Guillaume Touya

Matthieu Baley

Level of Details Harmonization Operations in OpenStreetMap Based Large Scale Maps

OpenStreetMap data comprise of very detailed (e.g. zebra crossing) and quite rough features (e.g. built-up area). But making large scale maps from data with inconsistent level of detail often blurs map comprehension. This paper explores the automatic harmonization of OpenStreetMap data for large scale maps, i.e. the process that transforms rough objects to make them consistent with detailed objects. A typology of the new operators that harmonization requires is presented and six algorithms that implement the operators are described. Experiments with these algorithms raise several research questions about automation, parametrization, or the level of abstraction of the transformation, which are discussed in the paper.

Introduction

As OpenStreetMap (OSM) is growing larger every day, practical applications based on OSM data are flourishing, but the initial goal of the project was to produce open topographical maps. A quick look at the default map output provided by OSM shows that it is difficult to create good legible maps out of the huge amount of data in OSM. One of the main obstacles to the creation of good legible maps from OSM data is the heterogeneity of its level of detail (LoD). For example, in the database, very detailed objects (e.g. zebra crossings) coexist with raw objects (e.g. shorelines extracted from Landsat imagery). This heterogeneity is troublesome for small scale maps, as detailed objects should be removed or simplified, but also for large scale maps, as rough objects are often inconsistent with detailed features of the map (Touya 2012a). Regarding small scale maps, it can be considered as a map generalization problem. Although there is little research effort on generalizing OSM data [START_REF] Klammer | TileGen -an open source software for applying cartographic generalisation to tile-based mapping[END_REF][START_REF] Schmid | A method for high-level road network extraction of OpenStreetMap data[END_REF][START_REF] Sester | Integrating and Generalising Volunteered Geographic Information[END_REF], this paper focuses instead on legibility problems caused by the level of detail heterogeneity in large scale maps, i.e. scales larger than 1: 25,000. In such large scale maps, detailed objects can be displayed in the map without any generalization transformation, because the scale ratio makes map symbols close to their real extent on the ground. But rough objects are also included in the map, because it is often better to display rough information than Fig. 1. Buildings on the right should be inside the built-up area (in gray) to make it a understandable spatial relation Dealing with LoD inconsistencies in large scale maps can be seen as a new automatic mapping process, namely to transform rough objects to make them consistent with detailed objects when both types of objects share a spatial relation that helps to understand the map. We call such a process harmonizing level of detail, implying that the harmonization increases LoD. It should be noted that generalizing heterogeneous data is also a kind of harmonization but it is not the focus of this paper. The automation of harmonization raises two questions. Is it possible to automatically harmonize OSM maps? Is it meaningful to transform data without any additional information from ground truth to make it more detailed? The work presented in this paper seeks to explore both questions by experimenting first attempts of automatic harmonization on OSM data.

After the introduction, the following second section of this paper, briefly discusses the notions of level of detail, scale and quality, and briefly describes a method to infer the level of detail, as a first step for harmonization. The third part precisely defines cartographic LoD harmonization and proposes a typology of possible harmonization operators. Section four is the core of the paper and

Level of Detail and Data Quality

The scale of a map is the mathematical ratio between a distance measured in the map and the same distance measured on the ground. But the scale is not only a ratio, it is also closely related to the content of the map and its resolution [START_REF] Mackaness | Understanding geographic space[END_REF]. Indeed, the scale limits the map to a certain extent, and the human perception limitations bound what can be displayed on the map. On the other hand, geographical information databases with vector data can be zoomed in and out, thus they cannot be defined by a single scale. So, we usually refer to the level of detail (LoD) when we want to define the resolution, or the granularity of a geographical database. Unlike scale, the level of detail is not a mathematically defined notion, and the fuzziness of its definition may make it hard to assess. In previous work, we defined the level of detail as a complex notion [START_REF] Touya | Detecting Level-of-Detail inconsistencies in volunteered geographic information data sets[END_REF]) that encapsulates elements of:

 conceptual schema (a tree representation is more detailed than a forest),  attribute resolution,  geometric resolution, i.e. smallest length between two vertices (Figure 2),  geometric precision or accuracy,  granularity (size of the smallest detail of geometries).

OSM can be considered as a large geographical database, where the LoD of features varies as the contributors with varying skills may use sources of varying scales or resolutions.

There is a substantial literature on all aspects of OSM data quality [START_REF] Haklay | How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets[END_REF][START_REF] Girres | Quality assessment of the french OpenStreetMap dataset[END_REF][START_REF] Mooney | The annotation process in OpenStreetMap[END_REF]. Although the level of detail comprises of some elements of data quality, the aim here is not exactly to assess data quality. A raw lake outline (i.e. with few vertices, see Figure 2a) can be considered as a bad quality feature if it is expected to have a high level of detail, or conversely a good quality feature if the expected level of detail is not so high (if the aim is to make a map at a small scale, for instance). In this work, the focus is not on quality, precision or accuracy alone, so an inaccurate position will only be considered as a (major) factor for a low level of detail. Scale can be inferred from the geometrical resolution and the analysis of similar features in existing maps [START_REF] Reimer | Assigning scale equivalencies to OpenStreetMap polygons[END_REF]. For instance, [START_REF] Biljecki | Formalisation of the level of detail in 3D city modelling[END_REF] proposed several metrics to infer LoD in 3D city models. Regarding OSM, the LoD inference is automatically possible using multiple criteria decision techniques (Touya and[START_REF] Touya | Detecting Level-of-Detail inconsistencies in volunteered geographic information data sets[END_REF][START_REF] Touya | Inferring the scale of OpenStreetMap features[END_REF], where the used criteria correspond to different aspects of LoD (resolution, precision, etc.). Then, the LoD inconsistencies can be identified by searching for key anomalous, or improbable spatial relations between detailed and rough features. For instance, trees should not be located on roads, or land use parcels should not extend over coastlines [START_REF] Touya | Detecting Level-of-Detail inconsistencies in volunteered geographic information data sets[END_REF]. Improvements and alternative methods are, of course, necessary to get a better inference of the individual level of detail of OSM data [START_REF] Touya | Inferring the scale of OpenStreetMap features[END_REF], but this is not the focus of the presented work. As a consequence, the results of the inference method from [START_REF] Touya | Inferring the scale of OpenStreetMap features[END_REF], i.e. the classification of OSM features into one of five LoD categories from street LoD to country LoD, are used as inputs for the harmonization methods presented in the next section.

The problem of LoD inconsistencies is not specific to the derivation of maps from OSM data, but may occur with any other volunteered geographical information. However, we choose to focus on OSM as the project is complete enough to derive large scale maps with a high density of information in areas with many contributors, such as, from Western Europe. OSM also contains data for every part of the world, making processes to derive maps automatically useful for many people.

Cartographic LoD Harmonization

Problem Statement

We define cartographic LoD harmonization as the mapmaking process that transforms features involved in a LoD inconsistency, in order to make the map more legible and comprehensible. When the target is a small scale map, or a map where the LoD of the rough feature of the inconsistency matches the scale, harmonization can be brought down to map generalization. But, in this paper, we only focus on large scale target maps, where simplification is not necessary and harmonization is a new problem. Moreover, the aim is not to provide quality control for the OSM dataset. When problematic LoD inconsistencies are identified, it is the readability that guides the transformation and not the quality control. In this case, the transformation should be a balance between position preservation and map legibility. As a result, caricature operations may be preferred to transformations guided by ground truth. OSM data are supposed to be more or less incomplete with some objects that could be in the data, but have not yet been captured. So, harmonization operations should take into account that the possible lack of detail may be due to incompleteness rather than LoD. For instance, when an object should be near a road and it is not, then the inconsistency may be caused by the inaccuracy of the object location, or by the absence of a road that exists in the real world. The key to harmonize LoD inconsistent spatial relations in the map is to transform the rough counterpart of the spatial relation while preserving the detailed member.

Research in cartographic generalization and multiple representation databases already focused on relations between geographical features at different LoDs. In multiple representation databases, spatial relations can be horizontal or vertical (Mustière andMoulin 2002, Burghardt et al 2010). Horizontal relations involve features with a similar level of detail, such as a building located at the end of a dead end road, while vertical relations involve two features at different LoDs that represent the same real world feature, such as a city represented by a polygon or a point. Spatial relations involved in LoD inconsistencies are neither vertical, nor horizontal. Following the same vocabulary, we can consider them as diagonal relations.

There is no way to transform rough features into detailed and accurate abstractions of real world entities they represent, so, in a way, harmonization aims to make the map more readable. As stated by [START_REF] Monmonier | How to Lie with Maps (2nd Edition)[END_REF], "not only it is easy to lie with maps, it is ESSENTIAL." Maps are systems of relationships [START_REF] Mackaness | Map generalisation: Fundamental to the modelling and understanding of geographic space[END_REF], which means that most of the meaning of the map is conveyed by relations, and preserving relations by making them more legible improves the way maps are understood. However, transforming map features too much without safeguard can damage map readability more than it improves it. Then, our safeguard is the evaluation of harmonization to verify that map features are not too much transformed.

Typology of Harmonization Operators

In order to derive legible maps from LoD inconsistent information, harmonization requires operation that can be related to other automated cartography processes, such as cartographic generalization or text placement. Typologies of generalization operators (e.g. simplification, displacement, elimination) already exist, see for instance the ones from Foerster, Stoter and Kobben (2007) and [START_REF] Regnauld | A synoptic view of generalisation operators[END_REF]. In our case, we preferred referring to a broader typology of operators for multiscale maps from [START_REF] Roth | A typology of operators for maintaining legible map designs at multiple scales[END_REF]. The following operators are introduced for harmonization as derivatives of Roth et al.'s operators:

 Merge/Dissolve  Adjust Shape  Displace and enhance  Disambiguation
Merge is defined by [START_REF] Roth | A typology of operators for maintaining legible map designs at multiple scales[END_REF] as a "replacement of a feature with a representative feature of equal dimensionality" and illustrated by a group of small islands merged to the nearby big island. Regarding harmonization, the merge operation is useful to improve the geometry of a feature by merging detailed features that should be part of the rough feature. Geographical datasets often comprise high level objects that are aggregates of lower level objects of the dataset. For example, a city is an aggregate of buildings, roads, and parks. In OSM, such aggregate objects are very common and are generally less detailed than their components. This generates the most frequent LoD inconsistencies with obvious components that lie just outside the aggregate (see section 4.1). But merge is also coupled with dissolve, because the detailed features might sometimes be dissolved from the rough feature rather than merged, when they should not be part of the aggregate. For instance, a primary road cannot be part of a school site (see section 4.2).

Adjust shape is the adjustment of a least detailed shape without changing feature dimensionality, to avoid an improbable relation. It derives from the [START_REF] Roth | A typology of operators for maintaining legible map designs at multiple scales[END_REF] Adjust shape operator that was only dedicated to symbols and not to feature geometry. Modifying the shape of a lake in order to avoid intersections with roads is an example of the adjust shape operator (see section 4.4).

Displace and enhance is a displacement of a map feature in order to preserve or emphasize a relation. It is a mix of the "displace" and "enhance" operators from [START_REF] Roth | A typology of operators for maintaining legible map designs at multiple scales[END_REF]. The displacement of trees along roads to enable a real alignment of tree symbols is an example of the "displace and enhance" operator (see sections 4. 3 and4.5). Sometimes, the map reader does not know if the improbable relation is true or if there is a problem of data quality (e.g. missing features). Disambiguation is a new operator that aims at the removal of this ambiguity in map reading without consideration for the ground truth (that we do not know). It arbitrarily removes the improbable spatial relation. For instance, when a group of close buildings is inside a forest without clearing, the addition of the clearing is a disambiguation operation (see section 4.6). Finally, harmonization also has to make use of existing operators such as displacement to correct inaccurate positions.

Examples of Algorithms for Harmonization Operations

This section describes several algorithms (Erreur ! Source du renvoi introuvable.) that implement the operations introduced in the previous section.

Table 1. Table summarizing algorithms described in this paper, with bold features in the inconsistency column represent the rough member of the relation.

Algorithm Operation LoD inconsistency

Built-up area extension

Merge

Buildings that lie just outside a built-up area

Functional site adjustment

Merge

A functional site that excludes some of its component/ includes false components

Tree alignment along roads

Displace and enhance

Trees along a road but not aligned

Intersection removal

Adjust shape Roads/paths intersecting a lake

Logical displacement

Displace and enhance

Bus stops too far from the nearest road

Clearing addition

Disambiguation

Building groups inside a forest area

Built-up Area Extension

Algorithm description

This algorithm seeks to extend the limit of built-up areas to include all buildings of the actual built-up area, i.e. buildings that lie just outside the limit, but also all buildings that are very close to these buildings. In this case, the only information available to draw a more detailed extent for the built-up area is that the components that caused the inconsistency should be inside the aggregate, and no longer outside. Several algorithms exist to compute the boundaries of built-up areas only using buildings [START_REF] Boffet | Creating urban information for cartographic generalisation[END_REF][START_REF] Chaudhry | Automatic identification of urban settlement boundaries for multiple representation databases[END_REF][START_REF] Walter | Automatic interpretation of vector databases with a Raster-Based algorithm[END_REF]. We propose to use a similar strategy based on buffering the buildings, but in an iterative way. At each step, the algorithm searches buildings that are within a radius around the built-up area, but not yet inside the area. The radius used is 15 m, derived from [START_REF] Boffet | Creating urban information for cartographic generalisation[END_REF]. Figure 3 shows the three steps of the algorithm, at each iteration: (i) buffers, with the same radius, are computed around the components to include, (ii) buffers are merged to the built-up area, and (iii) the outline is simplified to get a consistent resolution all along, as far as possible. In order to avoid too large extensions, the radius is cushioned at each iteration, with a 0.8 factor. The algorithm stops when there is no new building found within the search radius.

Evaluation

Constraint-based evaluation is commonly used to evaluate maps derived by automatic generalization [START_REF] Stoter | Methodology for evaluating automated map generalization in commercial software[END_REF], Touya 2012b). Constraints, such as building area should not be below 0.4 mm² on the map, are defined according to map specifications and known eye perception limits [START_REF] Salichtchev | Cartographic communication: A theoretical survey[END_REF]). Such constraints have been defined to evaluate this harmonization algorithm, some for preserving the initial shape/granularity of features, and some for assuring that inconsistencies are really removed. Three constraints are used to evaluate harmonization:  (C1) A shape preservation constraint that uses a surface distance [START_REF] Girres | Quality assessment of the french OpenStreetMap dataset[END_REF] to measure that the general shape has not been distorted too much.  (C2) A granularity preservation constraint to measure that vertex density, used as a proxy for granularity by [START_REF] Reimer | Assigning scale equivalencies to OpenStreetMap polygons[END_REF], has not increased too much after harmonization.  (C3) A constraint that counts the number of dilated buildings intersecting the built-up area outline. The constraint is fully satisfied when there is no intersection.

An instance, or a monitor (Touya 2012b), is created for each constraint and each built-up area object. The monitors assess the constraint satisfaction given the current geometry of the built-up area. In this evaluation, the satisfaction of the constraint is retrieved before and after the harmonization to verify that C3 satisfaction has increased, while the C1 and the C2 satisfaction remained stable. The constraint satisfaction is expressed from 1 (not satisfied at all) to 8 (perfectly satisfied), similarly to the generalization constraints from Touya (2012b). A test area was chosen in the south west of France comprising around 50 built-up areas of small/medium towns with LoD inconsistencies. The built-up extension algorithm is triggered automatically with parameters given in the algorithm description. Results show a significant increase of C3 satisfaction, while C1 and C2 satisfactions slightly decrease but remain satisfied (Figure 4). The mean of satisfactions only slightly increases, but the lack of an unsatisfied constraint after harmonization is a better indicator, and it proves the increased global quality of the harmonized map.

Functional Site Adjustment

Regarding functional sites [START_REF] Chaudhry | A functional perspective on map generalisation[END_REF][START_REF] Mackaness | Automatic classification of retail spaces from a large scale topographic data-base[END_REF], such as schools or hospitals, boundaries are often crisp and identified on the ground by buildings, walls, or barriers. So, in this case, the merge/dissolve operation should set more realistic bounds for the site, and as a consequence, an algorithm based on dilatation cannot be used. The principles of the proposed "functional site adjustment" algorithm is to infer the probability of features that are near the initial boundary, to be part of the functional site or not. Then, boundaries of the site are displaced around the included or excluded component, without introducing any gap.

First of all, a belonging function is defined for each type of site. The function uses semantics and topology to infer if an object is a component of the site, or not [START_REF] Chaudhry | A functional perspective on map generalisation[END_REF]). We decomposed the belonging function into two functions that are specialized for each type of functional site: A functional belonging that uses semantics, e.g. a library or a football field is more prone to be part of a school while a primary road is not; and a spatial belonging that uses topological and metrical measures, e.g. a building that is 99% inside the site is more likely to be a part of the site. Both belonging functions give negative values for unlikely partonomy and positive values for probable partonomy. These functions were implemented for two types of sites, namely schools and hospitals. For instance, schools functional belonging value is 3 for objects tagged as churches or sports fields, 10 for objects tagged as libraries, and -10 for buildings tagged as commercial, and -15 for roads tagged as highways (Table 2). The spatial belonging computes the percentage of overlap between both geometries with a positive value over 60% and a negative value below 30%. Then, functional and spatial belongings are summed and compared to two thresholds: A belonging threshold (2 was used for schools and hospitals) and an exclusion threshold (-2 was used). Between the thresholds, the boundary is not modified by the object tested. Finally, the boundary is adjusted to include or exclude the intersecting objects. When the object to include (or to exclude) has a polygonal geometry, a polygon union (or difference) is used with a smoothing of intersections, i.e. the vertices just before the intersections are removed from the geometry (see the zoomed image in Figure 5). When the object has a linear geometry, the vertices of the boundary between the intersections with the line are replaced with the vertices of the line between the intersections. Figure 5 shows the example of a high school whose initial boundaries intersect with several buildings, with some buildings (at the bottom and on the left) that are included in the high school and one (on the right) that is excluded.

Tree Alignment Along Roads

When the "displace and enhance" operator is required, we have to choose which feature is displaced to enhance the relation. In the case of trees along roads, trees are often poorly detailed because they are hard to capture precisely, which makes tree alignments often overlap with road symbols (Figure 6). Here, we propose to displace trees because their location is already inaccurate, and because their displacement does not cause many repercussions in terms of symbol overlap in the map. On the contrary, moving or distorting the road would require some propagation of the transformation to connected roads.

The algorithm displaces trees in order to reduce the overlap between symbols and enhances the relation by forcing the alignment to the road symbol (Figure 6). Tree alignments on the right and on the left of a road are first identified by computing left and right buffers on roads and counting the trees inside each buffer. Then, right and left offset lines (like buffers without the caps) are computed with a distance that is the addition of the road symbol width and half the tree symbol radius (to maintain some overlap that mimics the fact that tree branches might overlap with road pavement). Finally, trees are projected on the offset and moved apart a little, in case projections are too close to each other (the tree symbol diameter is used as a minimal distance). When a tree is along two roads (i.e. at a crossroad), which is detected by the fact that a tree belongs to the left and right side of two different roads, the projected position is the intersection of both offset lines.

Figure 6 shows some automatic results obtained at the 1: 5,000 scale of the French city of Bordeaux, which is quite a large city where trees have been captured extensively by OSM contributors. Hundreds of overlapping alignments have been automatically identified and successfully displaced. Some remaining problems have appeared when there are several rows of trees, because the row along the road sometimes overlaps with other rows inside a park or a square. This problem would require some displacement propagation, and this issue is discussed in section 5.2.

Intersection Removal

Intersection removal is the modification of a linear or areal object that is rough, with a more detailed boundary, using other detailed objects that are in relation with the rough object, in order to figure out how the more detailed outline needs to be drawn. For instance, when objects like paths or buildings intersect the outline of a lake, a new outline is drawn avoiding such inconsistencies. Depending on the type of objects involved in the inconsistency, the intersection removal has to be made differently, and different implementations are possible, e.g. if space is required between the intersecting objects or if objects might be adjacent. In this paper, we describe an algorithm for paths or roads that cross lakes, so a space is required between the path and the lake boundary.

Lakes have bona fide boundaries [START_REF] Smith | Fiat and bona fide boundaries[END_REF], i.e. there is a physical discontinuity that marks the boundary on the ground. So, more information on the intersection removal can be deduced from the geographical characteristics of objects involved in the inconsistency. For instance, a detailed bicycle path (captured by GPS tracks) can intersect the outline of an rough lake captured on satellite images (Figure 7). The bicycle path has a certain width, so the harmonized lake outline cannot be adjacent to the bicycle path. Then, a small gap is added between the path and the lake, using an offset on the lake side of the path. The algorithm has the following steps: (i) Determine which side of the line (given the order of the vertices) the main part of the lake is (i.e., the portion with the largest area); (ii) remove the portion of the lake that is on the other side of the line; (iii) define an offset of the line (i.e. a buffer without cap) only on the side where the main part of the lake is with a width related to the real width of the line (use some width information if available in the tags), plus a small gap (0.1 mm in the map), and remove the offset from the lake polygon; (iv) compute a cushioned version of the offset on the segments before and after the intersecting segments of the line to avoid sharp differences where the intersection has been removed (Figure 7). 7 shows that, sometimes, paths crossing a lake are just bridges and there is no inconsistency that needs to be resolved. To avoid bad harmonization in such cases (Figure 7c), the intersection removal algorithm is improved with a pre-step that automatically identifies parts of a path that probably belong to a bridge. First, the algorithm checks the following semantics: If there is a tag "bridge" or "man_made"='bridge' on the line, it is considered to be a bridge. Then, the two following characteristics of bridge sections are used for the automatic identification:

1. The middle of a bridge section is more "inside" the lake than either of its endpoints (is this what you want to say?) (Figure 8a andb), 2. The angle between the bridge section and the nearest lake shore is close to 90° (Figure 8c andd).

Figure 8. a) a segment whose middle point is as close to lake boundaries as ending points (criterion not met), b) a segment that meets distance to edge criterion, c) a segment not orthogonal to lake boundary (criterion not met), c) a segment orthogonal to lake boundary (angle shore criterion met).

Each segment of intersecting lines is inspected for both criteria. In order to check criterion the shortest distance to the boundary of the lake is computed for both end vertices and for the middle point of the segment. Only the minimum distance for the end vertices is kept and compared to the middle point distance. If the middle point distance is significantly longer (a factor of 1.1 was used in the tests), the criterion is met (Figure 8a andb). In order to check criterion 2, the orientation of the tested intersecting segment is computed and compared to the mean of the orientations of the shore segments around the tested segment. If the angle difference is close to 90° (in fact bigger than 60°), the criterion is met (Figure 8c andd). When both criteria are met, the segment is considered as part of a bridge. Figure 7d shows that the identification of bridges greatly improves the automatic harmonization.

Bus Stop Displacement

Bus stop displacement is an instance of logical displacement of bus stops that cannot be located where they are captured. This has two possible causes: Either the bus stop is misplaced by lack of precision, or the road that serves the bus stop is missing. The proposed algorithm has three steps: (i) Verify the context to decide if displacement is required; (ii) find the most probable serving road; (iii) compute the displacement vector for the most probable serving road.

The main criteria to decide whether displacement is required or not is the distance to roads: If it is over 12 m, the bus stop is considered misplaced unless a public transport area is also defined. If the bus stop is inside a building or other features, like sports fields, it strengthens the probability of a misplaced bus stop. In the case of Figure 9, the ambiguous bus stop is in the middle of a large building, which is unlikely a bus station (i.e. the only case where a bus stop should Post-print of the chapter published in Citizen Empowered Mapping http://dx.doi.org/10.1007/978-3-319-51629-5_1 16 be in a building) considering the semantics and the shape of the building. So the bus stop is most likely misplaced.

The most probable serving road is first computed by finding the nearest roads within a specified radius (roads further than 120 m are not considered in our experiments). The nearest road is the default serving road, but obstacles can discard this choice. The segment between the bus stop and its projection on the road is buffered and intersections with buildings, hydrological features or barriers are searched. If one obstacle is found, the nearest other road without an obstacle is then considered as the most probable serving road. But if the bus stop already intersects a building (like in Figure 9), this building is not considered as an obstacle. Finally, the displacement is computed in the direction of the most probable serving road, to be close to the road (the length of the road is estimated according to the semantics attached to the road), and in a location with space. This location with space is computed the same way as in [START_REF] Duchêne | The CartACom model: transforming cartographic features into communicating agents for cartographic generalization[END_REF], by removing the spaces already occupied by other objects (the same objects used to find obstacles). Figure 9 shows an example of a bus stop automatically displaced.

Clearing Addition

Algorithm Description

In the case of buildings grouped inside a forest, the proposed algorithm computes the extent of the building group and then computes a probable clearing geometry from the group extent and the surrounding objects. First, buildings are grouped using a process similar to [START_REF] Boffet | Creating urban information for cartographic generalisation[END_REF] and [START_REF] Chaudhry | Automatic identification of urban settlement boundaries for multiple representation databases[END_REF], and similar to the built-up extension algorithm, merging buffers computed around each building inside a forest. The buffer used here is larger, 25 m, in order to create a larger group of buildings. However, the choice of a unique buffer parameter value, effective on a large area, is a complex problem. See section 5.1 for further discussions. Then, when groups are identified, the buffer of the convex hull of the group is computed, as a gap between buildings and trees is quite probable (Figure 10b). Finally, the network helps to refine the clearing extent (Figure 10c). The clearing polygon is cut into several parts using intersecting roads and paths, and the small parts that contain no building are removed from the clearing geometry. As roads and paths are often "natural" boundaries to clearings/forests, this last step results in a more realistic clearing boundary.

Comparative Evaluation

Reference data from IGN, the French national mapping agency, were used to compare some harmonized results to existing high quality maps. Comparing clearings automatically obtained by this algorithm to reference clearings is a complex task because the reference is quite different from the OSM forest data (Figure 11). In particular, the difference is illustrated by clearings in the harmonized map that are not clearings in the reference, but just a recess in the forest (Figure 11). In order to compare clearings as two polygons, we have to close the clearings in the reference data when they actually are not holes. This is done by intersecting reference forests with an expanded envelope of the harmonized clearings. Then, shapes of clearings are compared using the surface distance between two polygons (Girres and Touya 2010) described in equation 1. Comparisons were carried out on 33 clearings automatically created in three different areas in France. The average surface distance is 0.73, which is quite a large value as 1 stands for disjoint polygons and 0 for equal polygons. However, we consider this as a validation that harmonized clearings approximately occupy the same space as actual clearings. The aim never was to create more realistic clearings, which is impossible only using OSM buildings.

𝑠𝑢𝑟𝑓𝑎𝑐𝑒_𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝐴, 𝐵) = 1 -𝑎𝑟𝑒𝑎(𝐴∩𝐵) 𝑎𝑟𝑒𝑎(𝐴∪𝐵)

(1) Figure 12 shows some clearing harmonizations used in this comparative evaluation with the reference forest data. Figure 12c shows that other objects (in this example, ponds) should be included in the computation of clearings and also that the incompleteness of OSM greatly penalizes harmonization, since ponds displayed in Figure 12c are not captured in OSM, but are extracted from the reference data.

Discussion

The Importance of Parameterization

Experiments on large datasets from different countries and landscapes confirmed our assumption on the difficulty to find the best parameter values for the proposed harmonization algorithms. First, harmonization algorithms are hard to parameterize as parameter values are hard to correlate with visual results. It is a classical problem in automatic mapping processes, such as map generalization algorithms [START_REF] Weibel | Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence[END_REF], or label placement. For instance, there is no obvious value for defining how far a building can be considered to be "just outside" a built-up area.

Moreover, it appears that harmonization algorithms parameters are context-dependent insofar as parameter values are adapted to some situations and other situations require different parameter values. For instance, Figure 13a clearing uses the standard set of parameters empirically defined (25 m buffer around buildings to cluster the buildings), but does not look like the real clearing drawn in the IGN map (Figure 13c). A specific set of parameters (75 m buffer radius to cluster the buildings), which fails for most other cases, gives, here, much Post-print of the chapter published in Citizen Empowered Mapping http://dx.doi.org/10.1007/978-3-319-51629-5_1 20 better results. This example suggests that trying different set of parameters and keeping the best result, and/or defining situation-specific parameters, might be a more robust solution than finding the best parameters and applying them everywhere.

Required Cartographic Knowledge and Degree of Automation

The automation of cartographic processes, such as label placement, style definition, or generalization often requires some acquisition and formalization of the knowledge of cartographers (Buttenfield and McMaster 1991). Automatic harmonization of large scale OSM maps does not avoid this bottleneck, and some kind of cartographic knowledge base is necessary for several steps of the process.

The first type of cartographic knowledge to formalize to enable harmonization is the identification of the key anomalous spatial relations. Some have been identified in this paper, but the list is not exhaustive, and additional relations may be of interest in other parts of the world (only French OSM datasets are used in this work) where landscapes are different and the detailed and rough features may not be the same. Then, there is what [START_REF] Taillandier | Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation[END_REF] call control knowledge, which allows the definition of good parameters for automatic operations, and also allows the guidance of processes that chain several operations. Control knowledge requires experimenting with harmonization techniques to find out what leads to better maps.

In order to acquire this knowledge base in a more generic way, learning and artificial intelligence techniques could be used [START_REF] Weibel | Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence[END_REF]. Formalizing the knowledge to share it would also be beneficial, and the collective project of building on ontology for on-demand mapping processes [START_REF] Gould | Collaboration on an ontology for generalisation[END_REF]) could be used in this way.

Then, this knowledge could be used in processes able to chain harmonization operations, to adapt parameters to specific geographical situations, and to handle propagations of transformations. It could be interesting to adapt optimization (e.g., [START_REF] Harrie | Simultaneous graphic generalization of vector data sets[END_REF]Sarjakoski 2002, Sester 2005) or multi-agent techniques (e.g. [START_REF] Duchêne | The CartACom model: transforming cartographic features into communicating agents for cartographic generalization[END_REF] used in automated map generalization to enable such harmonization processes. Nevertheless, problems should be less complex than map generalization, as large scale map symbols allow more free space in the map than small scale map symbols.

Abstraction versus Realism

Harmonization operations try to guess what the consistent detailed information would be from the detailed objects of the dataset. Thus, information that does not correspond to ground truth is introduced into the map, in order to make the map readable. For the same reason, map generalization also distorts ground truth, by moving or simplifying objects. Thus, it is necessary to wonder if harmonization should aim at realistic harmonized representations, which mislead the reader and making him/her believe that the map is a realistic view of ground truth. Or, aim at abstract harmonized representations that show to the map reader that the information is not exactly as it is represented on the map. Figure 14 illustrates both strategies for the clearing creation around building groups. None of the representations is close to ground truth, or even to its representation in the IGN map. But Figure 14b is clearly a more realistic representation of the clearing than Figure 14c, which is more abstract or sketchier. Research in computer graphics and non-photorealistic rendering (like maps) show that blurring an object or making it sketchier may convey information on data quality [START_REF] Wood | Sketchy rendering for information visualization[END_REF]. Dashes also proved to convey uncertainty information [START_REF] Boukhelifa | Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty[END_REF]. So, a sketchy or dashed clearing outline could also be an efficient alternative to our proposed realistic or schematic harmonization. In the case of bus stop disambiguation, some instances may remain unsolved (Figure 15), and blurring or sketching the bus stop symbol could convey the uncertainty and avoid misinterpretation for the map reader.

Update OpenStreetMap?

In a certain way, harmonization operations improve OSM data quality and correct some mistakes in the database. So it may be tempting to use the harmonized operations to push updates in the OSM database. However, harmonization is a process dedicated to cartography, so some transformations carried out may be irrelevant for the OSM database. Operations that include some kind of caricature should not be used to improve the OSM database. For instance, the alignment of trees along roads caricatures the tree alignment to make it straight, in order to improve the map clarity. But positional accuracy is then lost for some trees. However, occurrences of trees badly aligned are consistency problems of the OSM database that should be corrected, but with different operations that focus more on placement accuracy than map legibility. Some other operations could be pushed in the database as it improves the level of detail of some rough objects, but it should be done carefully as it breaks a general rule of OSM to rely on ground truth to contribute to the project. For instance, the extension of built-up area to nearby buildings improves the level of detail of the built-up area but there is no checking in the field or with images that the new extent is close to ground truth.

Finally, we believe that some operations could be included without further checking, because the modifications are sure. For instance, "Adjust shape" operations avoid situations that cannot exist, like a path intersecting a lake or a forest in the sea. Existing OSM tools like KeepRight1 or Osmose2 already search for such kind of problems, for further manual corrections by OSM contributors.

Conclusion

To conclude, this paper tackles a new cartography problem raised by OpenStreetMap data, namely the level of detail harmonization that improves the level of detail of some rough objects in large scale maps. Several types of harmonization operations are proposed and experimented on OSM datasets. Some problems raised by the experiments are discussed, such as the need for realism or abstraction and the automation of the proposed algorithms in a process to harmonize a complete map. The first results show that it is a promising topic to explore in automated cartography.

Further research should clearly focus on harmonization processes, to be able to automatically chain harmonization operations, and solve complex problems that involve many objects and require optimization techniques. The processes should tackle the dependency of parameters on the geographic context of features. Of course, each operation presented in the paper could be improved and new operations have to be designed for the LoD inconsistencies that are not mentioned in the paper. Furthermore, as harmonization operations transform data into something realistic but false, abstract harmonization should be investigated with user tests, to know if map user better understand realistic or abstract harmonization. Finally, one of the main characteristics of OSM is that contribution patterns change all over the world (Jokar [START_REF] Arsanjani | An introduction to OpenStreetMap in geographic information science: Experiences, research, and applications[END_REF] and the inconsistencies encountered in the datasets tested in this paper might not occur elsewhere. Recent research showed that there is some relation between social aspects and contribution patterns in a region of the world [START_REF] Mashhadi | The impact of society on volunteered geographic information: The case of OpenStreetMap[END_REF], so it can be inferred that LoD might differ according to these varying patterns. As a consequence, experiments should be carried out to analyze the influence of varying contribution patterns on LoD harmonization problems.

Fig. 2 .

 2 Fig. 2. Two captures of the same lake at different levels of detail

Fig. 3 .

 3 Fig. 3. Three steps of one iteration of the algorithm to extend built-up areas. (a) Initial state, (b) compute buffers around buildings, (c) merge the built-up area with buffers, (d) simplify to preserve the resolution.The boundary simplification algorithm is a simpleDouglas and Peucker filter (1973), because the sharp shapes created by this simplification algorithm look like the shapes of initial rough built-up areas.

Fig. 4 .

 4 Fig. 4. Mean constraint satisfaction evolution during built-up extension harmonizations.

Fig. 5 .

 5 Fig. 5. High school with rough limits harmonized to be consistent with the detailed buildings and sports grounds

Fig. 6 .

 6 Fig. 6. a) Inaccurate trees overlapping a road. b) computation of an offset on the side of the road with trees. c) trees aligned on the offset line

Fig. 7 .

 7 Fig. 7. (a) A bicycle path intersecting a lake; (b) harmonized lake outline; (c) an example of bridges with bad harmonization; (d) harmonization with automatic detection of bridges However, Figure7shows that, sometimes, paths crossing a lake are just bridges and there is no inconsistency that needs to be resolved. To avoid bad harmonization in such cases (Figure7c), the intersection removal algorithm is improved with a pre-step that automatically identifies parts of a path that probably belong to a bridge. First, the algorithm checks the following semantics: If there is a tag "bridge" or "man_made"='bridge' on the line, it is considered to be a bridge. Then, the two following characteristics of bridge sections are used for the automatic identification:

Fig. 9 .

 9 Fig. 9. One bus stop is far from the road and harmonized by displacement.

Fig. 10 .

 10 Fig. 10. a) an initial building group inside a forest, b) a first clearing geometry is computed by merging the dilated buildings, c) removal of clearing parts that lie over a path or a road.

Fig. 11 .

 11 Fig. 11. Obstacles to conduct comparative evaluation: The clearing is not a clearing in the reference data, but only a recess in the forest.

Fig. 12 .

 12 Fig. 12. Comparison of harmonized and reference clearings. a) and b) the actual clearings are bigger than the harmonized ones. c) The lack of ponds (to be included in the clearing) in the OSM makes shapes quite different.

Fig. 13 .

 13 Fig. 13. The clearing created with standard parameters (a) does not look like the clearing in the IGN map (c); however, different parameter values give a closer result to the IGN map (b), but would fail in most other cases.

Fig. 14 .

 14 Fig. 14. (a) The actual shape of a clearing in the IGN map; (b) a computed realistic shape; (c) a computed abstract/schematic shape

Fig. 15 .

 15 Fig. 15. Unsolved disambiguation (which road serves the stops?) could be overcome by blurring symbols to convey uncertainty

Table 2 .

 2 Bonuses and maluses affected by the function belonging of school components for specific tags of OSM.

	Tag key	Tag value	Functional
			belonging
	building	any value	1
	building	dormitory	5
	building	church	3
	building	chapel	3
	building	civic

Post-print of the chapter published in Citizen Empowered Mapping http://dx.doi.org/10.1007/978-3-319-51629-5_1

http://keepright.ipax.at/

http://osmose.openstreetmap.fr/fr/map/


E Saint-Etienne

G Blasquez
email: g.blasquez@laas.fr

P Pons

R Plana

C Douziech

P Favaro

Ph Menini

N Fabre

J Graffeuil

T Parra

J C Lalaurie

New Technological advances for microshielded coplanar circuits on silicon

Keywords: Silicon, technology, microwaves

An advanced technological process for microwave coplanar circuits on silicon oxide/nitride membrane is presented. This process allows to make thick conductors with high geometric definition. Furthermore, it permits to realise microshielded circuits with minimised losses in the access ports.

Introduction

In the course of the last decade, the feasibility of circuits on dielectric membrane has been demonstrated [1]. The potential domain of application of this technology encompasses centimeter and millimeter waves. Along the line of works previously initiated at the University of Michigan, we are developing since 1995 an optimised process where the membrane is made of a double layer of silicon oxide and silicon nitride. This process increases the production yield and minimises the manufacturing dispersion.

In 1998, we have developed new improvements which give the circuits a protection from mechanical damages, minimise electromagnetic interferences and reduce losses in the access ports.

The 1996 Issue

The original process technology [START_REF] Saint-Etienne | Dixièmes Journées Nationales Micro-ondes[END_REF] includes three main stages : a) dielectric film manufacturing, by thermal oxidation of the silicon wafer, and LPCVD deposition of silicon rich silicon-nitride, b) conductor manufacturing by photolithography or by local electrochemical deposition within a photoresist mould, the latter process being appropriate for thick conductors, c) anisotropic etching of the silicon wafer under the conductors.

In this issue, mechanical and thermomechanical stresses have been minimised. It gives the devices a high stability throughout a wide range of temperature.

Demonstrators of coplanar transmission lines and filters on membranes of 1.4 µm x 5 mm x 10 mm have been manufactured from Ø10 cm wafers, with a production yield near 100%. The loss factor per unit length is less than 0.1 dB/mm in the frequency range 1-65 GHz. The 1998 Issue of the technological process [START_REF] Saint-Etienne | [END_REF] allows to manufacture shields which protect the coplanar circuits from casual mechanical damages and prevents them from interaction with the electromagnetic environment and/or adjacent circuits. In other respects, the addition of shields increases the structural rigidity and makes the wafer slicing easier, whatever the operation is made, by cleaving or by sawing.

Shield manufacturing from silicon wafers includes : a) the deposition of an unalterable film used as an etching mask, b) the structuration of silicon wafers by chemical micromachining, c) the metallisation of the surface. Materials, equipment and processes used to make shields are identical with those used to make circuits.

Figure 2 shows eight covers in a perspective view. One can see the bridge, with windows on both sides for testing. Also visible are assembling wells used to join the circuit (here a bandpass filter) and the microshield cover. The total height of a microshielded filter is between 500 µm and 1 mm. In order to produce microshielded coplanar circuits with a good accuracy, it appears necessary to control accurately the shield height. Silicon micromachining fulfils that condition. In other respects, as the wafers used to manufacture and the shield are both made of silicon, there is no differential thermal expansion. As a consequence, such microshielded circuits are able to operate in a wide range of temperature.

Improvement of the access ports.

The 1998 Issue of the process includes another improvement : the dielectric losses in the access ports are considerably reduced [4]. In the 1996 developments, the silicon wafer was not micromachined in the portions located under the access ports in order to operate on-wafer probing without any risk of damaging membranes. Circuit losses appeared to be almost totally located in these portions. They were already reduced by using high resistivity silicon and reducing the size of the access ports.

In the 1998 process, the dielectric film has been removed from the wafer in region of the access ports. In that way the conductors lie directly on the wafer and the Schottky junction enhances circuit insulation. Such phenomenon has already been reported in [5]. In order to validate that operating mode, two transmission lines 6 mm long have been manufactured on non micromachined wafers, one where the conductors were laid down on uncovered wafer, the other on wafer covered with the oxide/nitride film.

Figure 5 shows the loss factor per unit length obtained in both cases. Removing the film reduces the loss factor by a factor of 5 to 10 in the band 1-70 GHz. So the loss factor in the access ports at 50 Ω will be less than 0.2 dB/mm in that band.

Including this new improvements, the process is now composed of four main stages : a) dielectric film deposition, b) local etching of the film in the access ports, c) conductor formation, d) etching of the silicon wafer under the central part of the circuit.

Figure 1 :

 1 Figure 1 : Loss factor per unit length of a 75 Ω transmission line on membrane

Figure 2 :

 2 Figure 2 : Microshields shown from below

Figure 3 :Figure 4

 34 Figure 3 : Two bandpass filters shown before and after shielding

Figure 4 :

 4 Figure 4 : Transmission factor of a bandpass filter with and without shield (bridge height 210 or 55 µm)

Figure 5 :

 5 Figure 5 : Loss factor per unit length of two 50 Ω transmission lines, one on bare wafer, the other on wafer covered with the silicon oxide and nitride films.


Juliette Van Dijk

Julie Miro

Jean-Marc Strub

Benjamin Lacroix

Alain Van Dorsselaer

Bernard Edde

Carsten Janke
email: carsten.janke@crbm.cnrs.fr

From

POLYGLUTAMYLATION IS A POSTTRANSLATIONAL MODIFICATION WITH A BROAD RANGE OF SUBSTRATES*

Polyglutamylation is a posttranslational modification that generates lateral acidic side chains on proteins by sequential addition of glutamate amino acids. This modification was first discovered on tubulins and it is important for several microtubule functions. Besides tubulins, only the nucleosome assembly proteins NAP1 and NAP2 have been shown to be polyglutamylated. Here, using a proteomic approach, we identify a large number of putative substrates for polyglutamylation in HeLa cells. By analyzing a selection of these putative substrates, we show that several of them can serve as in vitro substrates for two of the recently discovered polyglutamylases, TTLL4 and TTLL5. We further show that TTLL4 is the main polyglutamylase enzyme present in HeLa cells and that new substrates of polyglutamylation are indeed modified by TTLL4 in a cellular context. No clear consensus polyglutamylation site could be defined from the primary sequence of the here-identified new substrates of polyglutamylation. However, we demonstrate that glutamate-rich stretches are important for a protein to become polyglutamylated. Most of the newly identified substrates of polyglutamylation are nucleocytoplasmic shuttling proteins, including many chromatin binding proteins. Our work reveals that polyglutamylation is a much more widespread posttranslational modification than initially thought and thus that it might be a regulator of many cellular processes.

One fundamental aspect of proteomic complexity comes from the various processing events that many proteins undergo following their synthesis. Posttranslational modifications, such as phosphorylation, acetylation and methylation, are reversible monomodifications and are known to function as switches for the activity of many proteins. Polyglutamylation is a reversible polymodification generated by sequential covalent attachment of glutamic acids (up to 20 in some cases) to an internal glutamate residue of the target protein (1). The length of the resulting side chain is regulated by the balance between the enzymes that catalyze glutamylation, recently identified as members of the tubulin-tyrosine-ligase-like (TTLL) protein family (2)(3)(4), and yet unidentified deglutamylase enzymes (5). Thus, this modification does not only generate "on" and "off" states, but a range of signals that might allow for gradual regulation of protein functions. The only known targets of polyglutamylation are α-and β-tubulins, the structural units of microtubules (MTs (1,6)), and the nucleosome assembly proteins, NAP1 and NAP2 (7). Tubulins are modified in their acidic, glutamate-rich Cterminus (1), which is the binding site for most MT-associated proteins (MAPs; reviewed in (8)). Tubulin polyglutamylation was therefore proposed to generate functionally divergent MTs by regulating the affinity between MAPs and MTs (4,(9)(10)(11). It was also shown to be important for centriole stability (12), axonemal motility (2,13,14) and neurite outgrowth (15). The role of NAP polyglutamylation has not yet been addressed, but as for tubulins, it may regulate the affinity of NAPs for their binding partners. Additional polyglutamylated proteins probably exist as the polyglutamylation-specific antibody GT335 (16) recognizes protein bands besides those of tubulins and NAPs on western blots of HeLa cell extracts (4,7). Here, using a proteomic approach, we identify new substrates for polyglutamylation and show that they are modified by two polyglutamylases from the TTLL protein family. No clear "glutamylation motif" could be defined in their primary sequences, but all the here-identified new substrates contain glutamaterich stretches that are most likely the acceptor sites for the modification. Our study opens the door to further investigations of the role of polyglutamylation as a general regulatory event of a broad range of cellular functions.

Experimental Procedures

Purification of GT335-reactive proteins from HeLa cells by immuno-affinity chromatography-10 mg of GT335 antibody was coupled to an Nhydroxysuccinimide-activated Hi-Trap column (1 ml, GE Healthcare) according to manufacturer's instructions. HeLa cells were extracted in PBS containing 0.1% Triton X-100 and protease inhibitors (aprotinin, leupeptin, and 4-(2aminoethyl)-benzenesulfonyl fluoride, each at 10 µg/ml). The high speed supernatant (15 mg of protein) was loaded onto the column and after extensive washing with PBS, bound proteins were eluted with PBS containing 0.7 M NaCl and concentrated to 1 mg/ml on an Amicon ultrafiltration device (4 ml, cut-off 10 kDa, Millipore). Western blotting-Proteins fused to EYFP were immunodetected with rabbit anti-GFP antibody (1:5,000, Torrey Pines Biolabs) and HRPconjugated anti-rabbit IgG (GE Healthcare) followed by detection with enhanced chemoluminescence (GE Healthcare). GT335 antibody was biotinylated with NHS-LC-Biotin (Pierce) according to manufacturer's instructions and incubated simultaneously with HRP-labeled streptavidin (GE Healthcare). For producing polyclonal anti-TTLL4 and anti-TTLL5 antibodies, bacterially expressed and purified fulllength mouse TTLL4 or fragment 692-1152 of mouse TTLL5 were injected into rabbits. Anti-TTLL antibodies were then purified from antisera against their respective targets. Mass spectrometric analysis-To identify the protein content of the GT335-affinity-purified fraction, the Coomassie stained protein bands were excised from the gel and submitted to in-gel trypsin digestion (17). Peptides were then concentrated on a C18 pre-column, separated on a reversed-phase capillary column (Pepmap C18, 75 µm inner diameter, 15 cm length, LC Packings) and analyzed by MS/MS on a Q-Tof II mass spectrometer (Micromass Ltd., Manchester, UK). Data analysis was performed with Mascot (Matrix Science Ltd., London, UK) against the NCBI (The National Center for Biotechnology Information) Database. Double and triple charged peptides were used for the research in database and the peptide tolerance was set to 0.2 Da for MS and to 0.3 Da for MS/MS. One missed cleavage by trypsin was accepted and carbamidomethylated cysteine and oxidized methionine were set as variable modifications. All entries were selected for the taxonomy. The Mascot score cut-off value for a positive protein hit was set to 50 and peptides with a score below 40 were manually interpreted to validate or discard the identification. Identified proteins were submitted to a second research including the polyglutamylation as variable modification. Additions of 1 to 8 glutamic acids were considered and a new research has been done each time.

In vitro polyglutamylation of the GT335-affinitypurified fraction-The GT335-affinity-purified fraction (final protein concentration of 0.3 mg/ml) was incubated for 2 hours at 30°C in the presence of 50 mM Tris-HCl, pH 9.0, 0.5 mM ATP, 2.4 mM MgCl 2 , 0.5 mM dithiothreitol, and 60 µM L-[ 3 H]-glutamate (45-55 Ci/mmol, GE Healthcare). Following SDS-PAGE, gels were exposed to MP films (GE Healthcare) for 40 days at -80°C after enhancement with Amplify (GE Healthcare). Immuno-precipitation experiments-For the depletion of TTLLs, the GT335-affinity-purified fraction (10 µg of total protein) was incubated two hours at 4°C on a rotary agitator with protein G magnetic beads (Dynabeads) that were coupled to anti-TTLL4 or anti-TTLL5 antibodies (2 µg). After washing with PBS, the TTLL-depleted fraction was polyglutamylated in vitro as described above. For immuno-precipitation of the EYFP fusions of NAPs and their chimeras, the high-speed supernatant of HeLa cell extracts was incubated under similar conditions with beads linked to anti-GFP antibody. Following extensive washing, the beads were directly resuspended in Laemmli buffer. In vitro polyglutamylation assay with recombinant proteins-Putative substrates of polyglutamylation were cloned from mouse testis or brain cDNA libraries, expressed as polyhistidine tag-fusion proteins in bacteria and purified by nickel-affinity chromatography (except for EB1 which was a generous gift of S. Honnappa and M.O. Steinmetz). TTLL proteins were expressed as GST-fusions in bacteria and purified on glutathione sepharose as previously described (4). Since only TTLL5 (CAM84325) was soluble and active when produced in bacteria as its full-length version, we produced truncated version of TTLL4 (TTLL4_C639, last 639 residues of CAM84324) and TTLL6 (TTLL6_N705, first 705 residues of CAM84326) and a shorter, naturally occurring version of TTLL7 (TTLL7S, CAM84327) (3,4). These shorter enzymes have been previously demonstrated to possess similar activities as compared to the full-length TTLLs (4). The polyglutamylation assay was performed by incubating 1 µg of each purified TTLL enzyme with 4 µg of each putative substrate in 20 µl of 50 mM Tris-HCl, pH 9.0, 0.5 mM ATP, 2.4 mM MgCl 2 , 0.5 mM dithiothreitol, and 7.4 µM L-[ 3 H]glutamate (45-55 Ci/mmol, GE Healthcare) for 2 hours at 30°C. Alternatively, 4 µg of taxoterestabilized MTs, prepared from adult mouse brains (18), were used as a substrate. Quantification of the radioactivity incorporated into the different putative substrate proteins was done by scintillation counting of the protein bands after SDS-PAGE and electrotransfer onto nitrocellulose. Each polyglutamylation test was performed at least three times in independent experiments. The overall count (dpm) varied between each set of experiments; however, the relative values of glutamate incorporated into different substrates tested with the same enzyme dilution were similar in all experiments. Each figure is one set of a given experiment performed at the same time with all substrates presented. Background levels (no incorporation) are below 100 dpm. In vivo polyglutamylation of the new substrates-Putative substrates of polyglutamylation were cloned as EYFP fusion proteins in mammalian expression vectors and transfected into HeLa cells together with an active or an enzymatically inactive (mutation E901G in the ATP-binding site) version of TTLL4_C639-EYFP. After 24 hours of transfection, cells were lysed in Laemmli buffer and subsequently analyzed by gel electrophoresis followed by western blot. In Fig. 5C, NAPs and their chimeras were expressed alone and immunoprecipitated with anti-GFP antibody prior to analysis. Mutagenesis-In order to exchange the C-terminal tails between the different NAPs, we introduced the restriction site BstBI into a highly conserved region of the NAPs. This was done by mutagenesis of NAPs (NAP1: T972C, NAP2: T951C, NAP4: T1170C T1171G T1172A, introducing a L391E amino acid change) using the quick change PCR method. The resulting mutants were expressed and were shown to behave like the wild-type proteins in the polyglutamylase assay (data not shown). We then used BstBI in combination with other enzymes to exchange the NAP domains. The resulting chimeras were cloned into bacterial and mammalian expression vectors.

RESULTS

Purification and identification of putative substrates for polyglutamylation. Since the GT335 antibody detects protein bands besides those corresponding to the glutamylated forms of tubulins and NAPs on western blots of HeLa cell extracts, it is likely that this antibody specifically recognizes some other polyglutamylated proteins. We therefore performed a large-scale purification of GT335-reactive proteins from HeLa cell extracts by chromatography on a GT335-column. The affinity-purified fraction was submitted to SDS-PAGE and stained with Coomassie Blue (Fig. 1, lane 1) or transferred onto nitrocellulose and probed with GT335 antibody (Fig. 1, lane 2). We visualized 40 protein bands on the gel after Coomassie staining, and about half of them were detected with GT335 antibody on western blot. The remaining proteins might have been nonspecifically retained on the column, for example by interacting with GT335-reactive proteins. However, it is also possible that some proteins are recognized by GT335 antibody only in their native form. We therefore excised all 40 protein bands from the gel and determined their protein content by nano-LC-MS/MS after trypsin digestion. This analysis gave rise to the identification of about 170 proteins (Table S1). In order to identify true substrates of polyglutamylation, the masses obtained by nano-LC-MS/MS were re-analyzed to search for masses that could be attributed to polyglutamylated tryptic peptides. However, we found no masses corresponding to modified peptides (mass increments of n times 129 Da, the mass of a glutamyl unit, with n ranging from 1 to 8). It has been previously reported that the polyglutamylated tryptic peptides of tubulins and NAPs are undetectable with the classical mass spectrometry method used here (1,7). The same seems to be true for the other substrates of polyglutamylation. Actually, because of their high acidity and/or branched structure, the modified peptides are lost during the reverse phase chromatography used to fractionate the peptides (7). An alternative procedure would be to fractionate the tryptic peptides by ion-exchange chromatography followed by a desalting step. This method has been used in the case of NAPs (7), but in order to follow the modified peptides during the purification steps, radioactive glutamate had to be incorporated into the proteins. Another problem, due to the acidic character of the modified peptides, is that their ionization in electrospray or MALDI is very hard even in the negative mode. Several procedures have been proposed to solve this problem (19)(20)(21), but the method remains far from straightforward. Taken together, we judged a mass spectrometry approach as not appropriate to identify new substrates of polyglutamylation. To pre-select proteins with a high probability of being polyglutamylated, we took advantage of the fact that a polyglutamylase activity co-purifies with the GT335-reactive proteins (7). Incubation of the GT335-affinity-purified fraction with [ 3 H]glutamate and MgATP resulted in the radioactive labeling of 13 protein bands (Fig. 1, lane 3). Since the new substrates of polyglutamylation might, as tubulins and NAPs, be modified within a glutamate-rich sequence we searched for proteins containing such motifs among the proteins identified within the 13 [ 3 H]-labeled bands. We found at least one protein with a glutamate-rich stretch in each of these bands (Table 1) and thus expect that these proteins are very likely targets of polyglutamylation.

TTLL4 and TTLL5 modify new substrates in vitro.

To confirm whether the very likely targets of polyglutamylation, pre-selected in Table 1, can indeed be polyglutamylated, a selection of them was produced in bacteria and purified for analysis in an in vitro polyglutamylation assay. Not all proteins were tested because some could not be produced in bacteria due to their large size (nucleoporin, PELP1, Myb-binding protein 1A) and others shared a high homology with some of the tested proteins (PHAPII is a shorter version of SET that differs only in few amino acids at the Nterminus and ANP32E shares 57% identity with ANP32A ( 22)). To test the ability of the selected proteins to be polyglutamylated in vitro, we need a source of polyglutamylase activity. Presently, the only known mammalian polyglutamylase enzymes are members of the TTLL protein family (4). Following overexpression of the different TTLLs in HeLa cells, only TTLL4 and TTLL5 increased the GT335-reactivity of non-tubulin substrates (4). Thus, these two TTLLs are strong candidates for being the enzymes that polyglutamylate nontubulin substrates. We therefore used recombinant TTLL4_C639 (a truncated version of TTLL4, see Experimental Procedures) and TTLL5 for in vitro polyglutamylation assays. To investigate the specificity of different enzymes for the new substrates, we also tested two other polyglutamylases, TTLL6_N705 and TTLL7S. The tested proteins, NAP1, NAP2, NF45, SET, B23, ANP32A and ANP32B (Table 1, proteins in bold) are all strongly modified by TTLL4_C639 and to a lesser extent by TTLL5 (Fig. 2A). The two other enzymes, TTLL6_N705 and TTLL7S, which are both highly active on MTs, did not modify the new substrates (Fig. 2A). Thus, in vitro, non-tubulin substrates are specifically polyglutamylated by TTLL4 and TTLL5. Moreover, since all pre-selected proteins tested are modified, it is likely that the presence of glutamate-rich sequences is indeed, as proposed earlier, a signature of polyglutamylated proteins. Among the protein bands that were not labeled with [ 3 H]-glutamate by the polyglutamylase activity co-purified on the GT335-column, we identified several other proteins containing glutamate-rich stretches (Table S1, proteins in bold). These proteins might be polyglutamylated either to a lower extent or by a different enzyme, which was not co-purified on the GT335-column. To investigate this possibility, we repeated the in vitro polyglutamylation assays with the same set of TTLL enzymes on 6 of these proteins (GRP78, NASP, RANGAP, RNP-K, NCT and EB1; Fig. 2B). As before, TTLL4_C639 was the most efficient enzyme. It incorporated [ 3 H]-glutamate into RNP-K and EB1, to a lesser extent into RANGAP and NCT, but poorly into GRP78 and NASP. Note that TTLL6_N705 and TTLL7S, which were inactive on the previously tested nontubulin substrates, slightly modified EB1. This might be due to the close structural homology between EB1 and tubulins (23). Overall, the proteins modified in this test incorporated much less glutamate than the previously identified substrates, which might explain why they were not seen in the autoradiography (Fig. 1, lane 3). However, they have to be considered as potential substrates of polyglutamylation and their modification might be important in vivo. TTLL4 is the major polyglutamylase co-purified with the new substrates. Since the GT335-affinity purified fraction possesses a polyglutamylase activity able to modify the new substrates (Fig. 1, lane 3), we wanted to determine if it contains either TTLL4 or TTLL5, the two enzymes that modify the substrates in vitro. We therefore raised polyclonal antibodies against these TTLLs. The epitopes of both, anti-TTLL4 and anti-TTLL5 antibodies are located outside the domains that are conserved between the different TTLLs (Fig. S1). Accordingly, when we probed extracts of HeLa cells overexpressing the different full-length TTLL polyglutamylases, anti-TTLL4 and anti-TTLL5 antibodies specifically recognized their targets and exhibited no cross-reactivity with any other TTLL polyglutamylase (Fig. 3A). We then used these antibodies to probe the GT335-affinity purified fraction, but none of the antibodies detected a protein of the expected size on western blot (not shown), most probably because of the absence or low concentration of the enzymes. Since both antibodies have been demonstrated to precipitate their target TTLL proteins (not shown), we used them to deplete the GT335-affinity purified fraction. Following incubation of the depleted fractions with [ 3 H]-glutamate and MgATP, we observed a strong decrease of glutamate incorporation when TTLL4, but not when TTLL5 was depleted (Fig. 3B). Thus TTLL4 is the major enzyme involved in the polyglutamylation of the new substrates identified in HeLa cells. TTLL4 polyglutamylates the new substrates in HeLa cells. To show that the proteins that are polyglutamylated in vitro can also be modified by TTLL4 under in vivo conditions, we expressed them as EYFP-tagged versions in HeLa cells. In untransfected cells (not shown) or cells overexpressing an inactive (ATPase-dead) TTLL4_C639 (Fig. 4, first lane), only proteins in the 50 kDa region (tubulins, NAPs) and a 180 kDa protein are detected with GT335 antibody. This indicates that the endogenous polyglutamylase activity of HeLa cells might be too low to modify most substrates above detection levels of GT335 in western blot. Accordingly, in the absence of exogenous polyglutamylase activity, only substrates that were strongly modified in vitro (NAP1, NAP2, SET and the ANP32 proteins) could be detected by GT335 antibody following their overexpression (Fig. 4, lower panel, "D"). We then expressed the selected proteins together with an active version of TTLL4_C639 (Fig. 4; "A"). A significant increase in GT335-reactivity was observed on a range of endogenous proteins (Fig. 4, compare first and second lane), demonstrating that GT335 specifically detects polyglutamylation on a large range of proteins. Moreover, the GT335-reactivity of NAP1, NAP2, SET and the ANP32 proteins increased further, and other putative substrates (RANGAP and nucleolin) also became labeled with GT335 antibody (Fig. 4, lower panel, "A"). The expression levels of the substrates, as visualized with anti-GFP antibody, are similar in cells cotransfected with ATPase-dead and active TTLL4_C639 (Fig. 4, upper panel, "D", "A"). Thus, increase of GT335 detection levels clearly correlates with increased polyglutamylation of the respective proteins by the active TTLL4_C639. Moreover, a shift in migration was observed for most of the modified proteins, suggesting that the charge and/or size of these proteins have been changed (Fig. 4, upper panel, compare "A" with "D" for the modified proteins). Requirements for polyglutamylation. We identified a number of proteins that are subjected to polyglutamylation. All these proteins possess glutamate-rich stretches within their primary sequence, but no obvious polyglutamylation consensus site could be defined. To better understand the determinants of polyglutamylation, we analyzed the primary sequences of several members of the NAP family (Fig. 5A andS2A) and their ability to be modified in vitro by TTLL4_C639 (Fig. 5B) or in HeLa cells by endogenous polyglutamylases (Fig. 5C). NAP1 and NAP2 are, in contrast to NAP3 and NAP4, strongly polyglutamylated in vitro and in vivo (Fig. 5). NAP1 was previously shown to possess a major polyglutamylation site in its C-terminus and a minor site in its N-terminus (7). Both sites consist of glutamate-rich stretches that are not well conserved among the members of the NAP family (Fig. S2A and Fig. 5A). NAP2 diverges from NAP1 in the N-terminal site. Since this is a minor polyglutamylation site, NAP2 is still strongly modified. On the contrary, the sequence of NAP4 diverges in the major C-terminal site and thus NAP4 is only lowly modified. NAP3 diverges in both sites and accordingly, it is not modified. Thus, we have shown a correlation between the presence of the two previously identified polyglutamylation sites and the extent of modification. In addition to these two sites, all NAPs possess a central domain which is rich in glutamates (Fig. S2A). This domain does not seem to be targeted by the polyglutamylation, as NAP3 is not modified although it has the most extended glutamate-rich central domain (Fig. 5A). The fact that this domain is not modified, although it is located at the outer surface of the protein according to the structure of NAP1 (24), suggests that the presence of a glutamate-rich sequence is not the only prerequisite for its polyglutamylation. We then produced six chimeras of the NAP1, 2 and 4 proteins by swapping their C-terminal tails (Fig. 5A) and tested their ability to be modified by TTLL4_C639 in vitro (Fig. 5B) or by endogenous polyglutamylase activity in HeLa cells (Fig. 5C). Chimeras that possess the non-acidic C-terminal tail of NAP4 (NAP1_4 and NAP2_4), were poorly polyglutamylated both in vitro and in vivo, in good agreement with the lack of the major polyglutamylation site. On the other hand, chimeras that bear the highly acidic tail of NAP2 (NAP1_2 and NAP4_2) are, as NAP2, strongly modified. Concerning the chimeras that have the highly acidic tail of NAP1 (NAP2_1 and NAP4_1), NAP2_1 is modified to the same extent as NAP1 but NAP4_1 is less modified than one would expect according to the presence of the tail of NAP1. This again suggests that other, yet unknown determinants of polyglutamylation contribute to the modification. To further demonstrate that the presence of a glutamate-rich stretch is not the only prerequisite for a protein to become polyglutamylated, we analyzed different members of the nucleophosmin / nucleoplasmin family. Nucleophosmin B23 has two putative acidic polyglutamylation sites (Fig. S2B). NPN2, an oocyte-specific homologue of B23, is more acidic in the first site but less in the second (Fig. S2B). Although, taken together, the total number of glutamate residues in two potential polyglutamylation sites is similar, NPN2 is modified to a lower extent than nucleophosmin B23 by TTLL4_C639 (Fig. 6). This suggests that the second site contributes more to the degree of polyglutamylation than the first one. Consistently, NPN3, which completely lacks the second site, is poorly modified by TTLL4_C639 (Fig. 6). Again, these results reveal that not all glutamate-rich stretches present in proteins are equally subjected to polyglutamylation. We further analyzed the highly homologous proteins EB1 and EB3. Although both proteins carry a glutamate-rich C-terminus (Fig. S2C), only EB1 was modified by TTLL4_C639 in vitro (Fig. 6). Other TTLL enzymes that are active on EB1 (Fig. 2B) were inactive on EB3 as well (not shown). Finally, we tested the protein HMG-B1, which has not been identified as a potential substrate, but has an extended glutamate-rich C-terminus (Fig. S2D). HMG-B1 was almost not modified in the in vitro assay with TTLL4_C639 (Fig. 6). Taken together, these observations show that the presence of glutamate-rich sequences can be a prerequisite, but is not sufficient by itself for a protein to become a target of polyglutamylation, suggesting that other features are required.

DISCUSSION

Polyglutamylation was initially discovered on brain α-tubulin and subsequently also observed on β-tubulin (1,5,6). It was considered as a tubulinspecific modification until it was demonstrated to target also NAP1 and NAP2 (7). In the present study, we show that polyglutamylation is an even more general post translational modification. New substrates for polyglutamylation. Using the affinity of polyglutamylated proteins for the GT335 antibody, we purified many potential polyglutamylated proteins (Table S1). We demonstrated that several of these proteins are indeed subjected to polyglutamylation as they are modified by TTLL4 in vitro and/or in a cellular context. As we could not test all the putative substrates, a number of other targets of polyglutamylation might be present among the proteins listed in Table S1. Moreover, this list is by no means exhaustive as many of the substrates can be low-abundance proteins. It is also possible that not all polyglutamylated proteins interact with the GT335 antibody. This antibody has been raised against a synthetic peptide (EGEGEEEG) modified by the addition of two glutamyl units on the third glutamate (16) and it might, therefore, be biased towards proteins that are polyglutamylated within glutamate-rich sequences. Specificity of the polyglutamylation reaction. All the here-identified substrates of polyglutamylation are modified within glutamate-rich stretches, suggesting that an acidic environment at the acceptor site is generally necessary for polyglutamylation to occur. Nevertheless, we demonstrate that one cannot simply conclude that a protein possessing glutamate-rich sequences is subjected to polyglutamylation. Substrate recognition by polyglutamylases is probably a complex mechanism. Solving the crystal structure of the polyglutamylase enzymes and thus knowing the properties of the surface residues of the enzyme that are in contact with the substrate, will certainly facilitate understanding of the prerequisites for the polyglutamylation reaction. Taken together, we suggest that proteins with glutamate-rich sequences can be generally considered as potential substrates for polyglutamylation, although this needs to be tested for each single case.

Possible new roles of polyglutamylation.

Polyglutamylation generates acidic side chains of variable lengths on proteins. Such a modification might act as a fine-regulator of many cellular functions, for example by controlling the affinity of the here-identified target proteins for their binding partners, as earlier proposed for tubulins (reviewed in (25)). Many of the new substrates of polyglutamylation (ANP32 proteins, SET, nucleophosmin B23, nucleolin and RNP-K), as well as the NAP proteins, shuttle between the cytosol and the nucleus (22,26,27). Since nucleoporin and RANGAP, proteins involved in nucleo-cytoplasmic transport, are also modified, polyglutamylation could play an important regulatory role in nucleo-cytoplasmic shuttling. Moreover, most of these shuttling proteins are histone chaperones (NAP1, NAP2, B23, SET/PHAPII, nucleolin) and/or regulators of transcription (ANP32 proteins, SET/PHAPII, NF45, PELP1, RNP-K), suggesting that polyglutamylation might also regulate access to chromatin. Finally, EB1 and RANGAP are regulators of MT dynamics, suggesting that not only the polyglutamylation of tubulin, but also of its associated proteins could be important for the dynamics and functions of MTs. In cycling cells, the maximum yield of tubulin polyglutamylation is achieved during mitosis (18). It is thus tempting to speculate that, by targeting simultaneously MTs and chromatin binding proteins, polyglutamylation allows for a crosstalk between spindle dynamics and changes in chromatin structure accompanying mitosis.

FIGURE LEGENDS

Fig. 1. Purification of GT335-reactive proteins from HeLa cell extracts. The high speed supernatant of HeLa cell extracts was submitted to GT335-affinity chromatography. The bound fraction was run on a 10% SDS polyacrylamide gel and stained with Coomassie blue (lane 1), or transferred onto nitrocellulose for detection with GT335 antibody (WB, lane 2). The incorporation of [ 3 H]-glutamate by the co-purified polyglutamylase activity was visualized by autoradiography (lane 3). The protein bands (whose content is listed in Table S1) are numbered and their number is in a box for the ones that appear radioactively labeled following in vitro polyglutamylation. Molecular mass markers are indicated. Fig. 2. In vitro polyglutamylation of selected putative substrates with different TTLL enzymes. Proteins selected among the bands labeled by the co-purified polyglutamylase activity (A; Table 1) and proteins selected among the other bands according to the presence of glutamate-rich sequences (B; proteins in bold in Table S1) were expressed and purified from bacteria. They were then subjected to an in vitro polyglutamylation assay with recombinant TTLL4_C639, TTLL5, TTLL6_N705 and TTLL7S. The activity of each TTLL was verified by using MTs as a substrate. The amount of [ 3 H]-glutamate incorporated in the different substrates, was determined by scintillation counting. Fig. 3. Identification of the polyglutamylase activity co-purified with the GT335-reactive proteins. The specificity of polyclonal anti-TTLL4 and anti-TTLL5 was demonstrated on extracts from HeLa cells overexpressing the different TTLL polyglutamylases in fusion with EYFP (A). These antibodies were then used to immuno-deplete the GT335-affinity-purified fraction (B). As a control, depletion was also performed with rabbit IgG. The depleted fractions were incubated with [ 3 H]-glutamate for 2 hours, run on an SDS-PAGE and stained with Coomassie Blue. [ 3 H]-glutamate incorporation was visualized by autoradiography. Only the depletion with anti-TTLL4 antibody reduced the polyglutamylase activity in the GT335-affinity-purified fraction. Molecular mass markers are indicated. Fig. 4. In vivo polyglutamylation of selected putative substrates. The previously tested substrates of polyglutamylation (Fig. 2), as well as nucleolin which was not tested in vitro because it was hard to produce in bacteria, were co-transfected into HeLa cells with an active or inactive (ATPase-dead) version of TTLL4_C639 in fusion with EYFP. After 24 hours of transfection, the cells were lysed in Laemmli buffer, run on SDS-PAGE, transferred onto nitrocellulose and probed with anti-GFP antibody to check the expression level of the overexpressed proteins (upper panel) and with GT335 antibody to visualize the extent of polyglutamylation of the substrates (lower panel). Orange arrows point to the different overexpressed substrates visualized with anti-GFP antibody. In the lower panel, green arrows indicate the substrates that have been polyglutamylated in vivo, while red arrows point to proteins that were not modified. In general, we considered a substrate as being modified, when: (i) a band of the size of the protein (as compared to the anti-GFP signal) is recognized by GT335, and (ii) its GT335-reactivity increases upon co-expression of active TTLL4_C639 and (iii) this band does not correspond to endogenous GT335-reactive proteins (compare with cells not expressing the given substrate). Note that cells co-overexpressing B23 and active TTLL4_C639 were dying, explaining the decreased overall GT335-reactivity in the corresponding lane. Molecular mass markers are indicated. Fig. 5. In vitro and in vivo modification of NAPs and chimeras. The sequences of the NAP proteins (see Fig. S2A) and of different chimeras in which the C-terminal tails of NAP1, NAP2 and NAP4 were exchanged are schematized (A). Previously identified N-terminal and C-terminal polyglutamylation sites are shown in red according to their size (domain 1 and 3 in Fig. S2A). NAPs and their chimeras were expressed in bacteria, purified and subjected to in vitro polyglutamylation using recombinant TTLL4_C639 as a source of enzyme (B). The amount of [ 3 H]-glutamate incorporated in the different substrates was determined by scintillation counting. NAPs and chimeras were also cloned in fusion with EYFP in mammalian expression vectors and transfected into HeLa cells. Following 24 hours of transfection, they were first immuno-precipitated and then probed with either anti-GFP antibody to see the expression level, or with GT335 antibody to detect their polyglutamylation by endogenous enzymatic activity (C). Fig. 6. In vitro polyglutamylation of proteins related to the new substrates. In vitro polyglutamylation assays were performed using recombinant TTLL4_C639 on proteins from the nucleophosmin / nucleoplasmin and EB families as well as on HMG-B1 (see sequences in Fig. S2A). The amount of [ 3 H]glutamate incorporated in the different substrates was determined by scintillation counting.

TABLE

Table 1: Pre-selection of very likely targets of polyglutamylation. Among the proteins identified bands that were labeled with [ 3 H]-glutamate (Fig. 1 and Table S1), we selected those that possess long glutamate-rich stretches within their primary sequence and that thus are very likely targets of polyglutamylation. The band numbers correspond to those indicated in Fig. 1. In vitro polyglutamylation tests were performed on the proteins in bold and all of them did incorporate glutamate.

Band

Protein Accession Mass pI Sequence alignments were performed with MultiAlign (http://bioinfo.genopoletoulouse.prd.fr/multalin/) and corrected manually. Glutamate-rich domains have been highlighted and all glutamate residues in these regions are red. Note that not all regions are conserved between the different members of a family. For NAPs, the three glutamate-rich domains have been numbered and the site at which the C-terminal tails have been swapped in the chimeras have been underlayed with blue (X) (see Fig. 5A). 1: Identification of the GT335-affinity-purified fraction protein content. Forty protein bands were excised from the gel of Fig. 1, digested by trypsin and analyzed by nano-LC-MS/MS. The proteins identified in each band are listed. Proteins in bold contain at least one glutamate-rich sequence domain and are therefore considered as more likely substrates of polyglutamylation than others.

17 Figure S1 :Figure S2 :

 17S1S2 FigureS1: Epitopes of anti-TTLL4 and anti-TTLL5 polyclonal antibodies. (A) Anti-TTLL4 antibody was raised against the full-length protein. Out of four different truncated and EYFP-tagged versions of TTLL4 that were expressed in HeLa cells and analyzed on western blot, anti-TTLL4 recognized solely those containing N-terminal sequences located upstream of amino acid 555. Thus, all epitopes of this antibody are contained within the N-terminal domain of TTLL4, which is not conserved in any other TTLL. (B) Anti-TTLL5 antibody was raised against a fragment containing amino acids 692 to 1152 of TTLL5. This sequence is not conserved in any other TTLL protein. Schematic representation of TTLLs and their domains is according tovan Dijk et al., 2007 (4).