We investigated the effect of structural interdependencies between groups , and interdependencies between individuals on ingroup favoritism in minimal group situations. Previous research has attempted to determine whether ingroup favoritism is produced by categorization or intragroup interdependencies (reciprocation expectations), but recent literature suggests that it is not possible to tease these influences apart. We report two studies that investigate how ingroup favoritism evolves over time in social interaction. The levels of ingroup favoritism were affected by categorization and inequality, and the level of ingroup favoritism changed over time, increasing or decreasing depending on the nature of the initial intergroup structure. We conclude by providing two explanations for this change: emergent norms, and changes to the intergroup situation produced by interaction. Our experiments confirm the value of studying the evolution of minimal group behavior, especially for explaining why low status groups act to preserve intergroup inequalities. The underlying causes of ingroup favoritism—or “parochial altruism”–have been of enduring interest to social psychologists, economists, and others seeking to explain the persistence of intergroup conflict, prejudice and inequality. However, researchers have seldom studied the evolution of ingroup favoritism.In the first part of this article, we review experiments that have used the minimal group paradigm (MGP) to show that ingroup favoritism is a relational phenomenon, shaped by interdependencies between groups and individuals. Although relationships of interdependence evolve over time and in interaction, these temporal aspects of ingroup favoritism have only just begun to attract research attention. We then report the results of a program of experimental work that has sought to determine how the context and process of interaction can affect ingroup favoritism. We conclude by reflecting on our findings that interaction can promote both ingroup and outgroup favoritism, which can either accentuate or attenuate inequalities between groups.The MGP eliminates social interaction, thus giving researchers sufficient control to study the underlying causes of ingroup favoritism. The benchmark studies were conducted by Tajfel and his colleagues in the early 1970s .However, this was not the significance that Tajfel and his colleagues drew from the studies , 4. For “subjects define and evaluate themselves in terms of the imposed social categories, compare ingroup with outgroup in terms of the only available value dimensions  and seek positive distinctiveness for their own group by awarding it more money.”(p.30)We might characterize social identity theory as an intergroup interdependence theory of social behavior because it situates the phenomenon in the context of intergroup relations. Although Tajfel and his colleagues challenged Sherif’s economic explanation of prejudice, they endorsed his intergroup relations perspective. In his summer camp studies, Sherif , 8 had sSubsequent research has shown how other features of the intergroup situation can strengthen or weaken ingroup favoritism. Although low status groups demonstrate outgroup favoritism when the status differences are perceived to be stable and legitimate, they show ingroup favoritism when the status hierarchy is deemed illegitimate; and high status groups may respond to unfairness with compensatory favoritism toward low status groups , 11. SimRabbie, Schot and Visser  argued tThe effects of reciprocation expectation on ingroup favoritism are now well-established. Locksley, Ortiz, and Hepburn  showed tYamagishi and his colleagues have conducted a number of experiments to show that ingroup favoritism in the MGP depends on the expectation of ingroup reciprocation and is eliminated when reciprocation can be established directly with ingroup or outgroup members –21. TheySimpson  studied In response to Simpson’s experiment, Yamagishi and Mifune  have refAccording to Yamagishi and Mifune , reciprosocial expectations with which they enter an experiment—or any other social situation.” (p.72). He attributed ingroup favoritism to the human motive to act appropriately, and suggested that the MGP data be subject to “cultural analysis” of “the general norms and values that respondents were importing in order to deal with the novel situation.”. on.” informs participants that they received or did not receive tokens from ingroup or outgroup members in the past , 16expecst e.g. , 18, 29), 29expecresponses but one-off decisions. While second-player decisions in sequential games [Experimental games relax the restrictions on interaction that are enforced in MGP that use allocation matrices. Prisoner’s Dilemma games, for example, are interactional in the sense that outcomes depend on the actual decisions of both players . Howeveral games  are inteTrue interaction involves sequences of action, response, and reaction. Chen and Li  asked parespond by making an allocation to the original allocator or the other recipients in the subsequent rounds. Such behavior would constitute a fully interactive network because at each point in a sequence of behaviors each actor has the opportunity to respond to all prior interactions, allowing the participants to collectively constitute the environment for subsequent interactions.Chen and Li , CacaultWe have identified two studies that have investigated how participation in a fully interactive network affects ingroup favoritism. Goette, Huffman and Meier  studied The within-group interaction among platoon members in these experiments extended over time and allowed all group members to interact with each other, responding to previous actions. The network was fully interactive, but with uncontrolled and undocumented interaction, and so we have no way of knowing what features of interaction produced the outcomes of interest, or how prior interactions affected subsequent ones. The interaction was a ‘black box’ of unknown content—but which as a whole was shown to affect social identity, collective behavior, and ingroup favoritism.In recent years, computer simulations using simple artificial agents have gone some way to open the black box of interaction. They have shown how group-based phenomena such as ethnocentrism can emerge over time, as agents make simple decisions to either defect or cooperate with ingroup and outgroup agents . Such siThese software agents might make simple, rule-governed and self-interested decisions, stripped of the complexity of human social life, but the simulations lay bare the evolutionary dynamics of ingroup favoritism. These fully interactive social contexts show that the “dynamic nature of bias results from complex social network interactions which play a central role in human societies.” ( p.1). Thwww.viappl.org). We created exchange games in VIAPPL in which each participant was represented by an avatar located in a sparse virtual arena in which all other participant avatars were visible  prime. The design thus manipulated intergroup interdependencies, allowing us to investigate the exchange practices of equal status, high status, and low status groups and individuals in different normative climates. We report all measures, manipulations, and exclusions in these studies, and determined sample size in advance of data collection by ensuring that we had sufficient number of game replications in each condition of the design.Our dependent variable was defined by the target of giving. We recorded whether each player in each round gave their token to an ingroup member, an outgroup member, or to themselves. Given the 40-round game structure, each form of giving potentially ranged from 0 to 40, and there were dependencies between the three forms of giving which together summed to 40. Ingroup favoritism was indexed by higher ingroup and lower outgroup giving. Even though the avatars in the individual condition were not represented as distinguishable group members, to compare allocation practices in the individual and group conditions we treated each individual as having the group identity of the individual in the same position in the arena in the group condition.In the light of the substantial literature on ingroup favoritism in the MGP we propose the following general hypotheses for the effects of the experimental manipulations:G1. There will be more ingroup giving and less outgroup giving in the group than individual condition.HG2. In the inequality condition there will be more “compensatory” ingroup giving and less outgroup giving among low status groups, and there will be more “compensatory” outgroup giving and less ingroup giving among high status groups. We expect compensatory responses because our inequality condition is illegitimate in the sense that it is imposed by experimenters and not ‘earned’ by the participants [Hicipants . In contG3. Competition primes will strengthen ingroup favoritism whereas fairness norms will weaken ingroup favoritism.HWe anticipated that these effects would interact with each other by strengthening ingroup favoritism when they are aligned or by weakening ingroup giving when they are not aligned. For example, compensatory ingroup giving of low status groups and outgroup giving of high status groups will be further strengthened by the fairness prime, whereas the competitive ingroup favoritisms in the equality condition will be strengthened by the competition prime.It is only in Round 1 that the experimental situation is fully defined by the experimenters, as is usually the case in MGP experiments. As the games unfold, the exchanges that occur in each round begin to change the environment of individual and group balances and differences. Moreover, a sense of sociality emerges as ties are made between individuals and groups and players respond by reciprocating (or not), acting selfishly, or by ingroup or outgroup favoritism. Thus, each replication of a game with the same set of starting conditions will not be expected to produce the same outcome. This is the evolutionary process that is our primary interest.In this exploratory study we investigate how ingroup favoritism evolves over time and in response to different starting experimental conditions. We propose the following temporal hypotheses about the evolution of ingroup favoritism in social interaction.T1. Static model without interdependence: Behavior is prescribed by a fixed underlying cause or set of causes  that assumes a constant strength from round one. The motives and beliefs underlying such interaction are similar to the fixed “happiness rules” that inform agent behavior in simple simulations. Although no one explicitly defends this model, it is implicit in most research that only makes use of one-off observations to identify the cause—in an either-or fashion—of ingroup favoritism. This is the model, without interdependence, that John Turner spent a lifetime arguing against [H against .T2. Interpersonal interdependence model of emerging norms (Monotonic change model). This is similar to the static model, but with normative strengthening of tendencies. We expect monotonic change as initial behavioral tendencies strengthen over time as they acquire normative status.HT3. Intergroup interdependence model: This is similar to the interpersonal interdependence model, but we expect different emergent norms under different conditions of intergroup relations as predicted by Social Identity Theory [Hy Theory .T4. Dynamic interdependence model (non-monotonic change model). This model anticipates that behavioral imperatives and norms are (1) shaped by nature of the relations between groups and individuals within groups, and (2) change over time as they are shaped by prior actions. Intergroup hierarchies and the legitimacy and stability of intergroup relations are not stable ([H stable ( p.8), neT1 is opposed to the other temporal hypotheses, and while we don’t expect to find evidence for HT1, we did not hypothesize which of HT2, HT3 and HT4 we expected to identify, or which would be most prominent.HThe sample consisted of 1005 student volunteers  from the University of KwaZulu-Natal, South Africa. The mean age of the participants was 20.59 years (SD = 2.16 years). All participants provided written informed consent to participate in the study, which had been approved by the Human Sciences Research Ethics Committee of the University of KwaZulu-Natal.Participants played 40-round games in which they were required to allocate one token per round to any of the participants in the game, including themselves. We paid all participants a standard incentive of R20 for their participation irrespective of their final token balance, meaning that the tokens had a purely symbolic value. The requirement to allocate one and only one token per round resulted in a structural dependency between ingroup-, outgroup-, and self-giving, yielding compositional data in which each participant’s outgroup-, ingroup-, and self-giving summed to one in each round, and 40 over the rounds.Four experimental factors were operationalized as follows:Participants were randomly assigned to play either as undifferentiated individuals or as members of visually distinctive groups. Assignment to minimal groups was random but was done via a painting preference task in which participants indicated their preference for a painting by Klee or Kandinsky. In the individual condition, player avatars were notionally assigned to the same groups as in the group conditions, but participants were not aware of this group membership because no visual or textual indicators of group membership were given. In the individual inequality condition, this underlying group membership was made evident by the difference in starting balance between the high status and low status individuals.Equality condition was manipulated orthogonally to group condition. Half the games in the group and individual conditions were equal status games and the other half were unequal status games. In the equal status games, all participants were assigned the same starting token balance, namely 30 tokens. In unequal status games, participants assigned to the low status condition were allocated 20 tokens, whereas participants assigned to the high status condition were allocated 40 tokens. This manipulation created a second experimental factor, Equality Condition, with three levels—equal status (n = 503), low status (n = 251) and high status (n = 252).Immediately before starting the game, the participants completed an adaptation of the verbal memory task developed by Hertel and Fiedler , 48, whiWe partitioned the data for each game into 5 waves of 8 rounds each, and summed the tokens given to self, ingroup, and outgroup in each wave.When crossed, these factors resulted in a 12-cell design, where each of the 12 cells represented a VIAPPL game to be played by 14 participants under a specific set of conditions, with the game in each cell replicated six times. This resulted in a total of 72 VIAPPL games in the study with 1005 participants (three participants failed to complete the game), making a total of 5025 observations of ingroup- outgroup- and self-giving (1005 participants by five 8-round waves).Counts of ingroup-, outgroup-, and self-giving could each range from 0 to 8 (and jointly summed to 8) in each wave. Ingroup- and outgroup-giving were approximately normally distributed, but self-giving was positively skewed and zero-inflated. The frequency of self-giving was relatively low compared to ingroup- and outgroup-giving, but increased steadily over time See . There wWe planned to use multi-level modelling (NLME and LME4 packages in R) to account for the nesting of the three kinds of token giving (1) in waves, (2) repeated by individuals, who, in turn, were (3) nested in games. However, it proved infeasible to analyse our compositional, longitudinal data with multi-level models. We thus decided to analyze ingroup versus outgroup giving independently from self-giving, eliminating the compositional aspect of the data, and allowing us to use multilevel models. We operationalized ingroup favoritism as the proportion of non-self-giving that was ingroup giving:This removes the longitudinal trend of rising self-giving—which was evident in all cells of the design –from the dependent variable, and in the process rectifies the dependency in the compositional nature of the data. It also eliminates the problem of skewness and zero-inflation that was apparent with the self-giving data. Because self-giving was zero-inflated, we reduced self-giving to a binary variable for each individual in each wave to index whether any self-giving apparent in that wave. We then conducted a binomial regression  to predict self-giving from the main effects, Time, Group Condition, Equality Condition, and Norm Condition. Only Time significantly predicted self-giving . Models that included interaction terms would not converge, perhaps due to the zero inflation. This analysis together with an inspection of descriptive data showed that self-giving increased over time for all the experimental conditions, and that the mean level of self-giving was the same in all experimental conditions.null = 1183.19). We first included only games as a random factor, which improved fit over the null model , and then we tested the model that included individuals and games as random factors, which also improved fit significantly over the null model, and produced a much smaller value of AIC . We then added the four fixed effects to the model, and compared the model with random intercepts (AIC = -273.89) against the model with random intercepts and random slopes (AIC = -394.71), treating time as a random effect. Adding random slopes improved the fit . We decided on the basis of this preliminary testing to include the three nested random effects subjects, time, and games, in further multilevel models, estimating random effects for intercepts and slope terms.Preliminary analyses of ingroup favoritism indicated that the random intercept models significantly improved model fit, in comparison with the null model  and improved the distribution of the residuals, which were approximately normally distributed with the double nesting in place.2 values for the overall model, following the approach of Nakagawa and Schielzeth [2 values of 0.10 and 0.50 respectively . We then tested the full model by adding the 4-way interaction and all lower order interactions into the mixed effects multilevel model described above. The interactions of a higher order than two-way were not significant, nor were any of the effects involving norm condition. We thus ran the final model that included the two significant two-way interactions—Time by Group Condition and Time by Equality Condition—and the main effects see . The modhielzeth , which rThe main effect for Time indicates that, overall, there was a slight tendency for ingroup favoritism to increase over time. The interaction between Time and Group Condition shows that this effect was apparent among players who were assigned to minimal groups, but not among players in the individual condition—see Participants in the minimal groups started out making more ingroup versus outgroup allocations than participants in the individual condition, and this difference strengthened over the course of the games. Given that there is no significant difference in the mean level of self-giving in the individual and group conditions, these results suggest that self-giving replaces outgroup giving in the minimal groups, but replaces both ingroup and outgroup giving in the individual condition.The main effect for Equality Condition see  reflecteThe interaction between Equality Condition and Time provides very strong support for the need for an evolutionary approach to the study of ingroup favoritism. The pattern of compensatory giving to the low status groups is very strong among the high and low status groups at the beginning of the game, but declines for both groups in the second half of the game. Perhaps this decline can be anticipated for high status groups for whom ingroup favoritism begins to play a stronger role as the initial inequality begins to decline. It is surprising, however, that low status groups increase the rate of giving to the high status groups in later rounds even when the intergroup inequality persists. It appears as though the generosity from the high status outgroup in earlier rounds promotes reciprocation that serves to preserve the inequality. This is the case despite the fact that inequality persists throughout, with the mean round balances after each wave being as follows: 151:266, 161:256, 170:248, 177:241, and 180:237.G1 and HG2, but not HG3. Ingroup favoritism was stronger in the group than the individual condition (HG1), and we observed the pattern of compensatory outgroup giving among the high status groups and compensatory ingroup giving among the low status groups in the inequality condition (HG2). There was no effect for the norm condition manipulation (HG3), either because the prime was not powerful enough to induce norms, or because competition and fairness norms have no bearing on behavior in this context. In sum, these findings provide strong support for the social identity theory of ingroup favoritism among minimal groups. Ingroup favoritism was apparent in the allocation of tokens that had no monetary value and was much more frequent than self-giving. This pattern of giving appeared to be motivated by the desire to be in the ‘winning group’ irrespective of material rewards. Previous MGP research has been criticized for excluding self-interest [The results of Study 1 supported hypotheses Hinterest , but we T2 that proposed that initial behavioral tendencies would strengthen as they acquired normative status. This trend was apparent both in the case of ingroup favoritism and self-giving. The results for the inequality condition support the intergroup interdependence hypothesis, HT3, which proposed that different emergent norms would evolve under different conditions of intergroup relations.The finding that that the two significant interactions involved Time supports our argument for the importance of an evolutionary approach to studying ingroup favoritism. The data show that ingroup favoring norms tend to increase over time. This supports the hypothesis HStudy 2 was a replication and extension of Study 1, but we used monetary rather than symbolic rewards, and excluded Norm Condition, which was found to have no impact on the allocations in Study 1. In this replication of the experiment, conducted in 2014, each token represented one South African Rand (approximately US $ 0.09), and the participants were told that they would receive as payment the token balance they achieved at the end of the game. Given these material outcomes, we anticipated greater self-giving and ingroup favoritism as group members used ingroup affiliation and reciprocation expectations to increase their material rewards.The sample consisted of 224 student volunteers  who each participated in one of 16 14-player experimental games. The mean age of the participants was 20.47 years (SD = 2.28 years). All participants provided written informed consent to participate in the study, which had been approved by the Human Sciences Research Ethics Committee of the University of KwaZulu-Natal.During each of the 40 rounds of each game, participants were required to give one token to any of the participants in the game, including themselves. Three experimental factors were operationalized as follows:Participants were randomly assigned to play either as undifferentiated individuals (8 games) or as members of visually distinct groups (8 games). Assignment to minimal groups was random but was done via a dot estimation task by which participants were informed that they would be assigned to a group of ‘over estimators’ or ‘under estimators’. In the individual condition, player avatars were assigned to the same groups as in the group conditions, but participants were not aware of this group membership because no visual or textual indicators of group membership were given .Equality condition was manipulated orthogonally to group condition. Half the games in the group and individual conditions were equal status games and the other half were unequal status games. In the equal status games, all participants were assigned the same starting token balance, namely 20 tokens. In unequal status games, participants in the low status group were assigned 10 tokens, whereas participants in the high status group were assigned 30 tokens. This manipulation created a second experimental factor, Equality Condition—with three levels, equal status (n = 112), low status (n = 56) and high status (n = 56).We partitioned the data for each game into 5 waves of 8 rounds each, and summed the number of tokens given to self, ingroup, and outgroup in each wave.The total number of allocation decisions made in the experiment was 8960 (16 games x 14 participants x 40 rounds), which resulted in 1120 8-round waves.The distribution of ingroup-, outgroup-, and self-giving across all the 8-round waves revealed a stronger ingroup favoritism preference than in Study 1. Self-giving remained negatively skewed and very strongly zero inflated. Ingroup-giving was negatively skewed and outgroup-giving was positively skewed, reflecting the preference for ingroup-giving over outgroup-giving.We used multi-level modelling (NLME and LME4 packages in R) with maximum likelihood estimation to analyze the data while taking onto consideration the nesting of token giving (1) in waves, (2) repeated by individuals, who, in turn, were (3) nested in games. We computed ingroup favoritism in the same manner as we did in Study 1, as the ratio of ingroup-giving to ingroup- plus outgroup-giving, thus removing self-giving from ingroup favoritism. The skewness in the data that we report above was fairly small, and when we assessed the ML model residuals, these were approximately normal, suggesting that the skewness did not adversely affect model results.Preliminary analyses indicated that random intercept models significantly reduced the AIC in comparison with the null model (null AIC = 767.25). We first included only games as a random factor, which provided a better fit than the null model  and then we tested the model that included individuals and games as random factors, which provided a better fit too than the null model, with a lower AIC value . We then added the three fixed effects to the model, and compared the model with random intercepts (AIC = 309.41) to the model with random intercepts and random slopes (AIC = 271.78). Adding random slopes improved the fit . The AR1 correction for autocorrelation improved the model fit  and the residuals were approximately normally distributed with the double nesting in place.2 =.34, df = 2, p > .84); neither was the two-way interaction between Group Condition and Equality Condition . Our final model thus included the two-way interactions between Time and Group Condition and Time and Equality Condition and the three main effects  . We then tested the full model with the three-way interaction and all lower order interactions and main effects. The three-way interaction was not significant (χ.37) see . Estimathielzeth , which rAn inspection of the figures that represent how Group Condition  and EquaThe main effect for Equality Condition replicate the finding that the low status group showed higher ingroup favoritism than the equal status group , whereas the high status group showed lower levels of ingroup favoritism than the equal status group . This confirms our expectation that the high status group would engage in compensatory outgroup favoritism given that our manipulation of inequality was illegitimate. This difference between the high and low status groups decreased over time because the high status group showed more ingroup favoritism over time, whereas the low status group showed less ingroup favoritism over time. In the final period, however, the high status group showed sharply less ingroup favoritism as self-giving replaced ingroup- but not outgroup-giving. These results provide strong evidence of an intergroup interdependence effect. The high status groups started the games with a higher rate of outgroup- versus ingroup-giving, but this generosity decreased over time as the initial illegitimate advantage reduced but before the inequality was eliminated. The low status group members initially favored their own group-players, but this ingroup favoritism also decreased over time as which helped to preserve the intergroup inequality in later rounds . By the forth wave there is not difference ingroup favoritism between the high and lost status groups, because both groups are acting in a way that preserves the inequality.G1). The results for inequality conformed the compensatory giving we expected in the context of an illegitimate inequality manipulation (HG2). The results confirm the importance of the effect of group-based inequalities on social interdependencies.Study 2 found higher levels of ingroup favoritism and self-giving than Study 1, consistent with the idea that these strategies were vehicles for leveraging economic gain. Overall, however, the pattern of significant main and interaction effects was remarkably similar to that found in Study 1. The results provided support for the hypothesis that ingroup favoritism would be stronger under the group than individual condition (HT2 and HT3) as was the case in Study 1.The same interactions with Time were apparent in Study 2 as were evident in Study 1. The difference between the levels of ingroup favoritism between the individual and group condition increased over time; and the levels of ingroup favoritism by the high and low status groups converged over time. These findings support the interpersonal and intergroup interdependency hypotheses .A great deal of MGP research has been devoted to teasing apart social identity processes from interdependency processes in minimal group situations. Is ingroup favoritism produced by categorization and identification or is it produced by reciprocation expectations? Recent evidence and a number of reviews have reached the conclusion that both processes affect behavior in the MGP and, moreover, that the two processes are themselves mutually interdependent , 32. CatOne of the consequences of this avenue of investigation has been to fix attention on ingroup favoritism rather than, say, on the conditions that promote outgroup favoritism. This has positioned MGP research within the social psychological tradition of work that addressed the ‘prejudice problematic’. In contThe present research makes two novel contributions to this tradition of social psychological scholarship. Our first contribution was explicit right from the beginning of this research project: we have investigated how ingroup favoritism evolves over time. To achieve this goal we put in place two essential conditions for the evolution of social life, namely, interaction between social actors, and a temporal context in which behaviors are allowed to influence each other and begin to create emergent contexts for subsequent interaction. We developed VIAPPL as an experimental platform for an evolutionary social psychology (see viappl.org). Our minimal group studies in this interactive context confirm the expectations that categorization promotes ingroup favoritism and that an illegitimate status hierarchy can promote compensatory outgroup favoritism among high status groups and ingroup favoritism among low status groups.In addition, the present research shows how the strength of ingroup favoritism may change over time. One reason for this is because acts of ingroup-, outgroup- and self-giving may acquire normative status and acceptability as they are picked up by other participants during the course of the interaction. This appeared to be the case for self-giving, which was a dispreferred, low frequency behavior at the start of the games, but which became more popular over time. In addition to emerging norms, there was a second way in which interaction produces change: behavior that responds to initial structural conditions can change those conditions thereby weakening the rationale for that behavior. This was evident in the weakening pattern of compensatory ingroup and outgroup giving of the low and high status groups in both studies. This pattern of allocation gradually reduced the intergroup inequality which, in turn, weakened the need for compensation and reduced the outgroup favoritism among high status group members and ingroup favoritism among low status group members. These results indicate that compensatory ingroup favoritism of low status groups and outgroup favoritism of high status groups is quickly extinguished. This pattern of compensatory intergroup behavior that has been evident in non-evolutionary minimal group studies and in early rounds of the present studies does not appear to persist over time. Both high and low status groups changed their exchange behavior in later rounds to preserve the inequality that was initially deemed illegitimate.In addition to promoting an evolutionary approach, this study has made a second contribution to the MGP literature. Although our data confirms much of what we already know about ingroup favoritism, it also sheds light on some of the processes that may undermine social change orientation of minority group members. Social identity theory ascribes outgroup favoritism among minority group members to a stable and oppressive social system which prevents the cultivation of cognitive alternatives, and which fosters self-depreciating responses among minority group members , 53. JosIn contrast to both of these accounts, the present research proposes interactional reasons for the outgroup favoritism of minority group members. In both studies, the initial ingroup favoritism of the low status groups decreased over the course of the game to the point where it was equal to the ingroup favoritism of equal and high status groups despite the fact that substantial inequalities remained between the groups. Members of the poor group began to increasingly favor the wealthy group in making allocations. Why would they act against their own group interest in this way? One possibility is that they did so to reciprocate for the favor the high status group had shown them in earlier rounds of compensatory outgroup favoritism of the high status group. Another possibility is that they hoped to elicit future reciprocation and benefit by giving to wealthy outgroup members. Perhaps the lowered inequality and the early compensatory exchanges led the participants to view remaining equality as more legitimate than the initial unearned inequality that the experimenters had imposed.Whatever the reason, the results suggests that intergroup interdependencies created by inequality can affect the kind of interpersonal interdependences that social actors attempt to set up. Generous treatment from high status group members may build expectations of fair treatment, which may in turn promote outgroup favoritism among low status groups in the face of ongoing inequality, even when the expectations of fair treatment from this high status group are not met (cf. ).In the article we have advanced a minimal group interaction paradigm (MGIP) in order to study the evolution of ingroup and outgroup favoritism in interactive exchange networks. VIAPPL reintroduces social interaction into the context of social psychological experiments, marking a return to the interactive experiments of an earlier generation . However, in contrast to these earlier interactive experiments, the form of interaction in VIAPPL is strictly controlled and observed so that it is possible to document the evolution of social life and its outcomes. In this article, however, our progress in studying inter- and intra-group interdependencies has been limited by our use of aggregate data and statistical techniques that assume independence of observations, and that thus partial out the real interdependencies in the data at a local level (i.e. between actors). In future work we plan to study local structural interdependencies using the methods of network analysis.S1 File(XLSX)Click here for additional data file.S2 File(CSV)Click here for additional data file.S3 File(CSV)Click here for additional data file.


Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant  et al. show that the evolution of multi-drug-resistant bacteria in vitro can be manipulated by administering pairs of antibiotics and switching between them.It is unclear whether strategies involving antibiotic cycling can efficiently control the emergence of antibiotic-resistant bacteria. Here, Yoshida  In vitro pharmacokinetic/pharmacodynamic studies have identified optimal dosing regimens using multiple drugs that can effectively suppress bacterial growth and prevent the emergence of drug-resistant mutants34578910111315171921The emergence of multi-drug-resistant bacteria, or ‘superbugs', poses an imminent threat to our society and has been accelerated by a number of factors, such as the overuse and misuse of existing antibiotics as well as diminishing antibiotic pipelines1Despite a number of clinical, laboratory and theoretical studies with various antibiotic combinations2024252627121391031In this work, we investigate effective combinations of antibiotics that suppress the development of drug resistance under cycled stress and how the multi-drug evolution can be related to evolution under single antibiotic condition. We find unique evolutionary patterns  in which the emergence of drug resistance to one of the cycled antibiotics is completely suppressed. A simple mathematical model is derived from the obtained data, which allows us to predict the fate of multi-drug evolution from evolutionary patterns under single drug conditions. By combining the experimental data and model predictions, we then demonstrate that reversing evolved multi-drug resistance is possible by temporally modulating antibiotic stresses.Escherichia coli MG1655 strain as a susceptible strain, in contrast to the clinical definition in which a strain can be treated with an antibiotic at the recommended dosage. Resistant strains were defined as any (evolved) bacterial samples whose MICs exceed that of the wild-type strain.This study aims to gain insights into bacterial evolution under fluctuating antibiotic stress by the design of a series of long-term (24 days) laboratory evolution experiments. Experiments were designed to expose bacterial cultures to single antibiotic stress and a range of configurable alternating antibiotic stresses. The drug type and the concentration of the drug applied were either switched (on/off) or oscillated over a range of variable time periods . To achi34E. coli strain, a series of 24-day experiments in the morbidostat system was performed using two alternating antibiotic stresses  compared to the case of single antibiotic stress . In the latter case, the drug resistance was developed rapidly and at a similar rate to the single antibiotic case k=1.7±0.12 and 31.9±3.12 day−1 for kanamycin (KAN) and nalidixic acid (NAL), respectively, for cycling case and k=7.11±6.01 and 5.06±1.65 day−1 for single antibiotic case. However, the maximum level of NAL resistance was significantly lowered  shown in We hypothesized that the single drug-resistant state with POL might be related to the evolutionary patterns of resistance under single antibiotic condition. Indeed, the development of POL resistance under single antibiotic stress exhibited a unique evolutionary pattern with a relatively long ‘silent phase' where the population does not develop drug resistance (up to 6 days) followed by sharp increase as shown in 7We also investigated the cross resistance and collateral sensitivity profiles121329The multi-drug and single drug evolution experiments demonstrated various evolutionary patterns depending on the types and combinations of antibiotics. While antibiotic cycling strategies that eliminated bacteria with sublethal antibiotic dosages have been reportedWith the serial transfer method used in the previous studyIn contrast, the rate of resistance evolution in the morbidostat is primarily determined by the growth rate, in particular the growth rate of a new mutant relative to that of the populationE.coli populations in this study might have used the non-genetic mechanisms to cope with POL stress at the initial stage of evolution when the drug concentration was low. This could then have switched to genetic mechanisms, that is, beneficial mutations conferring POL resistance as the drug concentration increased, which may explain the step-like evolutionary pattern of POL resistance.The evolution under single POL stress showed a unique pattern with a relatively long silent phase that was not observed in the other antibiotics tested. A simple explanation for the pattern could be to assume that multiple mutations are required for POL resistance. However, it seems implausible because the reproducible evolutionary trajectories of POL resistance  the duration of silent phase, (2) the rate of adaptation and (3) cross resistance and collateral sensitivity profiles. To study the obtained results from a broader perspective, we derived a simple model incorporating the three factors to describe the evolutionary patterns . It shou842784445A, termed as RA, is constantly promoted during exposure to antibiotic A. However, the resistance level eventually saturates due to fitness costsRA negatively affects resistance to another antibiotic B, or RB largely if the antibiotics are a collateral sensitive pair1213θ, the rate of adaptation α and the collateral sensitivity/cross resistance coefficient β. These model parameters were determined based on the fitting parameters of experimental results under single antibiotic stress.The model was based on two assumptions: (1) The development of bacterial resistance to an antibiotic α) and POL resistance did not develop at all despite antibiotic cycling (θ). In contrast, the nullclines have only one intersection when the antibiotics other than POL were cycled  were plotted on the axes, while the latter ones  were in the upper right part of the plot. To illustrate the predictability of the theoretical model, the normalized predicted MIC values were plotted against the normalized observed MIC values . We also constructed a null model of the mathematical model as a comparison. In the null model, all the collateral sensitivity/cross resistance coefficient term β were set to zero. Comparison of predictions by the null model and experimental results showed poor correlations  while the model predicted that the bacterial population would result in multi-drug-resistant states . As disc41periment .A is determined as α and β was indeed seen in the experimental results (β) lowered the maximum resistance level compared to that with cross resistant pair , for instance, CHL and KAN, and CHL and NAL pairs, respectively. The equation also indicates that if antibiotic A with large α is cycled with antibiotic B with small α, the evolution results in We derived an approximate analytical solution of the model, which describes that the normalized maximum resistance to antibiotic est form . This me results : AntibioOne of the important implications in the model is that the evolution of bacterial drug resistance can be viewed as a dynamical system because the model proposed here is a simple deterministic system. Such dynamical systems view of biological systems was previously proposed in the context of stem cell differentiation and drug resistance evolution of caner cells48basS and secD genes as key mutations that confer POL resistance  and streaked to single colonies. One single colony was selected and used throughout this study. Bacterial cells were grown in 30 ml flat-bottomed glass vials  in a shaking incubator  for 18 h at 30 °C before experiments in the morbidostat. Miller Lysogeny Broth  was used as culture medium for all the experiments. All the antibiotics and culture media used in this study were purchased from Sigma-Aldrich unless otherwise noted. Antibiotic solutions were prepared from powder stocks. They are dissolved in solvents (E. coli (MG1655)solvents , filter A morbidostat system was built based on instructions 58. A mor−1. A 16-channel parallel peristaltic pump  was used as waste pump to remove excess liquid from morbidostat vials. Vials and pumps are connected with silicone tubing and PTFE tubing. All the pumps are computer-controlled by Arduino MEGA board (Arduino) with a custom-made mountable printed circuit board  at the flow rate of 1.2 ml minit board . The cusThe optical density (OD) of bacterial cells was measured via a matched pair of an infrared LED  and a phototransistor . Tube holder assemblies that accommodates infrared light source and photodiode were made by three-dimensional printer  using VeroBlack material . The infThe final assembled apparatus accommodates 16 morbidostat vials in a temperature controlled incubator  and ethanol individually, then rinsed with sterile water twice. Bacterial cells stored at −80 °C were thawed and transferred into autoclaved morbidostat vials with 12 ml of LB medium, in which bacterial cells were diluted 60-fold. The assembled vials containing cell cultures were then placed into the morbidostat vial holders.THR∼0.4), and (2) if the current OD is larger than the OD at the time of previous dilution. If both conditions are met, 1 ml of drug medium is added to the cell culture. Otherwise fresh medium is added. Note that we used a higher OD threshold than the previous study (ODTHR=0.15)THR is low.A custom software that monitors bacterial growth in the vials and controls pumps via Arduino boards was written in Python. This software implements the morbidostat algorithm as specified 50, whichThe concentration of antibiotic in the drug medium was started from 10 times of the MIC at the beginning of the long-term experiment. If the bacterial population gains antibiotic resistance and the drug concentration inside the morbidostat vial is more than 60% of the concentration of drug medium, then the antibiotic concentration in the drug media was increased to twofold of that of the previous concentration .A single morbidostat experiment was run for 22 h. No apparent formation of biofilms on the inner wall of morbidostat vial was observed at the end of experiment. Cell cultures were sampled from all the 16 vials in the morbidostat and stored as glycerol stocks at −80 °C.μ (unit: h−1) was calculated as n-th dilution and n-th dilution. Δt is 600 s. The concentration of an antibiotic A at n-th dilution [A]n was calculated by the following equation.Morbidostat operation log data were analysed by custom Matlab scripts. The growth rate V is the volume of cell culture in the morbidostat vial , ΔV the volume of fresh or drug media added at each dilution (1 ml), [A]stock is the concentration of the antibiotic A in the stock drug medium (varies). Statistical tests, that is, s.e.m. and P-value by Welch's two sample t-test, were performed using R.Where 2-transformed fold changes in MIC relative to wild-type strains, that is, WT and re MIC[A]EVO indicate the MICs of wild-type  and evolved mutant against antibiotic A, respectively.The MIC of samples collected during evolution experiments was measured by growing the samples on a set of LB agar plates with varying antibiotic concentrations , and m the midpoint of the curve. Fitting was performed using a custom-made Python script using SciPy package. The duration of the silent phase θ was calculated as the time point when f(x)≥0.1, that is, The evolutionary trajectories of bacterial resistance to an antibiotic was fitted with a logistic function where The evolved strains from the morbidostat experiments stored at −80 °C were streaked on LB agar plates to pick individual colonies for MIC assay. After 15 h of incubation at 30 °C, at least 25 colonies from each sample were chosen at random and dissolved into 180 μl of LB in 96-well plate individually. After overnight incubation of the 96-well plate at 30 °C, we remove 65 μl of those cell samples into empty 384-well plate for MIC assay as described in 2.5.Total 40 samples were sequenced using Illumina MiSeq desktop sequencer: The ancestor, day 12 samples of all 19 populations of single antibiotic conditions, and day 24 samples of 20 populations of antibiotic cycling conditions and control. Each evolved strain was diluted 1:100 in 5 ml of LB with no drug and grown overnight (18 h) at 30 °C before DNA extraction. We confirmed that the overnight growth without antibiotics does not change the antibiotic resistance level by two methods. First, the resistant samples evolved under single antibiotic conditions . Second,The obtained sequences were aligned to the MG1655 reference data (NC_000913.2). Alignment was performed using Bowtie2. SNPs and indels were identified using SAMtools. Identification of mutations were analysed using breseq ref. . A minimwww.dnabaser.com/). Although this is not an accurate way to determine allele frequencies, the analysis was used to determine whether an evolved population is genetically homogeneous or heterogeneous.Allele frequencies were determined from fluorescent signal intensities of Sanger sequencing data, that is, the ratio of a signal intensity of a mutant SNP and total signal. Sequences were aligned by DNA Baser  using Sundials package. Further details of the model can be found in Numerical solutions for the theoretical model were calculated in Matlab  and in Julia  Sequence Read Archive (SRA) with the following sample accession codes: SRS1749080 (CHL), SRS1749079 (POL), SRS1749078 (POL-CHL), SRS1749077 (CHL-POL_D6), SRS1749084 (CHL-POL_D10), SRS1749083 (CHL-POL_D18), SRS1749082 (CHL-POL_D24), SRS1749081 (MG1655 wild type) and SRS1749076 (Control). Custom software used in this study is available from How to cite this article: Yoshida, M. et al. Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro. Nat. Commun.8, 15589 doi: 10.1038/ncomms15589 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures, supplementary tables, supplementary methods and supplementary references.


The present study aims to obtain the tensile properties of early age concrete based on a uniaxial tension test employing RC slender members. First, the paper shows that concrete strain is equal to the strain of rebar at the mid-span of the RC member. The tensile Young’s modulus and the strain capacity of early age concrete are estimated using strain measurements. The experiment indicated that the tensile Young’s modulus at an early age is higher than the compressive modulus. This observation was similar to one found in a previous investigation which used a direct tension test of early age concrete. Moreover, the paper describes how an empirical equation for mature concrete can be applied to the relation between uniaxial tensile strength and splitting tensile strength even in early age concrete. Based on a uniaxial tension test, the paper proposes an empirical equation for the relationship between standard bond stresses and relative slip. The mechanical properties of concrete vary according to the degree of hydration as the strength develops with the age of the concrete. On the other hand, concrete volume always changes due to hydration heat during early age active reactions. Early age cracks often occur when the tensile stress due to the volume changes arising from thermal contraction and shrinkage is higher than the tensile strength at that age. To predict and prevent such cracking, designers and conductors often use a thermal stress simulation . Such siet al. investigated fracture characteristics of early age concrete by using wedge-splitting specimens [Many investigations dealing with early age tensile properties have been conducted for predicting cracking. ACI Committee 231 presented a report of early age cracking based on the latest studies . His reppecimens . They pri.e., 8, 24, 31 hours. The bond strength was evaluated as bond stress at slips of 0.1 mm and 1 mm. The values calculated from the predicting formula were approximately 30% lower than the early age experimental values. In addition, Sule and Breugel presented crack formation of an early age RC member [Chapman and Shah conducted pull-out tests of rebar from early age concrete to quantify bond strength . They reC member . They foAs summarized above, a number of investigations dealing with mechanical properties of early age concrete have been conducted. However, little research on the cracking behaviors of concrete with a long bond length of rebar has been conducted. By employing a uniaxial tension test with RC members, early age bond behavior may be appropriately obtained. In addition, the test method using bond properties of rebar in concrete may yield some early age mechanical properties, e.g., tensile strength and tensile strain capacity. This paper describes the tensile properties of normal strength early age concrete based on such a laboratory test.The present study uses the materials shown in Deformed steel bars used for reinforced concrete specimens are described in The uniaxial tension tests were conducted to obtain tensile properties of concrete at early age. For a comparison, compressive strengths, splitting tensile strengths and compressive Young's moduli were also tested. cf’), splitting tensile strengths (tf2) and compressive Young’s moduli (CcE) developing with age. This study used 3 cylindrical specimens  for each test. Herein, the tests were conducted referring to JIS-A-1108 [The fundamental properties of the concrete, shown in S-A-1108 , JIS-A-1S-A-1108  and JIS-S-A-1108  respectiBoth ends of the rebar were attached to a loading jig, and each of the end conditions was rotatable. tCσ) loaded to a concrete section.P is the providing force, A is the sectional area, E is Young’s modulus and C and S stand for concrete and rebar respectively.Equation (1) presents a tensile stress (CE (MPa) is Young’s modulus of concrete irrespective of compression or tension, and cf’ is the compressive strength (MPa).JSCE  and ACI-Herein, Equation (2) omits the coefficient considering the creep effect from the original formula of JSCE. It should be noted that these formulae are based on the test of compressive Young’s modulus.tf1 is the cracking stress (MPa), and tf2 is the splitting tensile strength (MPa).Cracking stress estimated by the equation becomes almost equal to the splitting tensile strength at 3.5 MPa, and all splitting tensile strengths in the present test are lower than the strength.−6) that were reported in previous investigations [igations ,24. The Z is the slip of rebar from concrete and x and x’ are location from the bond end and distance from the mid-span, respectively.In the present study, “slip” is estimated using only the strains of concrete and rebar. The study evaluates the slip by integrating both the strains from the mid-span, which has a boundary condition of zero-slip. That is to say, the sum of the two strains is the slip as given in Equation (5) and et al. estimated the slip using the equation under the assumption that the strain of concrete is constant in each loading [Mimura  loading . The asset al. used a similar equation for estimating the concrete strain, but that study employed compressive Young’s moduli obtained from cylindrical specimens [Mimura pecimens . TensileEquation (7) gives a definition of local bond stress. For simplification, this study assumes the deformed bar as a round bar having a nominal diameter of (13 mm). et al. [et al. [Yamao et al.  and Shim [et al.  used twobf) shown in Equation (8) [Herein, JSCE gives a design formula of bond strength (tion (8) .(8)Z/D). Based on the previous evaluation method, the present study also employs the standard bond stresses and the relative slip. In particular, the method using standard bond stress may be appropriate because the property of the early age concrete varies with time even in the same mix proportion.The formula shows that the bond strength of rebar in concrete is proportional to 2/3 power of compressive strength. To eliminate the influence of the bond strength of various concrete, most research employs standard bond stresses defined as the bond stress divided by 2/3 power of compressive strength. In addition, most research uses the relative slip (a and b are coefficients of the regression curve. The coefficient a is 0.39 (1 day), 0.70 (1.5 days) and 0.90 (after 2 days); the coefficient b is a constant 0.12.a represents the ultimate bond stress; it develops with age and becomes constant after the age of 2 days. Interestingly, the coefficient b of all the regression lines in the graph is approximately 0.12. The empirical equations may be useful for predicting the crack width of early age concrete, but they are limited to the mix proportions and materials used in the study.(1)Concrete strain can be estimated by using the strain of a rebar having a long embedment length. Tensile stress-strain responses of early age concrete are extremely linear.(2)Tensile Young’s modulus is higher than compressive modulus of early age concrete, and both moduli gradually become equal as hydration develops over time. The prediction formulae shown in the previous codes may underestimate tensile Young’s modulus of early age concrete.(3)The empirical equation for mature concrete can be applied to the relationship between uniaxial tensile strength and splitting tensile strength even in early age concrete.(4)Bond behaviors after the age of 2 days are almost equal, so an equation can be applied to this behavior. The empirical equation based on the test is proposed in the paper.The present study aimed to obtain the tensile properties of the concrete which is usually employed in construction of infrastructure in Japan. Based on the uniaxial tension test using RC slender member, this paper describes the cracking stress and bond properties of early age concrete. The conclusions of the study are listed as follows:


Endpoints included efficacy and event‐free survival (EFS). Finally, a repeated dose toxicity study of erymet combined with PN cofactor was conducted in CD1 mice. Recombinant MGL was cytotoxic on 4/6 cell lines tested. MGL half‐life was increased from <24 h to 9–12 days when encapsulated in RBC. Conversion of PN into PLP by RBC was demonstrated. Combined erymet + PN treatment led to a sustained Met depletion in plasma for several days with a 85% reduction of tumor volume after 45 days following cells implantation, and a significant EFS prolongation for treated mice. Repeated injections in mice exhibited a very good tolerability with only minor impact on clinical state  and a slight decrease in hemoglobin and triglyceride concentrations. This study demonstrated that encapsulation of methioninase inside erythrocyte greatly enhanced pharmacokinetics properties of the enzyme and is efficacy against tumor growth. The perspective on these results is the clinical evaluation of the erymet product in patients with Met starvation‐sensitive tumors.Erymet is a new therapy resulting from the encapsulation of a methionine gamma‐lyase  in red blood cells (RBC). The aim of this study was to evaluate erymet potential efficacy in methionine (Met)‐dependent cancers. We produced a highly purified MGL using a cGMP process, determined the pharmacokinetics/pharmacodynamics (PK/PD) properties of erymet in mice, and assessed its efficacy on tumor growth prevention. Cytotoxicity of purified MGL was tested in six cancer cell lines. CD1 mice were injected with single erymet product supplemented or not with vitamin B6 vitamer pyridoxine . NMRI  Cancer‐associated reprogramming of cellular metabolism was recently reconsidered as a core hallmark of cancer l‐Methionine (Met) is a dietary essential, sulfur‐containing proteinogenic amino acid involved in several cellular metabolism pathways Min/+ mice fed a normal diet supplemented with 0.7% additional Met for 4 weeks Met auxotrophy was observed in several xenograft models 11C‐MET positron emission tomography is also used in the clinical management of glioblastoma, highlighting the eagerness of aggressive brain tumor cells for Met Met auxotrophy was also reported in 25% of fresh and cultured patient tumors from different organs As Met restriction appeared to be appropriately selective of tumor cells, two different therapeutic approaches were considered for Met depletion at the systemic level using nutritional starvation or Met‐catabolizing enzymes . MGL is a pyridoxal 5′‐phosphate (PLP)‐dependent enzyme involved in the metabolism of Met. This enzyme is ubiquitous in all organisms, except in mammals. MGL specifically degrades Met to alpha‐ketobutyrate, ammonia, and thiols. Due to its enzymological properties, MGL was isolated from several bacteria, fungi, and protozoa, but the most studied form was purified from Recombinant MGL tumor growth inhibitory efficacy has been verified in numerous cancer cell lines, and its cytotoxic effect was far more significant in these cell lines than in human nonmalignant cells Even if pegylation has partially solved the problem of MGL half‐life, low bioavailability of enzyme cofactor PLP remains a major problem. MGL catalytic activity requires PLP, the biological active form of vitamin B6 as a cofactor. PLP is rapidly bound to plasma proteins, which limits its bioavailability. In addition, PLP's half‐life in the blood stream is only ∼15 min The aims of the present study were (1) to produce highly purified MGL on an industrial scale and to verify its in vitro cytotoxic effects, (2) to evaluate the PK‐PD characteristics of MGL‐loaded RBC in healthy mice, (3) to assess the role of RBC in PLP biosynthesis from the exogenous uptake of PN, and (4) to investigate the antitumoral effect of MGL‐loaded RBCs in a subcutaneous xenografted mouse model bearing glioblastoma tumors.Ammonia assay kit was from Roche Diagnostics . Vitamin B6 enzymatic assay kit was obtained from Bühlmann Laboratories AG . Cell Counting Kit‐8 (CCK‐8), Met, and PLP were from Sigma . PN hydrochloride solution was from DB Pharma . Culture reagents were obtained from GE Healthcare Europe, GmbH, , Invitrogen , and Sigma.P. putida was codon optimized for expression in Escherichia coli, synthesized, and cloned. The cGMP runs were conducted at a 350L fermentation scale. After two precultures (6 and 16 h) at 37°C in GY medium–glucose–kanamycin, and cooling at 28°C, protein expression was induced by 1 mmol/L IPTG. Cell sediment was harvested after 20 h and concentrated using a 500‐kDa hollow fiber. Cell pellet was recovered by centrifugation at 15,900g and suspended in lysis buffer (7 v/w). Lysis was performed at 10°C by high‐pressure homogenization at 1000, 600, and 600 bars. Lysate underwent clarification at 10°C by adding 0.2% PEI and centrifugation at 15,900g. The soluble fraction was sterilized by 0.2‐μm filtration before precipitation with ammonium sulfate (60% saturation) at 6°C, over 20 h. Two crystallization steps (PEG‐6000 at 10% and ammonium sulfate at 10% saturation and PEG‐6000 at 12% and 0.2 mol/L NaCl at 30°C) were then carried out. After centrifugation , pellet was resuspended in a solubilization buffer, filtered (0.45 μm), and subject to two anion exchange chromatography (DEAE sepharose FF) steps. A polishing step (Q membrane chromatography) led to removal of contaminants. Purified MGL was concentrated and diafiltered in formulation buffer  using a 10‐kDa cutoff TFF cassette, aliquoted, and stored at −80°C.MGL coding sequence from μmol/L Met. All studies were performed with mycoplasma‐free cells in logarithmic phase of growth and blank wells and viability control were included. Cell density, CCK‐8, and MGL incubation times were optimized for each cell line.AGS (ATCC CRL‐1739) and NCI‐N87 (CRL‐5822) human gastric adenocarcinoma, LN‐229 (CRL‐2611) and U‐87 MG (HTB‐14) human glioblastoma, and BxPC‐3 (CRL‐1687) and MIA PaCa‐2 (CRL‐1420) pancreatic carcinoma cell lines were grown according to ATCC recommended culture conditions. U‐87 MG‐luc2 cells (ref. 124577) were cultured following Caliper Life Sciences recommendations. Complete culture mediums contained 60–200 μL complete medium or Met‐deprived medium ± physiological dose of Met (40 μmol/L) and dispensed in a 96‐well plate. All conditions were tested in duplicate. Plates were incubated for 24 h for cell seeding and cell viability measurements, and renewal of medium were carried out every 24 h for 10 days. For each time point, 10 μL of CCK‐8 solution was added to each wells and plates were incubated for 1–4 h. Optical density (O.D.) was measured at 450 nm and percentage of growth was determined.Cells at a fixed concentration were prepared in 100 μL) were dispensed in 96‐well plates at a fixed concentration. All conditions were tested in triplicate. Cells were incubated for 24 h. Then, 10 μL of MGL, ranged from 0.001 to 14.6 U/mL , were added. After a 3 or 4 days incubation step, 10 μL of CCK‐8 solution were added and plates were incubated for 1–4 h. O.D. was then read at 450 nm. The concentration that gives a 50% inhibition of cell viability (IC50) was determined using Gen5™ Data Analysis Software .Cells cultured in complete medium . RBC were collected from mouse blood after centrifugation and plasma/buffy coat was removed and washed twice with 0.9% NaCl (v:v) solution. MGL was added to required concentration, leading to a suspension of 65% hematocrit (Ht). RBC were dialyzed against a hypotonic buffer of 40 mOsmol/kg and resealed by addition (10% v:v) of a hypertonic solution (detailed in nude mice (Rj:NMRI‐Foxn1nu/Foxn1nu) were from JANVIER Labs. Animals (5 weeks old) were allowed to acclimatize for 7 days, identified, weighed, and randomly assigned to study groups. Animals were cared for in an accredited facility  in accordance to ethical guidelines Male CD1 mice (Crl:CD1(ICR)) were from Charles River Laboratories. Male NMRI μL of whole blood diluted in 1 mL of PBS, 0.5% BSA, and passed in triplicate . Fluorescent RBC proportion was plotted as a function of time following erymet administration, and half‐life was determined by exponential regression.Fluorescently labeled erymet products were injected intravenously into CD1 mice. At various time points, mice were sacrificed and blood was collected in lithium heparinate tubes kept at +4°C away from light. The proportion of fluorescent RBC in whole blood was determined by flow cytometry: 5 g. Plasma was then aliquoted and stored at −80°C until Met quantification.CD1 mice were injected with erymet product(s) ± PN administration or with free MGL enzyme and were sacrificed at different time points. Blood was collected in heparinized tubes kept at +4°C and rapidly centrifuged  at 1000nude mice were subcutaneously injected (day 0) with 3 × 106 U‐87 MG‐luc2 cells in 100 μL 1× DPBS and were randomly divided into two groups of 10 mice. Treated group received five IV injections of erymet  on days 9, 16, 23, 30, and 37. Control group received SAG‐Mannitol–20% plasma vehicle. Nonimplanted mice (n = 12) were used to verify plasma Met depletion of erymet products, 24 h postinjections, and after the five treatments. From day 9 to day 45, mice received daily PN dose  by oral route. Tumor growth was followed by caliper measurement and bioluminescence imaging. Tumor volume  was determined by the standard formula TV = (L × W2)/2. For imaging, each mouse was injected i.p. with 300 μL of luciferin. Animals (under isoflurane/oxygen anesthesia) were placed in an optical imaging system  and measurements were performed within an optimum time frame determined by kinetic acquisitions on three mice. Mice were weighed once a week and examined frequently for drug‐related side effects. Animals were euthanized if (1) TV > 2000 mm3, (2) loss of body weight > 20% to the maximal body weight, (3) appearance of tumor necrosis and/or metastases, or (4) severe deterioration of the general status.Twenty NMRI nude mice were subcutaneously injected (day 0) with 3 × 106 U‐87 MG‐luc2 cells in 100 μL 1× DPBS and were randomly divided into three groups of 10 mice. Treated groups received five IV injections of erymet (30 or 45 U/kg) on days 8, 15, 22, 29, and 36. Control group received SAG‐Mannitol–20% plasma vehicle. PN cofactor (8.4 mmol/L) was administered orally in combination with erymet or vehicle from day 8 to day 43. Tumor growth was followed by caliper measurement.Thirty‐six NMRI n = 48) received four IV injections of erymet (63 U/kg) on days 1, 8, 15, and 22, and daily administration of PN from day 2 to day 22. Control groups (n = 48) received SAG‐Mannitol–20% plasma vehicle as reference item. Mice were daily observed throughout the study and erymet toxicity was investigated by evaluating (1) mice clinical state using a scored clinical follow‐up, (2) plasma methionine concentration, and (3) hematological and serum clinical chemistry parameters analysis, at both day 23 (day following end of treatment period) and day 37 (15 days after treatment period). Animals were euthanized if (1) loss of body weight > 20% to the maximal body weight or/and (2) total clinical score > 4.Evaluation of erymet toxicity was carried out at Voxcan . Ninety‐six CD1 mice  were used to investigate induction of side effects. Treated groups  and deposited in EDTA‐K2 or serum separator tubes. Hematological parameters included red blood cell count, hemoglobin (Hb), packed cell volume, mean corpuscular volume, mean corpuscular Hb, mean corpuscular Hb concentration, reticulocyte count, platelet count, total white blood cell count, and differential white blood cell count. Serum clinical chemistry parameters included analysis for sodium, potassium, chloride, calcium, glucose, urea, total cholesterol, triglycerides, total bilirubin, total protein, albumin, globulin, creatinine, alkaline phosphatase, aspartate aminotransferase, and alanine aminotransferase.μL) were deproteinized with 300 μL of methanol. After 2 min of vortex, and 5 min at room temperature, the samples were centrifuged . To 200 μL of supernatant, 200 μL of 1 mmol/L TDFHA containing a stable radioisotope of Met  as internal standard were added. Five μL were injected on Api 3200 (AbSciex) tandem mass spectrometer.Met level in murine plasma samples was quantified according to a RPLC‐ESI‐MS/MS validated method described by Piraud et al. 3 produced by MGL and assayed indirectly by enzymatic action of glutamate dehydrogenase (GLDH) according to a commercial kit (Roche). MGL standards were prepared in matrices  or in an aqueous solution. Alpha‐ketoglutarate, NADPH, and GLDH were added to samples or standards for endogenous NH3 removal. After 10 min, 75 μL of Met (78.3 mmol/L) were introduced and reaction mixtures were incubated for 30 min. Degradation of NADPH into NADP+ was continuously tracked by measuring the O.D. at 340 nm. Percentage of MGL holoenzyme fraction was determined by dividing MGL activity measured without PLP supplementation by MGL activity measured with addition of PLP in the assay.The assay is based on measurement of NH2 by a PLP‐dependent enzyme, tyrosine apodecarboxylase, whose activity is directly proportional to PLP concentration. Then, tyramine oxidase catalyzed the oxidation of tyramine in hydroxybenzyl aldehyde and H2O2. Finally, H2O2 was converted in quinoneimine whose absorbance was measured at 546 nm. Intraerythrocytic PLP levels were calculated as follows:Quantitative determination of PLP concentration in plasma and whole blood was assessed by an enzymatic assay (Bühlmann). Briefly, tyrosine was converted to tyramine and COt‐test was performed between groups using unequal variance. Group differences in tumor growth were tested using two‐way (time and intergroup variability) ANOVA. EFS was estimated by Kaplan–Meier estimator and compared between groups log‐rank test. Level of significance was set at P < 0.05.Collected data were described overall and by individual along time with mean ± standard deviation for continuous variables, and number, percentage for categorical data. For PK/PD studies, differences were tested using nonparametric Friedman's test. Unilateral Student's P. putida was adapted to HMS174 (DE3) host strain for large‐scale production of recombinant enzyme according to cGMP requirements. The defined method  was similar to proliferation in complete medium. All these results indicated the dependence of U‐87 MG cells on Met to grow, while LN‐229 proliferation was only slightly impaired by Met removal. The effect of purified MGL on proliferation was then investigated .Several arguments are in favor of a Met‐dependent phenotype of glioblastoma cells 50 values of 0.24 and 0.36 U/mL, respectively.The literature reports that gastric and pancreatic cancers harbor a Met‐dependent phenotype and are sensitive to MGL treatment μmol/L) 15 min after administration followed by a rapid return to the physiological level  24 h postinjection. By comparison, erymet administration  lowered Met concentration to ∼20 μmol/L at 24 h, corresponding to a 65% depletion. Met level slowly increased between day 1 and day 5, remained stable and under the lowest individual concentration observed in CD1 mice (35 μmol/L) from day 5 to day 9, and corresponded to a 34% depletion of the control level after 9 days, confirming the improvement of MGL pharmacodynamics by RBC entrapment.Intravenous injection of recombinant MGL  in CD1 mouse model Fig. A inducedμmol/L, 8.5 ± 1.1 μmol/L, and 17.1 ± 4.4 μmol/L on day 5, day 7, and day 8, respectively (P ≤ 0.002). From the third PN1 IV injection (day 8), Met concentrations began to increase to reach the level observed in the erymet‐only group with 33 ± 8 μmol/L on day 9, 24 h after the last PN1 injection. Three hours after PN2 IG administration, Met values also decreased rapidly to reach 14.3 ± 8.0 μmol/L, 8.1 ± 2.1, and 9.0 ± 2.0 μmol/L on day 5, day 7, and day 8, respectively (P ≤ 0.002). Even if this decrease was slower than in the erymet + PN1‐treated group on day 5, the effect on Met depletion appeared to be slightly prolonged with PN2 IG than with PN1 IV supplementation. Indeed, 24 h after the last PN injection, Met concentration of the erymet + PN2‐treated group (22.2 ± 8.3 μmol/L) did not reach value of the erymet‐treated group.To determine the impact of cofactor supplementation on erymet pharmacodynamics, administration of PN precursor, by intravenous (IV) or intragastric (IG) route, was tested in vivo. PN injections were started at day 5, which corresponds to the time point when Met depletion was stabilized after erymet injection. The number of PN injections was voluntarily limited to three administrations per mouse to avoid caudal vein lesions while PN IG administrations were done daily over a 4 days period. PN concentration was extrapolated from human maximal daily dose and corresponded to 300 mg/day orally and 200 mg/day intravenously for a 70‐kg adult μmol/L) 30 minutes after uptake and then slowly decrease to return to initial concentration (1.2 μmol/L) at 24 h. Impact of rapid intraerythrocytic conversion of PN into PLP on MGL enzyme activity was assessed in CD1 mice , without modifying the total PN concentration administered per day Fig. F. MGL haμmol/L). Twenty‐four hours after injection, each single administration lowered Met levels to a range between 21.5 ± 6.4 and 43.5 ± 29.0 μmol/L, corresponding to a 42–71% decrease in the control value. Met plasma level was decreased by 75% (19.0 ± 15.6 μmol/L) after the five cumulative erymet injections  . Additionally, mean bioluminescence at day 45 was reduced by 60%   prolongation of EFS of the treated mice  of erymet‐treated mice were still alive at the end of the study (day 112). At this time, mice were euthanized and mean tumor volume was limited to 295.1 ± 237.5 mm3. Follow‐up at day 112 indicated that two animals exhibited good clinical status while the third one presented diarrhea. All along the study, the impact of erymet + PN treatment was very limited with only a slight but stabilized decrease in mice body weight in the treated group  for 5 weeks, in association with daily PN supplementation by gavage, led to plasma Met level decrease in mice when compared to control group  equal to 0.0005. In conclusion, five administrations of erymet at a dose of 45 U/kg combined to daily PN injections in subcutaneous glioblastoma‐bearing mice resulted in a significant tumor growth inhibition and improvement of lifespan over time compared to control arm (without treatment). In addition, the treatment at this dose is comparable to results obtained for the dose of 76 U/kg for the period from day 32 to day 46 holding around 80% of tumor growth reduction.In addition, another in vivo study was performed to evaluate if same efficiency of erymet treatment would be observed at two lower doses  in the same mouse glioblastoma model. Significant tumor growth delay was observed from day 32  the lowest efficient dose that induced tumor growth regression . Number of injections was lowered from 5 to 4 administrations in order to be in line with the treatment envisioned in clinics.μmol/L in females and from 78.7 to 93.1 μmol/L in males. At each time step, the l‐methionine concentration measured in treated animals was lower than for mice treated with vehicle alone, traducing a good depletion in l‐methionine induced by the treatment . During the 23 days of the study and the following 15 days of recovery period, no animal mortality was observed in both vehicle and erymet groups (data not shown). A scoring grid  that is similar to the Met concentration in plasma The major limitations associated with MGL use are a short half‐life in the bloodstream and immunological reactions Another advantage of the enzyme‐loaded erythrocytes approach is avoiding hypersensitive reactions in patients by shielding the bacterial enzyme from the immune system. The tolerability, enzyme activity prolongation, and clinical benefits of RBC as enzyme carriers were clearly demonstrated in a recent phase III clinical trial on primary relapse/refractory acute lymphoblastic leukemia As Met is an essential amino acid involved in several metabolic pathways, including protein synthesis, protection from oxidative stress, nuclear and cell division, and DNA methylation Among Met‐dependent tumors, glioblastoma (GBM) was identified as a potential candidate for erymet therapy, as several clinical arguments support the interest in using Met deprivation therapy in patients suffering from these highly aggressive primary brain tumors. Indeed, radiolabeled Met (MET‐PET) uptake is one of the metabolic imaging tools used to determine brain tumor volumes and to identify residual tumors after resection or recurrent gliomas In parallel, a preclinical toxicity study in CD1 mice underlined the safe profile of repeated injections of erymet combined with PN administration. Pooled together, these preclinical results on erymet efficacy and safety will help us to anticipate extrapolation of efficient doses in clinics.P. putida was reported to possess the ability to partially catabolize l‐homocysteine or l‐cysteine MGL from In conclusion, we reported here that erymet, a RBC‐encapsulated form of MGL, acted as an intravascular microbioreactor, and effectively led to a long‐term, sustained significant plasma Met depletion in vivo, resulting in an efficient tumor reduction growth in a GBM tumor‐bearing mouse model.2/M phase, and forced cell to undergo apoptosis By limiting the intracellular level of Met, erymet is expected to alter several cellular pathways. Several works recently reported that dietary or enzymatic Met depletion caused a cell cycle arrest, preponderantly in G® technology offers a robust and reproducible process for entrapping therapeutic enzymes in human erythrocytes and has already been proven to provide clinical therapeutic benefits in the case of L‐asparaginase, as compared to the free enzyme.Erymet product manufacturing is currently being scaled up for human trial purposes. The osmotic‐based method using ERYcapsRegulatory toxicity studies (Good Laboratory Practice compliant) is planned for end of 2017 and if the safety profile on animals is confirmed, a phase I clinical trial will be set up to demonstrate the optimal tolerability and biological efficacy of erymet associated with PN administration in patients with cancer potentially sensitive to Met deprivation.None declared.


IgG4-related disease (IgG4-RD) is recognized as an immune-mediated condition with pathology features of lymphoplasmacytic infiltrate, storiform fibrosis, and obliterative phlebitis, accompanied with or without elevated serum IgG4 concentrations. However, few of pulmonary artery IgG4-RD causing pulmonary hypertension (PH) was reported.The medical records of 3 patients with pulmonary artery IgG4-RD inducing PH were analyzed retrospectively.Imaging findings demonstrated that the lesions of 3 patients located in pulmonary artery, which were initially diagnosed as pulmonary thrombus or malignant tumor. Computed tomography pulmonary angiography (CTPA), ultrasonic cardiogram, and positron emission tomography/computed tomography (PET/CT) didn’t support the diagnosis of pulmonary thrombus or malignant tumor. Right heart catheterization (RHC) showed definite PH. Biopsy by right heart catheterization in 2 patients or pneumonectomy in 1 patient confirmed the diagnosis as IgG4-RD. Treated with glucocorticoids and cyclophosphamide or rituximab, 2 patients’ IgG4 concentrations declined sharply and the lesions shrunk gradually. Another patient treated with glucocorticoids died of heart failure.IgG4-RD involved pulmonary artery causing PH was rare. A high index of awareness of this disease is required for early diagnosis and treatment. PET/CT might be a valuable approach to distinguish pulmonary artery IgG4-RD from pulmonary thrombus and malignant tumor. However, published literature on pulmonary artery IgG4-RD causing pulmonary hypertension (PH) is limited. In this study, we aim to improve awareness of the clinical and imaging manifestations of pulmonary artery IgG4-RD causing PH to avoid a delay in diagnosis or misdiagnosis.IgG4-related disease (IgG4-RD), involved single or multiple organs, is known as an immune-mediated disorder with typical histopathologic features, with or without elevated serum IgG4.1.1Three patients were diagnosed as pulmonary artery IgG4-RD which initially was misdiagnosed as pulmonary thrombus or malignant tumor. The diagnosis of this disease was based on biopsy by right heart catheterizationin in 2 patients and pneumonectomy in 1 patient. We retrospectively analyzed these cases by reviewing these patients’ medical records, laboratory findings, radiological findings and pathological changes, therapeutical strategies, and following times. The Ethics Board in our hospital  approved our study and all patients provided written informed consent.2The major clinical and auxiliary examination data of 3 patients (men) with pulmonary artery IgG4-RD were shown in Table Our initial management for these patients was to use glucocorticoids and cyclophosphamide for patient 3, glucocorticoids for patient 2, and glucocorticoids and rituximab for patient 1 as described in Table Before treatment, expression levels of serum cytokines in patient 1 and 3, including interleukin-4, interleukin-5, interleukin-10, interleukin-13, transforming growth factor-β (TGF-β), immunoglobulin E, IgG4, and endothelin were substantially higher than that in healthy person (except for IL-5 in patient 3 and IL-13 in patient 1). After treatment, the serum IgG4 and ILs returned to normal. Before treatment, flow cytometry showed that CD3+/CD4+(Th) and CD4+CD25+/CD127+(Treg) were obviously high, and after treatment, these lymphocytes reduced obviously.3Pulmonary artery IgG4-RD inducing PH is a rare form of IgG4-RD and might become another unnegligible cause of PH. IgG4-RD can involve virtually any organ. Besides the most frequently involved pancreas, biliary trees, and kidneys, it attacks large to medium-sized arteries extensively, such as abdominal aorta, thoracic aorta, iliac artery, coronary artery, renal artery. IgG4-RD involving pulmonary artery is rarely reported in published literature. From another angle, the lack of familiarity with its clinical and imaging manifestations may limit its diagnostic rate.In this study, these 3 patients’ symptoms and physical examination were unremarkable, and all of them have no multi-organ involvement, which can be a vital clue for differential diagnosis. Besides that, elevated serum IgG4 concentrations can be a valuable hint leading us to a quick diagnosis. However, the serum IgG4 level is not a routine item of blood tests in current clinical practices. Furthermore, as a general conclusion, pathological diagnosis is the gold standard for diagnosing IgG4-RD, but it may be difficult to obtain biopsy specimen in IgG4-RD with pulmonary artery involvement in some cases, and this main method for definite diagnosis make little sense in early diagnostic procedures. So the delay in diagnosis or even misdiagnosis is hardly avoidable. Back to this study, we find that imaging examination played a crucial role in the diagnosis process, and may be an alternative method for the diagnosis.–15 In our study, PET/CT is also considered as an effective method to distinguish IgG4-RD from malignant tumors and pulmonary thrombus. The malignant tumor showed higher SUV intake while pulmonary thrombus showed no SUV intake. Proven useful in diagnosing the pulmonary artery IgG4-RD, these image characteristics of PET/CT should be explored and validated in further study and may be updated into the consensus diagnostic criteria for IgG4-RD in the future.CTPA and ultrasonic cardiogram are important diagnostic approaches to pulmonary vascular lesion. However, it is difficult to identify pulmonary artery IgG4-related lesions from malignant tumors and pulmonary thrombus. Recently, several case reports have indicated PET/CT scan might be a valuable approach to diagnosis of some IgG4-RD. The guideline didn’t involve pulmonary artery IgG4-RD causing PH. In our study, we show 3 patients with pulmonary artery IgG4-RD causing PH, which presents one new contributory factor of the cause of PH.PH from the 5th World Symposium at Nice, France is divided to 5 classifications, including pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, pulmonary hypertension due to lung diseases and/or hypoxemia, chronic thromboembolic pulmonary hypertension and pulmonary hypertension with unclear and/or multifactorial mechanisms.,18 A Japanese study showed complete remissions in only 61% of IgG4-RD patients in 1 year following up. The response to glucocorticoids is different according to affected organs and the degree of fibrosis. Retroperitoneal fibrosis, sclerosing mesenteritis, and fibrosing mediastinitis are less effective to glucocorticoids. In our study, early treatment of glucocoricoids combined application conventional steroid-sparing agents or rituximab lead to good clinical outcomes, which may generate clues for further medication study. In addition, the serum IgG4 and cytokines concentration may become some good biomarkers for IgG4-RD improvement, which should be explored and validated in further study. The radiological assessment to pulmonary artery lesions by CTPA, MRI, and ultrasonic cardiogram is also an appropriate evaluation method for disease process.The glucocorticoids is the first-line, standard approach for most patients with IgG4-RD.,10,20–24 some immune cells and cytokines were measured in our study, and it suggested that the lesion of pulmonary artery was formed in multiple processes. To be specific, the changes of Th2 cell, Treg cell, and cytokines support the probable mechanisms mentioned in previous studies (Fig.  The presentation of ultrasonic cardiogram also approved this probable IgG4-RD causing PH mechanism (Fig. With the progression of diagnostic techniques, this disease has been recognized gradually, but the pathogenesis is not fully clear. According to previous study,ies Fig. .[2] The ism Fig. .In conclusion, to the best of our knowledge, these patients with IgG4-RD presenting pulmonary artery obstruction causing PH is rarely reported in previous studies. These diseases might be delayed or mistakenly diagnosed as pulmonary thrombus or malignant tumor. Therefore, a high index of awareness is required for the early diagnosis and treatment of this disease, which can prevent disabling tissue fibrosis, severe organ failure, and even death. The image characteristics of PET/CT might be valuable clues for the early diagnostic accuracy of this disease.Conceptualization: Yong Wang.Data curation: Hui Deng, Sheng Zhao, Yunlong Yue, Yong Liu, Yali Xu, Jin Qian, Xiaorong Ma, Peiliang Gao, Xiaoyan Yao, Xin Jiang, Xiqi Xu, Zhicheng Jing, Yong Wang, Lei Pan.Formal analysis: Xinying Xue.Funding acquisition: Xinying Xue.Investigation: Sheng Zhao, Zhicheng Jing, Lei Pan.Methodology: Sheng Zhao, Yong Liu, Zhicheng Jing, Lei Pan.Project administration: Yunlong Yue, Yong Liu.Resources: Yunlong Yue, Yong Liu, Yali Xu, Jin Qian, Xiaorong Ma, Xiaoyan Yao, Xin Jiang, Yong Wang.Software: Yunlong Yue, Yong Liu.Supervision: Zhicheng Jing, Yong Wang, Lei Pan.Writing – original draft: Peiliang Gao, Lei Pan, Xinying Xue.Writing – review & editing: Sheng Zhao, Lei Pan, Xinying Xue.