Erwan Bocher
email: erwan.bocher@ecnantes.fr

Thomas Leduc
email: thomas.leduc@cerma.archi.fr

Nathalie Long
email: nathalie.long@univnantes.fr

Fernando González Cortés

Guillaume Moreau
email: guillaume.moreau@ecnantes.fr

Fernando Gonzalescortes

urbSAT: from spatial SQL to urban indicators

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

GIS have become unavoidable tools for the development and management of territories. Deployed in urban environments, they are used for diagnosis, public debate and as decisionmaking tools for both experts and nonexperts. For urban issues, GIS need several models: 2D and 3D geometric modeling, simulation of natural phenomena (light, heat, water, wind, noise, etc.), multi temporal various scale data representation, visual representation. Due to this complexity and the amount of data, urban GIS are faced with a number of issues concerning data modeling, storage, querying and finally representation, hence the need for indicators to assist urban planning. As far as sustainable development is concerned, the main challenge with indicators is to integrate interdisciplinary knowledge in order to allow a better understanding of urban issues by a larger set of people that thus will be able to share their expertise.

In this paper, we propose both a tool for indicator production and a language with spatial operators in order to narrow the border between each expert (urban manager and scientist). In the first section we will explain the challenges that are specific to the urban context and our vision of knowledge sharing and mutual enrichment of both data and methods. The next section will make a short synthesis of existing works. Then, we will present the urbSAT (urban Spatial Analysis Tool) that includes a set of spatial operators and urban functions. The last section will present two urban indicators.

Motivation

At present, more than a half of the world population is living in urban areas. Urban areas are in fast expansion. Space and energy consumption are increasing. Therefore urbanization control has become a major issue. Moreover, the two most important greenhouse gases producers are transportation and elder buildings. Sustainable development that is seeking the best tradeoff between the fulfillment of human needs maintaining the quality of the natural environment indefinitely by applying different methods and criteria in urban planning and management. Among the social, economical and ecological factors that are taken into account in decisionmaking, environmental issues now have a greater impact.

In this domain, it is necessary to have a wide knowledge of physical, chemical and meteorological mechanisms that operate in urban environments to address such challenges as enhancing the environmental quality of the city, limiting energy consumption and the associated costs, reducing pollutions and preventing risks but also to able to answer public demand in terms of comfort, security and public infrastructures.

To address these issues, researchers are faced with severals problems among which are tool disparity, knowledge sharing and enhancement (both for data and methods). Indeed, different kinds of tools are used ranging from Fluid Mechanics (with Finite Elements Methods computation) to in situ social inquiries, hence the difficulty to integrate the knowledge produced by those tools. What they usually have in common is a geographic reference.

Recently, attention has been focused on the development of Spatial Data Infrastructures (SDI) at different levels (local, national) and for various policy areas and priority of actions such as social, economic, and environmental issues [START_REF] Clinton | Executive Order 12906: Coordinating geographic data acquisition and access: The National Spatial Data Infrastructure[END_REF], INSPIRE 2007). All of them share the same goal: maximize user's access to spatial data, minimize the redundancy of efforts and investments [START_REF] Nebert | Developping Spatial Data Infrastructures: the SDI Cookbook[END_REF]. The interoperability of the system is guaranteed by several standards for data exchange and processing (GML, WFS, WMS).

In the spirit of SDI, we will promote best practices and guidelines around geosciences, not only on sharing data, but also on sharing spatial processing method that are used to build urban knowledges.

Beyond the strict field set of practices, themes and business logics, we aim to set up and deploy a common language and the corresponding framework. This common will provide all researchers the ability to produce and share complex indicators using a spatial standardized SQL. In terms of uses and learning, the valueadded will be a common reusable syntax between many users whatever their speciality (climatology, transport, urban structure...). It provides an opportunity to avoid or eliminate all the barriers through integrated models that transcend disciplines.

Related work 3.1 Urban indicators

Within advancements in data collection, computer software including GIS, and image processing, several researchers have developed automated or semiautomated method to extract urban indicators like morphological information: [START_REF] Ratti | Urban texture analysis with image processing techniques[END_REF] use image processing with the MATLAB software package; [START_REF] Burian | Morphological analysis using 3D building databases[END_REF] propose an approach using GIS from US database to process 3D building datasets and then, multispectral images to compute an expanded set of parameters including surface cover fraction with the ESRI ArcGIS software package [START_REF] Burian | Development of gridded fields of urban canopy parameters for models3/CMAQ/MM5[END_REF].

At IRSTV institute a software called DFMap was developed to analyse urban tissue [START_REF] Long | Urban cover modes and thermodynamic parameters from urban data base and satellite data: comparison for Marseille during ESCOMPTE[END_REF]. It allows computing not only some statistical parameters describing the morphology of the buildings but also aerodynamic parameters like roughness length and displacement height. It was meant as a pre processor for atmosphere numerical simulation models that are based on a regular vector gridbased map of significant parameters such as rugosities, thermal and radiative flows.

DFMap uses the BDTopo® spatial database that is produced by the French National Geographic Institute (IGN) from aerial photographs. This database is composed of vector land cover data, including vegetation, hydrographic network, roads and buildings. The topography is also included in this database as well as contours and spot heights.

The DFMap computes several functions over a grid :

• Functions describing buildings morphology like height, surface, volume, perimeter,…

• Functions describing the cover mode with density calculation,

• Functions providing aerodynamic parameters like roughness length and displacement height thanks to Bottema's and Raupach's models [START_REF] Bottema | Urban roughness modelling in relation to pollutant dispersion[END_REF][START_REF] Raupach | Drag and drag partition on rough surfaces[END_REF].

The user can choose cell size, set wind direction to compute frontal and lateral surface densities used in the roughness calculation. The results are presented in cartographic map with associated numerical data. They are used to build a spatial typology about urban tissue.

Nevertheless DFMap has some limitations :

• It can only compute urban indicators from BDTopo® database, stored in DXF format. It is a strong limitation with respect to the availability of various databases like urban GIS, or satellite images.

• It has been developed from scratch as a monolithic application. It does not take advantage of GIS capabilities such as reprojecting and geometric computations or map generation facilities.

• The spatial functions are statics. It's not possible to combine each others to create new indicators.

• It is not pluggable and it is not freely available.

To solve these limitations and to facilitate the knowledge sharing as denote before we decided to extract the main DFMap functions and to integrate then onto a more generic spatial query language.

SQL spatial query language

The questions of spatial query language have been thoroughly studied over the last years. As reviewed by (Lopes de Oliveira, 1996), the need for spatial query formalism has been clearly identified by and answered in [START_REF] Egenhofer | Towards a Spatial Query Language: User Interface Consideration[END_REF], with GEOQL [START_REF] Ooi | Efficient Query Processing in Geographic Information Systems[END_REF], Spatial SQL [START_REF] Egenhofer | Spatial SQL: A query and presentation language[END_REF]. The SQL language has already been spatially enabled and extended by the Open Geospatial Consortium in the OpenGIS "Simple Features Specification for SQL" (Herring 2006a,b) Recently a generic processing library called GDMS (Generic Data Source Management System) has been developed at the IRSTV institute [START_REF] Bocher | GDMS: an abstraction layer to enhance Spatial Data Infrastructures usability[END_REF]. With GDMS, the idea was to postpone all problems of interoperability across data repositories, but also about the SQL semantics by developing a highly flexible, portable and standards compliant tool to build SQL queries. Thus GDMS is an unified abstract layer that allows addressing, managing and modifying any attributes and spatial data's from a single point of view. GDMS is able to make homogeneous SQL queries on data, so as to filter, order, update without worrying about the nature of the data carrier (databases or flat files). However it is possible to extend the semantics from typical SQL with customized functions and queries, developed in Java. At least, it's significant to mention that GDMS is open source and takes advantage of two famous libraries: Java Topology Suite for vector data and ImageJ for raster data. Theses choices allow us to concentrate the effort on the specific spatial processing need to build urban indicators and not on basic spatial operators that already exist.

UrbSAT

UrbSAT has been derived of the generic system GDMS. The provided interface is a SQL language that preserves the grammar of the SQL92 standard and adds to this standard the concept of geometry and spatial functions as defined by the OGC simple features SQL specification (Herring 2006a,b). The most interesting feature of the UrbSAT is the possibility to extend the semantics from typical SQL with customized functions and queries. Those functions and queries are artifacts developed in java that can be easily reused in another SQL statement by referencing its name. For example, the buffer function takes a geometry as input and outputs the buffered geometry. The following statement produces a new data source with as many rows as the original data source (mysource), having only one new field with the buffer of the original geometries:

SELECT buffer(the_geom, 20) from mysource;

As mentioned in the previous section, the mechanism to build urban indicators is to process vector data and to aggregate information on a vector grid. In order to obtain an abstract reusable workflow that will be used as common basis for all future studies and integrate it in the GDMS SQL semantic we have decomposed the DFMAP methodology. We have identified two types of indicators :

• case 1 : the one that can be compute using a sequence of spatial OGC operators and predicates.

• case 2 : the other that need specific function such as create a regular vector grid. UrbSAT is an application of the OrbisGIS open source GIS software developed at IRSTV. The extended query language is available using a SQL console that permits to deal with the layers loading in the OrbisGIS map viewer [START_REF] Leduc | OrbisGIS : A GIS for scientific simulation[END_REF].

Example case study

We will present two types of indicators that are computed over a grid with a 200m cell size : average building and clusters classification.

Average building

We have rebuilt the 3D objects in order to compute the average height for every building. The spatial vector layer, inherited from the new BDTopo® database, contains the altimetry data, relative to the sea level, of each building roof. The aim of the urbSAT GetZDEM custom query is to produce a new vector layer mixing the input DEM and the buildings vector layer (figure 2). This new vector layer enriches the previous one with a new numeric attribute that corresponds to the height of the corresponding building, and it adds to the 2dimensional geometric field copied from the vector layer a third dimensional value that corresponds to the Z of the underlying DEM cell.

GDMS underlying layer provides also a full extrude function that does not only create the ground footprint and the roof but also the walls. It gives the possibility to build a real three dimensional representation of parcels and buildings over a DEM. This function, from a single table of 2D geometries (buildings floor) and a numeric field (building height), is able to produce a volume that represents the corresponding building (Figure 3). At the end the new layer that represents the building in 3D is used to compute the average height by cells.

From densities to clusters classification

This step consists in computing a global envelope based on each input data source boundary. This envelope is the reference used to construct the regular grid (Figure 1). Then, the 5 following densities can be computed:

• Density of built areas

• Density of areas covered with natural vegetation,

• Density of asphaltcovered areas

• Density of watercovered surfaces.

• Average building density. An example is illustrated in figure 4.

These densities populate the grid and they are classified with the kmeans algorithm. The statement is expressed as : SELECT KMeans(9) from mydensities;

where 'mydensities' is a the vector grid that contains for each cells all the densities. '9' represents the number of clusters. Figure 5 shows the result on the French city of Le Havre.

Conclusion

We have presented the UrbSAT tool that is a set of spatial functions dedicated to urban indicators production. UrbSAT is based on GDMS, a generic library, and offers a common way to query spatial data in SQL. It is a large refactoring and generalization of DfMap as it is meant to be used for any simulation and is GISbased. This latter argument drastically reduces the amount of development needed to produce new indicators and should favor knowledge sharing among researchers.

Figure 1

 1 Figure 1 presents an example with the actual process required to obtain a density indicator. It shows step by step the SQL statements that are used to manipulate the different input data to produce expected result. The purpose of this indicator is to build a regular grid of polygons that will contain as an attribute the density of the features in the input data for each one of those polygons. Looking thoroughly to the sequence of instructions that compute the density indicator it is possible to see howUrbSAT takes advantage of some basic SQL functions to calculate the area of the parcels, to intersect different sources and to aggregate the results (case 1). Furthermore, we can show how the SQL

Figure 3 :

 3 Figure 3: 3D representation of urban buildings over DEM Query : select Extrude(id, height) from mybuilding;

Figure 4 :

 4 Figure 4: built area density on Le Havre Figure 5: Kmeans classification on Le Havre.


Sandra Rebouissou

Gabrielle Couchy

Louis Libbrecht

Charles Balabaud

Sandrine Imbeaud

Charles Auffray

Tania Roskams

Paulette Bioulac-Sage

Rebouissou Sandra

Couchy Gabrielle

Libbrecht Louis

Balabaud Charles

Imbeaud Sandrine

Auffray Charles

Roskams Tania

Bioulac-Sage Paulette

Zucman-Rossi Jessica

Service De H Pato-Gastroent Rologie

Andr

The beta

Keywords: Adult, Aged, Carcinoma, Hepatocellular, metabolism, pathology, physiopathology, Down-Regulation, genetics, Extracellular Matrix, genetics, metabolism, Female, Focal Nodular Hyperplasia, metabolism, pathology, physiopathology, Gene Expression Profiling, Humans, Liver Cirrhosis, metabolism, pathology, physiopathology, Liver Neoplasms, metabolism, pathology, physiopathology, Male, Middle Aged, RNA, Messenger, metabolism, Reverse Transcriptase Polymerase Chain Reaction, Up-Regulation, genetics, beta Catenin, genetics, metabolism Focal nodular hyperplasia, zonation, -catenin ß, glutamine synthetase, benign tumor, diagnostic markers, transcriptome, microarray

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Focal nodular hyperplasia (FNH), first described by Edmondson ( ), is the second most common benign liver tumor, outnumbered 1 only by hemangioma. FNH usually occurs in women 20 50 years old. A relation between FNH and oral contraceptive use has been

-
reported but is disputed ( ). Classical FNH is characterized by a central stellate fibrous region containing malformed vascular structures (

). The solitary central artery with high flow and the absent portal vein give the lesions a characteristic radiological appearance ( ) in more 4

than 80 of FNH cases. However, some classical FNHs, mainly those smaller than 3 cm, have no central scar at pathological examination % ( ). In all cases, the treatment of FNH is conservative because there is very low risk of complications such as bleeding, and malignant 5 transformation has not yet been proven ( ). 6

FNH typically occurs in normal or almost normal liver. The lesion is multinodular, composed of nearly normal hepatocytes arranged in 1 2 cell-thick plates. Bile ductules are usually found at the interface between hepatocytes and fibrous regions ( , ). It is thought that

-- 7 8
increased arterial flow hyperperfuses the local parenchyma, leading to secondary hepatocellular hyperplasia. FNH is therefore considered a hyperplastic response to increased blood flow, rather than a neoplastic process ( , , ). Various vascular abnormalities such as 3 9 10 telangiectases, hereditary hemorrhagic telangiectasia, arteriovenous malformation, and anomalous venous drainage may be present, especially in patients with multiple FNHs.

Clonal analysis demonstrated the reactive polyclonal nature of liver cells in FNH in 60 100 of cases, depending on the series ( -%

11-15

). Other studies analyzing chromosome gains and losses by comparative genomic hybridization, allelotyping, or karyotyping identified chromosome alterations indicating a clonal origin of the FNH nodules in 14 50 of cases ( , , ). However, genetic analysis of FNH -% 2 15 16 did not demonstrate somatic gene mutations in , , or ( , , ). Recently, it was shown that two angiopoietin CTNNB1 TP53 TCF1 APC [START_REF] Bioulac-Sage | morphologic, and molecular features defining so-called telangiectatic focal nodular hyperplasias of the liver[END_REF] 17 18 genes ( and ) involved in vessel maturation have altered mRNA expression levels, with a consistently increased ANGPT1 ANGPT2 / ratio in FNH.( , ) ANGPT1 ANGPT2 14 15 Focal nodular hyperplasia-like (FNH-like) nodules are lesions arising in cirrhotic liver that closely resemble FNH at both the morphological and radiological levels ( , ). It has been hypothesized that FNH-like nodules arise as a local hyperplastic response to 19 20 vascular alterations ( , ) known to occur in liver cirrhosis ( ). However, the exact pathogenesis of FNH-like nodules remains to be 20 21 3 elucidated.

To better understand the pathophysiology of FNH, we searched for genes deregulated in FNH by conducting a combined cDNA and oligonucleotide transcriptomic analysis exploring expression of more than 15,000 different genes. In this experiment, transcriptome profiles of eight FNHs were compared to six non-tumor liver samples. After the analysis, selected genes belonging to the most strongly activated or inactivated pathways were validated using quantitative RT-PCR in a larger series of 70 benign liver tumors including FNH, hepatocellular adenomas (HCA), macroregenerative and dysplastic cirrhotic nodules, and FNH-like lesions.

Material and Methods

Patients and Samples

A series of 11 FNHs, 15 HCAs, 6 normal liver tissues including samples taken at a distance from HCAs ( ), FNHs ( ), and Bordeaux and Leuven. HCAs were classified as described previously;( , ) five cases were HNF1 -inactivated, five were -catenin 22 23 α β mutated, and the remaining five HCAs were non-mutated and inflammatory. For all cases included in this study, full clinical and morphological data were available and are summarized in . Frozen tissue samples were available for all cases, and the prerequisite Table 1 to include samples in the study was an adequate sampling of fresh and fixed liver tissue. This study was conducted with the authorization of our local ethics committees (CCPPRB Paris Saint-Louis, France, and Committee from the University Hospitals, Leuven).

For each case, tissue from surgical samples including the nodules and corresponding non-tumor tissue was cut into thin slices. A representative portion of the nodule, as well as of the non-tumor liver, was immediately frozen in liquid nitrogen and stored at 80 C until Cergy-Pontoise, France). The quality of DNA and RNA was assessed by gel electrophoresis followed by staining with ethidium bromide, and degraded samples were excluded. RNA was accepted for quantitative RT-PCR if the 28S/18S ratio was greater than 1. For microarray analysis, RNA was analyzed by Agilent Bioanalyzer profiling, and RNA was qualified if the 28S/18S ratio was greater than 1.5 and if the RNA was not degraded.

Microarray Analysis

Transcriptional profiling of FNH lesions and non-tumor liver tissues was performed using two different microarray approaches (Affymetrix oligonucleotide GeneChips U133 and a cDNA microarray). Minimum Information about a Microarray Experiment (MIAME)-compliant data were deposited in Gene Expression Omnibus (GEO) at NCBI ( ) and are http://www.ncbi.nlm.nih.gov/geo/ accessible through GEO series accession number GSE9536. The characteristics of the in-house manufactured cDNA array are described in [START_REF] Graudens | Gremy G Deciphering cellular states of innate tumor drug responses[END_REF] Additional data file 2 ( ). The detailed procedure for the gene array analysis is provided in the 24 supplementary

. Gene ontology (GO) categories that were significantly over-represented in FNH were determined by the material and methods hypergeometric test using the web-based tool GOTree Machine (GOTM) (

). Distribution in GO http://bioinfo.vanderbilt.edu/gotm/ categories of genes significantly up-regulated and down-regulated in FNH were compared to the distribution of the overall genes included in the HG-U133A Affymetrix array. GO categories were considered significantly enriched when the -value was less than 0.01. P

Quantitative RT-PCR

Quantitative RT-PCR was performed as described previously ( ) using pre-designed primers and probe sets from Applied Biosystems 25 for the detection of , , , and to normalize the signals. Detection was performed using the ECL SuperSignal West Pico Chemiluminescent Substrate (Pierce) and horseradish peroxidase-conjugated anti-mouse IgG (1:4000, Amersham) as the secondary antibody.

Statistical Analysis

All reported values are mean SD. Statistical analysis was performed using GraphPad Prism version 4 software, and significance was ± determined using the nonparametric Mann-Whitney test for unpaired data. A difference was considered significant at < 0.05. P

Results

Identification of Overexpressed Genes Encoding Proteins of the Extracellular Matrix

Analysis of the Affymetrix and cDNA microarray results revealed 240 and 284 genes that were respectively significantly up-and down-regulated in FNH compared to control non-tumor liver samples ( ). We analyzed these sets of genes to Supplementary Table 1 identify biological pathways that were specifically altered. Using a gene ontology-based analysis, we identified several GO categories that were significantly over-represented in FNH (

). Among the up-regulated genes in FNH, the extracellular matrix and Supplementary Table 2 cell adhesion terms achieved the highest degree of significance ( 6.10 and 8.10 respectively) ( ). The overexpression of

P = 22 - P = 12 - Table 2
the genes encoding proteins of the extracellular matrix is consistent with the presence of fibrosis in most cases of typical FNH, particularly in the fibrous scar area. In FNH, fibrosis appears as a blue area as revealed using trichrome staining ( ) or is also Fig. 1A andB immunostained using anti-collagen IV antibodies (

). This profile of expression is characteristic of the TGF--dependent Fig. 1C E β pathways. Accordingly, we identified a two-fold overexpression of the TGF-1 gene in FNH, as well as overexpression of other key genes β involved in fibrogenesis such as and . A smooth muscle actin (SMA) staining was observed in myofibroblasts present at PDGFA PDGFRB the periphery and within the fibrotic area ( ). These results are consequently consistent with an activation of the TGF pathway Fig. 1G H β in cells located around the central fibrous scar.

Expression of Zonated Genes is altered in FNH

Nineteen regulated genes were identified whose expression is physiologically zonated in the hepatic lobule. In addition, the expression of these genes is known or was previously suggested to be regulated by -catenin ( ) ( , ). Among these, the expression of 13 β Table 3 26 27 genes physiologically expressed in the periportal area was down-regulated in FNH. Moreover, the expression of six genes normally expressed in the perivenous area was up-regulated in FNH nodules, suggesting altered zonation in FNH. To further explore this phenomenon, we analyzed the architectural distribution of glutamine synthetase (GS), a well characterized overexpressed protein in FNH that is physiologically expressed in only one or two plates of hepatocytes immediately adjacent to the central veins in normal liver (Fig. 1I ). In FNH, we observed the overexpression of GS in hepatocytes that were heterogeneously distributed in the nodules. A systematic analysis of the staining architecture showed a non-random distribution of the highly stained hepatocytes over relatively large areas, often anastomosed in map-like patterns centered on central veins, whereas they remained at a distance from fibrous bands ( ).

" "

Fig. 1J and 1K

This zonal distribution pattern of GS expression in FNH strongly differs from the homogeneous staining usually observed in -catenin

β - mutated adenomas ( ). 22
The expression of , the gene encoding GS, is known to be up-regulated by -catenin ( ). Accordingly, we found that GLUL β 28 GLUL mRNA overexpression significantly correlated with a slight but significant (the gene encoding -catenin) mRNA overexpression CTNNB1 β in FNH compared with normal liver tissues ( 0.33, 0.016, ). However, immunohistochemical analysis of FNH nodules r 2 = P = Fig. 1L using a -catenin antibody did not show any abnormal -catenin localization. -catenin was membranous in all hepatocytes, without β β β significant cytoplasmic or nuclear accumulation, even in hepatocytes that overexpressed GS ( ). Despite no detectable variation Fig. 1M O of the total -catenin expression in hepatocytes of FNH, we observed the overexpression of the active, unphosphorylated -catenin protein

β β
in FNH compared to normal liver samples ( ). Consistent with -catenin activation in FNH, our transcriptomic analysis revealed at Fig. 1P β least seven additional genes known to be regulated by the -catenin pathway but whose physiological localization in the lobule was β unknown (

). Taken together, these results suggest the up-regulation of the -catenin signaling pathway in areas surrounding the Table 3 β veins of FNH nodules. This up-regulation is related to overexpression of the active form of -catenin in the absence of β CTNNB1 -activating mutations, as evidenced in 11 screened FNH samples. As activated -catenin signaling is a known readout of Wnt signaling, we β compared the expression levels of Wnt regulators in FNH and normal liver tissues. We did not find any significant differences in GSK3β and phospho-GSK3 ser9 expression using western blotting (

). Similarly, no significant difference was observed β Supplementary Fig. 1 regarding the expression levels of , (14 genes analyzed), (Frizzled receptors) (9 genes analyzed), (3 genes AXIN1 WNT FZD DVL analyzed), and mRNA ( ). Surprisingly, against the hypothesis of a -catenin activation through APC LRP5 LRP6 Supplementary Fig. 2 β silencing, we found a mild but significant overexpression of the transcript in FNH (2-fold) when compared to non-tumor livers ( APC ). Supplementary Fig. 2

Expression of the FNH-deregulated Genes in Other Types of Benign Hepatocellular Tumors

In a series of 70 liver samples including cirrhotic and normal tissues, different subtypes of adenomas, typical FNH nodules, and FNH-like, dysplastic, and macroregenerative nodules developed in cirrhosis, we examined the specificity of FNH-deregulated genes as identified in our microarray analysis or as described previously in the literature (all the data are provided in the ). supplementary Table 3 The ratio of expression of two angiopoietin genes, , which was previously shown to be increased in FNH, ( ) ANGPT1/ANGPT2 29 demonstrated a significantly increased mean -fold change of expression (9.5 6.5) in FNH compared with non-tumor liver tissues.

±

However, in our series, the ratio also increased significantly, although at a lower level, in cirrhosis-related samples ANGPT1/ANGPT2 including random cirrhosis (3.9 0.4), regenerative and dysplastic nodules (7.5 0.9), and FNH-like nodules (4 0.6) (

). In contrast, ± ± ± Fig. 2 the overexpression of the transcript, which encodes neurotensin, was observed specifically in FNH ( ). The ratio of expression of NTS Fig. 2 was fully specific and sensitive to discriminate FNH from the other benign tumors ( ). NTS/HAL Fig. 2 The microarray results indicating significant overexpression in FNH of the -catenin target genes , , and were β GLUL GPR49 TBX3 confirmed using quantitative RT-PCR ( and data not shown). Interestingly, overexpression was lower than that observed in -catenin Fig. 2 β mutated adenomas. The other subtypes of lesions demonstrated normal or significantly decreased expression levels of the -catenin target β genes compared to FNH ( ). Similarly, two periportal genes, and , which encode the serine dehydratase and histidine Fig. 2 SDS HAL amomnia lyase, respectively, were down-regulated specifically in FNH and -catenin mutated adenomas ( ).

β -Fig. 2 Interestingly, FNH-like nodules demonstrated an expression profile similar to those of other cirrhotic samples but different from the profile observed in typical FNH. In particular, in FNH-like nodules, the -catenin induced perivenous genes encoding GS and GPR49

β -

were significantly down-regulated compared with non-tumor liver and FNH nodules ( ). Consistent with this result, very little Fig. 2 parenchymal GS staining was observed in FNH-like nodules (

). Some parenchymal staining was observed around venous structures, Fig. 3 in addition to the ductular reaction, of FNH-like nodules. Because ductules occupy only a small area of FNH-like nodules and no parenchymal GS staining was detectable, no GS over-expression was observed at the RT-PCR level. However, the immunohistochemical pattern of GS expression was distinct from that of FNH, thus confirming that FNH-like nodules represent a separate entity. Moreover, in FNH-like nodules, neurotensin and the selected genes displaying periportal localization were not found differentially expressed when compared to non-tumor liver tissues by quantitative RT-PCR (

). In fact, FNH-like nodules showed similar expression patterns as Fig. 2 other cirrhotic lesions. Consequently, our results support the idea that FNH-like nodules have a pathophysiological origin different from that of typical FNH.

Discussion

In the present work, we carried out a transcriptomic analysis which revealed the molecular pathways altered in FNH. For the first time, activation of the -catenin pathway was demonstrated in these tumors. Recently, -catenin was identified as a master actor of hepatocyte β β proliferation, liver regeneration, hepatocyte metabolism, and liver development and as a master regulator of liver zonation ( , ). In 26 [START_REF] De | Somatic mutations of the beta-catenin gene are frequent in mouse and human hepatocellular carcinomas[END_REF][START_REF] Miyoshi | Activation of the beta-catenin gene in primary hepatocellular carcinomas by somatic alterations involving exon 3[END_REF][START_REF] Monga | Beta-catenin antisense studies in embryonic liver cultures: role in proliferation, apoptosis, and lineage specification[END_REF][START_REF] Monga | Changes in WNT/beta-catenin pathway during regulated growth in rat liver regeneration[END_REF][START_REF] Tan | Conditional deletion of beta-catenin reveals its role in liver growth and regeneration[END_REF] normal hepatocytes, -catenin and its known target genes, such as GS, are expressed exclusively in the immediate perivenous areas ( ,

β
). This zonated defined pattern of expression differs from that observed in -catenin activated HCA or carcinoma, which usually shows β more diffuse cytoplasmic GS staining and nuclear -catenin staining, in accordance with the monoclonal origin of the -catenin activating

β β -
mutations in these tumors ( , ). 8 30 In FNH, -catenin activation showed a heterogeneous distribution along the hepatocellular nodules, with a characteristic architectural β distribution over relatively large areas, sometimes centered on visible veins and usually remaining at a distance from fibrous bands. This specific -catenin expression pattern may be interpreted as the result of an altered hepatocyte zonation in FNH leading to increased β β -catenin gradient starting from the veins. Because FNHs are most frequently polyclonal tumors, it is not surprising to find a heterogeneous distribution of -catenin pathway activation in the absence of gene mutation ( , ).

β 11 13-16

In FNH, sinusoidal blood flow is modified by arteriohepatic hyperperfusion, and sinusoids are directly irrigated by inlet arterioles coming from fibrous septa ( , ). The abnormal arteries drain indirectly into sinusoids via capillaries in the fibrous septa, leading to a 3 10 modified sinusoidal endothelial cell phenotype ( , ). This situation is entirely different from the normal lobulation of the liver ( 9 36-38 Fig. 4 ). Because the fibrous septa with arteries are the location of blood inflow, they can be considered portal tract equivalents, i.e., they have " "

the artery and the ductular reaction but not that of the bile duct and vein ( ). In FNH, we observed an extension from the centrolobular 39 equivalent area toward the portal tract equivalent. The blood composition may likely participate in zonal/restricted -catenin activation. remain stable, activation of these pathways is probably tightly controlled to balance cell proliferation rate.

Another important finding of our study was that FNH-like nodules gene expression profile significantly differs from that of typical FNH nodules. In particular, FNH-like nodules did not show -catenin activation. Moreover, they did not exhibit increased β NTS/HAL expression ratio as did typical FNH. None of the genes tested in FNH-like nodules demonstrated a significantly different level of expression from that of random cirrhotic samples or cirrhotic dysplastic nodules. Our results demonstrate that, despite an enhancement of the signal during the arterial phase in MR/CT imaging of both types of nodules, typical FNH and FNH-like nodules derive from different pathophysiological processes. The specific markers identified in the present study will be useful tools to correctly diagnose and classify benign lesions of the liver, including the different molecular subtypes of HCA ( ). 22

In conclusion, abnormally zonated -catenin activation is a particular feature of typical FNH that may explain the slight polyclonal β over-proliferation of hepatocytes at the origins of the lesions. In contrast, FNH-like nodules, as random cirrhosis, do not share the same molecular defect, indicating a different pathogenesis. Finally, molecular and immunohistochemical markers will be useful tools in clinical practice to diagnose and classify benign hepatocellular tumors.

Ackowledgements:

We thank Eric Chevet for critical reading of this manuscript. This work was supported by Inserm (R seaux de recherche clinique et r seaux (I, J, K) Immunohistochemistry using GS antibody in non-tumor liver (NTL) (I) and FNH (J, K). In NTL, GS is expressed only by hepatocytes of 1 or 2 centrolobular plates (arrow); in FNH, typical aspect of GS staining in a map-like pattern (J); this large GS area is - Transforming growth factor beta 1 induced transcript 1 ns 1.9 ns: not significant; na: not analyzed; nd: not done; NTL: non tumor-livers

  nodules including 3 high-grade dysplastic nodules (HGDN), 1 low-grade dysplastic nodules (LGDN), and 4 2 ] [ macroregenerative nodules (MRN) , 11 FNH-like nodules, and 19 samples of random cirrhotic liver including samples taken at a distance ] [ from hepatocellular carcinoma ( ), FNH-like nodules ( ), and dysplastic nodules ( ) were collected at the University Hospital of 8 7 4 ]

  studies. Paraffin tissue sections were routinely stained with H&E and Masson s trichrome for diagnostic purposes. ' Pathological diagnosis of FNH and HCA was made on the basis of classical criteria ( , ). 7 8 DNA and RNA were extracted as described previously.( ) DNA was quantified by fluorometry (Fluoroskan Thermo Labsystem, 15

40 β 43 β

 4043 Activation of the -catenin pathway in FNH could consequently contribute to tumor formation as -catenin may promote hepatocyteβ βproliferation and regeneration. However, in contrast to an activating mutation, hepatocytes in FNH remain sensitive to repression of the β -catenin pathway in regions surrounding fibrosis. The present study also shows that fibrosis in FNH is related to the overexpression of several genes encoding extracellular matrix proteins, in addition to increased TGF-expression. This result is in agreement withβ in situhybridization experiments in which a high expression level of TGB-1 was detected in mesenchymal cells in FNHs ( ). TGF-1 is a β multifunctional cytokine that is involved in development and tissue differentiation through its effects on cell proliferation, migration, differentiation, and apoptosis. TGF -1 is also important in the pathological process of fibrosis. Interestingly, TGF-and Wnt/ -cateninβ β βpathways regulate a number of common processes during embryonic development, such as the patterning of imaginal discs in Drosophila and tissue specification and organogenesis in vertebrate embryos ( ). Several recent reports have highlighted the complex, intertwined 41 nature of the two signaling pathways ( ). Zhang and collaborators supported the hypothesis that -catenin in renal epithelial cells, rather 42βthan in mesenchymal cells, may suppress TGF-1 dependent signaling ( ). Furthermore, analysis of TGF-1 transgenic mice showed βreduced GS expression resulting from apoptosis of GS-positive hepatocytes ( ). However, Lotz and collaborators showed that in FNH, 44 TGF-activation was not related to increased hepatocyte apoptosis ( ). Consequently, additional functional analyses of the interplay β 40 between TGF-and -catenin pathways in hepatocytes are required to better understand the pathogenesis of FNH. One hypothesis could β β be proposed in which TGF overexpression in FNH may occur to counteract the proliferative effect of -catenin activation. In another β β hypothesis, -catenin activation could represent a response to an abnormal TGF -activation in FNH. Considering that most frequent FNH β β

Figure 1

 1 Figure 1 Expression of the TGF and -catenin pathways in FNH β β (A, B) Two serial sections of FNH stained with Masson s trichrome (TC) showing fibrous bands stained in blue. (C, D, E) Immunostaining of ' collagen IV (Col IV) in NTL (C) and FNH (D, E). In NTL, some Co IV fibrils are found around vessels and biliary channels in portal tract; in FNH, immunostaining showed a diffuse pattern with more intense expression in and around fibrous bands (black arrows), also found along sinusoids. (F, G, H) Immunostaining of smooth muscle actin (SMA) in NTL (F) and FNH (G, H). In NTL, SMA is expressed only by myofibroblasts of vascular walls (black arrow): artery (right), central vein (left); in lobules, only a few hepatic stellate cells (HSC) express faintly SMA (red arrow); in FNH, SMA is overexpressed by myofibroblasts in and around fibrous bands (black arrow) and veins (red arrow).

  centered on a central vein and remains at a distance from the fibrous band (K). (L) Correlation between and mRNA GLUL CTNNB1 overexpression in FNH and in NTL. Expression of (encoding GS) and (encoding -catenin) was measured in FNH (n 11) GLUL CTNNB1 β = and in NTL (n 6) with quantitative RT-PCR. (M, N, O) Immunohistochemistry using a total -catenin antibody in NTL (M) and FNH (N, = β O). In NTL, -catenin immunostaining showed a normal membranous staining in hepatocytes and an obvious cytoplasmic staining in biliary β cells (arrows); in FNH, no cytoplasmic nor nuclear -catenin staining was observed in hepatocytes that overexpressed GS, but there was β normal membranous expression. In fibrotic areas, biliary cells of the ductular reaction showed normal strong cytoplasmic expression of β -catenin (arrows). (P) Overexpression of active, unphosphorylated -catenin in FNH (n 4) compared with NTL tissues (n 4).

Figure 2

 2 Figure 2 Validation of deregulated genes in well-defined subtypes of benign liver tumors Gene expression was examined by quantitative RT-PCR in different subgroups of samples: N, normal liver samples ( 6); FNH-like nodules n = ( 11); Cirr N, dysplastic or macroregenerative nodules in cirrhosis ( 8); Cirr, cirrhotic nodules ( 19); FNH ( 11); Inflam. Ad, n = n = n = n = inflammatory adenomas ( 5); HNF1 Ad, HNF1 -mutated adenomas ( 5); -cat. Ad, -catenin mutated adenomas ( 5). All results n = α

Figure 3

 3 Figure 3Expression of glutamine synthetase (GS) in FNH-like nodule and cirrhosis. (A) FNH-like nodule with a diameter of 9 mm (left side) and some surrounding regenerative cirrhotic nodules (S). The FNH-like nodule contains several septa that are surrounded by reactive ductules (arrows). (B) GS-staining of the FNH-like nodule and surrounding regenerative cirrhotic nodules. In the FNH-like nodules, there is staining of the reactive ductules (arrows), but parenchymal staining is very limited, in contrast to focal nodular hyperplasia. In the surrounding cirrhotic nodules, there is some patchy parenchymal staining, mostly related to venous structures (arrowheads).

Figure 4

 4 Figure 4 Schematization of glutamine synthetase staining in normal liver ( ) and in FNH ( ). GS staining is indicated in brown. CV, central vein; PT, A B portal tract; V, vein; DV, dystrophic vessels; fib, fibrosis (in grey); DR, ductular reaction; hep GS , glutamine synthetase-positive +

*

  Two patients presented simultaneously with alcohol intake and hepatitis B viral (HBV) or hepatitis C viral (HCV) infection. AAT1: alpha-1 antitrypsin deficiency.

  One could therefore hypothesize that the almost completely arterial inflow in FNH, without portal venous blood inflow, may cause β -catenin activation to extend much farther toward the inflow area than in normal liver or in cirrhotic liver, where there is still some portal venous inflow. The mechanism of -catenin activation in FNH remains to be elucidated since we did not identify any alteration of the main

	GSK3 , β AXIN1	and	APC	and positive regulators such as	, WNT FZD	(Frizzled),	DVL	and	( LRP Supplementary Fig. 1	and ). Moreover, 2
	in FNH we did not identify any activating mutations of -catenin. Furthermore a previous work showed the lack of β	AXIN1	and	APC
	inactivating mutations in those tumors ( ). 17

β β known regulators of the Wnt pathway. As such, we did not observe any variations in the expression level of negative regulators such as

Table 1

 1 Clinical Features of Included Samples and Patients

		Number of analyzed	Number of	Age (mean ±	Gender	Tumor diameter (mean SD, ±	Number of nodules	Risk factor of
		samples	patients	SD)	(F/M)	cm)	(mean)	cirrhosis
	Normal liver	6	6	48 14 ±	6/0			-
	FNH	11	8	41 7 ±	8/0	5.1 1.8 ±	2	-
	FNH-like	11	6	62 7 ±	2/4	1.2 0.5

Table 2

 2 Genes Encoding Extracellular Matrix Proteins Up-regulated in FNH

	Gene Symbol	Gene Name

Table 3

 3 Known Zonated and -Catenin Target Genes Deregulated in FNH significant; na: not analyzed; nd: not done; NTL: non tumor-livers

		β
	G e n e
	Symbol	Gene Name

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]

[START_REF] Paradis | Evidence for the polyclonal nature of focal nodular hyperplasia of the liver by the study of X-chromosome inactivation[END_REF]


Yannick Ladeiro

Gabrielle Couchy

Charles Balabaud

Paulette Bioulac-Sage

Laura Pelletier

Sandra Rebouissou

Jessica Zucman-Rossi

MicroRNA profiling in hepatocellular tumors is associated to clinical features and oncogene/tumor suppressor gene mutations

Keywords: Hepatocellular carcinoma, Hepatocellular adenoma, HBV, HCV, Alcohol consumption, Focal nodular hyperplasia, miRNA, Benign and malignant tumors, biomarkers, HNF1α, microarray, ß-catenin, transcriptome, liver cell adenoma

est destinée au dépôt

MicroRNA profiling in hepatocellular tumors is associated with clinical features and oncogene/tumor

suppressor gene mutations.

Introduction

Hepatocellular adenomas (HCA) and hepatocellular carcinomas (HCC) are liver tumors heterogeneous in nature with various risk factors, genetics alterations, and clinical features [START_REF] El-Serag | Hepatocellular carcinoma: recent trends in the United States[END_REF][START_REF] Laurent-Puig | Genetics of hepatocellular tumors[END_REF][START_REF] Villanueva | Genomics and signaling pathways in hepatocellular carcinoma[END_REF]. HCA are rare benign liver tumors usually developed in women after an oral contraceptive use [START_REF] Baum | Possible association between benign hepatomas and oral contraceptives[END_REF]. They are sometime difficult to discriminate from well-differentiated HCC or from focal nodular hyperplasia (FNH), a benign regenerative lesion. Recently, we defined a new molecular classification of HCA according to genetic (HNF1α or ß-catenin mutations) and phenotypic (inflammation) features [START_REF] Bioulac-Sage | Hepatocellular adenoma subtype classification using molecular markers and immunohistochemistry[END_REF][START_REF] Zucman-Rossi | Genotype-phenotype correlation in hepatocellular adenoma: new classification and relationship with HCC[END_REF]. We showed that ß-catenin activated HCA were more at risk of malignant transformation than the other HCA subtypes.

Hepatocellular carcinoma (HCC) is the most common malignant tumor in the liver and the third cause of death from cancer. Viral (HBV and HCV infection) and non-viral (Alcohol and aflatoxin B1 ingestion) agents are known to be associated to HCC occurrence. In each tumor, several oncogenic pathways are deregulated [START_REF] Laurent-Puig | Genetics of hepatocellular tumors[END_REF] and different subtypes of tumors are defined according to genetic and transcriptomic based classification [START_REF] Boyault | Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets[END_REF][START_REF] Laurent-Puig | Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis[END_REF]. Briefly, two main tumor groups related to chromosome stability/instability were identified and six subgroups (called G1 to G6) were found closely related to genetic (TP53 and ß-catenin mutations), activation of oncogenic pathways (AKT and IGF activation), clinical features (local invasion), and risk factors (HBV exposure).

Apart of genetic and epigenetic abnormalities modifying oncogene and tumor suppressor genes, deregulation of microRNAs (miRNAs) may also contribute to carcinogenesis. miRNAs are small endogenous non-coding RNAs (~22 nucleotides) responsible for a posttranscriptional expression regulation of targeted genes. They promote mRNA degradation and repress mRNA translation by sequence-specific interaction with the 3'-untranslated region of targeted mRNAs [START_REF] Bartel | MicroRNAs: genomics, biogenesis, mechanism, and function[END_REF]. Physiologically, miRNAs have been shown to be involved in several processes such as development, apoptosis, proliferation and differentiation [START_REF]The functions of animal microRNAs[END_REF][START_REF] Miska | How microRNAs control cell division, differentiation and death[END_REF].

Moreover their levels of expression are deregulated in a large number of tumors, some of them were found directly implicated in carcinogenetic mechanisms and several altered expression of miRNAs has previously been described in rat and human HCC [START_REF] Kutay | Downregulation of miR-122 in the rodent and human hepatocellular carcinomas[END_REF][START_REF] Meng | MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer[END_REF][START_REF] Murakami | Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues[END_REF].

We hypothesized that miRNAs deregulation could be related to specific sub-groups of hepatocellular tumors defined by either oncogene/tumors suppressor mutations, histological or clinical features. We first analyzed the expression of 250 miRNAs in a series of 46 malignant and benign hepatocellular tumors extensively characterized compared to 4 normal liver tissues. According to tumor classification, the most significant deregulated miRNAs were selected and their differential expression was validated in a second series of 43 tumors and 16 non-tumor liver tissues including cirrhosis and chronic hepatitis of various etiologies.

Patients and Methods

Liver Samples and Clinical Data

A whole series of 109 liver samples (55 HCC, 29 HCA, 5 Focal Nodular Hyperplasia, 30 non tumor liver samples) were collected from 93 patients surgically treated in Bordeaux from 1992 to 2004. Liver tissues were immediately frozen in liquid nitrogen and stored at -80°C until used for molecular studies. All the patients were recruited in accordance with French law and institutional ethical guidelines. Liver samples were fully clinically, histologically and genetically characterized and divided in two sets (Table 1). Hepatocellular adenomas (HCA) were classified according to the patho-molecular classification taking into account the HNF1α or ß-catenin mutations and the presence of inflammatory infiltrates [START_REF] Bioulac-Sage | Hepatocellular adenoma subtype classification using molecular markers and immunohistochemistry[END_REF][START_REF] Zucman-Rossi | Genotype-phenotype correlation in hepatocellular adenoma: new classification and relationship with HCC[END_REF].

HCC were classified according to the clinical, pathological and genetic features as classification previously described [START_REF] Boyault | Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets[END_REF].

Samples total RNAs extraction

Total RNAs extraction was performed using TRIZOL ® Reagent (Invitrogen). A 2 mm 3 frozen tissue sample was homogeneized in 800µl of TRIZOL ® Reagent using a Tissue Lyser Mixer Mill MM 300 (Quiagen). The extraction was performed according to Invitrogen protocol.

RNAs were quantified using NanoDrop ® ND-1000 and quality was tested by migration of 200 ng RNA on 0.8% agarose gel.

Quantitative RT-PCR

miRNAs quantitative RT-PCR was performed using TaqMan ® MicroRNA Human Assays and the ABI PRISM ® 7900HT System (Applied Biosystems). The first set of sample was analysed using a set of 250 miRNAs (Applied Biosystems, Supplementary Table 1). The expression of 22 miRNAs was further analysed in the validation set of tumors. For each sample and each miRNA tested, an independent retrotranscription using a specific reverse transcription primer was performed on 5 ng of totals RNAs according to manufacturer's protocol. Ribosomal 18s (R18S) was used for the normalization of expression data. The relative amount of miRNAs and all mRNAs studied in samples, was determined using the 2 HNF1α/TCF1, GLUL, LGR5, TBX3 and PANK1 mRNA expression were measured as previously described (15) using pre-designed primers and probe sets from Applied Biosystems (probe set numbers: Hs00167041_m1, Hs00374213_m1, Hs00374213_m1, Hs00255591_m1, Hs00379740_m1).

Clustering analysis

In the first set of 50 samples, quantitative RT-PCR expression results for the 250 tested miRNAs were filtered, 130 miRNAs were selected on the basis of a Ct value below 31 in at least 10% of the tested samples (Supplementary Table 1). These 130 selected miRNA were used for an unsupervised clustering analysis. Dendrograms classifying samples and genes, with average linkage methods using Pearson correlation as a distance metric, was obtained based on the expression profile of quantitative RT-PCR using the dChip 2006 software (http://biosun1.harvard.edu/complab/dchip/). Clusters were considered significantly associated with a feature when the P value was less than 0.05 with Pearson correlation.

miRNAs significantly differentially expressed according to clinical or genetic features identified in the unsupervised clustering analysis were selected for supervised analyses (Supplementary Table 3).

Statistical Analysis

Statistical analysis was performed using dChip and GraphPad Prism softwares.

Quantitative RT-PCR datas were first analysed for differences of expression between samples subgroups using dChip t-test and the nonparametric Mann-Whitney test. miRNAs with a fold change superior to 3 and statistically significant in the two test were selected for validation (Supplementary Table 4). All miRNAs expression data were presented as mean ± SEM in subgroups of tumors. Non-parametric Spearman test was used to compare quantitative values of expression. All reported P values were two-tailed and differences were considered significant when the P value was under 0.05.

Cell Culture and Transfections

HepG2 cells were cultured in Dulbecco's modified Eagle Medium High Glucose (Invitrogen) supplemented with 10% fetal bovine serum, and penicillin/streptomycin. Cells were transfected using Lipofectamine RNAiMax (Invitrogen), in six wells plates, according to the manufacturer's protocol. HNF1α extinction was performed with a siRNA targeting exon 8 and 9 of HNF1α (siRNA 3544, sens :GGUCUUCACCUCAGACACUtt ; antisens : AGUGUCUGAGGUGAAGACCtg, Ambion). Several siRNA concentrations were tested (0, 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 5, 10, 50 nM) in order to modulate the HNF1α expression. Maximal inhibition of HNF1α protein was observed 72h post transfection (see Supplementary Fig. 1). BLOCK-iT Alexa Fluor Red Fluorescent Oligo (Invitrogen) was used as transfected dsRNA control. Western blot was performed using HNF1α antibody sc-6548 (Santa Cruz Biotechnology). At this time, cells were lysed in TRIZOL ® Reagent (Invitrogen)

for RNA extraction.

Results

Global profiling of miRNAs is related to well defined-subgroups of hepatocellular tumors

We analyzed the expression level of 250 miRNAs in the first set of samples that included 28 HCC, 13 HCA and 5 FNH compared to 4 normal liver tissues. An unsupervised analysis was performed after selection of the 130 miRNAs expressed in at least 10% of the samples (Fig. 1A). It showed a significant partition between non-tumor and tumor samples (P<0.01), benign tumors were divided in three clusters significantly parted from HCC (P<0.01). Adenomas were grouped in two distinct clusters corresponding to HNF1α mutated (P<0.01) and

Inflammatory HCA (P<0.01) respectively. Focal Nodular Hyperplasias were located on a third cluster (P<0.01) distinct from HCA. In contrast, ß-catenin mutated HCA were spread between two HCA clusters. HCC samples were partitioned in three different clusters significantly characterized either by a specific clinical risk factor (HBV, P<0.001; Alcohol intake, P<0.05)

or by a gene mutation (ß-catenin, P<0.01). Two supervised analyses were performed to classify separately benign and malignant tumors using the list of genes differentially expressed in each clinical or genetic features significantly associated to clusters in the unsupervised analysis (Fig. 1B and C, Supplementary Table 3).

Specific miRNA deregulations related to benign or malignant features.

As shown above, miRNA profiling could classify hepatocellular tumors according to pathological, clinical and genetic features. Thus, we aimed to identify and validate, in a second series of tumors, miRNA with a deregulated expression the most closely associated to such specific features. First, we identified miRNAs specifically deregulated in tumors when compared with non-tumor liver samples, and several were validated (Fig. 2). Three miRNAs were found significantly up-regulated (miR-224) or down-regulated (miR-422b and mir-122a) in both benign and malignant tumors (Fig. 2A). Second, we validated miRNAs with a level of expression significantly different between malignant and benign tumors. Corresponding to these criteria, miR-200c and miR-203 were under-expressed in benign tumors (Fig. 2B) while mir-21, miR-222 and miR-10b were significantly over-expressed in HCC (Fig. 2C). Finally, miR-224, that was differentially expressed between tumor and non-tumor liver tissues, was also significantly over-expressed in HCC when compared to benign tumors (Fig. 2A).

miRNAs deregulation related to tumor suppressor/oncogene mutations in HCA

We searched for miRNAs deregulation that could characterize specific subgroups of benign tumors. While unsupervised clustering enabled to correctly classify inflammatory HCA and FNH in two specific clusters, no miRNA displayed a sufficient fold change of expression to grant further validation. In contrast, a robust under expression of miR-375 was validated in ßcatenin-mutated HCA when compared to the other HCA (fold change: -14.2, P<0.001 in overall samples, Fig. 3A). Among 15 miRNAs significantly deregulated in HNF1α-mutated HCA, we validated the under expression of miR-107 in HNF1α mutated HCAs when compared to the other HCA (fold change: -3.6; P<0.001, in overall samples, Fig. 3B). This observation suggested that the HNF1α transcription factor could regulate, directly or not, the expression of miR-107. To test this hypothesis, we performed an HNF1α silencing using several doses of specific siRNA targeting HNF1α in HepG2 hepatocellular cell line and we assayed the consequences on miR-107 expression level. The silencing of HNF1α significantly correlated with the extinction of miR-107 in a dose-effect manner (Spearman r: -0.92; P<0.0001, Fig. 3C).This result suggested that HNF1α may control the expression of miR-107. MiR-107 is located in the intron 5 of PANK1 gene coding for the pantothenate kinase 1. We tested if the miR-107 and PANK1 expression could be co-regulated. We showed that PANK1 mRNA expression was not affected by HNF1α silencing, suggesting that regulation of miR-107 by HNF1α was independent of PANK1 status (Supplementary Fig. 2).

According to the hypothesis of a direct regulation of miR-107 by HNF1α, the in silico search (using Genomatix softwares) revealed two potential HNF1α binding sites located -1852 and -1543 base pairs upstream of the pre-miRNA initiation site. However, the direct role of HNF1α remained to be fully demonstrated by a functional analysis of the putative promoter region.

miRNAs deregulation related to genetic features and risk factors in HCC

As shown in Fig. 1, miRNAs deregulations were associated with alcohol consumption and HBV infection in HCC. We validated miR-126* under-expression in HCC related to alcohol abuse when compared to the other HCC (fold change: 2.7; P<0.001, in overall samples, Fig. 4A). Similarly, we also validated a significant over-expression of miR-96 in HBV related HCC when compared to non-infected tumors (fold change: 5.7; P<0.01, in overall samples, Fig. 4B). In contrast, the level of expression of miR-126* and miR-96 did not vary significantly among the various subtypes of non-tumor liver tissues (Supplementary Fig. 3). In particular, no significant variation was observed in cirrhosis or in HBV related non-tumor samples. As suggested by the unsupervised clustering, no miRNA were found significantly deregulated in HCCs HCV infected versus non-infected HCCs. Similarly, no significant difference was found according to the level of cell differentiation.

microRNAs deregulations were also related to the ß-catenin oncogene mutation in the unsupervised clustering analysis (Fig 1). As in ß-catenin mutated HCAs, we validated the extinction of miR-375 in HCC characterized by a ß-catenin activating mutation (fold change: -44; P<0.001, in overall samples, Fig. 4C). We also found a significant anti-correlation between the level of expression of miR-375 and three genes regulated by ß-catenin, TBX3,

LGR5 and GLUL in HCA as well in HCC (Fig. 4D). Finally, no significant differences in miRNA expression was observed in non-tumor liver tissues according to HCV or HBV infection, alcohol intake and cirrhosis.

Discussion

Aberrant patterns of miRNAs expression have already been described in several hematological and solid tumors, including HCC (see for review [START_REF] Calin | MicroRNA signatures in human cancers[END_REF]). In the present work, we have identified, for the first time, miRNAs expression patterns that can unambiguously differentiate benign, malignant and several subtypes of hepatocellular tumors according to specific risk factors, oncogene and tumor suppressor gene mutations. These new findings and the most significant deregulated miRNAs are summarized in Fig. 5.

In the present work, several miRNAs previously associated with malignancy were identified as significantly deregulated in hepatocellular subgroup of tumors. Most notably, we showed that miR-224 previously identified as over-expressed in HCC ( 14) was also upregulated in benign hepatocellular tumors, including adenomas and focal nodular hyperplasia that are regenerative polyclonal nodules ( 17), but at a lesser degree. Similarly, miR-122a has been previously found down-regulated in human and rodent HCC [START_REF] Kutay | Downregulation of miR-122 in the rodent and human hepatocellular carcinomas[END_REF]; this microRNA, highly expressed in the liver, is essential for HCV replication [START_REF] Jopling | Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA[END_REF] and it modulates cyclin G1 expression in HCC cell-line [START_REF] Gramantieri | Cyclin G1 is a target of miR-122a, a microRNA frequently down-regulated in human hepatocellular carcinoma[END_REF]. Here, we showed that miR-122a is also down-regulated in both human benign and malignant hepatocellular tumors. Thus, concomitant analysis of benign and malignant tumors enabled us to associate deregulation of expression of miR-224, 422b and 122a with common early events in hepatocellular tumorigenesis.

Only few miRNAs deregulations have been previously described in benign tumors [START_REF] Bottoni | Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas[END_REF][START_REF] Weber | A limited set of human MicroRNA is deregulated in follicular thyroid carcinoma[END_REF]. Interestingly, in pituitary adenoma, miRNAs pattern of expression were significantly related to tumor subtype according to histotypes and secreting characteristics [START_REF] Bottoni | Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas[END_REF]. In thyroid nodules, most of the miRNAs deregulated in carcinomas have demonstrated also an altered expression in adenomas [START_REF] Weber | A limited set of human MicroRNA is deregulated in follicular thyroid carcinoma[END_REF]. Here, we identified miRNA (miR-200c and miR-203) with an expression specifically down-deregulated in benign hepatocellular tumors when compared to normal liver tissues or HCC. A lower expression of miR-203 was also previously described in non-secreting pituitary adenomas when compared with growth hormone pituitary secreting adenomas [START_REF] Bottoni | Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas[END_REF]. In hepatocellular tumors, we showed that miRNAs profile of expression in benign tumor differed according to tumor subtype defined by oncogene and tumor suppressor gene mutation or by histological phenotype. These results enlightened the importance of refining classification based on molecular and meticulous characterization of the tumor phenotypes, including for benign tumors.

Here we validated a significant over-expression of miR-10b, miR-21 and miR-222 in HCC when compared with benign tumors and non-tumor liver tissues. Interestingly, overexpression of these three miRNAs was previously identified as important key factors to promote cell invasion or proliferation in various epithelial tumor types. Recently, Ma and collaborators identified miR-10b as a microRNA highly expressed in metastatic breast tumors that promotes cell migration and invasion [START_REF] Ma | Tumour invasion and metastasis initiated by microRNA-10b in breast cancer[END_REF]. They showed that miR-10b expression is induced by twist allowing miR-10b to inhibit translation of the messenger RNA encoding homeobox D10, resulting in increased expression of a well-characterized pro-metastatic gene, RHOC. miR-222 over expression promotes cell proliferation by targeting p27Kip1 in prostate and thyroid carcinoma [START_REF] Galardi | miR-221 and miR-222 expression affects the proliferation potential of human prostate carcinoma cell lines by targeting p27Kip1[END_REF][START_REF] Visone | MicroRNAs (miR)-221 and miR-222, both overexpressed in human thyroid papillary carcinomas, regulate p27Kip1 protein levels and cell cycle[END_REF]. Finally, miR-21 over-expression down-regulates the Pdcd4 tumor suppressor and stimulates invasion, intravasation and metastasis in colorectal cancer [START_REF] Asangani | MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer[END_REF]. Higher over-expression of miR-21 was also previously associated with poorly differentiated HCC and it participates to down-regulate the level of expression of PTEN [START_REF] Meng | MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer[END_REF].

Our results identified the deregulation of two miRNAs, miR-96 and miR-126*, as significantly associated with different risk factors of HCC, HBV infection and alcohol consumption, respectively. To our knowledge, miR-96 and miR-126* have not been directly implicated in pathways related to carcinogenesis. Because no significant variation in the expression of these two microRNAs was observed in non-tumor tissues according to HBV infection or alcohol intake, it suggests that microRNAs deregulations are directly linked to carcinogenetic processes specifically induced by these two risk factors. In fact, using transcriptomic and genetic global analyses we have previously shown that carcinogenesis mechanisms in HBV related HCC were particular, closely related to chromosome instability, AKT activation and fetal features in gene expression [START_REF] Boyault | Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets[END_REF][START_REF] Laurent-Puig | Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis[END_REF]. In contrast, microRNA profiling is the first global genomic approach that enabled to discriminate HCC related to alcohol abuse from the other tumors. Consequences of the miR-126* deregulation remained to be explored in alcohol related tumors and HCC associated to other risk factors.

For the first time, we identified miRNAs deregulation in hepatocellular human tumors closely related to specific oncogene or tumor suppressor gene mutations. However, in cell line models, some miRNAs have been shown to be under control of oncogene [START_REF] Schulte | MYCN regulates oncogenic MicroRNAs in neuroblastoma[END_REF] or tumor suppressor [START_REF] He | microRNAs join the p53 network--another piece in the tumour-suppression puzzle[END_REF]. The low expression of miR-375 in both HCA and HCC mutated for ß-catenin together with the highly significant anti-correlation between miR-375 level of expression and genes targeted by ß-catenin argue for a direct link between β-catenin activation and repression of miR-375. This is an important result since ß-catenin is a major oncogene activated in 30 to 40% of the HCC. It increases the complexity of regulation of expression by ß-catenin adding yet another layer of control with microRNA to the RNA and translation control level. In HCA, we showed a specific under-expression of miR-107 in HNF1α inactivated samples. It has been suggested by in silico analysis that miR-107 could regulate the expression level of several predicted target proteins involved in lipid metabolism. Underexpression of miR-107 is predicted to promote acetyl-coA and lipids levels [START_REF] Wilfred | Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways[END_REF]. Interestingly, we previously showed that HNF1α inactivated HCA exhibited a steatotic phenotype resulting from an activation of lipogenesis [START_REF] Rebouissou | HNF1alpha inactivation promotes lipogenesis in human hepatocellular adenoma independently of SREBP-1 and carbohydrate-response element-binding protein (ChREBP) activation[END_REF]. Thus, we can hypothesize that miR-107 under-expression specifically observed in HNF1α inactivated HCA may contribute to the steatotic phenotype and lipid accumulation. Our data suggest that this miRNA could be regulated by HNF1α like the recently identified miR-194 during intestinal epithelial Caco-2 cell line differentiation [START_REF] Hino | Regulatory interaction of HNF1-alpha to microRNA-194 gene during intestinal epithelial cell differentiation[END_REF].

In conclusion, this work revealed an additional layer of gene alterations to better characterize hepatocellular tumors. It allowed the identification of miRNAs deregulation closely correlated to clinical and histological subtypes of tumors as well as to oncogene and tumor suppressor gene mutations. The gene and protein networks directly targeted and affected by these miRNAs that participate to tumorigenesis remain to be extensively explored.

Figures:

Fig. Table 3) in the 28 HCC samples. The different significant tumors subgroups are indicated in color. The heat map, in the lower panel, shows the expression of miRNAs across samples.

For each miRNA probeset, data are median-centered (black), with the lowest and highest intensity values in green and red, respectively.

  Fig. 1. (A) Unsupervised hierarchical clustering of miRNA profiling in 50 liver samples. The dendrogram in the upper panel was obtained on the basis of the quantitative RT-PCR data of the 130 miRNAs expressed in at least 10% of the liver samples that included 4 normal livers, 28 HCC, 13 HCA and 5 FNH. (B) Supervised clustering analysis of miRNA significantly expressed according to each sub-group (see Supplementary Table3) in the 18 benign

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  ) in the 18 benign tumors samples. (C) Supervised clustering analysis of miRNA differentially expressed in βcatenin mutated HCC, alcohol related HCC and HBV infected HCC (see Supplementary

Fig. 2 .Fig. 3 .

 23 Fig. 2. miRNAs the most significantly deregulated in hepatocellular tumors. (A) miR-224 was found over-expressed, miR-422b and miR-122a were found down regulated in tumors when compared to non tumor liver. (B) miR-200c and 203 were found under-expressed in benign tumors when compared to HCC. (C) miR-21, 222 and 10b were found over-expressed in HCC when compared to benign tumors. FS=First Set of tumors, VS=Validation Set of tumors. B=Benign tumors (FS n=18; VS n=12); HCC=Hepatocellular Carcinomas (FS n=28; VS n=22); NL=Normal Livers (n=4), NTL=Non Tumor Liver (n=16, including cirrhosis n=9; HBV infected n=6; HCV infected samples n=5; alcohol liver disease n=5). Data are presented as the mean fold compared to the mean value in non tumor samples ± SEM. Statistical analysis was performed using Mann-Whitney test, *P<0.05; **P<0.01; ***P<0.001.

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 2 (C) Correlation between miR-107 and HNF1α transcript level of expression in HepG2 cell line. The indicated miR-107 expression is the observed miR-107 expression divided by the level of miR-107 expression in basal HNF1α condition. Statistical analysis by Spearman test.

Fig. 4 .

 4 Fig. 4. miRNAs deregulation associated to risk factors and oncogene mutation in HCC. (A) miR-126* was under-expressed in HCC related to alcohol abuse (Al+; FS n=7; VS n=6) vs others HCC (Al-; FS n=10, VS n=8). (B) miR-96 was over-expressed in Hepatitis B Virus infected HCC (HBV+; FS n=9; VS n=9) versus non infected HCC (HBV-: FS n=10; VS n=8). (C) miR-375 is down expressed in β-catenin mutated HCC (β-c Mut; FS n=7, VS n=8) versus non mutated (β-c Wt; FS n=21, VS n=14) HCC. For Normal Liver (NL) and Non Tumor Liver (NTL) distribution, see Fig. 2. (D) Correlation between miR-375 and three genes regulated by ß-catenin in hepatocellular tumors. FS=First Set of tumors, VS=Validation Set of tumors.

Fig. 5 .

 5 Fig. 5. miRNAs expression is associated to different subgroups of hepatocellular tumors. HCC: Hepatocellular Carcinomas; HCA: Hepatocellular Adenomas; : over-expression; ✁ : under-expression

We thank Philippe Bois for critical reading of this manuscript. We thank Aurélien de Reynies for statistical help. This work was supported by the ANRS, Inserm (Réseaux de recherche clinique et réseaux de recherche en santé des populations), and the program "Carte d'identité des tumeurs" initiated, developed and funded by the Ligue Nationale Contre le Cancer. YL is supported by an ANRS doctoral fellowship. JZR is supported by a "contrat d'interface" between Inserm and CHU Bordeaux.

Supplementary Figures:

Supplementary Fig