Giovanni Fusco
email: giovanni.fusco@unice.fr

Andrea G B Tettamanzi
email: andrea.tettamanzi@unice.fr

Multiple Bayesian Models for the Sustainable City: The Case of Urban Sprawl

Keywords: Urban Sprawl, Uncertainty, Model Selection, Belief Revision, Bayesian Networks, Grenoble. Model Centrality Weakened

whether they are published or not. The documents may come

Introduction

Practitioners and policy-makers assume that attaining sustainable urban development is essentially a question of data monitoring, decision making and policy implementation in a context of well-established scientific theories and positive knowledge of the urban realm. On the contrary, understanding, measuring and managing urban sustainability is a complex task and uncertainty is omnipresent in the kind of knowledge we have on the sustainable city. Urban sustainability is in fact a multi-dimensional issue, involving socioeconomic, environmental, urban design and governance aspects. Cities are complex systems, whose knowledge is always partial, incomplete, if not contradictory (when different points of view are taken into account). Even more, when dealing with sustainable development, researchers and practitioners have to foresight alternative possible futures, whose knowledge is by definition uncertain. This paper will focus on the case of the possible effects of policies aimed at limiting urban sprawl. Urban sprawl is a central issue for the sustainable city. Letting the city grow through low-density, functionally specialized new suburban developments produces direct consequences (over-consumption of natural and agricultural land) and indirect ones (longer trips, car-dependence, increased greenhouse gas emissions, need of new expensive road infrastructures and/or high level of road congestion) which challenge the goals of sustainable urban development [START_REF] Foley | Global consequences of land use[END_REF][START_REF] Mcdonald | The implications of current and future urbanization for the global protected areas[END_REF]. The spatial interaction between the city and its suburbs also plays an important role. Camagni, Capello and Ni-jkamp [START_REF] Camagni | Towards sustainable city policy: an economy-environment-techno-logy nexus[END_REF] highlight that the city/suburbs opposition poses a social dilemma: households want to take advantage of economy of agglomeration offered by the city (in terms of job opportunities, services, etc.) but try individually to avoid the diseconomies of agglomeration of living in the dense city (congestion, poor environmental quality). By moving to the suburbs they obtain better environmental amenities and are still capable of profiting of jobs and services offered by the city thanks to increased car-mobility. By so doing, they increase congestion and pollution in the city, worsening quality of life for city dwellers and pushing more households to opt for a suburban residence. Densification strategies, both in the inner city and in its suburbs have thus been proposed [START_REF]European Commission: European Sustainable Cities. Report of the Expert Group on the Urban Environment, Sustainable City Project[END_REF][START_REF] Calthorpe | The Regional City: Planning for the End of Sprawl[END_REF][START_REF] Duany | Suburban Nation: The Rise of Sprawl and the Decline of the American Dream[END_REF] in order to limit and eventually revert urban sprawl. These strategies make nevertheless strong assumptions on the impacts and even on the acceptability of densification by resident populations [START_REF] Breheny | Urban compaction: feasible and acceptable?[END_REF]. Conflicting hypothesis can thus be identified in an extremely rich literature on the ability of planning policies to curb urban sprawl, namely through densification strategies [START_REF]High Urban Densities -A Solution for our Cities? Consulate General of France in Hong Kong[END_REF][START_REF] Charmes | La densification en débat[END_REF][START_REF] Puca | Vers des politiques publiques de densification et d'intensification douces? Workshop proceedings[END_REF][START_REF] Laugier | L'étalement urbain en France. Synthèse documentaire[END_REF]. In North America, authors like Gordon and Richardson [START_REF] Gordon | Beyond Polycentricity: The Dispersed Metropolis, Los Angeles 1970-1990[END_REF][START_REF] Gordon | Are Compact Cities a Desirable Planning Goal[END_REF] have challenged both the feasibility and the opportunity of sprawl containment, seeing suburbanization as the most efficient marketdriven allocation of land respecting consumer preferences. Consensus is wider in Europe on the impossibility of accommodating uncontrolled sprawl in much more constrained geographical settings. Negative consequences on the traditional city centers (often observed in North America) are also seen as a major challenge for European cities, given the economic, heritage and symbolic value of traditional urban cores.

But even within this consensus, different assumptions can lead to different models of the interplay between population growth, congestion, city/suburbs relations, densification policies, resident perceptions and urban sprawl. These underlying assumptions also reflect different beliefs in the most plausible outcomes of observed trends of urban sprawl and of the capacity of densification policies to have a real impact on these trends. We think that modelers accompanying decision-making and policy formulation should integrate and not ignore this multiplicity of possible models. We follow in this the principle of multiple explanations first formulated by the ancient Greek philosopher Epicurus: if several theories are consistent with the observed phenomena, retain them all.

Within this paper we will thus propose in Section 2 two extremely simplified alternative models of the interplay between densification policies and urban sprawl. These models reflect two particularly prominent views in the debate on urban densification that can be found in Europe and, more specifically, in France. We will formalize the models as Bayesian networks (BN) organizing expert knowledge in the form of probabilistic relations. At the same time the two BN should not be considered as the only possible explanatory models. An expert within a decision-making context could be more or less confident in each of the two models, but should also allow some skepticism in the ability of either of them to capture reality. Some plausibility should thus be given to the fact that both models fail to explain urban sprawl and that a third, unknown model or even no model at all links urban sprawl to underlying trends and to densification policies. In Section 3, we will assess how new evidence could be used to review beliefs in models through a Bayesian framework and how beliefs in model and evidences could be combined to calculate beliefs in outcomes of urban sprawl. The fi-nal section will explore how different theoretical frameworks could be used instead of Bayesian pro-babilities to integrate uncertainties linked to multiple possible models of urban sprawl.

2

Two Alternative Models of Urban Sprawl

Bayesian Networks to Model Uncertain Relations Behind Urban Sprawl

Two alternative models for the relation between densification policies and urban sprawl have thus been formalized as Bayesian Networks [START_REF] Jensen | Bayesian Networks and Decision Graphs[END_REF][START_REF] Korb | Bayesian Artificial Intelligence[END_REF]. Bayesian Networks (BN) are systems of probabilistic relations implemented on a directed a-cyclical graph which can be used to model uncertain causal knowledge among stochastic variables.

BN have already been proposed as models of spatial systems [START_REF] Fusco | Démarche géo-prospective et modélisation causale probabiliste[END_REF][START_REF] Scarella | La ségrégation résidentielle dans l'espace-temps métropolitain[END_REF] once appropriate expert knowledge is elicited in terms of probabilities. Marcot et al. [START_REF] Marcot | Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation[END_REF] give guidelines for BN development and update/revision, combining expert knowledge and empirical evidence, in the field of ecological modelling and conservation.

Figure 1 shows the causal graph of the two models that we developed. Both models include the same fifteen variables, seven of which correspond to the city sub-model and seven to the suburbia sub-model while the last variable, the demographic growth of the whole study area, is considered external to the system. To simplify domain knowledge, variables are either binary (yes/no, stable/increase, preserved/endangered, etc.) or ternary (stable/decrease/increase, stopped/limited /accelerated, etc.). Within each sub-system, decision variables are identified. Densification is a policy applicable both to the city and to the suburbs. Within the city, it can correspond to urban infill, to brownfield development or to replacement of smaller buildings with bigger ones. In the suburbs soft densification (infill of single-family houses) is often opposed to hard densification (where bigger buildings are mixed to single-family houses). A common urban policy in France is also city renovation, which includes the development of modern transit (usually light-rail transit), requalification of public space and publicfunded renovation of old buildings. A contested policy is the development of employment in suburbs: some see it as a way of containing daily mobility of suburbanites, other see it as an encouragement to further urban sprawl. Many internal variables are perceptions by resident households: perception of city quality of life, perception of suburban amenities (mainly environmental amenities linked to low-density urbanization) and perception of suburban quality of life (combining environmental amenities with presence of jobs and services). These are often non-observable variables, unless expensive ad hoc surveys are carried out. The four decision variables and the external one are the independent variables of the model.

Even by adhering to a given set of theoretical assumptions, BN are particularly well-suited tools to model the kind of knowledge experts have on the phenomena under study. Cause-to-effect relations between geographic and sociodemographic variables are relatively "dirty", uncertain: the same causes can produce different effects and several additional variables are missing in the model. Causal probabilistic relations are thus a good option to model uncertain causal knowledge. But even with bi - The Suburbia Subsystem nary and ternary variables, the number of probabilistic parameters in the BN is daunting. The ten dependent variables are governed by almost 300 probabilistic parameters in Model 2 (almost 250 in Model 1), which are clearly impossible to elicit from experts. Noisy logical gates NoisyOr, NoisyAnd and NoisyMax [START_REF] Henrion | Some practical issues in constructing belief networks[END_REF][START_REF] Diez | Canonical Probabilistic Models for Knowledge Engineering[END_REF] have thus been used to model in a simplified way, under the assumption of independence of causal impact, the sufficient/necessary causal links among the variables. Leak parameters can also be used to take into the account the effect of additional variables which are missing in the models. Model 1 thus required the elicitation of only 38 non-null parameters, Model 2 required 42. These parameters are judgments on the probabilistic force of the relations within a limited number of possible values (0.4, 0.5, 0.6, 0.7, 0.8 and 0.9, the value 1 being reserved to deterministic relations). Two possible leak parameters were also considered: 0.1 for relations, which the expert considers knowing with relatively little uncertainty (there is a 0.1 probability that the phenomena are produced by variables omitted in the model), and 0.2 for relations with higher uncertainty due to missing variables. For every model, we thus had to produce around forty numerical assessments within a very limited number of possible values. This is a realistic effort requested on domain experts. Discrete probabilistic functions associated to the noisy logical gates can then be used to derive the complete conditional probability table for each dependent variable of the models.

Analogies and Differences between the two Models

Models 1 and 2 are not based on completely different theoretical assumptions and formalize expert knowledge on the possible impacts of densification policies in European cities on a time span of 10-20 years. They share several common points, justifying the use of the same fifteen variables even if, sometimes, with slightly different probabilistic parameters. Both models consider that urban sprawl is the combination of increased suburban population and spatial extension of the suburbs in natural and agricultural land. Both give an important role to perceived quality of life from urban and suburban dwellers. For both models, for examples, perceived quality of life in the city tends to improve with city renovation, with maintained city centrality and with city gentrification (bringing more affluent dwellers to the urban core) as well as with a reduction of city congestion (conversely, perception of quality of life worsens when city congestion increases). For both models, perceived suburban quality of life combines the perception of suburban amenities with the practical aspects related to the presence of jobs and services. Both models finally agree on the demographic connections between city and suburbs. Demographic growth of the whole urban area fuels both city population and suburban population. Gentrification (which is often catalyzed by city renovation) tends nevertheless to diminish city population (wealthier dwellers normally occupy more space than poorer ones), whereas densification increases the ability of the city to absorb its share of demographic growth. Households which are not retained by the city (because of gentrification or because of a desire to leave the city due to a worsened perception of its quality of life) increase the demographic pressure on suburbs. When perceived quality of life in the suburbs worsens, households tend to migrate further away and this produces a spatial extension of suburbs and fi-nally contributes to the acceleration of urban sprawl. But the two models make opposite assumptions on the role and the impacts of densification policies. On the whole, Model 1 is relatively neutral on the direct impact of densification policies on household perceptions, considering that the majority of people are not necessarily hostile to densification, though recognizing an important role to city renovation on city congestion and on the perceived quality of life of its inhabitants. On the contrary, it assumes that densification can produce positive indirect effects: it allows the city and the suburbs to accommodate demographic growth within the present urban boundaries and, above all, it limits the risks that population growth in the suburbs endangers the perceived quality of their residents (densification policies aim at controlling the quality of urban and suburban infills). For Model 1, suburban jobs development weakens the traditional city-center and both directly and indirectly (by diminishing the perceived quality of life in the city), it favors further suburbanization, spatial extent of suburbs and ultimately accelerates urban sprawl. Model 1 corresponds to the majority view of European urban planners and to the recommendations of official documents from the French ministry and the EU Commission [START_REF]European Commission: European Sustainable Cities. Report of the Expert Group on the Urban Environment, Sustainable City Project[END_REF].

Model 2 assumes a positive role of suburban jobs development in reducing city congestion, an assumption that was traditionally underlying many urban plans by local authorities in France and in Europe, even if it has been later put in question by authors like Wiel [START_REF] Wiel | La transition urbaine ou le passage de la ville pédestre à la ville motorisée[END_REF]. Above all, Model 2 assumes that densification policies, both in the city and in the suburbs, will be negatively perceived by a majority of households and will have negative effects on city congestion, even more if they are not accompanied by city renovation policies. On the whole, even without accepting the extreme positions found in the American literature [START_REF] Gordon | Beyond Polycentricity: The Dispersed Metropolis, Los Angeles 1970-1990[END_REF][START_REF] Gordon | Are Compact Cities a Desirable Planning Goal[END_REF], the assumptions behind this model are more pessimistic on the ability of urban planning to stop urban sprawl and suggest more limited goals of sprawl containment.

Using BN models to Revise Beliefs

Once elements of evidence are entered in the models, the BN can propagate this information and revise the beliefs on non-observed variables. Bayesian belief revision, based on Bayes' theorem (1) and on its generalization given by Jeffrey's rule (2), can thus be used to propagate both elements of certain and uncertain evidence [START_REF] Bilmes | On Virtual Evidence and Soft Evidence in Bayesian Networks[END_REF][START_REF] Pan | Belief Update in Bayesian Networks Using Uncertain Evidence[END_REF].

p ( H i ∨E ) = p ( E | H i ) p ( H i ) ∑ i ❑ p ( E∨H i ) p( H i ) (1)
Where Hi are the different hypotheses for which beliefs have to be revised given evidence E.

q

( H i ∨E ) = ∑ j ❑ q j p ( H i ∨E j ) (2)
Where beliefs qj are distributed over several possible outcomes for E and each p(Hi| Ej) is calculated according to (1).

To be more precise, Bayesian belief revision within a BN is done by considering the probabilities of a child variable for a given configuration of the values of its parent variables as a multinomial distribution (binomial distribution for binary variables). The distributions are defined a priori by expert elicited parameters and can be used to revise beliefs on the marginal probabilities of variables of interest once elements of evidence are entered in the model. Following a thorough Bayesian approach, the probabilistic parameters can also be updated through the use of the corresponding conjugate prior distributions (Dirichelet and Beta distributions) if an incremental learning of model parameters is sought for, like in [START_REF] Josang | Dirichelet Reputation Systems[END_REF]. Within our research, we are not interested in parameter updating (see discussion further).

We can thus suppose we implement a given set of policies and calculate the new beliefs on the variable "urban sprawl" given by the two models (Figure 2). Model 1 suggests that the densification of city and suburbs, together with city renovation and a policy aiming at hindering job developments in the suburbs will very probably stop urban sprawl (p=0.78) or possibly limit it (p=0.14). These beliefs are particularly insensitive to the demographic growth scenario. Model 2, on the contrary, favors the implementation of a completely opposite set of policies: no densification, whether in the city or in the suburbs, no city renovation but development of jobs in the suburbs. The final impact on urban sprawl is relatively uncertain (even if it is clearly better than any other set of policies implementing densification): sprawl could be stopped (p=0.35), limited (p=0.26) but also accelerate (p=0.39). These beliefs are very sensitive to the demographic growth scenarios, though remaining relatively uncertain: by increasing population, urban sprawl should probably accelerate (p=0.47, with probability of stop falling to 0.25); in the absence of population growth, urban sprawl should be probably stopped (p=0.45, with probability of acceleration falling to 0.32). Uncertain evidence could also be propagated, as for example if we consider population growth very probable (p=0.8) but not certain.

3

Belief Revision between Alternative Models

Problem statement

The central point in our paper is not belief propagation within a BN. A much more crucial goal is to make the best use of available evidence from case studies (which are always relatively rare) in order to reassess our beliefs in models. Subsequently, revised beliefs in models and belief propagation within each model could be combined to derive new beliefs on a few variables of interest, given the available evidence.

The main characteristic of our epistemic framework is considering Models 1 and 2 as two alternative plausible explanations of urban sprawl, together with a more uncertain option considering urban sprawl as the consequence of an unknown third model (which could also be a model of stochastic independence from any other domain variable). Raftery [START_REF] Raftery | Bayesian model selection in social research[END_REF][START_REF] Raftery | Rejoinder: model selection in unavoidable in social research[END_REF] has already proposed Bayesian frameworks for model selection in the social sciences, an indication subsequently made by Withers [START_REF] Withers | Quantitative methods: Bayesian inference, Bayesian thinking[END_REF] within the geographical science. The core of model selection lies in the identification of the best model between Model 1 (M1) and Model 2 (M2), knowing some empirical evidence E and revising some prior belief on model plausibility (p(M1), p(M2)). Using Bayes' theorem [START_REF] Foley | Global consequences of land use[END_REF], one can evaluate the posterior odds of the two models, given the evidence:

p ( M 1|E ¿ ¿ p ( M 2|E ¿ ¿= p ( E|M 1¿ ¿ p ( E|M 2 ¿ ¿ p (M 1) p (M 2) (3)
The first term of the right side of the equation is known as Bayes factor, defined as the ratio of the likelihoods of the evidence given each model. When the parameter space of each model is considered, likelihoods must be calculated as a complete integral on all parameter space, which is often intractable. Bayesian Information Criterion (BIC) is then used as an approximation of Bayes factor. If, as we will do here, we consider model parameters as fixed, Bayes factor can be directly calculated through belief propagation in the two Bayesian networks. At the same time, as already anticipated, our frame of discernment of possible models is significantly richer:

Models = {M1, M2, …, Other}
Where Mi are the possible models considered and Other stands for any other possible model not yet formulated. The modeler can have prior beliefs on these possible options (including the option Other) and can calculate likelihoods from available models. It cannot however calculate the likelihood p(E|Other), which is necessary in order to apply Bayes theorem for revising the beliefs on the possible models. We thus propose an approach to estimate the likelihood of the option Other given the evidence E respecting the following principles:

1. p(E|Other) = 0 if at least one of the formulated explanatory models Mi is completely plausible (for at least one i, likelihood Mi = 1); 2. p(E|Other) = 1 if no other formulated explanatory models is minimally plausible (for all i, likelihood Mi = 0). 3. p(E|Other) should grow as the likelihood of the formulated explanatory models diminishes.

These principles correspond to the human quest of explanation for observed phenomena, considering the current accepted explanatory knowledge and its ability to explain the new phenomena. If, among several currently accepted models, at least one of them is a plausible explanation of newly observed phenomena, no push for new models is felt, and the newly acquired empirical evidence is used to arbitrate between available models. The plausibility of new, yet to be formulated models is high when newly observed phenomena cannot plausibly be accounted for by any of the available models. History of science often witnessed this course of events. We could then write:

p(E|Other) = 1 -f(Max (p(E|Mi))) ( 4
)
Where f is a suitable monotonic decreasing function varying between 0 (when Max(p(E|Mi))=0) and 1 (when Max(p(E|Mi))=1). One possible operationalization of (4) is:

p(E|Other) = 1 -Max (p(E|Mi))  (5)
if  < 1 the push to look for new models is sublinear with the lack of plausibility of the best available model, if  >1 it is super-linear. Once an appropriate  is chosen, reflecting the attitude to lack of plausibility of the best available model, we can calculate thanks to ( 5) likelihoods for all model options given observed evidence E and, through Bayes' theorem (1), we can revise our beliefs in the different model options.

If, instead of certain evidence E, we have uncertain evidence p(Ei), Jeffrey's rule (2) can be used to revise beliefs.

Application to the Possible Models of Urban Sprawl

Let us imagine that an urban geographer is relatively confident in Models 1 and 2 as possible explanations of urban sprawl and its relations to densification policies in French cities. He does not want to commit to any of the two models in particular and he does not want to bar the possibility that other models, not yet formulated, could explain urban sprawl in French cities. He could thus assign prior probabilities 0.45, 0.45 and 0.1 to Model 1, Model 2 and Other within . How should he revise these beliefs . How s with newly acquired knowledge on a case study to which the models could be applied?

Fig. 3. Densification policies in Grenoble regional master plan of 2000. Source: [START_REF] Withers | Quantitative methods: Bayesian inference, Bayesian thinking[END_REF] The metropolitan area of Grenoble, in the alpine region of southern France, offers a good feedback on densification policies in French cities (Figure 3). At its core lies the city of Grenoble, a mid-sized city of 155 000 inhabitants. Around the central city, a vast peripheral area hosts a population three times more important. The periphery develops along three main axes going south, north-east and north-west and following three main alpine valleys. Most of these peripheral areas are made of residential suburbs, surrounding a few historical villages and industrial or commercial surfaces. We will focus our attention on this study area for a period stretching from 1999 to 2013. Over this period, Grenoble and its surrounding municipalities have implemented one of the most coherent densification policies in France, both in the central city and in the suburban areas, promoted by the adoption of the development and town planning master plan of 2000 [START_REF]DDT Isère: Comment favoriser la densification? Direction Départementale du Territoire[END_REF][START_REF] Aurg | Schéma Directeur de la Région Grenobloise[END_REF] and of several local town plans. The central city has also implemented important policies of city renovation, as the Urban Renovation Program of 2005 [START_REF]GRA: Programme de Rénovation Urbaine de l'agglomération grenobloise[END_REF], the extension of the LRT lines A and B and the opening of two new LRT lines in 2006 and 2007. The same coherence cannot be found in terms of suburban jobs development. The master plan in 2000 foresaw the development of industrial, service and retail jobs in a few key areas of the suburbs. Indeed, jobs have steadily grown between 1999 and 2013 both in the central city (passing from 85 000 to 93 000) and in the rest of the metropolitan area (passing from 136 000 to 207 000 within a much larger geographic perimeter, see further).

We could thus easily assess that, for the Grenoble study area, variables City Densification, Suburban Densification, City Renovation and Suburban Jobs Development all have value "yes" over the period 1999-2013. As for the population growth of the study area, it passed from 631 000 inhabitants to 684 000, with an 8.4% increase, which is in line with the general steady demographic growth of France during the same period. We will thus assign value "increase" to variable Demographic Growth. These five values define a scenario of policies and demographic growth for our study area, which are independent from the probability relations of our two models.

A few easily measurable variables can also be used to introduce observed evidence in this scenario and evaluate model likelihood given the scenario and the evidence. City population was thus relatively stable over the period, oscillating between 153 000 and 156 000 inhabitants. Suburban population, on the other hand, rose from 478 000 to 528 000, a 10.5% increase over the 15 years. We can thus assign values "stable" and "increase" to variables City Population and Suburban Population, respectively.

Finally, the spatial extension of the suburbs within the metropolitan area was particularly important. Metropolitan areas are defined in France by the National Institute of Statistics and Economics (INSEE) as functional urban areas where more than 40% of the active population in each municipality commutes to the central city or to other municipalities in the metro area. Thus defined, the metropolitan area of Grenoble included 119 municipalities in 1999 but well 197 in 2013. Even if the newly incorporated municipalities were already well developed in 1999, their population increase strongly depended on household migration from the central city and other central municipalities and they were progressively integrated in the urban functional area. We can thus assign the value "yes" to the variable Spatial Extension of Suburbs between 1999 and 2013.

When these new findings are entered in the BNs, we can calculate marginal joint probabilities of 0.0636 for Model 1 and of 0.0992 for Model 2. These values correspond to the likelihoods of the two models given the scenario (defined by independent variables) and the observed findings (i.e. the three further empirically determined variables). We can thus revise our beliefs in the different model options using Equation [START_REF] Calthorpe | The Regional City: Planning for the End of Sprawl[END_REF].

Table 1 shows the results of the belief revision assuming two different attitudes to lack of plausibility of the best available model: linear (=1) and sublinear (=0.25). The case study of Grenoble is clearly a counter-example for the two acknowledged models of urban sprawl. The most conflicting element of evidence of the case study with the models is the relatively stable city population in spite of overall demographic growth and the city densification policies (for both models city gentrification becomes then the most plausible cause of the observed phenomena). Overall, the likelihood value is relatively low for both models (even if it is higher for Model 2 than for Model 1). Equation ( 5) thus produces relatively high likelihoods for other possible models, compatible with the observed evidence: 0.4388 in the sublinear case, 0.9008 in the linear case. Bayesian belief revision (Equation 1) thus results in much more uncertainty within our frame of discernment of possible models. In the sublinear case, belief in Model 2 is almost the same as the one in other possible models (0.38 and 0.37, respectively) and slightly higher than for Model 1. In the linear case, the most plausible model is the one yet to be discovered (posterior belief = 0.55), highlighting the weaknesses of available models.

Discussion

The interpretation of the Grenoble case study in terms of hard evidence could be challenged. Even if this example was selected for its paradigmatic role and for the ease of interpretation of the available evidence, a soft/virtual evidence approach could be more appropriate [START_REF] Bilmes | On Virtual Evidence and Soft Evidence in Bayesian Networks[END_REF][START_REF] Pan | Belief Update in Bayesian Networks Using Uncertain Evidence[END_REF]. In the absence of an unambiguous protocol allowing the interpretation of available evidence, expert-based interpretation of evidence could be used. This could possibly result in soft or virtual evidence, assigning probabilities or likelihoods (respectively) to the fact that city or suburban densification has really taken place in the study area during the 1999-2013 period. Equation (2) would then be used to propagate evidence in the Bayesian networks. By so doing, the likelihoods of the two models would increase accordingly (reducing as a consequence the likelihood of other models), but the main conclusion would be the same: belief in Model 2 increases slightly compared to belief in Model 1, but belief in other models, which was particularly low a priori, becomes much higher now, increasing considerably uncertainty among model options. Should Models 1 and 2 be discarded altogether in the analysis of urban sprawl in French metropolitan areas during the last decades? Our Bayesian belief revision doesn't reach this conclusion, as a unique counter-example is not sufficient to invalidate our models, given our prior beliefs. France has around 50 metropolitan areas of more than 200 000 inhabitants, having applied more or less coherent policies of densification to counter sprawl. The proposition to be evaluated is indeed the ability of our two models to cover this domain, and only a more thorough analysis of this set could possibly arrive to such a conclusion. If such analysis were to produce a very low value of posterior belief for one of the two models, this could indeed be disqualified as a pertinent model for our domain. If both models were characterized by very low posterior beliefs, the geographer's conclusion should be the need to look for alternative models, based on different theoretical assumptions and possibly more attentive to specific dynamics observed in French metropolitan areas. As often in the social sciences, we remain within a falsification approach with is much weaker than Popper's [START_REF] Fusco | Faire science avec l'incertitude : réflexions sur la production des connaissances en Sciences Humaines et Sociales[END_REF]: evidence concurs to make theoretical explanations incrementally more or less plausible, up to the point where the scientific community decides that a given model is no longer acceptable.

From this point of view, we are not interested in Bayesian updating of model parameters. Posterior expected values of parameters could indeed be calculated through conjugate priors of the multivariate distributions in the Bayesian Networks. Parameters in the conditional probability tables would then be allowed to drift considerably from the values derived from the elicited knowledge modelled through the noisy logical gates. Sophisticated parameter updating schemes could also be used, by assigning experience equivalents to the prior knowledge incorporated in the models. Prior knowledge could then be possibly swamped by data, as new case studies are presented to the models. But the resulting models would be less and less linked to precise theoretical assumptions, and finally harder to falsify, given their ability to adaptively incorporate newly acquired evidence.

It is also worth highlighting how our problem differs from more classical robust Bayesian modelling as presented by Bolstad [START_REF] Bolstad | Introduction to Bayesian Statistics[END_REF]. When modelling binomial or multinomial stochastic processes, Beta and Dirichelet priors are normally linearly combined with a flat prior, to which the modeler assigns a very low prior belief. By doing so, the modeler makes sure that possible miss-specifications of the priors don't result in a wrong posterior distribution: when data contradict the prior, the posterior depends more on the data than on the prior. An important theoretical assumption underpins these operations: the observed phenomena are governed by a simple binomial or multinomial distribution whose parameters can possibly be wrong, but the posterior model will always have the same functional structure. In our problem, the possible models are Bayesian Networks, which are complex combinations of binomial and multinomial distributions. It is impossible to find appropriate conjugate distributions to combine with a flat prior. Moreover, the models have different structures (and not just different parameters) and possible other models are not just the "flat equivalent" of the available ones. They are yet to be developed models with particular structure and parameters, which are presently unknown. Our approach is thus more a belief revision heuristic among available and unavailable models backed by Bayesian calculus, than a precise Bayesian calculus of posterior model parameters within a given model structure.

Conclusions: Further Frameworks for Multiple Models of Urban Sprawl

This paper showed how Bayesian belief revision together with a particular framework assigning likelihood to unknown models can be used in order to assess prior beliefs on multiple models. This approach seems particularly interesting whenever the modeler is able to express his beliefs in terms of additive probabilities and when the number of available evidence is relatively small, giving thus great importance to prior probabilities. This last condition is very frequent in research on sustainable cities: a few case studies are the empirical evidence of broadly formulated, alternative models of sustainable urban development. The case of urban sprawl in the metropolitan area of Grenoble also highlighted issues of knowledge elicitation and domain definition for the models. It was finally shown how a single counter-example is not sufficient to invalidate the two proposed models, given our prior beliefs. However, Bayesian networks and Bayesian calculi are by no means the only tool that can be employed to reason about alternative hypotheses in the light of the available evidence. Artificial intelligence, in particular, has dedicated much effort to devising formal and computational frameworks for representing and managing uncertainty, on the one hand, of which Bayesian networks are but one offshoot, and belief revision on the other hand.

When it comes to representing and managing uncertainty, it is important to underline that two aspects of uncertainty must be distinguished: (i) stochastic uncertainty, resulting from a system behaving in a random way, and (ii) epistemic uncertainty (or ignorance), resulting from a lack of knowledge about a system.

Dempster-Shafer theory of evidence [START_REF] Shafer | A Mathematical Theory of Evidence[END_REF], and Phil Smets' transferable beliefs model [START_REF] Smets | The transferable belief model[END_REF], which is an interpretation thereof, is a theory of uncertainty whose aim is to give a mathematical account for epistemic uncertainty, besides stochastic uncertainty. The theory of evidence is rooted in probability theory, but it innovates in allowing for the allocation of a probability mass to events, rather than just elementary events or individual sample points.

The basic ingredients of the Dempster-Shafer theory of evidence are:  a basic belief assignment, in the form of a probability mass distribution m over (sets of elementary) events A included in Ω, such that the mass of the empty set is null and the mass of all events sums up to one.  a belief function Bel, determined by the basic belief assignment: Bel(A) is the sum of the masses of all events included in A;  a plausibility function Pl, also determined by the basic belief assignment and dual to the belief function: Pl(A) is the sum of the masses of all events that have a non-null intersection with A; thus Bel(A) = 1 -Pl(complement of A). The process of changing beliefs when new evidence becomes available is central to the evidence theory. The intuition behind it is that a basic belief assignment m represents an epistemic (or credal state of an agent and, if further evidence becomes available to the agent, in the form of a proposition φ, the agent should change m to reflect this new evidence. In particular, the agent should rule out all worlds where φ does not hold and transfer their previous belief mass to worlds where φ holds. It should be noticed that this is essentially a conditioning process, whereby m( • ) is conditioned by φ to yield m( • | φ). One possible way to perform such a belief transfer is by means of the so-called Dempster rule of conditioning.

Possibility theory [START_REF] Dubois | Possibility Theory[END_REF] is another mathematical theory of uncertainty based on fuzzy set theory, alternative to probability theory. It differs from this latter by the use of a pair of dual set-functions (possibility and necessity measures) instead of only one. This feature makes it easier to capture partial ignorance. Besides, it is not additive and makes sense on ordinal structures. Possibility theory has a strong relation with the theory of evidence, not only because the two share basically the same objec-tive, but also because both theories use monotonic (or non-additive) measures to represent beliefs.

The advantage offered by these two theories, with respect to Bayesian probabilities, is their capability of representing epistemic uncertainty in an explicit fashion. The theory of evidence, in addition, may represent stochastic uncertainty as well in a way that is fully compatible with probability theory, while the relationship between possibility theory and probability is subtler; nevertheless, transformations from possibilities to probabilities and vice versa have been proposed and studied in the literature [START_REF] Dubois | On Possibility/Probability Transformations[END_REF]. How these two theories might be applied to our problem of model selection is left for future work. The main issue that would have to be addressed to this purpose is a suitable definition of the "sample space" Ω underlying the model selection problem, given which, the uncertainty associated with the models might be represented, respectively, as a probability mass distribution over the power set of Ω or a possibility distribution over Ω. It should finally by assessed how these more sophisticated AI theories of uncertain knowledge could be integrated in decision making in the field of sustainable urban development.

Fig. 1 .

 1 Fig. 1. Two Bayesian Network Models for Urban Sprawl.

Perceived Suburban Quality of Life

		City Congestion
		Decrease	54.9
		Increase	45.1
		Suburban Jobs Development
		Yes	50.0
		No	50.0
	Demographic Growth
	Yes	50.0
	No	50.0
		Stable	61.5
		Improves	26.4
		Worsens	12.1

Perceived City Quality of Life

	Stable	20.1
	Improves	36.3
	Worsens	43.7

Perception of Suburban Amenities

			City Renovation
			Yes	50.0
			No		50.0
					City Centrality
					Weakened	55.1
					Maintained	44.9
					Spatial Extention of Suburbs Yes No 54.7 45.3	Urban Sprawl Stopped 39.6 Limited 23.9 Accelerated 36.4
					Suburban Population
					Stable	40.6
					Increase	59.4
					Suburban Densification
					Preserved	79.8	Yes	50.0
					Endangered	20.2	No	50.0
		The City Subsystem
	City Renovation Yes No 50.0 50.0	Decision Variable		Suburban Densification Yes No 50.0 50.0	Demographic Growth Yes No 50.0 50.0
	Yes No	Gentrification 50.0 50.0	Internal Variable	Suburban Population Stable Increase 40.8 59.2

Perceived Suburban Quality of Life

	City Densification Yes 100		City Renovation Yes 100		Model 1
	No	0		No	0
		Yes No	Gentrification 84.0 16.0		Perceived City Quality of Life Stable 12.7 Improves 61.2 Worsens 26.1
	City Population
	Stable Decrease Increase	11.1 16.9 72.0	City Congestion Decrease Increase 63.0 37.0	City Centrality Weakened 18.6 Maintained 81.4
					Spatial Extention of Suburbs Yes No 13.8 86.2	Urban Sprawl Stopped 77.6 Limited 14.1 Accelerated 8.27
			Suburban Jobs Development
			Yes	0
	Demographic Growth Yes No 50.0 50.0	No	100	Suburban Population Stable 15.0 Increase 85.0
			Perceived Suburban Quality of Life Stable Improves Worsens 6.00 0 94.0	Endangered Preserved Perception of Suburban Amenities 10.0 90.0	Suburban Densification
	City Densification Yes 0 No 100			City Renovation Yes 0 No 100		Model 2
			Gentrification	Perceived City Quality of Life
			Yes	20.0	Stable	25.9
			No	80.0	Improves	24.7
					Worsens	49.4
	City Population Decrease Stable Increase 49.3 20.7 30.0	City Congestion Decrease 51.7 Increase 48.3	City Centrality Weakened Maintained 71.9 28.1
					Spatial Extention of Suburbs
					Yes	55.0
					No	45.0	Urban Sprawl
							Stopped	34.8
			Suburban Jobs Development			Limited	25.6
	Demographic Growth	Yes No	100 0	Suburban Population	Accelerated	39.6
	Yes	50.0			Stable	68.4
	No	50.0			Increase	31.6
							Suburban Densification
			Worsens Improves Stable	20.9 55.3 23.7	Perception of Suburban Amenities Preserved Endangered 29.9 70.1	Yes No	0 100

Yes

Fig.

2

. Implementing Different Policies on the two Models.

Table 1 .

 1 Belief revision for model options given the case study of Grenoble.

		Model 1	Model 2	Other
				sublinear (=0.25)
	prior belief	0.45	0.45	0.1
	likelihood	0.0636	0.0992	0.4388
	posterior belief	0.24	0.38	0.37
				linear (=1)
	prior belief	0.45	0.45	0.1
	likelihood	0.0636	0.0992	0.9008
	posterior belief	0.18	0.27	0.55


Li Zhong

Rustem Uzbekov

Chloé Journo

Philippe Roingeard

Andrea Cimarelli
email: acimarel@ens-lyon.fr

A novel domain within the CIL regulates egress of IFITM3 from the Golgi and prevents its deleterious accumulation in this apparatus

Keywords: IFITM, Interferon, HIV, virus, innate immunity, Golgi, Trafficking, PRRT2

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The InterFeron-Induced TransMembrane proteins (IFITMs) are broad viral inhibitors that prevent membrane fusion between viral and cellular membranes, thus protecting the cell from infection [START_REF] Diamond | The broad-spectrum antiviral functions of IFIT and IFITM proteins[END_REF] . IFITMs belong to the dispanin/CD225 family of proteins that originated from metazoan lineages and diversified into four distinct subfamilies (A through D) that include proteins of known and yet unknown functions [START_REF] Sällman Almén | The dispanins: a novel gene family of ancient origin that contains 14 human members[END_REF] . The A subfamily regroups all IFITMs which in humans are: IFITM1, 2 and 3, that we will refer to hereafter as IFITMs, that are interferon (IFN)-induced proteins essentially studied in the context of viral infection [START_REF] Brass | The IFITM proteins mediate cellular resistance to influenza A H1N1 virus, West Nile virus, and dengue virus[END_REF] ; IFITM5 that plays undefined roles in Osteogenesis imperfecta type V, a bone-specific disease [START_REF] Lim | A novel Ser40Trp variant in IFITM5 in a family with osteogenesis imperfecta and review of the literature[END_REF] and IFITM10 whose functions are unknown. An additional known member of the dispanin/CD225 family is the neuron specific PRoline-Rich Transmembrane protein 2 (PRRT2, B subfamily) that is involved in neurotransmitter vesicles regulation and has been genetically linked to benign familial infantile seizures (BFIS) and to paroxysmal kinesigenic dyskinesia (PKD), the most common type of paroxysmal movement disorder [START_REF] Calame | Presynaptic PRRT2 Deficiency Causes Cerebellar Dysfunction and Paroxysmal Kinesigenic Dyskinesia[END_REF] .

Members of the dispanin/CD225 family are characterized by a similar structure consisting of an intramembrane domain (IMD, previously defined as transmembrane domain 1, TM1), a cytoplasmic intracellular loop (CIL) and a transmembrane domain TMD (previously defined as transmembrane domain 2, TM2, as schematically shown in Fig. 1a for IFITM3). By virtue of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. this structure, IFITMs decorate cellular membranes in which they act as membrane fusion inhibitors. The distribution and activities of IFITMs on cellular membranes are highly regulated and are driven not only by distinct N and C termini responsible for the more prominent distribution of individual IFITMs at the plasma or at inner membranes as is the case for IFITM1 with respect to IFITM2 and 3, respectively, but also by several post-translational modifications and in particular palmitoylation on cysteine residues, ubiquitination and methylation on lysines, as well as phosphorylation on a specific tyrosine residue [START_REF] Chesarino | Phosphorylation of the Antiviral Protein Interferon-inducible Transmembrane Protein 3 (IFITM3) Dually Regulates Its Endocytosis and Ubiquitination[END_REF][START_REF] Yount | S -Palmitoylation and Ubiquitination Differentially Regulate Interferon-induced Transmembrane Protein 3 (IFITM3)-mediated Resistance to Influenza Virus[END_REF][START_REF] Shan | Histone demethylase LSD1 restricts influenza A virus infection by erasing IFITM3-K88 monomethylation[END_REF][START_REF] Jia | The N-terminal region of IFITM3 modulates its antiviral activity by regulating IFITM3 cellular localization[END_REF][START_REF] Jia | Identification of an endocytic signal essential for the antiviral action of IFITM3[END_REF] .

More recently, IFITMs have been linked to cellular functions independent from their protective role against viral infection in the regulation of glucose metabolism in mice, of phosphatidylinositol 3-kinase (PI3K) mediated signaling in B cells [START_REF] Lee | IFITM3 functions as a PIP3 scaffold to amplify PI3K signalling in B cells[END_REF] and of trophoblast fusion during placental formation in vivo [START_REF] Buchrieser | IFITM proteins inhibit placental syncytiotrophoblast formation and promote fetal demise[END_REF] , overall supporting the notion that IFITM proteins likely act well beyond a viral context.

In this study, through an in-depth characterization of a novel gain-of-function IFITM3 mutant, we have been able to identify a novel protein domain with the CIL of IFITM3 that regulates its egress from the Golgi apparatus. Mutations within this domain lead to the retention of IFITM3 in this organelle, where IFITM3 interferes with the fusion mediated between vesicle and target Soluble N-éthylmaleimide-sensitive-factor Attachment protein REceptor (v-and t-SNAREs), impairing the functionalities of the Golgi secretory pathway.

Functional as well as in silico analyses allowed us to precisely define the Golgi egress 81S-x- K-x-R-D86 domain which is positioned after the IMD, at the beginning of the CIL and it is conserved across vertebrate members of the dispanin/CD225 A subfamily. Through the analysis of mutations identified in patients affected by PKD, we do show here that, despite no conservation at the amino acid level, this domain is functionally conserved also in PRRT2, a member of the B subfamily, thus raising the possibility of a general functional role in Golgi trafficking common to dispanin/CD225 subfamilies.

Overall, these results identify a domain that regulates egress of IFITM and IFITM-like proteins from the Golgi and given the deleterious effects that dysregulation of this process bears on the overall functionalities of this apparatus, they highlight the importance that correct regulation of IFITM trafficking has for the cell.

Results

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A gain of function IFITM3 mutant affects glycoprotein trafficking. During a previous mutagenesis study of the broad viral inhibitor IFITM3, we identified a mutant in the cytoplasmic intracellular loop region of IFITM3 (CIL, mutant 85-90 in which six consecutive residues were mutated to alanine, Fig. 1a) that exerted a strong inhibitory effect on the incorporation of HIV-1 Envelope glycoproteins at the surface of released virion particles [START_REF] Appourchaux | Functional Mapping of Regions Involved in the Negative Imprinting of Virion Particle Infectivity and in Target Cell Protection by Interferon-Induced Transmembrane Protein 3 against HIV-1[END_REF] . At its simplest, the formation of infectious HIV-1 virion particles requires the gathering on the plasma membrane of two classes of structural viral proteins: Gag (and Gag-Pro-Pol, not marked here for clarity's sake) and Env (Fig. 1b). While Gag is translated from cytoplasmic mRNAs from free ribosomes, the Envelope glycoprotein undergoes co-translational ER translocation and reaches the plasma membrane following the ER-Golgi secretory pathway 14 . As such, loss of Envelope incorporation in virion particles may reflect potential effects of IFITM3 along this axis that we decided to investigate further. Indeed, when expressed in HEK293T cells undergoing HIV-1 virion assembly (Fig. 1b-c), the 85-90 IFITM3 mutant induced only a moderate, yet detectable, decrease in the levels of cellassociated Gag and Env proteins when compared to control or WT IFITM3-expressing cells, but as expected it induced a drastic decrease in the amount of Env glycoprotein incorporated in purified virion particles (Fig. 1c). From this starting observation, we decided to determine whether this defect could be more generally reflective of an interference with Golgi-mediated trafficking, independently from HIV-1. To this end, the 85-90 IFITM3 mutant was co-expressed along with a fluorescent G protein of the Vesicular Stomatitis Virus (mEm.-VSV-G, Fig. 1d), prior to confocal microscopy analysis. Under these conditions, a strong perinuclear accumulation of mEm.-VSV-G was observed in cells expressing the mutant IFITM3, overall suggesting a defect in Golgi-mediated trafficking (Fig. 1d).

To further support this result, we analyzed the EndoH susceptibility of a non-tagged VSV-G followed by WB analysis, as an independent and commonly used technique to appreciate the progression of glycosylated proteins from the ER through the Golgi (Fig. 1e). Under these conditions, expression of WT IFITM3 led to a small but detectable accumulation of EndoHsensitive VSV-G when compared to control cells which however did not reach statistical significance under the conditions used here. However, the proportion of EndoH-sensitive VSV-G protein was significantly increased upon expression of the 85-90 IFITM3 mutant, indicating a strong defect in the ER-Golgi secretory pathway in the presence of this mutant protein.

Overall, these results indicate that the expression of the 85-90 IFITM3 mutant affects the normal functionalities of the cellular ER-Golgi secretory pathway, highlighting it as a novel gain-of-function IFITM3 mutant.

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The 85-90 IFITM3 mutant accumulates in and leads to structural changes of the Golgi apparatus. The 85-90 IFITM3 mutant did not significantly colocalize with the ER, nor with endosomal vesicles (Extended data Fig. 1). Instead, this mutant was strongly localized at the cis-Golgi (GM130 marker, Fig. 2a and 2b for a 3D-reconstruction of positive cells) and induced gross morphological changes in the appearance of the Golgi: punctiform in control and WT-IFITM3 expressing cells and inflated in cells expressing the 85-90 IFITM3 mutant. These structural changes were not only apparent after 3D-reconstruction of positive cells following confocal microscopy analysis (Fig. 2b), but also after electron microscopy analysis which more precisely indicated that the shape of the Golgi apparatus had lost its classical punctiform distribution in favor of an expansion of large vesicles (Fig. 2c).

The 85-90 IFITM3 mutant affects fusion between t and v-SNAREs. One of the key features of IFITM family members is their ability to interfere with viral-to cellular membranes fusion 15- 17 . We thus hypothesized that Golgi dysfunctions could be caused by the interference of IFITM3 with the ability of v-SNARE vesicles to fuse to t-SNAREs compartments, a process that is key to maintain protein and membrane fluxes, that is key for the integrity of the Golgi and that is reminiscent of the fusion between membranes that is observed after the engagement of a viral envelope glycoprotein with its cellular receptor 1819 . To test this hypothesis, we analyzed the ability of the 85-90 IFITM3 mutant to inhibit the fluorescent resonance energy transfer (FRET) that occurs upon fusion between GS15-CFP and ERS24-YFP bearing vesicles (v-and t-SNAREs, respectively), as a measure of membrane fusion inhibition in this compartment, as described [START_REF] Fukasawa | Selective control of SNARE recycling by Golgi retention[END_REF] (Fig. 3a). Given that this analysis was carried out in living cells, an excess of the 85-90 IFITM3 mutant was used to ensure that the majority of GS15-CFP and ERS24-YFP double positive cells were also IFITM3-positive. Under these conditions, a statistically significant decrease in FRET was observed upon expression of the 85-90 IFITM3 mutant with respect to control cells, indicating that this mutant does indeed interfere with v-to t-SNAREs fusion, in agreement with the major function ascribed to IFITM3 as a membrane fusion inhibitor.

To further support this observation, we overexpressed the v-SNARE GS15 and determined whether this could lead to Golgi trafficking normalization despite the presence of the 85-90 IFITM3 mutant. Indeed, v-SNAREs overexpression is known to bypass membrane fusion defects between Golgi membranes [START_REF] Babcock | Genetic analysis of soluble Nethylmaleimide-sensitive factor attachment protein function in Drosophila reveals positive and negative secretory roles[END_REF][START_REF] Kweon | Ykt6p is a multifunctional yeast R-SNARE that is required for multiple membrane transport pathways to the vacuole[END_REF][START_REF] Nichols | SNAREs and membrane fusion in the Golgi apparatus[END_REF] and we thus hypothesized it could relieve the defect imposed by the IFITM3 mutant. To this end, increasing levels of GS15 were expressed along with a constant amount of both 85-90 IFITM3 mutant and VSV-G, prior to the analysis of the distribution of VSV-G within triple-positive cells (Fig. 3b). Under these conditions, GS15 was able to relieve the perinuclear accumulation of the VSV-G glycoprotein induced by the IFITM3 mutant and was also able to act similarly on the 85-90 IFITM3 mutant itself, supporting our contention that a main disruptive event due to the accumulation of the 85-90 IFITM3 mutant in the Golgi is the inhibition of v-to t-SNAREs fusion.

The 85-90 mutation in the IFITM3 CIL defines a domain that regulates IFITM3 egress from the Golgi. To assess whether the 85-90 mutation induced retention or relocalization of IFITM3 at the Golgi, we performed a time-course analysis of both WT and mutant IFITM3 from early to late time points after ectopic DNA transfection (Fig. 4). While both proteins exhibited a predominant Golgi distribution at 6 hours post transfection, such concentration gradually diminished in the case of WT IFITM3, in agreement with its functional exit from the Golgi. On the contrary, little changes were observed over time in the case of the 85-90 IFITM3 mutant that remained in this compartment, supporting the notion that the 85-90 mutation induces the retention, rather than the redistribution, of IFITM3 in the Golgi.

In agreement with this observation, incubation of cells with the broad trafficking inhibitor Monensin induced accrued Golgi accumulation of WT IFITM3, but as expected did not modify the distribution pattern of the 85-90 mutant (Extended data Fig. 2).

Overall, these results concur in indicating that the 85-90 mutation defines a protein domain within the CIL that regulates the normal egress of IFITM3 from the cis-Golgi.

Fine mapping of the Golgi egress domain of IFITM3 within the CIL. To more precisely map the residues that regulated the egress of IFITM3 from the Golgi, individual amino acids spanning the entire CIL were mutated to alanine and mutants were analyzed by confocal microscopy (Fig. 5a-b and Extended data Fig. 3, alanine residues in the CIL were instead mutated to glycines). With the exception of two mutants that were barely detectable by WB (D92A and G95A), the remaining mutants were expressed to detectable levels upon WB analysis. We noticed that the D92A mutant acquired also a nuclear localization unusual for IFITM proteins, but this was not characterized further. This mutagenesis study identified a number of residues whose mutation led to accrued accumulation of IFITM3 into the Golgi (S81, V82, K83, S84, R85, D86, A96, Q97, Q98, S101, T102, A103, K104, L106 and N107), indicating that several residues within the CIL participate to this phenotype. Interestingly however, the mutations that resulted in levels of IFITM3 localization in the Golgi equivalent to those of the 85-90 mutant were essentially clustered in a patch (from residues S81 to D86) that was posited at the boundary between the end of the intramembrane domain of IFITM3 (IMD) and the start of the CIL and that overlapped with the 85-90 stretch.

Thus, the complete mutagenesis of the CIL allowed us to more precisely refine the domain responsible for the exit of IFITM3 from the Golgi (81SxKSRD86).

A generalized Golgi egress domain is conserved among members of the dispanin/CD225

subfamily A and is functionally conserved in PRRT2, a member of the dispanin/CD225 subfamily B, genetically associated to paroxysmal kinesigenic dyskinesia (PKD).

IFITMs belong to the larger dispanin/CD225 family which is itself divided into four subfamilies (A to D, Fig. 6a) [START_REF] Sällman Almén | The dispanins: a novel gene family of ancient origin that contains 14 human members[END_REF] . The A subfamily is composed of IFITM1, 2 and 3, as well as IFITM5 and IFITM10, which are not IFN regulated and exert unclear functions that in the case of IFITM5 are associated to Osteogenesis imperfecta type V, a bone-related genetic disease [START_REF] Lim | A novel Ser40Trp variant in IFITM5 in a family with osteogenesis imperfecta and review of the literature[END_REF] . The alignment of residues spanning the IFITM3 SxKSRD domain in human IFITMs, as well as their comparison in vertebrates (Fig. 6a, aligned sequences on the left for human IFITMs and Logo on the right for vertebrate IFITMs, respectively) indicated the presence of a consensus sequence conserved across vertebrate members of the dispanin/CD225 A subfamily: S-x-K-x-R-D. This patch was not conserved at the amino acidic level in the remaining subfamilies.

To determine whether this consensus domain exerted similar functions in other IFITM proteins belonging to the A subfamily, a single point mutation was introduced in the corresponding domain of IFITM1 (R64A), that contrarily to IFITM3 is essentially localized at the plasma membrane due to the lack of specific trafficking domains at its N-terminus when compared to IFITM3. Under these conditions, the introduction of a single point mutation in the IFITM Golgi egress domain was sufficient to drive the redistribution of IFITM1 from the plasma membrane to the Golgi, as observed by confocal microscopy analysis (Fig. 6b).

Thus, in silico and functional analyses indicate the presence of a conserved S-x-K-x-R-D domain important for the exit of members of the dispanin/CD225 A subfamily from the Golgi.

Despite the fact that the functions of certain proteins belonging to the dispanin/CD225 family remain unknown (as is the case for TMEME90A, TMEM91, TMEM233 and TMEM265), the information existing on other members (TUSC5, TMEM90B and PRRT2) indicate functions revolving around vesicular trafficking and vesicular behavior 5 24,25 . Among them, PRRT2 acts as a regulator of neurotransmitter vesicles in neuron cells and is genetically linked to paroxysmal kinesigenic dyskinesia (PKD, the most common type of paroxysmal movement disorder), as well as benign familial infantile seizures (BFIS). While the Golgi egress domain identified in IFITM proteins did not appear to be conserved at the amino acid level in other subfamilies, we decided to investigate whether it could be functionally conserved in PRRT2 as a prototype member of the B subfamily. Indeed, we noticed that two mutations in PRRT2 have been identified in patients affected by PKD in the same CIL region at the boundary between the IMD and the CIL (A291V and R295Q, corresponding to the underlined positions 1 and 5 of the S-x-K-x-R-D domain) [START_REF] Becker | PRRT2-related disorders: further PKD and ICCA cases and review of the literature[END_REF][START_REF] Liu | Novel PRRT2 mutations in paroxysmal dyskinesia patients with variant inheritance and phenotypes[END_REF] .

To test this hypothesis, the R295Q and 1291V mutations were introduced along with a R295A alanine mutation in PRRT2 and analyzed by confocal microscopy (Fig 6c). While WT PRRT2 exhibited a predominant plasma membrane distribution, introduction of the above-mentioned mutations led to accrued redistribution of PRRT2 in the Golgi, similarly to what described for IFITM3.

Thus, these results indicate that the boundary between the IMD and the CIL of members of the dispanin/CD225 family is important for the regulation of their trafficking from the Golgi. This domain can be clearly defined in members of the dispanin/CD225 A subfamily as a consensus sequence conserved in vertebrates, and despite sequence dissimilarity, it maintains similar functions in other subfamilies, at least as appreciated in PRRT2 using de novo generated as well as PKD-associated genetic mutations.

Conclusions

In the present study, we have defined a novel domain within the CIL of IFITM3 that regulates egress from the Golgi through the characterization of a novel gain-of-function IFITM3 mutant. This 81S-x-K-x-R-D86 domain is conserved at the amino acid level in vertebrate members of the dispanin/CD225 A subfamily that regroups all IFITM members. In addition, we do show here that, despite no sequence similarity, this domain is nonetheless functionally conserved in at least one member of the B subfamily (PRRT2), raising the possibility of a general functional role across all dispanin/CD225 subfamilies.

Mutations of the Golgi egress domain lead to the retention of IFITM3 in the Golgi, where IFITM3 interferes with v-to t-SNARE vesicles fusion, ultimately driving macroscopic changes in the Golgi apparatus and glycoproteins trafficking defects.

The inhibitory effect of the 85-90 IFITM3 mutant on v-to t-SNARE vesicles membrane fusion is highly reminiscent of the well-described negative effects of IFITM proteins on the fusion between viral and cellular membranes after viral envelopes engage their cellular receptors and page 9 9 in this respect it is not surprising that increased accumulation of IFITM3 at the Golgi affects SNAREs fusion similarly. However, these results stress the importance of a controlled regulation of the exit from the Golgi of IFITM3, and possibly of other members of the family, to avoid deleterious effects on the functionality of the entire apparatus.

While ER-sorting protein domains have been identified [START_REF] Gomez-Navarro | Protein sorting at the ER-Golgi interface[END_REF] , similar domains have not been characterized for the Golgi and our results thus provide an important piece of the puzzle to decorticate how proteins that transit through the Golgi may be sorted out of it.

The Golgi egress domain of IFITM3 can either serve as a docking site for specific co-factors that mediate trafficking within and from the Golgi (for instance Rab proteins), it can play a regulatory role of other IFITM regions, or of course a combination of both.

The 81S-x-K-x-R-D86 domain is specially posited between the IMD and the beginning of the CIL and it is thus in close proximity with a short amphipathic helix important for antiviral functions 29 , with two cysteine residues that can be palmitoylated and thus affect IFITM association to membranes (C71, C72 in human IFITM3; a third cysteine C105 is also present that appears less important in IFITM3 functions [START_REF] Yount | Palmitoylome profiling reveals S-palmitoylation-dependent antiviral activity of IFITM3[END_REF][START_REF] Mcmichael | The palmitoyltransferase ZDHHC20 enhances interferon-induced transmembrane protein 3 (IFITM3) palmitoylation and antiviral activity[END_REF] ) and a G-x3-G domain that has been shown to be important for IFITMs oligomerization and functions [START_REF] Rahman | Homology-guided identification of a conserved motif linking the antiviral functions of IFITM3 to its oligomeric state[END_REF] . Furthermore, the K83 residue present within the 81S-x-K-x-R-D86 domain can be itself ubiquitinated [START_REF] Chesarino | Phosphorylation of the Antiviral Protein Interferon-inducible Transmembrane Protein 3 (IFITM3) Dually Regulates Its Endocytosis and Ubiquitination[END_REF] and together with residue K104 it has been recently described to mediate PIP3 binding of IFITM3 at the plasma membrane of B cells, in a process that is required for the correct formation of a PI3K signaling platform [START_REF] Lee | IFITM3 functions as a PIP3 scaffold to amplify PI3K signalling in B cells[END_REF] . Although, our confocal microscopy data suggest that the K83 IFITM3 mutant may not act as a functional PI3K platform simply because it is retained in the Golgi, the possibility of ubiquitination of the 81S-x-K-x-R-D86, at least in members of the dispanin/CD225 A subfamily, implies the potential prospect of finely tuning Golgi egress according to the dynamic status of ubiquitination of IFITM3, thus adding an additional layer of regulation to this process.

Given that the 85-90 IFITM3 mutant is a proficient membrane fusion inhibitor, we believe it unlikely that the 81S-x-K-x-R-D86 domain plays a role in IFITM oligomerization driven by the G-x3-G domain, as this would have been expected to lead to a non-functional IFITM3 mutant incapable to inhibit v-to t-SNAREs fusion.

The model we favor is that the 81S-x-K-x-R-D86 domain regulates the access of proximal domains to post translational modifications that in turn affects IFITM trafficking and more specifically access to palmitoylation at proximal cysteines. This model is in agreement with a recent one proposed by the Rothman's lab in which Golgi exit of certain proteins relies on a gradient of palmitoylation [START_REF] Ernst | S-Palmitoylation Sorts Membrane Cargo for Anterograde Transport in the Golgi[END_REF] . The model could also explain why this domain plays a conserved page 10 10 functional role in diverse subfamilies, despite no discernable sequence similarity, as is the case for PRRT2. The presence of two cysteines proximal to the region corresponding to the domain that regulates egress from the Golgi in the A subfamily and in PRRT2 is indeed a common feature in all members of the different dispanin/CD225 subfamilies (C276/C278 in PRRT2; C114/116 in TUSC5 as members of the B subfamily; C171/C172 in TMEM90A; C015/C106 in TMEM91 for members of the C subfamily and C152/C153 for PRRT1, member of the D subfamily).

PRRT2 is a neuronal specific protein involved in pre-and post-synaptic neurotransmitter vesicles regulation and has been genetically linked to PKD. While the majority of PKD patients suffer from mutations that result either in absent or severely truncated proteins, a few exhibit non-synonymous changes in the context of the full-length protein. Our results indicate that among them, mutations in the Golgi egress domain of PRRT2 may work by causing relocation of the protein, thus diminishing the concentration of active PRRT2 at its normal functional sites (i.e. the plasma membrane).

Although IFITMs have been predominantly studied in a viral infection context, several reports indicate that these proteins can play more pleiotropic functions: they interfere with trophoblast fusion during placental formation by affecting syncytin-mediated membrane fusion [START_REF] Buchrieser | IFITM proteins inhibit placental syncytiotrophoblast formation and promote fetal demise[END_REF][START_REF] Zani | Interferon-induced transmembrane proteins inhibit cell fusion mediated by trophoblast syncytins[END_REF] ; they lead to glucose-related metabolic dysfunctions in mice through unknown mechanisms [START_REF] Wee | Age-related onset of obesity corresponds with metabolic dysregulation and altered microglia morphology in mice deficient for Ifitm proteins[END_REF] and they concur to B cell signaling [START_REF] Lee | IFITM3 functions as a PIP3 scaffold to amplify PI3K signalling in B cells[END_REF] . Interestingly, ectopic expression of IFITM proteins has been in some cases reported to decrease the overall levels of viral glycoproteins and even to act more generally on translation, although the underlying mechanism(s) remain unclear [START_REF] Ahi | IFITM3 Reduces Retroviral Envelope Abundance and Function and Is Counteracted by glycoGag[END_REF][START_REF] Lee | IFITM proteins inhibit HIV-1 protein synthesis[END_REF] . In this respect, our results may provide a mechanistical explanation for the decreased levels of glycoproteins, by suggesting that the phenotype can be driven by high levels of IFITM3 accumulation in the Golgi, for instance in conditions of prolonged interferon stimulation, that in turn alter glycoproteins trafficking and stability through this secretory pathway.

More generally, our results highlight the fine line that separates the protective effects of innate defense proteins against viral aggression, from the deleterious side-effects that they may play against the cell itself. The literature provides other examples of this ambivalence for example in the family of apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3 proteins (APOBEC3), prototypical anti-retroviral defense proteins. While the main function of APOBEC3 members is to mutate and thus inactivate retroviral genomes thanks to their cytidine page 11 11 deaminase activity, this same enzymatic activity also contributes to the tumorigenic process when it is turned against the cellular genome [START_REF] Uriu | The Battle between Retroviruses and APOBEC3 Genes: Its Past and Present[END_REF][START_REF] Law | APOBEC3A catalyzes mutation and drives carcinogenesis in vivo[END_REF][START_REF] Granadillo Rodríguez | The interesting relationship between APOBEC3 deoxycytidine deaminases and cancer: a long road ahead[END_REF] .

IFITMs are potent inhibitors of membrane fusion in the context of viral infection, but since membrane fusion is a common process in the cell, their biology (intended here as the sum of activity, dynamic location, stability etc) must be tightly regulated to avoid deleterious effects on the cell itself. Together with other examples mentioned above and recently described in the literature, our results suggest that dysregulated expression of IFITM molecules and in particular with signals that interfere with its egress from the Golgi may lead to Golgi-related dysfunctions.

This may be an as yet unappreciated contribution of IFITM proteins to some pathological conditions for example in cancers or in interferonopathies in which the expression of IFITM proteins appears deregulated.

Methods

Cells, plasmids and antibodies

Human embryonic kidney cells (HEK293T, ATCC cat. CRL-3216) were maintained in complete DMEM media with 10% Fetal Calf Serum (Sigma cat. F7524). Untagged WT IFITM3 (gene ID: 10410) and IFITM1 (gene ID: 8519) and corresponding mutants were either previously described (85-90; [START_REF] Appourchaux | Functional Mapping of Regions Involved in the Negative Imprinting of Virion Particle Infectivity and in Target Cell Protection by Interferon-Induced Transmembrane Protein 3 against HIV-1[END_REF][START_REF] John | The CD225 domain of IFITM3 is required for both IFITM protein association and inhibition of influenza A virus and dengue virus replication[END_REF] ), or cloned (all others) by standard mutagenesis techniques in the plasmid pQCXIP (Clontech). WT human PRRT2 (gene ID: 112476) was synthesized as Nterminal HA tag fusion protein (Genewiz) in pcDNA3 vector (Thermofisher) and single point mutants were generated on this matrix by standard molecular biology techniques.

The HIV-1 proviral clone NL4-3 and the VSV-G expression constructs have been described elsewhere [START_REF] Tartour | IFITM proteins are incorporated onto HIV-1 virion particles and negatively imprint their infectivity[END_REF] The following secondary antibodies were used for WB: anti-mouse, anti-rabbit and anti-sheep IgG-Peroxidase conjugated (Sigma, cat. A9044 and cat. AP188P and Dako cat. P0163); while the following ones were used for confocal microscopy: donkey anti-rabbit IgG-Alexa Fluor 594 conjugate and donkey anti-mouse IgG-Alexa Fluor 488 conjugate (cat. A-21207 and cat.

A-21202; Life Technologies).

Ectopic DNA transfections, viral production and confocal microscopy analyses

HEK293T cells were directly seeded on 0.01% poly-L-lysine-coated coverslips (Sigma, cat. P4832) and analyzed 24 hours after ectopic DNA transfection (unless otherwise specified, Lipofectamine 3000 cat. L3000008, ThermoFisher, according to the manufacturer's instructions). Cells were washed three times with PBS 1x, fixed with 4% paraformaldehyde (Euromedex, cat. 15713) for 10 min, quenched with 50 mM NH4Cl (Sigma cat. A4514) for 10 min, and permeabilized with PBS-0.5% Triton X-100 (Sigma, cat. X100) for 5 min. After a blocking step in PBS-5% milk, cells were incubated with primary antibodies for 1 hour at room temperature (dilution 1:100), washed and then incubated with fluorescent secondary antibodies (dilution 1:100). A 4'-5-diamidina-2-phenylindole (DAPI)-containing mounting medium was used (ThermoFisher, cat. 62248). Images were acquired using a spectral Zeiss LSM800 confocal microscope and analyzed with Fiji software (version 2.0.0). For 3D-reconstruction, zstack collections were analyzed with the Imaris 9.2.0 software (Oxford Instruments Group).

Colocalisations were quantified using the Pearson overlap coefficient (Fiji software).

IFITMs and PRRT2 coding DNAs were routinely transfected at a concentration of 1 µg per well of a 24 well plate along with 0.2 µg of VSV-G or of mEm.-VSV-G. When specified, increasing doses of GS15 coding DNA were also added (0, 0.2, 0.5 and 1 µg).

Monensin was used at a final concentration of 0.2 mM for 3 hours, prior to analysis (Sigma, cat. M5273).

Production of HIV-1 virions particles was instead carried out by calcium phosphate DNA transfection of the HIV-1 proviral clone NL4-3 (ratio 3 to 1, NL4-3 over IFITM). Virion particles released in the supernatant of transfected cells were harvested forty-eight fours post transfection, syringe-filtered (0.45µm filters, Minisart, cat. 146622) to remove cellular debris page 13 13 and purified by ultracentrifugation over a 25% sucrose cushion (28.000 rpm for 2 hours; Beckman Coulter ultracentrifuge).

In confocal microscopy experiments using the GM130 Golgi marker, IFITM or VSV-G relocalization was quantified for each cell as the ratio between the signal present at the Golgi over the signal present in the cytoplasm (Golgi/cytoplasm ratio). When the GM130 marker was not used, perinuclear accumulation was either scored visually (as in

Fluorescence Resonance Energy Transfer (FRET) microscopy

Analyses were performed on live cells plated on a 35 mm glass-bottom dish and transfected with 2 µg of DNA coding the 85-90 IFITM3 mutant and 0.5 µg each of GS15-CFP and ERS24-YFP to ensure that the majority of cells examined were IFITM3-positive. After replacing media with phenol-red MEM supplemented with 10%FCS, cells were analyzed by confocal microscopy and images were acquired at a temperature of 37°C with a spectral Zeiss LSM710 confocal microscope, equipped with an argon laser and a 63X oil immersion objective. CFP and YFP were excited with a 458 and a 514 nm lasers (emission windows set at 465-505 nm and 525-600 nm, respectively) and images were acquired with a 512x512 resolution, 8 bit line average. Photobleaching of the donor YFP was performed with 4 sequential illuminations of the selected cell (4 frames, line average 2, iterations at 400, scan speed at 8, 514 nm laser AOTF setting at 100%). Four scans were acquired after each bleaching (two before and two after).

FRET efficiency was calculated from the ratio of the CFP fluorescence measured post-and pre-YFP bleaching. Analyses were performed on 15-20 individual cells per sample group. The FRET control plasmid pECFP18aaEYFP from addgene.) was used for setup (Addgene cat. 109330, gift from Gabriele Kaminski Schierle) 44 . Images were analyzed using the ROI manager in Fiji software.

Glycosidase treatment assays and WB quantification

Cell lysates expressing IFITM3 and VSV-G proteins were collected and split in three aliquots that were either untreated, or treated with Endoglycosidase H (EndoH, NEB, P0702S) or Nglycosilase F (PNGaseF, NEB P0704S), according to the manufacturer's instructions. Treated lysates were then analyzed by WB, images acquired using Image Lab Touch Software (version 2.0.0.27, Chemidoc Imaging System from Bio-Rad) and bands were quantified by densitometry using the volume tool in the same software.

Softwares

Statistical analyses

The statistical analyses used in this study were calculated with the Graphpad Prism8 software: 6b and 6c, which for lack of space presents only overlays but not separated channels. Source data is provided in the relevant section of Figure 6.

  . The mEmerald-VSV-G (mEm.-VSV-G) coding construct was a gift from Michael Davidson (Addgene, 54307); pcDNA-D1ER coding for an ER targeted enhanced CFP was a gift from Amy Palmer & Roger Tsien (Addgene cat. 36325 43 ). GS15-CFP and ERS24-YFP (cyanin and yellow fluorescent proteins, respectively) were obtained by Oleg Varlamov from the Oregon National Primate Research Center, USA 20 . The following primary antibodies were used for WB or confocal microscopy, as indicated. Mouse monoclonals: anti-α-Tubulin, anti-HA and anti-VSV-G (Sigma cat. T5168, cat. H3663 and cat. V5507, respectively), anti-LAMP2 (Santa Cruz Biotechnology, cat. sc-18822), anti-CD63 and anti-GM130 (BD biosciences cat. 556019 and cat. 610823); anti-HIV-1 p24 (obtained through the NIH HIV Reagent Program, Division of AIDS, NIAID, NIH, contributed by Dr. Bruce Chesebro and Kathy Wehrly, cat. 12 ARP-3537). Rabbit polyclonal antibodies: anti-IFITM3 (Proteintech cat. 11714-1-AP), anti-IFITM1 (Proteintech, cat. 60074) and anti-GM130 (abcam cat. Ab52649). The sheep polyclonal antibody anti-HIV-1 Env was obtained through the NIH HIV Reagent Program, Division of AIDS, NIAID, NIH (contributed by Dr. Michael Phelan, cat. ARP-288).

Fig 1 ,

 1 in light of the extremely clear phenotype; binary scoring: perinuclear or non-perinuclear), or quantified by determining the average percentage of accumulation of VSV-G at the left or right part of the nucleus (asymmetric distribution= perinuclear accumulation; symmetric distribution= nonperinuclear accumulation). To bypass artificial measurements due to differences in size between left and right portions of the cell, the avg of the signals measured at each side of the cell were used. This measurement diminishes the absolute values of the protein present at one side, but offers the advantage of making measures independent from cell size differences. Visual scoring in Fig 1 was also confirmed through this analysis.Electron microscopySamples were fixed in mixture of 4% paraformaldehyde (TAAB Laboratories Equipment Ltd, United Kingdom, cat. F/P001) and 1% glutaraldehyde (Electron Microscopy Science, USA, cat. 16310) in 0.1 M phosphate buffer (pH 7.4) for 24 hours, washed three × 30 min in 0.1 M of phosphate buffer, and post-fixed for one hour with 2% osmium tetroxide (Electron Microscopy Science, USA, cat. 19190) in 0.15 M of phosphate buffer. After washing in 0.1 M of phosphate buffer for 20 min and two x 20 min in distillated H2O, samples were dehydrated in a graded series of ethanol solutions (50% ethanol two x 10 min; 70% ethanol three x 15 min and last portion for 14 hours; 90% ethanol three x 20 min; and 100% ethanol three x 20 min).

Final

  dehydration was performed by 100% propylene oxide (PrOx, ThermoFisher (Kandel) GmbH, Germany, Lot X19E013) three x 20 min. Then, samples were incubated in PrOx/EPON epoxy resin (Fluka, Switzerland) mixture in a 3:1 ratio for two hours with closed caps, 16 hours with open caps, and in 100% EPON for 24 hours at room temperature. Samples were replaced in new 100% EPON and incubated at 37 °C for 48 hours and at 60 °C for 48 hours for polymerization. Serial ultra-thin sections (thickness 70 nm) were cut with a "Leica Ultracut UCT" ultramicrotome (Leica Microsysteme GmbH, Wien, Austria), placed on TEM nickel oneslot grids (Agar Scientific, Ltd. United Kingdom, cat. G2500N) coated with Formvar film and stained 20 min with 5% uranyl acetate (Merck, Darmstadt, Germany, cat. 8473) and 5 min Reynolds lead citrate. The sections were then observed at 100 kV with a Jeol 1011 TEM (JEOL, page 14 14 Tokyo, Japan) connected to a Gatan digital camera driven by Digital Micrograph software (GMS 3, Gatan, Pleasanton, CA, USA).

  Electron microscopy: Digital Micrograph software (GMS 3, Gatan, Pleasanton, CA, USA). Confocal microscopy: Fiji software (version 2.0.0), Zen (version 2.3, Zeiss) and Imaris 9.2.0 software (Oxford Instruments Group). WB: Image Lab Touch Software (version 2.0.0.27, Chemidoc Imaging System from Bio-Rad).

  page

15 15

 15 Statistics and graphs: Graphpad Prism8 (8.4.3, Graphpad software, LLC).

  Student t tests (unpaired, two-tailed), one-way Anova tests with either Tukey's or Dunnett's multiple comparisons, as indicated in the figure legends. 43. Palmer, A. E., Jin, C., Reed, J. C. & Tsien, R. Y. Bcl-2-mediated alterations in endoplasmic reticulum Ca2+ analyzed with an improved genetically encoded fluorescent sensor. Proc Natl Acad Sci U S A 101, 17404-17409 (2004). 44. Kaminski, C. F., Rees, E. J. & Schierle, G. S. K. A quantitative protocol for intensity-based live cell FRET imaging. Methods Mol Biol 1076, 445-454 (2014).

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 20

Fig. 1 .

 1 Fig. 1. A novel gain-of-function IFITM3 mutant induces secretory pathway defects that affect glycoproteins trafficking. a) Genomic structure of IFITM3 with a highlight on amino acids of the cytoplasmic intracellular loop (CIL). In mutant 85-90, red residues have been mutated to alanine. b) Schematic representation of HIV-1 virion assembly. c) HEK293T cells were transfected with DNAs coding for the HIV-1 proviral clone NL4-3 along with control, WT or 85-90 mutant IFITM3. Cells were lysed 48 hours after transfection, while virion particles released in the supernatant were first purified by ultracentrifugation through a 25% sucrose cushion. Both cellular and viral lysates were analyzed by WB. d) HEK293T cells were ectopically transfected with DNAs coding for the indicated IFITM3s along with the G protein of the Vesicular Stomatitis virus fused to the mEmerald fluorescent reporter (mEm.-VSV-G), prior to confocal microscopy analysis twenty-four hours later. Representative confocal microscopy images and graph presenting the proportion of double-positive cells displaying perinuclear accumulation of mEm.-VSV-G (binary scoring; three independent experiments; between 50 and 100 cells scored per sample). *, p value <0.0001 following a one-way Anova, Tukey's multiple comparison test; non-statistically significant differences, not shown. e) Lysates obtained from cells expressing VSV-G along with control, WT and mutant IFITM3 were either untreated or treated with EndoH or PNGaseF prior to WB and densitometry quantification of the EndoH-sensitive and -resistant VSV-G forms. Panels present representative results, while the graph presents the proportions of EndoH-sensitive andresistant VSV-G obtained in 5 independent experiments. *, p value of 0.0021 following a oneway Anova, Tukey's multiple comparison test over control. Uncropped blots and source data are provided in the relevant section.

Fig. 2 .

 2 Fig. 2. The 85-90 IFITM3 mutant colocalizes with and induces gross morphological changes in the Golgi. a and b) Confocal microscopy analysis and 3D-reconstruction of cells expressing the indicated IFITM3 proteins. Representative images and graph presenting phenotype proportions in at least 100 cells scored per condition (binary scoring: inflated or normal Golgi, three independent experiments). *, p=8.3x10 -9 following an unpaired, two-tailed Student t test. c) Representative electron microscopy analyses of HEK293T cells expressing WT or 85-90 IFITM3 proteins. The region enlarged at the right of each panel corresponds to the red inset. Source data is provided in the relevant section.

Fig. 3 .

 3 Fig. 3. The 85-90 IFITM3 mutant impairs fusion between v-and t-SNAREs. a) Schematic representation of the FRET experiment and representative images pre and post ERS24-YFP bleach. FRET experiments were performed on live HEK293T cells in which an excess of DNA coding for the 85-90 IFITM3 mutant was used over GS15-CFP and ERS24-YFP (five-fold excess) to ensure that the majority of cells analyzed were also IFITM3-positive. The boxes and whiskers plot presents the normalized decrease and values distribution in FRET observed in control and 85-90 IFITM3 expressing cells from three independent experiments and >20 cells examined. *, p value of 0.0008, following an unpaired two-tailed Student t test. b) HEK293T cells expressing constant amounts of 85-90 IFITM3 and VSV-G were co-transfected with increasing amounts of GS15-CFP coding DNA, prior to confocal microscopy analysis (GS15/85-90 ratios of 0.2 and 0.5). Given that VSV-G accumulation in the Golgi leads to its asymmetrical perinuclear distribution in the cell, VSV-G fluorescence was determined for each cell according to the presented scheme. The fluorescence measured over distance in the cell was used to calculate the proportion of VSV-G protein present at one side (the left side was set as the side with higher protein accumulation). Representative pictures and graph presenting the distribution of VSV-G at one side of the cell in triple-positive cells (n=2 with 20 to 31 cells analyzed per condition). White circles highlight example cells of increased FRET (a) and of phenotypic differences in the perinuclear accumulation of VSV-G (b), respectively. ****, p value of <0.0001 following a one-way Anova, Tukey's multiple comparison test over 85-90 IFITM3 condition with no GS15-CFP. Source data is provided in the relevant section.

Fig. 4 .

 4 Fig. 4. Time course confocal microscopy analysis indicates that the 85-90 IFITM3 mutation affects the normal egress of IFITM3 from the Golgi. HEK293T cells were examined by confocal microscopy at different times post ectopic DNA transfection to determine the localization of IFITM3 proteins over time. Representative pictures and graph presenting the distribution of IFITM3 in the Golgi (calculated as a Golgi/cytoplasm ratio on 39 to 62 cells per time point and per condition in two independent experiments, AVG, SEM and individual values). The table presents p values obtained after a one-way Anova, Tukey's multiple comparison test between the indicated conditions: ns: non significant; *, p value <0.05; ****, p value <0.0001. Source data is provided in the relevant section.

Fig. 5 .

 5 Fig. 5. Mutagenesis of the entire CIL of IFITM3 finely maps the domain involved in the egress of IFITM3 from the Golgi. a) Individual amino acid of the CIL were changed to alanine or to glycine when alanine residues were present, prior to confocal microscopy analysis.

Fig. 6 .

 6 Fig. 6. The Golgi egress domain of IFITM3 is conserved across vertebrate members of the dispanin/CD225 subfamily A and is functionally conserved in PRRT2, a member of the B subfamily as shown by genetic mutations associated to PKD. a) Alignment of the indicated portions of human members of the different dispanin/CD225 subfamilies. The relevant domain is circled in red. The position of the first amino acid of each sequence is shown within parentheses. Left, logo files obtained after alignment of vertebrate orthologues of the indicated genes for each subfamily, except D. b) Single point mutation and confocal microscopy analysis of the Golgi egress domain of IFITM1. c) Point mutations and confocal microscopy analysis of PRRT2 mutants in the region corresponding to the Golgi egress domain of IFITM3. R295Q and A291V are genetic mutations associated to PKD. Representative pictures are shown for each protein, while separated channels are provided in the extended data Fig. 4. Whiskers and boxes plots of the quantification of the proportion of each protein in the Golgi as a Golgi/cytoplasm ratio (25-75 percentiles with individual cells represented as dots; group average indicated by an asterisk, n=20 in three independent experiments). Colored boxes represent statistical significant differences after a two-tailed Student t test or a One-way Anova Dunnett's multiple comparison test (b and c, respectively) of the examined mutant over WT. **, p value of 0.0019; ****, p value <0.0001. Source data is provided in the relevant section.

  ---------A----------------------IFITM3 (75) FIAFAYSVKSRDRKMVGDVTGAQAYASTAKCLN PRRT2(285) IVAFAYAVMSRNSLQQGDVDGAQRLGRVAKLLS CIL IMD PRRT R295A ----------A----------------------PRRT R295Q ----------Q----------------------PRRT A291V ------V--------------------------

Acknowledgements

Thanks to Delphine Muriaux for sharing insights, to Romain Appourchaux for his initial input, to Léa Picard and Lucie Etienne for help with bioinformatic analyses and evolutionary discussions and to Federico Marziali for help with FRET experiments. We are indebted to Oleg Varlamov from the Oregon National Primate Research Center, USA for v-and t-SNARE constructs as well as to the different authors of plasmids retrieved through Addgene cited in the Methods section.

LZ is the recipient of a PhD fellowship of the Chinese Scholarship Council (CSC). Work in the laboratory of AC is supported by grants from the ANRS (AO-2019-1 and AO-2021-1), as well as the ANR (ANR-20-CE15-0025-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge the contribution of the microscopy (LYMIC-PLATIM) platform of SFR BioSciences Gerland Lyon Sud (UMS3444/US8).

Data availability

Source data is provided with this paper. There is no restriction on data availability.

Author Contributions

L.Z. designed and performed most of the experiments. R.U. and P.R. performed and analyzed electron microscopy. A.C. supervised the research and wrote the manuscript. All authors commented on the manuscript.

Competing Interests statement

The authors declare no competing interests.