N = 77; Mage = 19.45 years; 73% female; 62% Caucasian, 13% African American) were recruited from the psychology participant pool at a midwestern U.S. metropolitan university. Participants made financial decisions in easy and hard versions of a paid card task resembling a standard gambling task, with a learning component. During half the trials, they made decisions with a No-Choice Manager who controlled their decisions, versus a Choice Manager who granted decision control. The hard task was designed to be too difficult for most participants, undermining their efficacy and security, and ensuring financial losses. The No-Choice Manager was designed to perform moderately well, ensuring financial gains. Participants felt greater outcome satisfaction (utility) for financial gains earned via Choice, but not losses. Participants (85%) preferred the Choice manager in the easy task but preferred the No-Choice Manager (56%) in the hard task. This change in preference for choice corresponded with self-efficacy and was mediated by perceived security. We used Decision Field Theory to develop potential cognitive models of these decisions. Preferences were best described by a model that assumed decision-makers initially prefer Choice, but update their preference based on loss-dependent attentional focus. When they earned losses (hard task), decision-makers focused more on economic payoffs , causing them to deemphasize procedural utility. Losses competed for attention, pulling attention toward economic survivability and away from the inherent value of choice. Decision-makers are more likely to sacrifice freedom of choice to leaders they perceive as efficacious to alleviate perceived threats to economic security.Individuals typically prefer the freedom to make their own decisions. Yet, people often trade their own decision control  to gain economic security (outcome utility). Decision science has not reconciled these observations. We examined how decision-makers’ efficacy and security perceptions influence when, why, and how individuals exchange procedural and outcome utility. Undergraduate adults ( Decades of research demonstrates that self-determination—whether individual or collective ,2—is a fThis observation is sometimes treated as a core principle of human nature ,8. HowevFaustian bargain , because losses drew attention away from the procedural utility (freedom of choice) dimension, causing them to prioritize economic welfare over self-determination. However, the authors did not use computational cognitive models to represent and test these assumed processes. DeCaro et al. also did not examine self-efficacy or security perceptions as a potential mechanism of these effects. It is important to consider the involvement of self-efficacy and security because prior theory suggests there is a crucial link ,25,29,33We extended DeCaro et al.’s (2020)  laboratoWe hypothesized attentional focus to be an important cognitive mechanism underlying preference for choice in the easy versus hard task. To investigate this possibility, we used Decision Field Theory’s  attentioS1.0 Study Materials in Participants completed a modified version of DeCaro et al.’s (2020)  card-basM = 19.45 years, SD = 2.56; 73% female; 62% Caucasian, 13% African American, 8% Biracial, 7% Asian, 4% Hispanic) recruited from the intro psychology participant pool maintained at a Midwest metropolitan university in the U.S . Individuals participated for partial course credit, as well as any payments earned during the study. Participants were paid their average earnings in the decision task . Any individual at least 18 years old and able to follow verbal and written instructions in English were eligible to participate. This study was conducted for approximately three months, from September to November 2020.Eighty-two individuals were recruited to participate in this study. The final sample consisted of 77 undergraduate students .We tested our hypotheses using a 2  × 2  × 2  within-subjects design. Order of task difficulty was counterbalanced between-subjects  after each card-based decision task. Participants completed a final demographics survey, were paid, and debriefed. The study lasted approximately one hour.The purpose of the card task was to give participants experience with each manager in the easy and hard performance environments, and to assess participants’ outcome satisfaction prior to the preference task. If procedural utility, indeed, matters, then participants should report greater satisfaction with financial payoffs earned via choice. If negativity bias or loss aversion also matters, then this satisfaction should be contingent upon valence .Participants were informed that they would complete a card-based decision task and be paid between $1 to $9 based on their overall  performance. They were told that they would see four card decks during each trial  and thatAfter reading the task instructions, participants completed a quiz assessing their understanding of the task, payment, and status quo goal/reference point . The computer provided feedback on the quiz, including correct answers for questions answered incorrectly. This feedback was given to ensure that participants understood the payoff scale ($1 through $9), status quo ($5), and the fact that their payment would be contingent on their performance .Decks A and C $2 (losses), Deck B $5 (status quo), and Deck D $8.70 , or “B, A, C, D, D, A, C, B, A” for short. Sequence-learning research and pilot testing indicated that this sequence would be extremely difficult to learn, given its length and task complexity .S1.2.2 Decision Trials and Payoffs: Hard Task in Correct trials yielded outcomes drawn from status quo and gains pools, $4 through $9 (mimicking Decks B and D in the easy task). Incorrect trails yielded outcomes drawn from the losses pool, $1 through $3 (mimicking Decks A and C in the easy task). The payoffs of these pools were carefully calibrated to exactly match those in the easy task  original experiment, each trial was accompanied by “guidance” from automated managers, identified simply as “Day” and “Night” managers . The Chovery dissatisfied) to 11 (very satisfied), with a neutral point at 6 .Participants reported their outcome satisfaction after each trial, immediately after selecting a deck and viewing the card’s monetary value ($1 to $9). They rated their satisfaction on an 11-point scale, ranging from 1 , divided evenly between choice and no-choice . The number of trials was designed based on DeCaro et al. (2020)  and pilodiagnostic outcome-satisfaction trials to compare outcome satisfaction for each payoff $1 to $9 across choice and no-choice conditions. Pilot testing revealed that individuals perceived these “random” diagnostic trials as variability or “noise” in the payoff distribution of the decks, enhancing the immersive experience of the task, which was designed to simulate a probabilistic system. Payoffs from the remaining 54 trials (67%) came directly from the underlying payoff distribution associated with the chosen deck (easy task) or sequence (hard task), as described earlier.For comparison across choice procedures  and tasks , we ensured that participants received a specific minimum set of common outcomes across choice and no-choice trials in both the easy and hard tasks. Specifically, 27 trials (33%) were designed so that each payoff $1 to $9 was presented at random, an average of three times per condition , regardless of the deck chosen. This design ensured that we had at least an average of three observations per payoff $1 to $9, regardless of participants’ skill level . We used these trials as The No-Choice Manager was designed to perform moderately well, earning $5.71 (a slight gain) in both tasks. During the easy task, the No-Choice Manager required participants to choose the best deck (Deck D) 56% of the time, and the second-best deck (Deck B) 22% of the time, for an overall accuracy of 78% (earning $5.71). In the hard task, the No-Choice Manager instructed selection of correct deck sequence 78% of the time (earning $5.71). Thus, participants were presented with a moderately effective coercive manager, who was likely to perform better and earn more than participants on the hard task, but not the easy task. This design created a situation where participants may wish to sacrifice decision control to the comparatively more efficacious decision-maker in the hard task in exchange for greater economic security.No-Choice Manager’s $5.71). In contrast, if the participant chooses the optimal deck (Deck D) every trial, then they will earn approximately $7.08, which is a gain (and $1.37 greater than the No-Choice Manager’s $5.71). In DeCaro et al.’s (2020) experiment, using a similar task, most participants learned the optimal deck after about a third of the trials, achieving high accuracy thereafter. We therefore expected participants’ performance to fall between these extremes, earning approximately $6.02 on the easier task and better than the No-Choice Manager  to learn. In the easy task, if a participant chooses completely at random , then they will earn approximately $4.52, which is a loss that falls just below the $5 status quo (and $1.19 less than the ager see . Based oStrongly prefer NOT TO work with this manager to 11 Strongly prefer TO work with this manager). We also asked participants how satisfied they would be working with each manager, on an 11-point scale, (1 Very Dissatisfied to 11 Very Satisfied). We averaged responses to the latter two items into a single preference strength indicator (α = .84). We expected both preference  and preference strength to be similar, providing two indicators of preference for choice for greater validity.To assess preference for choice after each task , we asked participants who they would prefer to work with if they did that task again: Night Manager or Day Manager . In addioptimal response question was presented first; the other items were presented in random order.Participants completed several follow-up measures after each part of the card task . These measures consisted of manipulation checks, assessing participants’ perceptions of the task and each manager, in addition to their need satisfaction , thought to capture important elements of procedural utility and economic utility. Items were presented individually on separate screens. The As an additional manipulation check, we asked participants whether they had learned the optimal response for each task. For the easy task, participants were asked to identify which deck  yielded the highest payoffs, on average. For the harder task, participants were asked to input the correct sequence, using the same keys they used during the actual task.Very easy to 11 Very difficult).To assess perceived task difficulty, we asked participants, “How difficult was the card task that you just completed?” on an 11-point scale (1 α = .84) to assess participants’ perceived self-efficacy . Participants responded on an 11-point scale (1 Strongly Disagree to 11 Strongly Agree). The items focused on personal efficacy when making decisions themselves, rather than across the entire study, because this was the critical aspect that participants controlled ; hard: M = 5.86, 95%CI), F = 91.50, р < .003, ηp2 = .11. As expected, participants perceived the Choice Manager as providing greater PJSD  than the No-Choice Manager , F = 177.75, р < .001, ηp2 = .70. The No-Choice Manager’s perceived PJSD was below the neutral point (“6”) of the scale (see 95%CI), indicating that participants perceived the No-Choice Manager as coercive. This effect was qualified by a difficulty × manager interaction, F = 11.75, р < .001, ηp2 = .14. Participants perceived the Choice Manager as lower in PJSD in the hard task , F = 16.19, р < .001, ηp2 = .18. This result is consistent with a slight decrease in desire for choice in the hard task ; hard: M = 3.78, 95%CI). F = 1.16, р = .284, ηp2 = .02. The latter result is consistent with a floor effect: the coercive No-Choice Manager continues to be perceived as coercive .Perceived PJSD was overall higher during the easy task , F than the harder task , F = 86.68, р < .001, ηp2 = .54. Participants’ self-efficacy in the hard task fell significantly below the neutral point (“6”) of the response scale (see 95%CI), indicating that participants felt inefficacious at the hard task.Manager Efficacy. To examine the perceived efficacy of the managers, we conducted a 2  × 2  within-subjects factorial ANOVA. Overall, participants perceived that the Choice Manager , which granted participants full decision control, was more efficacious than the No-Choice Manager , which controlled each decision, F = 9.31, р = .003, ηp2 = .11. This effect were qualified by a difficulty × manager interaction  = 65.64, р < .001, ηp2 = .47. During the easy task, participants perceived the Choice Manager as more efficacious  than the No-Choice manager , F = 53.77, p < .001, ηp2 = .42. This pattern reversed in the hard task. During the hard task, participants perceived the No-Choice Manager  as more efficacious than the Choice Manager , F = 11.51, p = .001, ηp2 = .13.eraction , FRelative Efficacy. To compare how participants perceived their self-efficacy relative to the No-Choice Manager, we computed a difference score (Self-Efficacy–No-Choice Manager’s Efficacy). As anticipated, participants felt more efficacious than the No-Choice Manager during the easy task , F = 65.23, р < .001, ηp2 = .47.M = 6.13, 95%CI ) than the harder task , F = 44.94, р < .001, ηp2 = .38. They also felt more secure making decisions themselves , F = 4.89, р = .030, ηp2 = .06. These effects were qualified by a difficulty × manager interaction, F = 37.72, р < .001, ηp2 = .34. During the easy task, participants felt more secure making decisions themselves , F = 41.80, p < .001, ηp2 = .56. During the hard task, they felt more secure when the No-Choice Manager dictated their decisions , F = 4.93, p = .029, ηp2 = .04.To examine the perceived security associated with each manager, we conducted a 2  × 2 manager  within-subjects factorial ANOVA. Overall, participants felt more secure during the easy task  and gains positively , F = 772.20, p < .001, ηp2 = .91. Participants also reacted more intensely to losses than gains: the average, absolute deviation was greater for losses  than gains , F = 62.91, p < .001, ηp2 = .46. These findings affirm our manipulation of the outcomes $1 through $4 as losses, and $6 through $9 as gains.To determine whether participants indeed perceived the outcomes $1 through $4 as losses, we quantified the extent to which outcome satisfaction ratings for losses and gains deviated from “6,” the neutral point, in both relative and absolute value. As expected, participants rated losses negatively  of losses compared to equally-sized gains, under strict assumptions of loss aversion ) compared to hard , F = 35.66, p < .001, ηp2 = .32. This result translates into an approximately 41% decrease in preference for Choice in the hard task.To examine percentage preference for choice, we calculated the number of participants who selected the Choice Manager after the easy versus hard task. As anticipated, a greater number of participants preferred the Choice Manager when the task was easy , F = 1.11, p = .295, ηp2 = .02. There was no gender × difficulty interaction, easy , hard , F<1, p = .724, ηp2 = .00.Fifty-six individuals identified as female, 20 as male, and 1 as non-binary. To examine potential effects associated with gender, we included gender  in the above analyses. We excluded the one individual who identified as non-binary due to insufficient sample size . There was no main effect of gender , F<1, p = .843, ηp2 = .01. There was no education × difficulty interaction , F<1, p = .584, ηp2 = .02. There was no race × difficulty interaction . There was no main effect of race , representing the security participants felt when making decisions themselves, with the Choice Manager, versus under the direct control of the No-Choice Manager. Positive difference scores represent greater security making decisions oneself, whereas negative scores represent greater security making decisions under the direct control of the No-Choice Manager. As expected, participants felt more secure making decisions themselves during the easy task , but felt more secure making decisions under the control of the No-Choice Manager during the hard task , F = 37.72, p < .001, ηp2 = .34.To test this hypothesis, we first created a difference score (M1: R.SEC) mediated the effect of task difficulty  on percentage preference for choice (Y: P.Choice). We quantified this indirect effect using MEMORE 2.1 [minus hard).Next, we investigated whether the changes in relative security (MORE 2.1  and HayeMORE 2.1  index ofM1: R.SEC) making decisions themselves during the easy task . Second, task difficulty (X), B = 0.24, p = .003, and relative security (M1: R.SEC), B = 0.06, p < .001, each had direct effects on percentage preference for the Choice Manager (Y: P.CHOICE). Specifically, when accounting for both task difficulty and relative security, preference for Choice was an average of 24% higher during the easy task. Participants were 6% more likely to prefer Choice with each 1-unit increase in perceived security.The detailed results are reported in X) to change in preference for Choice (Y: P.CHOICE), via corresponding changes to relative security (M1: R.SEC), was significant . Due to this pathway, preference for Choice increased by an additional 17% for each 1-unit increase in relative security, caused by the easy versus hard task. These results support our assumption that performance-contingent perceptions of security contribute to preferences for choice.Importantly, the hypothesized mediational pathway  linking task difficulty (Decision Field Theory (DFT) [To better understand the potential cognitive processes involved in performance-contingent preference for Choice, we used Decision Field Theory’s computational cognitive modeling framework to develop and test three diagnostic models. ry (DFT)  has beenry (DFT) ,73. Builry (DFT) , we hypory (DFT) ,75. The sequential sampling model. Such models posit that choices are made by accumulating information over time, until preference strength for one option reaches a critical threshold [DFT is a hreshold . Accordioutcome utility dimension represents the value derived from the monetary payoffs earned while working for each manager. For our models, we used participants’ final earnings in each of the card tasks , with the Choice versus No-Choice Manager. The procedural utility dimension refers to the value participants derive from exercising freedom of choice. We used participants’ ratings of PJSD associated with each manager , during the easy versus hard task, to determine this value.Consider the decision between the Choice and No-Choice managers. We assume that participants define these options along two dimensions. The These two dimensions  likely compete for attention and, therefore, influence over people’s preferences for choice. DFT posits that, at each moment in time, an individual attends to only one dimension, when comparing options. For example, an individual may first pay attention to the outcome utility dimension, considering the value they derive from the monetary payoffs associated with the Choice and No-Choice Managers. The next moment, they may focus attention to the procedural utility dimension, considering the value they derive from the PJSD of the Choice and No-Choice Managers. This information is updated over time, adjusting the overall value associated with each option, until a final selection is made. Individuals may also enter a decision situation with an initial preference, or bias, in favor of a particular option. For example, in Western democracies, individuals often have an initial preference for personal decision control ,77. As dvalence, V, which represents the current difference in value associated with the Choice Manager, νC, versus the No-Choice Manager, νNC:V  is updated by comparing the value associated with Choice versus No-Choice. In our study, the Choice Manager was perceived as having greater PJSD  than the No-Choice Manager. Thus, V will typically be V > 0, adding to the overall positive value of Choice. When attention is focused on the outcome utility dimension, the update to V , will depend on the participants’ actual earnings. First consider the easy task. Most participants earned gains with the Choice Manager, and these gains were greater than the gains earned with the No-Choice manager. Therefore, when deciding which manager to work for in the easy task, V will be V > 0 for most participants, adding positively to the perceived value of Choice. Now consider the hard task. Most participants earned losses with the Choice Manager but earned gains with the No-Choice Manager. Therefore, V will be V < 0, adding positively to the perceived value of No-Choice.The underlying utility calculations are computed as follows. At each moment in time, DFT computes a preference state P(t) for a particular manager (option) at a particular time, t. The preference state is defined as the sum of the previous preference state, P(t-1), and the new valence, V:A participant’s final decision to select Choice over No-Choice in the easy or hard task is the total of these momentary valences, at the moment of decision. In DFT, this updating process involves updating the bias parameter, ϐ, which represents any potential initial bias in favor of a particular option. Specifically, at the beginning of deliberation, the initial preference state, P(0) = ϐ, can be used to account for an initial bias. As previously stated, individuals in Western democracies can be assumed to have an initial bias in favor of Choice. DFT allows the bias parameter to be treated as a free-parameter, estimating the degree of bias present in individuals’ decisions, if any. Thus, if ϐ > 0, then there is an initial bias in favor of the Choice Manager.DFT contains a decision threshold, ϑ. DFT assumes that ϑ represents a person’s level of caution, or stringency in making a decision. This factor is typically estimated from the observed decision data, without an a-priori reason to select a particular value up front. The bias parameter, ϐ, affects the decision by decreasing the amount of evidence/information needed to select the initially favored option (and increasing the information needed to select the disfavored option). In the current study, bias in favor of Choice means that less evidence is needed for an individual to reach the decision threshold, ϑ, and select the Choice Manager.The bias parameter influences the final decision as follows. According to DFT, individuals consider each option until a sufficient amount of evidence is collected to push them beyond some attention parameter, w. In our model, w, defines the probability that an individual attends to the outcome utility dimension, and 1-w is the probability of attending to the procedural utility dimension. Thus, w determines which dimension is currently being considered and, therefore, the momentary values of νC and νNC in the overall valuation process, V, noted earlier. We estimated separate attention weights for the easy versus hard tasks  to capture changes in the balance of attention, if any, associated with participants’ performance in each of those tasks.As stated earlier, we hypothesized that the final decision for/against Choice depends on task difficulty, and which dimension  individuals focus on in the easy versus hard task. Our theory assumes that, when deciding which manager to work for, individuals place more attention (“weight”) on the outcome utility dimension when facing a net loss . DFT determines the focus of attention, based on an ϑ), bias parameter (ϐ), and attention weights , as free parameters, to determine the most likely value of those parameters to best describe participants’ decision behaviors in the preference task. Model 1 (Null) represents a standard economic utility model that ignores procedural utility. This model bases the valence of Choice entirely on the average economic outcomes (monetary payoffs) participants earned. Thus, it must predict preference for Choice in the easy task, if participants earned more money with the Choice Manager in the easy task. Conversely, Model 1 (Null) must predict preference for No-Choice in the hard task, if participants earned more money with the No-Choice Manager. Model 2 (Biased) adds the bias parameter (ϐ), representing a potential initial preference for Choice, similar to standard assumptions in procedural utility theory. Model 3 (Full) represents our hypothesis that decision-makers consider both economic outcomes (outcome utility) and decision procedures  but attend to these dimensions to different extents when facing losses (hard task) or gains (easy task).We created and tested three diagnostic models, meant to represent three dominant theoretical perspectives . We treaminnew = 0 to maxnew = 1, according to:We used the models to predict participants’ percentage preference for choice data (from the preference task). Prior to modeling, we standardized the underlying scales for monetary payoffs  and perceived PJSD  to improve parameter interpretability. To do so, we followed the procedure use by Berkowitsch, et al. (2015) . We rescold is the theoretical minimum value of the original attribute range (2.99 for payoffs and 1 for PJSD ratings) and maxold is the theoretical maximum value of the of the original attribute range (7.08 for payoffs and 11 for PJSD ratings).Where minWe evaluated each model’s performance in terms of its ability to replicate the overall (mean) preference patterns for the easy versus hard tasks. We also used standard model-fitting indices to gauge statistical fit. The models differed in their complexity . For example, the Biased Model has one free parameter, representing initial predisposition, whereas the Full Model has two additional free parameters, representing task-dependent attentional focus. Therefore, we computed the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) of model fitness, which evaluate model fit while penalizing models that use more free parameters. Thus, the AIC and BIC tend to favor less complex models, all else being equal. Lower AIC and BIC values indicate better fit. To assist with interpretation of our result we also computed AIC weights that roughly estimate the probability of each model being a reasonable account of the data, according to the observed data .https://osf.io/sgpd4/?64405714ed6840a9874f9ef79b0b87c5.When fitting each model, we assumed a binomial error process to connect the predicted choice probabilities to the observed choices. Given the limited number of observations per individual, rather than create a different set of models and model parameters for each individual, we estimated a single set of parameters for each model and used these calculate predictions for all participants. This approach essentially creates a single version for each of the three models tested, which assumes that all individuals shared approximately the same basic decision-making process. In this sense, the models address questions about the typical or “average” decision-maker. However, our analysis did respect individual differences in monetary payoffs earned during each task and individuals’ subjective perceptions of PJSD. That is, to calculate the likelihood that a particular individual would choose the Choice Manager, our models used that individual’s mean payoffs and PJSD ratings within the easier and harder tasks to make its predictions. We used a grid search method to find parameter values that maximized the likelihood of responses according to each model. MATLAB code for this modeling can be found on OSF: P(0), is ϐ = 1.00. This value means that participants required substantially less evidence to select the Choice Manager than the No-Choice Manager. Specifically, according to the model, three-times more evidence was required to choose the No-Choice manager. To calculate this difference in evidence, we compared the initial preference state, P(0) = 1.00, to the decision threshold, ϑ = 2.00. The Choice manager is chosen when P(t) ≥ 2, which is 1 point above the initial starting point of 1.00. In contrast, the No-Choice manager is chosen when P(t) ≤ -2, which is 3 points below the initial starting point.The model results and estimated parameters are shown in Tables weasy = 0.70). In the hard task, where individuals typically earned net losses with the Choice Manager, attention to freedom of choice was reduced by half, with an estimated 85% attention to outcome utility, and 15% procedural utility (whard = 0.85).The Full model also indicated that attention to procedural utility differed across tasks. In the easy task, where most participants earned net gains under the Choice Manager, they were estimated to focus on monetary payoffs (outcome utility) 70% of the time, and freedom of choice  30% of the time , and only 12% would choose the Choice Manager in the hard task . The Biased Model improved predictive accuracy for the easy task, with 86% of individuals predicted to choose the Choice Manager , but underestimated preference for Choice in the hard task by 9% .Overall, these findings support our hypothesis, and the plausibility of DeCaro et al.’s (2020)  originalIt is generally thought that individuals prefer freedom of choice  ,5,35. HoWe addressed this gap in the current study, examining decision-makers’ preference for choice in a decision task with performance-contingent financial payoffs, spanning well-defined losses and gains. Participants completed both an easy version of the task and a hard version designed to generate failure , decrease their sense of self-efficacy, and threaten their sense of security. They were supervised by two managers: a Choice Manager that granted personal decision control, and a coercive No-Choice Manager who denied personal decision control but ensured successful performance  on the hard task. We used mediational analyses and computational cognitive models to better understand the psychosocial  and cognitive  mechanisms involved in participants’ decision to retain choice or abandon choice in exchange for better financial outcomes.Participants reported greater procedural utility  when working with the Choice Manager. Participants also felt more satisfied earning gain outcomes with Choice, versus No-Choice, indicating a bonus to felt utility. However, there was no such enhancement for loss outcomes, indicating a weaker procedural utility effect for losses, which fell below the financial goal point ($5). These findings replicate and extend DeCaro at al.’s (2020)  originalThis study also extended DeCaro et al.’s (2020)  prior exHowever, these same participants performed poorly in the hard task, failing to learn the optimal response and earning just $3.84 (a loss) under the Choice Manager. The No-Choice Manager continued to perform moderately well in the hard task, earning $5.71 (gain). Importantly, participants reported feeling inefficacious and less secure when making decisions themselves in the hard task. Most participants (57%) preferred the No-Choice Manager, reversing their preference. This reversal was mediated by perceived security.These findings indicate that participants would rather relinquish freedom of choice to a controlling leader who could protect their security and ensure better outcomes than retain decision control and incur a financial loss. This finding supports prior theoretical speculation that security is a determining factor behind individuals’ decisions to not only grant decision control to others ,14, but This research integrates and extends decades of prior research on desire for control, autonomy, procedural justice and economic rational choice see . The besThe Full model’s predictive accuracy differed substantially from the Null Model, representing standard outcome-based utility. The Null Model must predict preference for whichever manager was associated with higher net earnings. Thus, the Null Model missed observed preference for Choice by 24% in the easy task and 31% in the hard task, where some individuals retained preference for Choice, despite earning less than the No-Choice Manager.In summary, the current results support the hypothesis that individuals (a) consider both procedural and outcome utility when striking Faustian Bargains, and (b) calibrate their preference for choice contextually, based on initial bias , anticipated outcomes of decision control , performance-contingent needs , and their capacity to satisfy those needs themselves . The current study advances prior research in several ways. We discuss these advancements, paying special attention to theory development and future research directions.how decision-makers value choice, when they trade choice, and why they do so, rather than if they value choice.Research indicates that individuals desire choice ,5,15 andMany individuals indeed value choice, but there are important factors to consider. First, individuals may be more likely to relinquish decision control when the decision is, for example, among tragic alternatives, uninteresting, unimportant, or too taxing , or the decision-maker feels too inefficacious to handle the decision properly ,23,45. Sϐ), attention weight(s) (w), and/or decision threshold (ϑ).Using DFT, we identified a potentially important, basic cognitive model of the Faustian bargain. We chose DFT for two reasons. First, unlike many other utility-based computational frameworks of decision-making, DFT explicitly models the attention mechanism thought to be involved in utility updating during preference formation ,73. Thisϐ) for choice , as well as greater attention (w) to procedural utility .For example, our results and cognitive model align with observations made in the work sector. Even though individuals value organizational and procedural fairness, and often desire self-determination in the workplace ,87, mostϐ) will be low in domains where individuals are typically decision averse, such as when making tragic decisions  may be more strongly drawn to outcomes (who lives or dies), rather than procedural fairness and self-determination. More generally, there is a norm for patients to relinquish considerable decision-making authority to doctors, whom many individuals perceive as benevolent experts [ϐ), decision thresholds (ϑ), and/or focus of attention (w) in different settings, where they inhabit different roles and, therefore, have different norms and expectations of decisional control [cf. We anticipate that initial bias for Choice (supplies ). In add experts ,45. A si experts ,90. Henctrol cf. ,82,91.ϐ) for particular kinds of Choice [e.g., w), may also be affected, if there are context-specific (sub)cultural differences in attention to decision processes versus outcomes. The current study was not designed to examine the potential effects of race, class, or gender, nor did this study have sufficient sample size to do so. However, understanding the influence of such factors in future studies will better inform public policy and facilitate better match of governmental systems  to stakeholder preferences [Preference for Choice is also affected by individual differences and culture. Procedural justice and self-determination are fundamental needs ,92. Howece e.g., ,93,94. Rce e.g., . For exace e.g., . We did ferences ,82.The questions we have raised in this study are important to society, because preference for choice—and by extension, particular types of rule-systems  that affect choice—influence acceptance, cooperation and compliance ,89. ThesFor example, to understand how governmental autonomy-support affected compliance motivation during the first wave of the U.S. 2020 COVID-19 pandemic, DeCaro and DeCaro (2022)  surveyedw, to the security dimension (emphasizing safety or economic survivability), deemphasizing the procedural justice/self-determination dimension. Cultural or individual preferences for particular forms of government  may likewise be captured by initial bias, ϐ. Hence, the conceptual and methodological innovations introduced in the current study may offer novel ways to examine classical and important questions about institutional preferences.Acceptance, motivation, and compliance appeared to be heightened in the governmental situation that provided the most procedural utility cf. ,8. PrefeWe assumed that attentional focus was an important feature in determining the influence of procedural versus outcome utility. However, we did not measure direction and level of attention. Future studies could provide greater insight into these covert attentional processes using eye-tracking and other process-tracing techniques ,99,100. As many prominent scholars, politicians, and scientists have stated, human decision-makers inherently face a fundamental paradox: choice is valuable  but not S1 File(DOCX)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


The molecular mechanisms underlying obesity-induced increases in β cell mass and the resulting β cell dysfunction need to be elucidated further. Our study revealed that GPR92, expressed in islet macrophages, is modulated by dietary interventions in metabolic tissues. Therefore, we aimed to define the role of GPR92 in islet inflammation by using a high-fat diet–induced (HFD-induced) obese mouse model. GPR92-KO mice exhibited glucose intolerance and reduced insulin levels — despite the enlarged pancreatic islets — as well as increased islet macrophage content and inflammation level compared with WT mice. These results indicate that the lack of GPR92 in islet macrophages can cause β cell dysfunction, leading to disrupted glucose homeostasis. Alternatively, stimulation with the GPR92 agonist farnesyl pyrophosphate results in the inhibition of HFD-induced islet inflammation and increased insulin secretion in WT mice, but not in GPR92-KO mice. Thus, our study suggests that GPR92 can be a potential target to alleviate β cell dysfunction via the inhibition of islet inflammation associated with the progression of diabetes.  Obesity-induced low-grade tissue inflammation manifests in multiple tissues, including pancreatic islets, and plays a critical role in mediating insulin resistance and β cell dysfunction –3. Obesi+ and CD4+)  –16.T cells and macrophages contribute to the rapid increase in islet inflammation by further releasing inflammatory cytokines. Therefore, they contribute to the persistence of inflammatory reactions within the pancreas and worsen autoimmune β cell depletion , 11, 16.G protein–coupled receptors (GPCRs) are the most prevalent signal-transducing proteins on the cell surface. GPCRs regulate numerous physiological and pathological processes, making them the most promising and crucial targets for novel drug discovery and effective therapeutic strategies . We prevSince the identification of GPR92 agonists, several physiological functions of GPR92 have been elucidated. GPR92 plays an important role in nociception and pain hypersensitivity , 20. GPROur data suggest that GPR92 might play an important role in metabolism. Among the several GPCRs surveyed by RNA-Seq, GPR92 was modulated by dietary interventions in metabolic tissues . Interestingly, pancreatic islets , skeletal muscle, and dorsal root ganglion ; Supplemc islets  and stroc islets  of obesec islets  or glucac islets . In addier F4/80 .+ cells (+CD11b+ cells (IMs), but not in F4/80–CD11b– cells from isolated islets, while insulin mRNA was highly enriched in F4/80–CD11b– cells  or microglia. However, HFD significantly increased GPR92 mRNA expression in IMs ; ref.26.F4/80, Cd11b, and Cd11c, which were enhanced by a HFD, were downregulated by clodronate treatment , and an upregulation of MAF BZIP transcription factor B (MafB), an immature β cell marker . These aThe islets of NCD-fed GPR92-KO mice exhibited reduced insulin secretion, which was restored by the depletion of IMs by clodronate treatment . Upon HFSince a marked expansion of immune cells and increased production of proinflammatory cytokines and chemokines leading to the abrogation of β cell functions are common features of diet-induced obesity and T2D , 8, 11, Tnfa, Ccl2, Il6, and Tlr4, compared with WT mice on a HFD   . HEK293 SRE-luc) . SimilarSRE-luc) .Tnfa, Mcp1, and Il6, induced by LPS treatment in cultured islets from WT mice. However, FPP failed to modulate the LPS-induced inflammation in cultured islets from GPR92 KO mice  in WT peritoneal macrophages (pMacs), whereas GPR92-KO pMacs did not respond to GPR92 agonist or FPP pretreatment . Similar KO mice . Togethe+CD11b+ cells) from WT and GPR92-KO mice. In the presence of high glucose levels, the CM from WT IMs treated with FPP increased insulin secretion in the islets similar to the addition of CM from WT pMACs with FPP treatment, whereas the CM from FPP-treated GPR92-KO IMs did not further enhance the release of insulin  from pMacs to WT islets. The ratio of CM to islets was 1 to 5, similar to the macrophage to β cell ratio observed in HFD mouse islets. As seen in  insulin .To gain insight into how FPP would modify macrophage function to influence β cell activity, we performed a proteomic analysis of the CM obtained from pMacs treated with FPP . The genBesides its antiinflammatory role, FPP treatment increased the expression of GPR92  and insuAdditionally, we determined that systemic administration of FPP improved glucose intolerance in HFD-induced obese WT mice , an effeIn this study, we present an assessment of immunological and metabolic functions of what we believe to be a novel GPCR, namely, GPR92. We showed that GPR92 is highly expressed in IM populations and its expression is closely related to diet-induced obesity. In GPR92 deficiency, the islets from obese mice exhibit an increased expression of inflammation-associated mediators and enhanced M1-like macrophages. These IMs lead to β cell dysfunctions and impair the insulin secretion in HFD-fed GPR92-KO mice, an effect that is eliminated when these immune cells are depleted. Additionally, GPR92 activation by FPP modulates the macrophage inflammatory response, thereby restoring the insulin secretion by β cells. Our results clearly indicate that GPR92 deficiency increases the activation of IMs and leads to a proinflammatory phenotype. Moreover, our findings suggest that enhanced islet inflammation contributes to the abrogation of insulin secretion observed in islets of GPR92-deficient mice upon HFD feeding.+ cells), whose population is substantially increased during obesity , where it can be driven to several fates, or even be converted into a variety of lipid anchors, such as small GTP-binding proteins like Cdc42 , which mOur findings strongly suggest that GPR92 regulates the function of pancreatic islets by modulating the IM inflammatory response. While the purpose of our current study is not to develop novel antidiabetic drugs, GPR92 in IMs could potentially be targeted to transactivate β cell function as a proficient therapeutic strategy to avert the damage caused by islet inflammation — which is caused by diet-induced obesity — and to regulate β cell-mediated insulin secretion in patients with T2D. In a future study, it would be worth determining whether the systemic treatment of FPP can be as potent as antidiabetic effects versus the current standard of care  or that FPP acts synergistically with these compounds when given chronically in a proper preclinical model of T2D.Altogether, our findings demonstrate that GPR92, a GPCR expressed in IMs and highly modulated by a HFD, controls the chronic inflammatory pathways that drive the pathogenesis of obesity-related diabetes. GPR92-KO mice exhibit enhanced islet inflammation and severely diminished insulin secretion. However, GPR92 agonist, FPP, markedly reduced the inflammation in macrophages. Moreover, IMs treated with FPP positively modulated β cell GSIS. Therefore, our study establishes what we believe to be a novel endocrine role of GPR92 in islet-resident macrophages as it controls islet inflammation and β cell dysfunction. It also proposes GPR92 as a potentially novel therapeutic target to improve the clinical outcomes of T2D.GPR92 (LPAR5)-KO mice were gifted by Jerrold Chun   and housFor glucose tolerance tests (GTTs) or GSISs, mice were fasted for 4 hours before the administration of glucose . At the indicated times, venous blood samples were collected in heparin-coated capillary tubes. Glucose levels were measured using an oxidase-peroxidase assay (Sigma-Aldrich). Insulin levels were measured by a commercial ELISA kit  as per the manufacturer’s instructions. Differences in glucose plasma levels against baseline were used to calculate the AUC values.https://imagej.net/software/fiji/).For immunofluorescence paraffin sections, the pancreas tissues extracted from WT or GPR92 KO mice were fixed in PBS-buffered 10% formalin for 24 hours, embedded in paraffin, and sectioned (5 μm). The sections were fixed afterward in xylene and ethanol and subjected to antigen retrieval  before i–ΔΔCT) compared with those of the control group. Primer sequences are listed in Total RNA isolation and quantitative PCR were performed as described previously . Gene exSequencing libraries were prepared according to the NEBNext Ultra II RNA Library Prep Kit for Illumina (New England BioLabs Inc.). Islets in Lysis Buffer were used as the starting material. RNA was extracted using the RNAqueous-Micro Total RNA Isolation Kit (Invitrogen). RNA-Seq analysis was performed as described by Shan and colleagues  generated by 3 layers, Histopaque 1119 (Sigma-Aldrich), Histopaque 1077 (Sigma-Aldrich), and HBSS (Gibco) with 0.1% BSA. The islets were collected from the interface between the second and third layers.The islets were isolated as described previously . BrieflyPrimary mouse islets isolated from both NCD and HFD WT mice, fed for 18–20 weeks, were used to evaluate the effects of macrophages on GSIS. To deplete islet macrophages, the primary islets were handpicked (30 islets per each replication) and incubated in DMEM with 10 mL of clodronate liposome at 7 mg/mL (FormuMax). In control groups, the mouse islets were treated with empty liposomes at 7 mg/mL (FormuMax). After 24 hours, the islets were washed and incubated in Krebs-Ringer buffer (Sigma-Aldrich) with 2.8 or 16.7 mmol/L glucose for 1 hour. Insulin concentrations in the supernatant were determined by Ultrasensitive insulin ELISA kits .g at 4°C for 5 minutes (+ and CD11c+ IMs were washed once with PBS with 2% FBS and then resuspended in PBS with 2% FBS for sorting. IMs were sorted for collection using a BD Biosciences FACS Aria cytometer (UTSW Flow Cytometry Core Facility). Flow cytometry plots were generated with FlowJo version 10.Isolated islets were dissociated in enzyme-free dissociation buffer (Sigma-Aldrich) at 37°C for 10 minutes, washed, filtered twice on a 40 μm cell strainer, and centrifuged at 600 minutes . Cell peWe harvested primary macrophages from the peritoneal cavity of WT and GPR92-KO mice as described previously , 51. AftThe previously-isolated islets were incubated with the CM (1:5) from pMacs or IMs for 24 hours and subjected to GSIS with glucose doses of 2.8 mM and 16.7 mM for 1 hour, as described previously (http://www.uniprot.org/). The FDR cutoff was 1% for all peptides.For proteomics assays, the CM from primary macrophages was concentrated into Amicon Ultra-15 Centrifugal Filter Unit (Millipore). A total of 30 μg of protein loaded into a SDS page gel was removed and submitted to the UT Southwestern Proteomics Core. Briefly, samples were digested overnight with trypsin (Pierce) following reduction and alkylation with DTT and iodoacetamide (Sigma–Aldrich). The samples then underwent solid-phase extraction cleanup with an Oasis HLB plate (Waters) and were subsequently dried and reconstituted. A total of 2 μL of these samples were injected onto a QExactive HF mass spectrometer (ThermoFisher Scientific) coupled to an Ultimate 3000 RSLC-Nano liquid chromatography system. Samples were injected onto a 75 μm inner diameter, 15 cm EasySpray column (ThermoFisher Scientific) and eluted with a gradient from 0% to 28%, over 90 minutes with a flow rate of 250 nL/minute. MS scans were acquired at 120,000 resolutions in the Orbitrap and up to 20 MS/MS spectra were obtained for each full spectrum acquired using higher-energy collisional dissociation for ions with charges from 2 to 8. Dynamic exclusion was set for 20 seconds after an ion was selected for fragmentation. Raw MS data files were analyzed using Proteome Discoverer v2.4 (ThermoFisher Scientific), with peptide identification performed using Sequest HT searching against the mouse protein database from UniProt  was delivered to the ligated pancreas through the celiac artery at 1 mL/minute using a circulation pump  and an in-line heater (Warner Instrument) set at 37°C. This solution was freshly prepared, filtered (0.45 mm), and equilibrated for over 10 minutes with 95% oxygen and 5% CO2. After an equilibrium period of 10 minutes, the perfusates were collected at 1 minute intervals for 45 minutes. Pancreas was perfused with a basal glucose concentration (2.8 or 5 mmol/L) for 35 minutes; 20 μM FPP was added to the perfusate during minutes 10–25, and 10 μM arginine was added between minutes 35–45. Insulin was measured in the perfusate using an assay kit (Cisbio).The pancreas was perfused through the celiac artery and the perfusate was collected from the portal vein following a modified procedure of previous protocols , 53. Brit test. The difference among various treatments was determined by 1-way ANOVA or 2-way ANOVA with the Bonferroni’s post hoc using GraphPad Prism 7.0 software. Differences were considered significant when P < 0.05.The values presented are expressed as the mean ± SEM. The statistical significance of the differences among 2 groups was determined by a Student’s 2-tailed All procedures were approved by the IACUC at UT Southwestern Animal Resource Center.DYO and COS conceived the project and designed the studies. COS performed most of the experiments. COS, VAP, and XS performed the mouse experiments. LV, QZ, and MS performed RNA-Seq library prep and data analysis. TO analyzed proteomic database. VAP, COS and NJ performed flow cytometry and cell sorting experiments. SC performed the pancreas perfusion assay. and LMAB assisted in experiments. RKG reviewed the manuscript and contributed to discussions. DYO and COS analyzed, interpreted data, and cowrote this manuscript.


Schizosaccharomycespombe Lem2, an NE protein, regulates nuclear-exosome-mediated RNA degradation. Lem2 deletion causes accumulation of RNA precursors and meiotic transcripts and de-localization of an engineered exosome substrate from the nuclear periphery. Lem2 does not directly bind RNA but instead interacts with the exosome-targeting MTREC complex and its human homolog PAXT to promote RNA recruitment. This pathway acts largely independently of nuclear bodies where exosome factors assemble. Nutrient availability modulates Lem2 regulation of meiotic transcripts, implying that this pathway is environmentally responsive. Our work reveals that multiple spatially distinct degradation pathways exist. Among these, Lem2 coordinates RNA surveillance of meiotic transcripts and non-coding RNAs by recruiting exosome co-factors to the nuclear periphery.Transcriptionally silent chromatin often localizes to the nuclear periphery. However, whether the nuclear envelope (NE) is a site for post-transcriptional gene repression is not well understood. Here we demonstrate that  Schizosaccharomycespombe.The Braun lab shows that the conserved nuclear membrane protein Lem2 interacts with the MTREC complex of the nuclear-exosome pathway to promote recruitment and degradation of ncRNAs and meiotic transcripts at the nuclear periphery in  Other coding and non-coding RNAs are continuously transcribed but function during specific developmental stages; for example, fission yeast meiotic mRNAs are expressed but rapidly degraded in vegetative cells5. Constant surveillance is required to prevent the aberrant accumulation of these transcripts, which is mediated by both nuclear and cytosolic pathways. However, how RNA-degradation pathways are coordinated in the nucleus is not well understood. Although the role of perinuclear anchoring in transcriptional silencing has been intensely studied, the effect of nuclear organization on post-transcriptional repression remains unclear.Eukaryotic genomes are pervasively transcribed, giving rise to sense and antisense RNAs from intra- and intergenic regions and repetitive elements. Accumulation of cryptic unstable transcripts (CUTs) may cause genome instability through RNA-DNA hybridization6). RNA-surveillance pathways contribute to substrate specificity and RNA processing, assisting exosome-targeting complexes through polyadenylation and RNA helicase activities that unwind RNA secondary structures6. In Schizosaccharomyces pombe, these targeting complexes include TRAMP (Trf4/5–Air1/2–Mtr4 polyadenylation) and MTREC (Mtl1–Red1 core). The TRAMP complex comprises a non-canonical poly(A) polymerase , a zinc-knuckle RNA-binding protein (Air1), and an RNA helicase (Mtr4). Fission yeast TRAMP degrades transcripts derived from pericentromeric repeats and plays a minor role in CUT elimination and small nucleolar RNA (snoRNA) processing9. The MTREC complex, also known as NURS (nuclear RNA silencing), comprises the Mtr4-like helicase Mtl1 and the zinc-finger domain protein Red1 (RNA elimination defective 1). MTREC mediates turnover of CUTs and meiotic and non-spliced transcripts11 and is orthologous to the human PAXT (poly(A) tail exosome targeting) complex12. MTREC assembles into an 11-subunit ‘super complex,’ in which Red1 scaffolds different submodules and recruits Rrp6 via Mtl1 (refs. 13). The submodules have different activities and include the canonical Poly(A) polymerase Pla1 and the complexes Red5–Pab2–Rmn1, Ars2–Cbc1–Cbc2, and Iss10–Mmi1 (refs. 19).Eukaryotic nuclear RNA degradation is mediated by the nuclear exosome, a multiprotein complex containing two 3′→5′ exoribonucleases, Rrp6  and Dis3/Rrp44 (ref. S. pombe. The YTH (YT521-B homology) protein Mmi1 recognizes hexanucleotide motifs (UNAAAC) known as DSR  sequences20. Substrate binding requires Mmi1 dimerization and interaction with its partner Erh1 (enhancer of rudimentary homolog 1) to form the tetrameric Erh1–Mmi1 complex (EMC). EMC sequesters RNA substrates, preventing their nuclear export and translation25. Mmi1 further binds the Ser- and Pro-rich protein Iss10 , which bridges the Mmi1-MTREC interaction26. Several DSR-containing genes are marked by histone H3 lysine 9 methylation (H3K9me) in an Mmi1-, Red1-, and Rrp6-dependent manner, potentially adding another layer of control10. During Rrp6 inactivation or stress exposure, an alternative degradation pathway using RNA interference (RNAi) results in H3K9me deposition at several loci known as HOODs (heterochromatin domains)27.Exosome targeting and function are best understood in the context of meiotic transcript turnover in 28), whereas Iss10 is critical for Red1 foci formation and MTREC co-localization with EMC28. During meiosis, Iss10 becomes unstable and Red1 foci disappear29. This causes Mmi1 to dissociate from MTREC and collapse into the Mei2 dot, a nuclear structure comprising the RNA-binding protein Mei2 and the non-coding RNA sme2 30. Together, Mei2 and meiRNA sequester Mmi1 at the genomic sme2+ locus, resulting in the inactivation of Mmi1-dependent RNA elimination32. Iss10 degradation and the disassembly of nuclear foci coincide with the accumulation of meiotic transcripts29. It has therefore been proposed that Iss10-dependent foci represent specific RNA-degradation sites28.In vegetative cells, exosome factors assemble into one or several nuclear foci. EMC-containing foci require the association of Mmi1 with Erh1  chromodomain proteins36. This heterochromatic platform recruits the Snf2-like/HDAC-containing repressor complex SHREC, restricting access to RNA polymerase II (Pol II)37. Several NE proteins contribute to silencing and perinuclear localization of heterochromatin40. Lem2 is a conserved integral protein of the inner nuclear membrane (INM) containing the LEM  and MSC  nucleoplasmic domains. Although the LEM domain contributes to centromere tethering, the MSC domain mediates heterochromatin silencing43. Lem2 promotes recruitment of SHREC to heterochromatin, thus linking heterochromatin silencing and nuclear organization38. Whether Lem2 contributes to other modes of gene regulation remains unknown.Transcriptionally silent chromatin and repressive histone-modifying enzymes are often sequestered at the nuclear peripheryHere, we uncover a global role for Lem2 in repressing non-coding RNAs and meiotic genes, which is distinct from its function in heterochromatin silencing. We show that Lem2 cooperates with the nuclear exosome and physically interacts with the MTREC subunit Red1 and its human homolog. Importantly, Lem2 is critical for the perinuclear localization of exosome substrates and their recognition by Mmi1 and Red1. Lem2-assisted RNA targeting is largely independent of Iss10 and Erh1 and their assembly into nuclear foci. Furthermore, Lem2 contributes to CUT degradation and snoRNA 3′ processing. Altogether, our data imply that multiple RNA-degradation pathways exist and localize to distinct subnuclear structures. We propose that Lem2 supports RNA surveillance by coordinating the degradation of exosome targets at the nuclear periphery.43. To examine whether Lem2 regulates gene expression through additional mechanisms, we performed transcriptome analysis by RNA sequencing (RNA-seq). A substantial portion of transcripts were upregulated in cells deleted for lem2+ (lem2∆), whereas only a few were decreased  were significantly over-represented (61%) among the upregulated transcripts in lem2∆ cells , RNAi (ago1∆), and SHREC . In stark contrast to heterochromatic transcripts, sme2 and sno20 transcript levels were unaltered in these mutants  with Ser5-phosphorylated RNA polymerase II (Pol II-S5P). As expected, clr4∆ cells showed strong enrichment of Pol II-S5P and increased transcription of tlh1+, a subtelomeric gene within heterochromatin , Red1 (MTREC), Erh1 (EMC complex), Iss10 (MTREC/EMC-bridging factor), Ccr4 (CCR4–NOT complex), or Air1 (TRAMP) . Cluster 1 was enriched for features related to early meiosis (63% frequency) and Red1-mediated degradation (30%). Many of these transcripts were also increased in cells lacking Erh1, consistent with its function in binding to exosome substrates as part of the EMC  Fig. . PrincipMP) Fig. . Differer4∆ Fig. . PairwisEMC Fig. . In cont51. In accordance with transcriptomics -binding protein Pab2 ics Fig. , RT–qPCRab2 Fig. . Convers5. We noticed that genes upregulated in lem2∆, such as mei4+, are often part of these HC islands, whereas Red1-independent HC islands were mostly unaffected by lem2∆ , but observed no H3K9me2 increase in lem2∆ cells  islandssme2, ssm4, sno20, and snR42) in single and double mutants lacking these factors in combination with lem2∆ by RT–qPCR. Although pab2∆, red1∆, and rrp6∆ single mutants displayed higher transcript levels of sme2 and ssm4 than did the lem2∆ mutant . Consistent with a role for Lem2 in 3′ processing, we found that the precursors of sno20 and snR42, but not their mature forms, accumulated in lem2∆ cells  revealed that Red1 associates with Lem2 in vivo . Although deleting the amino terminus did not impair Red1 association, removing the carboxy-terminal MSC domain abolished Red1 binding  cells undergoing meiosis  stem loops that can be visualized by co-expressing U1A-yellow fluorescent protein (YFP) , whereas only 20% of the lem2∆ cells showed this pattern, indicating that Lem2 promotes the perinuclear localization of this exosome RNA substrate   Fig. . We confFP) Fig. . Using l I) Fig. . These d54 and contains an RNA-recognition motif55. However, S. pombe homologs lack this C-terminal extension, and whether Lem2 can bind RNA is unknown.Given its interaction with Red1 and role in repression and localization of exosome targets Figs. –4, we spsme2 or ssm4 transcripts, which contain several DSRs . Moreover, these substrates can be captured by Mmi1 even when they accumulate to high levels, as seen in red1∆ cells, implying that the substrate-binding capacity of Mmi1 is not limited under these conditions  followed by RT–qPCR to assess binding of SRs Fig. 3. When ipts Fig. . We therpts Fig. . In agreed1 Fig. . Strikined1 Fig. . Bindinged1 Fig. , which i56. While EMC and MTREC can form independent foci, their mutual interaction depends on Iss10, which physically interacts with Red1 (refs. 26). We investigated whether the formation and localization of these nuclear foci require Lem2. Using live-cell imaging, we confirmed that the exosome-targeting factors Mmi1, Erh1, and Red1 form multiple nuclear foci. However, in agreement with our findings for Mmi1 , we found that GFP-Mmi1 association with Red1-6×HA was unaffected by lem2+ deletion  although lem2+ deletion causes accumulation of various targets , Pol II abundance is increased at heterochromatin but not at ncRNA genes; (2) repression of targets co-regulated by Lem2 and the exosome is unaffected by silencing factors  that work with Lem2 at heterochromatin; (3) mutants lacking Lem2 and exosome factors  display overlapping but non-additive phenotypes in the repression of several meiotic transcripts; (4) Lem2 physically interacts with the MTREC subunit Red1 via its MSC domain; (5) Lem2 does not affect chromatin binding of Mmi1 and Red1; (6) instead, Lem2 promotes Mmi1 and Red1 binding to RNA substrates; (7) Lem2 is critical for the localization and association of a DRS-containing transcript with a subnuclear pool of Mmi1 at the NE. On the basis of these findings, we propose that Lem2 recruits exosome co-factors to the nuclear periphery to coordinate post-transcriptional RNA processing and degradation, a function independent of heterochromatin silencing.Transcriptionally silent chromatin often localizes to the nuclear periphery, which is thought to provide a specialized compartment for gene repression38. Similarly, we find that Lem2 facilitates the association of MTREC with RNA substrates , implying that multiple spatially distinct degradation pathways exist. Indeed, lem2∆ mutants that also lack the EMC or TRAMP complexes show synergistic upregulation of various transcripts . Similarly, cells shifted from rich to minimal medium show upregulation of key meiotic regulators in a Lem2-dependent manner  except for the data shown in Figures pFA6a or pYM-based vectors67 and expressed from the chromosomal locus using the endogenous promoter. For Lem2-GFP fusions expressing strains, the respective fragments were cloned into a pJK210 vector and integrated into lem2∆ background strains in the endogenous locus. The soluble MSC-GFP overexpression construct was expressed under control of the TEF (Ashbya gossypii) promoter.Strains expressing epitope-tagged proteins were generated through homologous recombination using man1∆ and clr4∆, three biological replicates were processed. Single-end, 50-bp sequencing of libraries was performed on a HiSeq1500 sequencer in the LAFUGA core facility at the Gene Center, LMU Munich. Raw reads were de-multiplexed using Je (v1.2). Adapter-trimmed reads were aligned to the S. pombe reference genome (ASM294v2.27) and a custom GTF file using STAR (v2.7.3a), then processed using RSEM (v1.3.3). Differential expression was analyzed using the DESeq2 (v1.22.2) and tximport (v1.10.1) R libraries. For PCA plotting, data were batch normalized using the sva (v3.30.1) R library. Bedgraph coverage files for plus and minus strands were generated using genomecov (bedtools v2.29.1). RNA-seq data have been deposited in Gene Expression Omnibus (GEO) database under the accession number GSE174347. The full code for all NGS-related workflow is available at: https://github.com/Tsvanemden/Martin_Caballero_et_al_2021.For RNA-seq, 1 μg of RNA was used as starting material to prepare libraries, following the manufacturer’s instructions for NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB). Two or more biological replicates were used for generating libraries in parallel. For 68. Briefly, cells were lysed by bead beating  using TRIzol reagent and zirconia/silica beads (BioSpec), followed by centrifugation at 13,000 r.p.m. for 15 minutes at 4 °C. Recovered supernatant was extracted with chloroform and reprecipitated with isopropyl alcohol. Resuspended RNA was treated with DNaseI, and 10 mg of RNA was used in standard RT reactions using oligo((dT)20-N) primers. cDNAs were quantified by qPCR using primaQUANT SYBR Master mix (Steinbrenner Laborsysteme) and a QuantStudio 3 or QuantStudio 5 Real-Time PCR system (Applied Biosystems/Thermo Fisher). Data from 2–6 independent biological replicates are shown as individual data points together with the median.RT–qPCR experiments were performed as previously describedFor northern blotting and RT–qPCR analysis of snoRNAs Fig. , total Ract1 and then dividing by the mean of all samples from the same experiment , as previously described69. When analyzing single and double mutants for epistatic interactions, statistical testing was performed using R. Multiple testing was performed using ANOVA followed by a Tukey’s post hoc test at a 0.05 significance level.Primers used for qPCR are listed in Supplementary Table Three micrograms of RNA were separated on a 2% agarose gel and transferred overnight by capillarity on a nylon membrane  in SSC 10× buffer. RNAs were then UV-crosslinked to the membrane using a Stratalinker apparatus. In vitro-transcribed dig-labeled RNA probes were generated and incubated with the membrane using the Dig-Northern-Starter Kit (Roche), following the manufacturer’s instructions. Membranes were washed twice in 2× SSC 0.1% SDS and once in 1× SSC 0.1% SDS, for 10 minutes at 65 °C. Revelation was done according to the kit instructions using Chemidoc Imaging MP detection device (Biorad). Oligonucleotides used to generate DNA templates for RNA probes are listed in Supplementary Table pGADT7 or pGBKT7 vectors (Clontech). The S. cerevisiae strain Y2H Gold (Takara) was used to co-transform the plasmids, following the manufacturer’s instructions. Spotting assays were performed 3–5 days after transformation of the plasmids. Different dropout mixes were used to assess the strength of the interaction: SDC-Leu-Trp (Formedium), SDC-Leu-Trp-His (Formedium) and SDC-Leu-Trp-His-Ade (Formedium).Constructs were cloned into either 70 with a few modifications in the lysis buffer. The lysis buffer contained 50 mM HEPES pH 7.4, 100 mM NaCl, 10% glycerol, 1 mM EDTA pH 8, 2.5 mM MgCl2, 0.5% NP-40, 1× complete EDTA-free protease inhibitor cocktail (Roche), 2 mM PMSF (Serva), 20 mM N-ethylmaleimide . Cell lysates were prepared by resuspending the pellets from 150 to 200 OD600 units in 800 μl lysis buffer. Cells were lysed by bead beating  with zirconia/silica beads (BioSpec), and lysates were cleared by centrifugation . Clarified extracts were incubated with pre-equilibrated GFP-Trap or Myc-Trap (Chromotek) for 1.5 hours at 4 °C. Following immunoprecipitation, the bound material was incubated with RNase (Roche) or benzonase (Sigma) in the reactions, as indicated. Beads were washed four times with lysis buffer and two times with wash buffer . Proteins were eluted with 30 μl HU buffer  and bromophenol blue 1.5 mM) and analyzed by immunoblotting.CoIP assays were performed following a previously described protocol600 = 1 (≈2 × 107 cells) were pelleted from a suspension culture grown to mid-log phase. Total protein extracts were made using trichloroacetic acid  precipitation68. Proteins were solubilized in HU buffer  and bromophenol blue 1.5 mM). Proteins were resolved on NuPAGE 4–12% gradient gels (Invitrogen) or self-made 8% gels. Proteins were transferred onto PVDF membranes  and analyzed by standard immunoblotting techniques using specific antibodies. H3 antibody was used as a loading control.Cells corresponding to OD38. In brief, cells were grown overnight on rich medium (YES) to the logarithmic phase. Prior to imaging, cells were attached with lectin (Sigma) to glass-bottom dishes with a micro well (MatTek). Cells were imaged on a Zeiss AxioObserver Z1 confocal spinning disk microscope with an EMM-CCD camera  through a Zeiss Alpha Plan/Apo ×100/1.46 oil DIC M27 objective lens. Z-stacks were obtained at focus intervals of 0.4 μm. FiJi/ImageJ software was used to measure the distances between the foci and the periphery.Live-cell imaging was essentially performed as describedZ-stacks were obtained at focus intervals of 0.25 μm. Images were deconvolved using the SVI Huygens suite and FiJi/ ImageJ software was used to measure the distances between the foci and the periphery.For the imaging of cells expressing CFP-Mmi1, the following setup was used: confocal microscopy was performed at the Core Facility Bioimaging of the Biomedical Center (LMU Munich) with an inverted Leica SP8 X WLL microscope, equipped with 405-nm laser, WLL2 laser (470–670 nm), and acusto-optical beam splitter. Images were acquired with a HC PL APO ×93/1.30 GLYC motCORR-STED WHITE objective, and 71. Cells of WT (H1N2330), lem2Δ (H1N2324), and red1Δ (H1N2328) were pre-cultured in minimum medium (EMMG5S) for overnight at 30 °C, then transferred into rich medium (YES) and cultured for 16 hours. The cells (~1 × 108) were mixed with one-tenth of meiosis-induced PSB1940 (YY548-13C) cells (~1 × 107) as a positive control for smFISH, and then fixed with 4% formaldehyde (Polysciences) at 30 °C for 30 minutes. The cells were labeled with Quasar 570-labeled RNA probe set for sme2 (See Supplementary Table 4 in ref. 71 in detail), then mounted in ProLong Glass Antifade Mountant (Thermo Fisher Scientific).smFISH was performed as described in the previous report with slight modification72. The images were denoised by the ND-safir program73, deconvolved using the built-in SoftWoRx software (v7.0.0), and then projected by maximum-intensity projection. The brightness of the images was adjusted using the Fiji software74 for better visualization, without changing the gamma settings.The cells were observed using ×60 PlanApo N OSC oil-immersion objective lens  = 1.4, Olympus) on the DeltaVision Elite system  equipped with pco.edge 4.2 sCMOS camera (PCO). Chromatic shifts were corrected using Chromagnon software (v0.87) using a bleed-through fluorescence image as a reference600) were fixed with 1% formaldehyde (Sigma) for 15 minutes at RT, followed by quenching 5 minutes at RT with 125 mM glycine (Sigma). Cultures were spun down, washed once with 1×PBS and frozen in liquid nitrogen. Cells pellets were resuspended in lysis buffer  and lysed with glass beads (Roth) in a bead beater . Fragmented material was sonicated (Qsonica Q800R1) for 1 hour with cycles of 30 seconds ON/OFF at 4 °C. The lysate was cleared and used for immunoprecipitation with 15 μl GFP-trap or Myc-trap (Chromotek). Beads were washed with lysis buffer, and bound material was eluted from beads with elution buffer  with 15 minutes of incubation at RT and 15 minutes at 65 °C. RIP samples along with inputs were de-crosslinked at 95 °C for 10 minutes. Samples were then incubated with 40 μg of proteinase K (Sigma) for 4 hours at 37 °C. RNA was recovered with a phenol-chloroform-isoamyl alcohol extraction (Thermo Fisher Scientific) followed by precipitation with sodium acetate, isopropanol, and glycogen (Thermo Fisher Scientific). Precipitated RNA was digested with DNase I (Thermo Fisher Scientific) for 2 hours at 37 °C. Purified RNA was used for reverse transcription following manufacturer’s instructions  and used for qPCR, as described for RT–qPCR samples. The primers used are listed in Supplementary Table Cell lysates prepared from equal amounts of cells  and quenched with 125 mM glycine (Sigma). Following lysis and sonication, solubilized chromatin corresponding to approximately 5–6 × 108, 4 × 108, and 9 × 108 cells was immunoprecipitated with antibodies against Pol II-S5P , H3K9me2 (2 μl), and Myc-trap , respectively.ChIP was performed as previously describedact1+, tef3+, ade2+). The data were shown as relative to the untagged strain.RT–qPCR experiments were performed as described above, using the primers listed in Supplementary Table Rat monoclonal anti-HA  and mouse monoclonal anti-GFP  antibodies were purchased from Roche and Santa Cruz Biotechnology, respectively. Mouse monoclonal anti-H3  was obtained from Active Motif. Rabbit polyclonal anti-Myc  or mouse monoclonal anti-Pol II-S5P  antibodies were kindly provided by M. Spletter and by A. Ladurner, respectively. Mouse monoclonal H3K9me2 ChIP grade (ab1220) was purchased from Abcam. Secondary antibodies fused to HRP were used for detection P values were generated using two-tailed Student’s t-tests or chi-square (χ2) analyses; n.s., P ≥ 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.Representative results of at least two independent experiments were presented in all of the figure panels for all blots. No statistical methods were performed to predetermine the sample size, and the number of biological replicates was based on similar studies for each experiment. Analyses of the variance were performed, and pairwise differences were evaluated with Tukey’s post hoc test using R statistical language ; different groups are marked with letters at the 0.05 significance level. For all graphs, data the individual replicates are shown in a floating bar plot, and the line depicts the median. Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-022-00831-6.Supplementary InformationSupplementary Tables 1–4Reporting SummaryPeer Review FileSupplementary Data 1lem2∆ analyzed by AnGeLiGO terms of upregulated transcripts in Supplementary Data 2lem2∆, rrp6∆ and red1 mutants analyzed by AnGeLiGO terms in top clusters containing upregulated transcripts in


With the development of high-throughput sequencing and experimental technologies, extensive ncRNA interactions have been accumulated. Therefore, we updated the NPInter database to a fifth version to document these interactions. ncRNA interaction entries were doubled from 1 100 618 to 2 596 695 by manual literature mining and high-throughput data processing. We integrated global RNA–DNA interactions from iMARGI, ChAR-seq and GRID-seq, greatly expanding the number of RNA–DNA interactions (from 888 915 to 8 329 382). In addition, we collected different types of RNA interaction between SARS-CoV-2 virus and its host from recently published studies. Long noncoding RNA (lncRNA) expression specificity in different cell types from tumor single cell RNA-seq (scRNA-seq) data were also integrated to provide a cell-type level view of interactions. A new module named RBP was built to display the interactions of RNA-binding proteins with annotations of localization, binding domains and functions. In conclusion, NPInter v5.0 ( XIST, can regulate dosage compensation by interacting with X chromosome in the female embryo , we manually curated interaction pairs from public literature documented in PubMed. Over ten thousand related papers published between May 2019 and March 2022 were collected. After manual and stringent curation, we obtained 8587 interactions with experimental evidence from 5143 articles. The keywords for the PubMed literature search are listed in the Supplementary Material. In addition, we specifically collected SARS-CoV-2 related RNA interactions from literature published since 2020.To collect RNA–DNA interactions from high-throughput datasets, we downloaded and processed 156 raw SRA ChIRP-seq datasets  and 117 For global RNA–DNA interactions, we collected data from techniques including iMARGI, ChAR-seq and GRID-seq, which globally localize chromatin-associated ncRNAs, much more efficiently than the single RNA-targeting methods of ChIRP-seq and CHART-seq. ncRNA molecules were extracted and annotated with disease information from raw datasets of these three technologies. Information of experimental technologies for each interaction was retained. We compared the RNA–DNA binding sites with ncRNA annotations deposited in NONCODE v6 database  and alloP value threshold was set to 0.001. RNA–protein binding sites were also compared with ncRNA annotations in NONCODE v6 database and ncRNAs were allocated NONCODE IDs. Evolutionary conservation score of binding site was calculated for each interaction using PhastCons   databasehastCons .P value threshold was also set to 0.001 in software piranha for good quality of peaks. BEDOPS (We processed Argonaute targeted CLIP-seq (AGO-CLIP) datasets based on the method described in NPInter v4.0 . The P v. BEDOPS  was used. BEDOPS  and Targ. BEDOPS  and miRNWe re-annotated biomolecules in NPInter with standard IDs from reference databases. Specifically, lncRNAs, miRNAs, circRNAs, mRNAs and proteins were annotated with NONCODE IDs , miRBasehttps://portal.gdc.cancer.gov/), we collected information of over 50 tumor scRNA-seq datasets from the literature and downloaded related data sources from GEO. After screening, 20 scRNA-seq datasets representing 20 cancer types were selected with complete cell type annotation and available expression count matrix. The Seurat  from 1 100 618 in NPInter v4.0 to 2 596 695 interactions in NPInter v5.0 covering 60 species by literature mining and high-throughput data processing Table .http://bigdata.ibp.ac.cn/npinter5/browse/). For each interacting molecule, such as lncRNA, circRNA and miRNA, we re-annotated with NONCODE IDs, circBase IDs and miRBase IDs respectively. We arranged the type, description, alias, interaction tables and related disease annotation into the Molecule Profile page (e.g. http://bigdata.ibp.ac.cn/npinter5/molecule/22789/). Users can easily browse and view these data on our website.Apart from basic information such as species, cell line and experimental methods, biomolecules and interactions were provided with more detailed annotation, including interaction level, interaction class, tags of interactions and PhastCons sequence conservation score of the binding site. Levels were defined as ‘RNA–protein’, ‘RNA–RNA’, ‘RNA–DNA’ and ‘RNA-TF’ based on the types of interacting molecules. According to the interacting classes, interacting pairs were classified as different types . Tags including ‘miRNA target interaction’, ‘ncRNA–protein binding’ and others were applied to briefly describe the mechanism of each interaction with visualization displayed on the website. For each binding site detected in CLIP-seq, we provide an average PhastCons score to present evolutionary conservation. All the detail information of interactions can be viewed on the Interaction Profile page  has caused large-scale outbreaks of coronavirus disease 2019 (COVID-19) all over the world. It is an RNA virus whose success as a pathogen relies on its abilities to repurpose host RBPs and to evade antiviral RBPs . NumerouMEG3 was mostly contributed to metastatic PDAC  is a highly heterogeneous and constantly evolving milieu composed of various cell types ranging from normal to malignant cells . These ctic PDAC . scRNA-sdatasets ,52. HereHOTAIR as an example, which is one of the most extensively studied lncRNAs. Users could search HOTAIR at webpages of ‘Home’ and ‘Browse’.  to coordinate chromatin occupancy , in which the interaction network is displayed  when processing high-throughput data. In summary, NPInter is dedicated to providing multidimensional interactions for each ncRNA while maintaining high data quality.We integrated abundant RNA interactions with experimental evidence from published literature (18 181 interactions from 7311 articles) and applied strict We updated existing modules and rearranged our web interface to incorporate the new modules mentioned above for better user interaction and experience. The Browse module was updated to be categorized as traditional interactions and ncRNA-genome bindings. The former contains interactions of RNA–protein, RNA–RNA and RNA-TF derived from the existing Browse module, while the latter deposits chromatin-associated ncRNAs. The SARS-CoV-2 module was built to exhibit SARS-CoV-2 related interactions and annotations of each biomolecule. Also, the scNPInter module was developed for data visualization and display of cell type-specific lncRNAs and their interaction pairs from scRNA-seq datasets. Additionally, we integrated a new module named RBP to provide the interactions of RNA-binding proteins with corresponding annotations.RBPs are proteins that bind to double or single stranded RNA to play essential roles in RNA-mediated gene regulation . The regUnlike protein-protein interactions, which have been extensively investigated, ncRNA interactions have received less attention and remain largely unknown. To well document the ncRNA interactions and make a comprehensive complement to the biological regulatory network, we constructed the NPInter database and have updated it to a fifth version. In NPInter v5.0, we doubled the number of ncRNA interactions primarily by literature mining and high-throughput data processing. The volume of experimentally validated ncRNA interactions has been greatly expanded from the large accumulation of recently published literature. Each interaction pair was annotated with detailed information and conservation scores, and each molecule was annotated with disease information. In addition to ChIRP-seq and newly added CHART-seq which can only target one chromatin-associated ncRNA, we have integrated global RNA–DNA interactions derived from iMARGI, ChAR-seq and GRID-seq technologies. This has greatly increased the number of RNA–DNA interactions and chromatin-associated ncRNAs. To provide reference for the study of COVID-19, we also collected different types of SARS-CoV-2–RNA interactions between virus and host. Specificity of lncRNA expression in different cell types from tumor scRNA-seq data were also integrated in this version to provide a cell-type level view of interactions. Based on user feedback, a new RBP module was presented to provide RBP-based interactions classified according to annotation of localization, binding domains and functions. Compared with other RNA interaction databases, NPInter v5.0 covers global RNA–DNA interactions and integrated SARS-CoV-2–RNA interactions with functional annotations.The study of RNA interaction has expanded into various areas, such as tumor biology and COVID-19 study. Moreover, new technologies for identifying interactions are constantly being developed. We will continuously update and maintain the NPInter database to document such interactions. There are several techniques that investigate RNA interaction directly in a small number of cells or at single cell level, such as LACE-seq . Detectihttp://bigdata.ibp.ac.cn/npinter5.NPInter v5.0 is free to access, browse, search and download at gkac1002_Supplemental_FileClick here for additional data file.


PICU inpatients are likely to be at increased risk of having unmet SRH needs due to barriers to accessing services. Since May 2018, an in-reach SRH assessment has been available to all psychiatric inpatients on ES1 ward, if referred. Analysis of referrals over 15 months identified only 24 had been made during this time.To assess the SRH needs of women admitted to ES1 PICU, the feasibility of providing a SRH in-reach clinic, and the acceptability of delivering a nurse lead referral programme.A bi-monthly SRH in-reach clinic and a nurse led SRH referral pathway were implemented on ES1 over a seven-month period. A staff training needs assessment was performed followed by training, a protocol was developed, staff attitudes were explored, and patient engagement was sought.A total of 41% (32/77) of patients were referred, which was a 29% increase. 53.1% (17/32) of the total referrals had a true SRH need, equating to a 10% increase and 22% (17/77) of all PICU admissions. 90% of referrals were made by nursing staff. A staff focus group (n15) highlighted the acceptability and perceived importance of offering SRH care in PICU, if interventions were appropriately timed and the patient’s individual risk profile was considered.Results identify that SRH needs for PICU admissions are greater than previously realised. Providing a nurse led referral pathway for an SRH in-reach clinic is acceptable, feasible and beneficial for PICU patients. This project has resulted in service improvements including offering asymptomatic STI testing to all PICU admissions.No significant relationships.


Helicobacter pylori (H. pylori) infection can cause systemic multiple organ diseases and even tumors, such as gastric cancer (GC)  . Other pcer (GC) , 3. Non-Long et al. identified the stage-associated exosome miRNAs in colorectal cancer by improved robust and corroborative approach embedded miRNA-target network, and three miRNAs  were demonstrated to be the most stably expressed stage-associated miRNAs in CRC serum exosomes, cell exosomes and tissues, suggesting that these exosome miRNAs could be potential biomarkers for colorectal cancer. Not only miRNAs, the circulating Epstein-Barr virus DNA could also be a diagnostic and prognostic biomarker for the gastrointestinal tumor. He et al. further showed that circulating EBV DNA might play a predictive role in distinguishing EBVaGC from EBVnGC , and was associated closely with better overall survival . EBV infection in patients with gastric cancer may be linked to hepatic impairment and immune response. Circulating cell-free EBV DNA is not only a biomarker for the screening of an EBV-related GC subtype, but also an independent prognosis factor for the long-term survival benefit in GC patients. Next, Li et al. described some mechanisms about the infection of hepatitis B virus (HBV). They found that a clathrin-binding membrane protein epsin3 (EPN3) could negatively regulate the expression of HBV RNA. EPN3 expression was induced by transfection of an HBV replicon plasmid, while the HBV-RNA level was reduced in hepatic cell lines and murine livers hydrodynamically injected with the HBV replicon plasmid. Viral RNA reduction by EPN3 was dependent on transcription, and independent of epsilon structure of viral RNA. Viral RNA reduction by overexpression of p53 or IFN-α treatment, was attenuated by knockdown of EPN3, suggesting its role downstream of IFN-α and p53. Taken together, their studies demonstrated the anti-HBV role of EPN3.In this Research Topic, Yan et al. summarized the roles of Non-coding RNA in hepatitis B virus-related hepatocellular carcinoma (HCC) using a bibliometric analysis method, and Liu et al. reviewed the roles of non-coding RNA in the diagnosis and treatment of Helicobacter pylori-related GC, with a focus on inflammation and immune response. The former review analyzed the top 100 keywords using a co-occurrence cluster method and found four clusters which were the main hot topic for the association between non-coding RNAs and HBV-related HCC: (1) non-coding RNA as a molecular marker for the diagnosis and prognosis of HBV-related HCC; (2) dysregulation of non-coding RNA by HBV X protein (HBx); (3) effects of non-coding RNA on the biological behaviors of HBV-related HCC; and (4) epidemiological study for the effects of non-coding RNA on the risk of HBV-related HCC. The latter one briefly described the effects of non-coding RNAs on H. pylori-related GC from the perspective of inflammation and immune response, as well as their associations with inflammatory reactions and immune microenvironment. They outlined the potential of ncRNAs as markers for the early diagnosis, prognosis, and treatment of H. pylori-related GC. The ncRNAs involved in H. pylori-related GC may hold great promise as novel therapeutic targets for immunotherapy.In addition, there were two reviews summarizing the roles of non-coding RNAs in the microbial infections related tumors in this Research Topic. The role of non-coding RNA in the diagnosis and treatment of infection induced digestive system cancers” represents an important contribution to the regulatory mechanisms of non-coding RNAs in the complex molecular network of digestive system tumors caused by microbial infections, reveals current research gaps, and finally stimulates new ideas for future researches on the topic.The Research Topic “QX wrote the article. JL revised the manuscript. All authors shared and approved the contents.


The genome of influenza A virus (IAV) consists of eight unique viral RNA segments. This genome organization allows genetic reassortment between co-infecting IAV strains, whereby new IAVs with altered genome segment compositions emerge. While it is known that reassortment events can create pandemic IAVs, it remains impossible to anticipate reassortment outcomes with pandemic prospects. Recent research indicates that reassortment is promoted by a viral genome packaging mechanism that delivers the eight genome segments as a supramolecular complex into the virus particle. This finding holds promise of predicting pandemic IAVs by understanding the intermolecular interactions governing this genome packaging mechanism. Here, we critically review the prevailing mechanistic model postulating that IAV genome packaging is orchestrated by a network of intersegmental RNA–RNA interactions. Although we find supporting evidence, including segment-specific packaging signals and experimentally proposed RNA–RNA interaction networks, this mechanistic model remains debatable due to a current shortage of functionally validated intersegmental RNA–RNA interactions. We speculate that identifying such functional intersegmental RNA–RNA contacts might be hampered by limitations of the utilized probing techniques and the inherent complexity of the genome packaging mechanism. Nevertheless, we anticipate that improved probing strategies combined with a mutagenesis-based validation could facilitate their discovery. The natural reservoir of influenza A viruses (IAVs) are aquatic birds, yet spill-over events have led to the introduction and subsequent establishment of numerous IAV lineages in other species, including swine, poultry, bats and humans ,2. The aRecent research findings suggest that reassortment is driven by a selective genome packaging mechanism that assembles the eight viral genome segments as a supramolecular complex into the virus particle, thereby allowing the exchange of cognate genome segments between co-infecting IAVs . The curThe IAV genome comprises eight negative-sense, single-stranded viral RNA segments (vRNAs) that vary in length from 890 to 2341 nucleotides . All genFollowing viral infection, these vRNPs are released from the infecting virus particles into the cytoplasm and imported into the nucleus to be transcribed and replicated. Newly formed vRNPs are then exported out of the nucleus and transported to viral budding sites at the plasma membrane. The prevailing model assumes that during this transport, individual vRNPs are progressively assembled into an octameric supramolecular genome complex Figure  27–33)..27–33). Past research suggests that IAV genome packaging is coordinated by a network of specific intersegmental RNA–RNA interactions that is formed by discrete genomic loci known as packaging signals. While initial studies have mapped these packaging signals towards the segment termini, more recent studies suggest that they are also present in internal vRNA regions lying beyond the vRNA termini. In the following sections, we review the current knowledge on packaging signals and revisit their presumed role in forming intersegmental RNA–RNA interactions.Terminal packaging signals were first proposed in studies analysing the genome content of defective interfering (DI) IAV particles. Unlike ‘standard’ virus particles which package eight full-length vRNAs, DI particles commonly package one genome segment that harbours a large internal deletion but retains the NCRs plus short portions of the adjacent coding regions ,39. SuchTo map these proposed terminal packaging signals of each vRNA in detail, artificial genome segments were created in which a GFP reporter gene was flanked by the NCRs plus varying portions of the adjacent coding region of the studied vRNA Figure . In plasAfter the discovery of terminal packaging signals, research was intensified to understand their mechanism of action. Since terminal packaging signals contain conserved nucleotide stretches, a series of WSN/H1N1 and PR8/H1N1 mutants were created, by altering either a single 3′ or 5′ terminal packaging signal of a given genome segment with synonymous nucleotide substitutions ,57–65. TSince 3′ and 5′ terminal packaging signals are present in all genome segments, it was envisioned that they collectively participate in a network of vRNP–vRNP interactions involving all vRNPs. Recently, Bolte and colleagues provided compelling evidence for this idea . IntriguThis apparent flexibility of the vRNP–vRNP interaction network is further supported by findings that mutated terminal packaging signals have varying effects on genome packaging depending on the analysed IAV strain ,63. ThusAlthough numerous terminal packaging signals were identified, their mechanism of action has remained under investigation ever since. As they consist of RNA nucleotides, it was speculated that they form intersegmental RNA–RNA interactions. Early hints supporting this idea came from electron-tomography experiments that visualized electron-dense structures between neighbouring vRNPs in the (7 + 1) genome complex of budding viral particles Figure . Fourniein vitro transcribed vRNAs and computational predictions have shown that the terminal packaging signals of several genome segments adopt defined RNA secondary structures   rescue assay  Figure  36,82).,82.in viy Figure , suggestFurther evidence that terminal packaging signals form intersegmental RNA–RNA interactions was provided by Essere and colleagues while studying reassortment events between Moscow/H3N2 and Finch/H5N2 viruses using a co-transfection-based rescue assay . They obRecently, Miyamoto and colleagues described a functional interplay between the terminal packaging signals of vRNAs 1 and 4 in WSN/H1N1 . By intrIn conclusion, the experimental data reviewed thus far suggest a crucial role of the terminal packaging signals in the formation of an octameric genome complex and its incorporation into the viral particle. For some genome segments, the terminal packaging signals are known to adopt RNA secondary structures responsible for coordinated genome packaging. Although a few studies using virus mutants suggest that terminal packaging signals establish RNA–RNA interactions between genome segments, this mechanistic concept remains to be conclusively proven.trans-complemented for the missing viral protein. Subsequent analyses of viral particle preparations by plaque assay (on the trans-complemented cells), HA-assay and RT-qPCR  and a human isolate (e.g. Udorn/H3N2). The resulting reassortant viruses ideally replicate well in eggs and possess vRNAs 4 and 6 of the seasonal virus to elicit an immune response against the surface glycoproteins hemagglutinin and neuraminidase. While analysing reassortant viruses produced from co-infections between PR8 and Udorn, Cobbin and colleagues observed that the Udorn-vRNA 6 often co-segregated with the Udorn-vRNA 2 into the PR8 genetic background but rarely with the PR8-vRNA 2 . This imIn conclusion, two intersegmental RNA–RNA interactions between internal vRNA regions could be precisely mapped and validated to coordinate genome packaging. However, the short list of internal packaging signals, in contrast to the extensive list of terminal packaging signals, remains a challenge in evaluating their general role in genome packaging.Techniques coupling RNA–RNA crosslinking to next generation sequencing have recently enabled the high-throughput identification of intersegmental RNA–RNA interactions. These techniques commonly use psoralen derivates which intercalate into double-stranded RNA regions and crosslink them upon UV irradiation. Ligation of the crosslinked RNA regions creates chimeric RNAs, which are subsequently reverse-transcribed and sequenced. Computational analysis of the chimeric reads recovers the initially crosslinked RNA–RNA interactions, which can be used to build up an interaction network with precise intermolecular base-pairings.et al. performed sequencing of psoralen crosslinked, ligated, and selected hybrids (SPLASH)   Figure  on purif) Figure . They id) Figure  using vi) Figure . While tet al. focused on a ‘hotspot’ region in vRNA 5 that interacted with multiple partner sites on different genome segments  competition assay that a preferential co-segregation of Udorn-vRNAs 2 and 6 is mediated by specific base pairings between these two genome segments . They alsegments . Its mutThe eight IAV genome segments are known to be selectively packaged into viral particles as an octameric genome complex. Several lines of evidence support that this process is facilitated by an extensive and partially flexible network of intersegmental RNA–RNA interactions formed by terminal and internal packaging signals. However, two key aspects of this mechanistic model still lack conclusive evidence. Firstly, it has not yet been proven that the terminal packaging signals coordinate genome packaging by forming intersegmental RNA–RNA interactions Figure . AlthougDespite recent progress, our current mechanistic understanding of the genome packaging process is not sufficient to fully accept the prevailing genome packaging model. To develop a robust understanding of the genome packaging mechanism we (i) propose avenues to test the biological significance of the experimentally postulated intersegmental RNA–RNA interaction networks, (ii) suggest to evaluate and improve the accuracy of the existent RNA–RNA interaction probing strategies and (iii) encourage the field to explore the potential role of the IAV nucleoprotein (NP) in modulating intersegmental RNA–RNA contacts.trans-complementary virus mutants  the roles played by other viral proteins and host cell factors in IAV genome packaging and (ii) a systematic analysis of the current bioinformatics approaches to predict and study the involved intersegmental RNA–RNA interactions lie beyond the scope of this review and therefore have not been reviewed here. Understandably, insights obtained on these aspects could contribute significantly to our current knowledge of IAV genome packaging and genetic reassortment.