Ming-Fei Lang

Su Yang

Chunnian Zhao

Guoqiang Sun

Kiyohito Murai

Xiwei Wu

Jinhui Wang

Hanlin Gao

Christine E Brown

Xiaoxuan Liu

Jiehua Zhou

Ling Peng

John J Rossi

Yanhong Shi
email: yshi@coh.org

Genome-Wide Profiling Identified a Set of miRNAs that Are Differentially Expressed in Glioblastoma Stem Cells and Normal Neural Stem Cells

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

According to the World Health Organization (WHO) classification of tumors, a grading scheme, which represents a malignancy scale and a key factor influencing the choice of therapies, has been successfully applied to astrocytomas, the most common type of glioma [START_REF] Louis | The 2007 WHO classification of tumours of the central nervous system[END_REF]. The WHO defines pilocytic astrocytoma as grade I, diffuse astrocytoma as grade II, anaplastic astrocytoma as grade III, and glioblastoma as grade IV, the most malignant grade [START_REF] Louis | The 2007 WHO classification of tumours of the central nervous system[END_REF]. Glioblastoma is the most common and aggressive primary brain tumor with median survival time of 14 months after diagnosis [START_REF] Louis | The 2007 WHO classification of tumours of the central nervous system[END_REF]. Until now, no effective treatment has been developed for glioblastoma patients. The goal of our research is to identify novel molecular targets for this malignant tumor, and thus glioblastoma is the main interest of this study. Recent studies have led to the hypothesis that glioblastomas are maintained by a small population of cancer stem cells that retain stem cell properties, are highly tumorigenic, and display increased resistance to radiation and chemotherapy [START_REF] Singh | Identification of human brain tumour initiating cells[END_REF][START_REF] Bao | Glioma stem cells promote radioresistance by preferential activation of the DNA damage response[END_REF][START_REF] Godlewski | MicroRNAs and glioblastoma; the stem cell connection[END_REF]. These treatmentresistant tumor cell subpopulations are the cell populations that effective therapies must target [START_REF] Godlewski | MicroRNAs and glioblastoma; the stem cell connection[END_REF].

miRNAs are short 20-22 nucleotide RNA molecules that are expressed in a tissue-specific and developmentally-regulated manner and function as negative regulators of gene expression in a variety of eukaryotes. miRNAs are involved in numerous cellular processes including development, proliferation, and differentiation [START_REF]The functions of animal microRNAs[END_REF][START_REF] Bartel | MicroRNAs: genomics, biogenesis, mechanism, and function[END_REF][START_REF] Shi | MicroRNA regulation of neural stem cells and neurogenesis[END_REF]. Increasing evidence has linked miRNAs to cancer [START_REF] Esquela-Kerscher | Oncomirs -microRNAs with a role in cancer[END_REF]. miRNAs are important regulators of many key pathways implicated in tumor pathogenesis [START_REF] Asadi-Moghaddam | Potential role of miRNAs and their inhibitors in glioma treatment[END_REF]. They can function as either oncogenes or tumor suppressors in various tumors [START_REF] Cheng | Potential therapeutic implications of cancer stem cells in glioblastoma[END_REF].

Recently, miRNAs have been shown to be differentially expressed in glioblastoma tissues compared to normal brain tissues. For example, miRNA 21 is overexpressed in glioblastoma tissues, relative to surrounding normal brain tissues [START_REF] Conti | miR-21 and 221 upregulation and miR-181b downregulation in human grade II-IV astrocytic tumors[END_REF]. miR-26a is also amplified in glioblastoma tissues. By targeting the tumor suppressor Pten, overexpression of miR-26a facilitates tumorigenesis and predicts a poor survival [START_REF] Huse | The PTEN-regulating microRNA miR-26a is amplified in high-grade glioma and facilitates gliomagenesis in vivo[END_REF][START_REF] Kim | Integrative genome analysis reveals an oncomir/oncogene cluster regulating glioblastoma survivorship[END_REF]. On the other hand, miR-124, miR-137 and miR-451 exhibit reduced expression in malignant glioblastoma tissues relative to normal brain tissues [START_REF] Silber | miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells[END_REF][START_REF] Gal | MIR-451 and Imatinib mesylate inhibit tumor growth of Glioblastoma stem cells[END_REF]. The expression of these miRNAs is also reduced in glioblastoma stem cells relative to bulk tumor cells. Overexpression of these miRNAs in glioblastoma stem cells inhibits cell proliferation and induces neural differentiation, suggesting a tumor suppressor role for these miRNAs. These studies suggest that some miRNAs may be used as therapeutic agents for targeting glioblastoma stem cells. However, brain tumor stem cells share a core developmental program with normal neural stem cells [START_REF] Cheng | Potential therapeutic implications of cancer stem cells in glioblastoma[END_REF]. Optimal therapies should target tumor stem cells, but spare normal stem cells. Therefore, identifying miRNAs that are differentially expressed in glioblastoma stem cells and normal neural stem cells becomes essential for the development of optimal miRNA-based therapies for glioblastoma patients.

In this study, we present the results of a genome-wide miRNA expression profiling in human glioblastoma stem cells and normal neural stem cells using combined miRNA microarray and deep sequencing analyses. This study led to the identification of eight miRNAs that are substantially up-regulated and two miRNAs that are significantly down-regulated in glioblastoma stem cells, relative to normal neural stem cells. Differential expression of four of these miRNAs, 2 up-regulated and 2 down-regulated, was further validated by real-time RT-PCR in both glioblastoma stem cells and glioblastoma patient tumor tissues. Moreover, we demonstrate that these up-regulated or down-regulated miRNAs inhibit the expression of genes that are involved in tumor suppression or tumorigenesis, respectively.

Results

Differential miRNA expression in glioblastoma stem cells and normal neural stem cells

In order to identify miRNAs that are differentially expressed in glioblastoma stem cells and normal neural stem cells, we established three primary glioblastoma stem cell lines and three normal human neural stem cell lines to determine if there were significant differences in miRNA expression in tumor stem cells from normal stem cells. Human primary glioblastoma stem cells were derived from newly diagnosed glioblastoma multiforme IV patients and cultured in DMEM/F12 media supplemented with epithelial growth factor (EGF), fibroblast growth factor (FGF), and B27 supplement. Human normal neural stem cells were derived from normal human brain tissues and cultured in the same media. Both glioblastoma stem cells and normal neural stem cells grew as neurospheres under the culture condition (Fig. 1A). Both types of cells are multipotent, having the ability to differentiate into Tuj1-positive neurons and GFAP-positive astrocytes when induced into differentiation using fetal bovine serum and all-trans retinoic acid (Fig. 1B). However, the glioblastoma stem cells were able to generate tumors when transplanted to the immunodeficient NSG mice (Fig. 1C), whereas the human neural stem cells did not (data not shown).

Combined microarray and deep sequencing analyses were performed to determine the expression profile of miRNAs in glioblastoma stem cells and normal neural stem cells. Total RNAs were prepared from both glioblastoma stem cells and neural stem cells for miRNA microarray analysis. In microarray analysis, we identified 10 miRNAs that are more than 5-fold up-regulated in glioblastoma stem cells and 8 miRNAs that are more than 5-fold down-regulated in glioblastoma stem cells, relative to neural stem cells (Table 1). The differentially expressed miRNAs that exhibit more than 1.5-fold difference in the expression between glioblastoma stem cells and neural stem cells were shown in Table S1.

Using an Illumina Genome Analyzer II (GAII) sequencing system, we performed the whole-genome small RNA sequencing in glioblastoma stem cells and neural stem cells. Significantly more miRNAs were detected to be differentially expressed in glioblastoma stem cells and neural stem cells in deep sequencing analysis. For example, deep sequencing analysis revealed 105 miRNAs that were up-regulated more than 5-fold in glioblastoma stem cells. However, microarray analysis revealed only 10 miRNAs showing more than 5-fold increase in glioblastoma stem cells. Interestingly, 8 out of the 10 miRNAs that were up-regulated more than 5-fold in microarray analysis also exhibited significantly increased expression in deep sequencing analysis (Table 2). Two of the miRNAs that had more than 5-fold decrease of expression in microarray analysis also showed more than 5-fold reduction of expression in deep-sequencing analysis (Table 2). Thus, combined microarray and deep sequencing analyses allowed us to identify a set of miRNAs that are differentially expressed in glioblastoma stem cells and normal neural stem cells.

Validation of the differentially expressed miRNAs using real-time RT-PCR

The distinct expression of these miRNAs in glioblastoma stem cells and neural stem cells was further validated using real-time RT-PCR analysis. RT-PCR results of the top three miRNAs that are up-regulated in glioblastoma stem cells in both microarray and deep sequencing analyses (Table 2) are shown in Fig. 2A-C. All three miRNAs showed a significant up-regulation in the three primary glioblastoma stem cell lines (GSC1-3) tested, compared to three lines of normal neural stem cells. miR-10a revealed a dramatic increase of expression in all three glioblastoma stem cell lines tested, with more than 100-fold up-regulation of expression in two of the glioblastoma stem cell lines (Fig. 2A). miR-10b exhibited even higher expression in glioblastoma stem cell lines GSC1 and GSC3, with up to 2,505-fold increase of expression in GSC1 (Fig. 2B). miR-140-5p also displayed significant increase of expression in all three glioblastoma stem cell lines tested, although with much lower fold induction (Fig. 2C).

There are two miRNAs that are down-regulated more than 5fold in glioblastoma stem cells in both microarray and deep sequencing analyses. The expression of these two miRNAs was also validated using real-time RT-PCR assays. Both miR-124 and miR-874 exhibited reproducible decrease of expression in all three glioblastoma stem cell lines tested, compared to normal neural stem cells (Fig. 2D,E). Specifically, the relatively new miRNA miR-874 that has not been studied extensively exhibited a significant reduction of expression in all three glioblastoma stem cell lines, with more than 20-fold reduction in two of the glioblastoma stem cell lines GSC1 and GSC3 (Fig. 2E).

In addition to cultured cells, we next tested whether the miRNAs that are differentially expressed in glioblastoma stem cells and neural stem cells exhibit a distinct expression pattern in normal and glioblastoma brain tissues. For this purpose, RNAs were isolated from 9 grade IV glioblatoma multiforme brain tissue samples and 4 non-tumor normal brain tissue samples. Real-time RT-PCR analyses were performed to detect the expression of two up-regulated miRNAs and two down-regulated miRNAs. Consistent with the results from tumor stem cells, miR-10a exhibited a substantial increase of expression in most glioblastoma tissues (Fig. 3A). miR-10b also exhibited a dramatic up-regulation of expression in all of the glioblastoma tissues tested, with an average increase of 142-fold (Fig. 3B). For miRNAs that were downregulated in glioblastoma stem cells, both miR-124 and miR-874 displayed a significant decrease of expression in most of the glioblastoma tissues tested, compared to their average expression in normal brain tissues (Fig. 3C,D).

Target identification of the differentially-expressed miRNAs

By using Targetscan algorithm [START_REF] Lewis | Prediction of mammalian microRNA targets[END_REF], we identified CUB and SUSHI multiple domain protein 1 (CSMD1) as a candidate target for miR-10a and miR-10b, the most highly up-regulated miRNAs in glioblastoma stem cells in our profiling analyses. CSMD1 is a tumor suppressor gene that maps to chromosome 8p23, a region deleted in many tumor types [START_REF] Kamal | Loss of CSMD1 expression is associated with high tumour grade and poor survival in invasive ductal breast carcinoma[END_REF]. Sequence analysis revealed that the seed region of both miR-10a and miR-10b could form complementary base pairs with the 39 untranslated region (39 UTR) of human and mouse CSMD1 mRNAs (Fig. 4A,B). To demonstrate a direct interaction between the 39 UTR of CSMD1 and miR-10 (miR10a and miR-10b), we inserted the 39 UTR region of human CSMD1 that contains the putative miR-10 recognition sites and flanking sequences downstream of a Renilla luciferase reporter gene into a siCheck vector. RNA duplexes of mature miR-10a or miR-10b were transfected into human embryonic kidney HEK293 cells along with the reporter gene. Significant repression of the reporter gene was observed in both miR-10a and miR-10b-transfected cells (Fig. 4C,D). Mutation of the miR-10 targeting sites abolished the repression (Fig. 4C,D). Furthermore, treatment of the inhibitors of miR-10a and miR-10b reversed the inhibitory effect of miR-10a and miR-10b on the luciferase reporter activity, respectively (Fig. 4C,D). These results suggest that both miR-10a and miR-10b repress CSMD1 expression through the predicted targeting sites in CSMD1 39 UTR.

Since miR-10a and miR-10b are both up-regulated in glioblastoma stem cells relative to neural stem cells, we next examined the expression of CSMD1 in both cell types. Dramatic reduction of CSMD1 mRNA expression was detected in glioblastoma stem cells by RT-PCR analysis, compared to neural stem cells (Fig. 4E), consistent with the observation that CSMD1 expression is repressed by miR-10 (Fig. 4C,D). The homeobox transcription factor HOXD10 was identified as a tumor suppressor gene targeted by miR-10b in breast cancers recently [START_REF] Ma | Tumour invasion and metastasis initiated by microRNA-10b in breast cancer[END_REF]. We showed here that the HOXD10 mRNA expression is also dramatically reduced (.20-fold) in glioblastoma stem cells examined, compared to normal neural stem cells (Fig. 4F). Together, these results suggest that miR-10 targets the expression of tumor suppressor genes, CSMD1 and HOXD10, in glioblastoma stem cells.

Furthermore, using the Targetscan algorithm, we predicted oncogenes NRAS and PIM3 as putative target genes of miR-124, one of the down-regulated miRNAs in glioblastoma stem cells. NRAS is a small guanine-nucleotide binding protein and one of the three RAS (KRAS, NRAS, HRAS) isoforms [START_REF] Kiessling | High-throughput mutation profiling of CTCL samples reveals KRAS and NRAS mutations sensitizing tumors toward inhibition of the RAS/RAF/MEK signaling cascade[END_REF]. The RAS signaling pathway plays a crucial role in many cancers by regulating cell proliferation, differentiation, and survival [START_REF] Kan | Diverse somatic mutation patterns and pathway alterations in human cancers[END_REF]. Using Targetscan algorithm, miR-124 was predicted to have a targeting site at the 39 UTR of the NRAS gene. This targeting site is conserved in human, mouse, and dog NRAS (Fig. 5A). To validate the targeting of NRAS by miR-124, we made a luciferase reporter construct with human NRAS 39 UTR containing the predicted miR-124 targeting site and the flanking sequences inserted into the 39UTR of a Renilla luciferase reporter gene in a siCHECK vector. Transfection of miR-124 RNA duplexes led to significant repression of the reporter gene (Fig. 5B). Mutation of the putative miR-124 targeting site abolished the repression (Fig. 5B). Furthermore, treatment with a miR-124 inhibitor reversed the inhibitory effect of miR-124 on the luciferase reporter activity (Fig. 5B). These results suggest that miR-124 represses NRAS expression through the predicted targeting site in NRAS 39UTR.

Next, we tested whether miR-124 targets NRAS expression in glioblastoma stem cells. Mature miR-124 RNA duplexes were introduced into GSC1 cells using a cationic triethanolamine-core polyamidoamine (PAMAM) dendrimer-mediated small RNA delivery system [START_REF] Zhou | PAMAM dendrimers for efficient siRNA delivery and potent gene silencing[END_REF][START_REF] Zhou | Systemic administration of combinatorial dsiRNAs via nanoparticles efficiently suppresses HIV-1 infection in humanized mice[END_REF]. A control RNA duplex was included as a negative control. NRAS expression levels were examined by Western blot analysis. Reduction of NRAS protein level was detected in miR-124-transfected cells. Co-transfection of a miR-124 RNA inhibitor abolished the inhibitory effect of miR-124 on NRAS expression (Fig. 5C). This result indicates that miR-124 down-regulates endogenous NRAS expression in glioblastoma stem cells. We also examined the expression of NRAS in glioblastoma stem cells and neural stem cells, where miR-124 exhibits differential expression. A significant increase of NRAS mRNA expression was detected in glioblastoma stem cells, compared to neural stem cells (Fig. 5D). The inverse expression pattern of NRAS and miR-124 is consistent with the observation that NRAS expression is repressed by miR-124 (Fig. 5B,C).

A putative targeting site of miR-124 was also identified in the 39 UTR of both human and mouse PIM3, a proto-oncogene with serine/threonine kinase activity (Fig. 5E). PIM3 has been shown to promote tumor cell growth through modulating cell cycle regulators [START_REF] Wu | Accelerated hepatocellular carcinoma development in mice expressing the Pim-3 transgene selectively in the liver[END_REF][START_REF] Brault | PIM serine/ threonine kinases in the pathogenesis and therapy of hematologic malignancies and solid cancers[END_REF]. To validate the targeting of PIM3 by miR-124, we made a luciferase reporter construct with human PIM3 39 UTR containing the predicted miR-124 targeting site and the flanking sequences inserted into the 39UTR of a Renilla luciferase reporter gene. Transfection of miR-124 led to significant repression of the reporter gene and mutation of the putative miR-124 targeting site abolished the repression (Fig. 5F). Furthermore, treatment with a miR-124 inhibitor reversed the inhibitory effect of miR-124 on the luciferase reporter activity (Fig. 5F). These results suggest that miR-124 represses PIM3 expression through the predicted targeting site in its 39 UTR.

To test whether miR-124 targets PIM3 expression in glioblastoma stem cells, mature miR-124 RNA duplexes were introduced into GSC1 cells using the dendrimer-mediated delivery system [START_REF] Zhou | PAMAM dendrimers for efficient siRNA delivery and potent gene silencing[END_REF][START_REF] Zhou | Systemic administration of combinatorial dsiRNAs via nanoparticles efficiently suppresses HIV-1 infection in humanized mice[END_REF]. A control RNA duplex was included as a negative control. Reduction of PIM3 protein level was detected in miR-124transfected cells by Western blot analysis. Co-transfection of a miR-124 RNA inhibitor abolished the inhibitory effect of miR-124 on PIM3 expression (Fig. 5G). This result indicates that miR-124 down-regulates endogenous PIM3 expression in glioblastoma stem cells. Moreover, a significant increase of PIM3 mRNA expression was detected in the glioblastoma stem cells tested, compared to neural stem cells (Fig. 5H), further supporting the idea that miR-124 represses PIM3 expression.

It is increasingly clear that miRNAs are important regulators of key signaling pathways implicated in tumorigenesis. Using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we compared the predicted targets of miRNAs that showed more than 5-fold up-regulation or down-regulation in glioblastoma stem cells in our microarray analysis. Seven of the ten miRNAs that were up-regulated more than 5-fold in glioblastoma stem cells are predicted to have components of the p53 pathway as common targets (Fig. 6A). The p53 pathway has been shown to be involved in cell cycle arrest, apoptosis, inhibition of cell migration, inhibition of angiogenesis, and affect genomic stability [START_REF] Junttila | p53-a Jack of all trades but master of none[END_REF]. In contrast, five out of eight miRNAs that exhibited more than 5-fold down-regulation in glioblastoma stem cells were predicted to target components of the IGF pathway (Fig. 6B) that has been implicated in promoting cell growth, survival and migration [START_REF] Clayton | Growth hormone, the insulin-like growth factor axis, insulin and cancer risk[END_REF].

Discussion

In the present study, we investigated genome-wide miRNA expression in tumor stem cell populations of glioblastoma, the most frequent and malignant primary brain tumor. In spite of recent improvement of surgical and radiotherapeutic techniques, the prognosis for glioblastoma patients is still very poor. The search for molecular targets is fundamental to develop effective treatments for glioblastoma.

Global profiling is one of the most effective approaches to identify abnormally expressed miRNAs in tumor genomes. In this study, we used three different technical platforms to determine the differential expression of miRNAs in glioblastoma stem cells and neural stem cells. We combined the microarray platform with the newly emerged small RNA deep sequencing technology to profile miRNA expression in glioblastoma stem cells and normal neural stem cells and validated the profiling results using quantitative RT-PCR. Although the absolute fold change obtained from each platform is different due to the different sensitivity of the techniques, the trend of the change for the miRNAs studied is consistent. The miRNA expression profile could clearly distinguish glioblastoma stem cells from normal neural stem cells, allowing us to identify a miRNA signature of glioblastoma stem cells that were significantly up-regulated or down-regulated in glioblastoma stem cells, relative to neural stem cells.

In line with our findings that a set of miRNAs are differentially expressed in glioblastoma stem cells and normal neural stem cells, certain miRNAs also exhibit distinct expression profiles in glioblastoma tissues and normal brain tissues. For example, we demonstrate for the first time in this study that the expression of miR-874 is dramatically reduced in glioblastoma tissues, compared to normal brain tissues. miR-124, another miRNA that was down-regulated in glioblastoma stem cells, also exhibited reduced expression in glioblastoma tissues in this study, consistent with the results of previous glioblastoma tumor tissue profiling [START_REF] Huse | The PTEN-regulating microRNA miR-26a is amplified in high-grade glioma and facilitates gliomagenesis in vivo[END_REF][START_REF] Silber | miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells[END_REF][START_REF] Ciafre | Extensive modulation of a set of microRNAs in primary glioblastoma[END_REF][START_REF] Godlewski | Targeting of the Bmi-1 oncogene/stem cell renewal factor by microRNA-128 inhibits glioma proliferation and self-renewal[END_REF][START_REF] Xia | Loss of brainenriched miR-124 enhances the stem-like traits and invasiveness of glioma cells[END_REF][START_REF] Skalsky | Reduced expression of brain-enriched microRNAs in glioblastomas permits targeted regulation of a cell death gene[END_REF][START_REF] Fowler | miR-124a is frequently down-regulated in glioblastoma and is involved in migration and invasion[END_REF].

In this study, we show that miR-10b is highly expressed in both glioblastoma stem cells and in glioblastoma tumor tissues. Upregulation of miR-10b was also observed in other glioblastoma samples [START_REF] Huse | The PTEN-regulating microRNA miR-26a is amplified in high-grade glioma and facilitates gliomagenesis in vivo[END_REF][START_REF] Ciafre | Extensive modulation of a set of microRNAs in primary glioblastoma[END_REF][START_REF] Godlewski | Targeting of the Bmi-1 oncogene/stem cell renewal factor by microRNA-128 inhibits glioma proliferation and self-renewal[END_REF], suggesting an important role for miR-10b in glioblastoma tumorigenesis. Moreover, a recent study revealed that miR-10b expression is inversely correlated with glioblastoma patient survival [START_REF] Gabriely | Human glioma growth is controlled by microRNA-10b[END_REF]. Interestingly, miR-10b was also found to be up-regulated in breast cancer, leukemia, and pancreatic cancer and promote tumor invasion and metastasis in breast cancer [START_REF] Ma | Tumour invasion and metastasis initiated by microRNA-10b in breast cancer[END_REF][START_REF] Calin | MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias[END_REF][START_REF] Bloomston | MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis[END_REF]. Together, these results suggest that some miRNAs, such as miR-10b, may function as a global oncogene to stimulate tumorigenesis in multiple tissues. Likewise, miR-124 is also frequently down-regulated in other cancers, such as medulloblastoma, hepatocellular carcinoma, and oral squamous carcinoma [START_REF] Li | miR-124 is frequently down-regulated in medulloblastoma and is a negative regulator of SLC16A1[END_REF][START_REF] Furuta | miR-124 and miR-203 are epigenetically silenced tumor-suppressive microRNAs in hepatocellular carcinoma[END_REF][START_REF] Hunt | MicroRNA-124 suppresses oral squamous cell carcinoma motility by targeting ITGB1[END_REF], suggesting that it may function as a general tumor suppressor. Therefore the knowledge of miRNAs that we have obtained for glioblastoma stem cells may shed lights to other cancer stem cells.

Pathway analysis revealed that most of the significantly upregulated miRNAs, with more than 5-fold increase in glioblastoma stem cells as shown in our microarray analysis, have putative targets in a common pathway, the p53 pathway. The dysregulation of the p53 pathway has been shown to be an underlying mechanism for tumorigenesis [START_REF] Junttila | p53-a Jack of all trades but master of none[END_REF], thus the up-regulated miRNAs may function as oncomiRs by targeting the p53 pathway if their role in regulating the p53 pathway is confirmed. On the other hand, most of the down-regulated miRNAs, with more than 5-fold decrease in glioblastoma stem cells, share their predicted targets in the IGF signaling. Repression of the IGF signaling has been shown to inhibit tumorigenesis [START_REF] Clayton | Growth hormone, the insulin-like growth factor axis, insulin and cancer risk[END_REF]. Thus, these down-regulated miRNAs may assume a role of tumor suppressors by targeting components of the IGF pathway if their role in regulating the IGF signaling is confirmed. The identification of miRNAs as oncogenes or tumor suppressors holds the promise of identifying novel diagnostic markers or molecular targets for antitumor therapies. The prediction that the differentially expressed miRNAs have the ability to target multiple components in one or more pathways makes them potential molecular targets for cancer therapy.

Glioblastoma stem cells represent a subpopulation of cancer cells with extraordinary capacities to promote tumor growth, invasion and therapeutic resistance, making them an ideal target cell population for anti-glioblastoma therapies. However, a major challenge is to define functional and molecular features that can distinguish cancer stem cells from normal stem cells in order to develop therapeutic strategies that specifically target the tumor population, but leave normal stem cells intact. Therefore comparing miRNA expression between glioblastoma stem cells and normal neural stem cells is highly relevant in that it may lead to the identification of glioblastoma stem cell-specific miRNAs, thus resulting in the development of novel glioblastoma therapies by targeting only tumor stem cells. We performed the comparison of miRNA expression between glioblastoma stem cells from adult glioblastoma patients and normal neural stem cells from human fetal brains instead of human adult brains due to the inaccessibility of normal human adult brain tissues containing neural stem cells. Although differences do exist between embryonic neural stem cells and adult neural stem cells, it has been proposed that embryonic neural stem cells resemble adult neural stem cells in many ways [START_REF] Ming | Adult neurogenesis in the mammalian brain: significant answers and significant questions[END_REF]. Therefore this comparison will provide useful information regarding glioblastoma stem cell-specific miRNA expression and provide a basis for strategic targeting glioblastoma stem cells through modulation of tumor stem cell-specific miRNA expression.

miRNAs have been shown to be involved in tumor initiation and progression, functioning as oncogenes or tumor suppressor [START_REF] Asadi-Moghaddam | Potential role of miRNAs and their inhibitors in glioma treatment[END_REF][START_REF] Cheng | Potential therapeutic implications of cancer stem cells in glioblastoma[END_REF]. Therefore, modulation of miRNA expression provides great hope for potential cancer therapy [START_REF] Verissimo | Neuroblastoma therapy: what is in the pipeline?[END_REF]. Furthermore, since each miRNA may have more than one targets, miRNA-based gene therapy offers the therapeutic appeal of targeting multiple gene networks that are controlled by a single miRNA [START_REF] Asadi-Moghaddam | Potential role of miRNAs and their inhibitors in glioma treatment[END_REF]. Strategies for miRNA-based cancer therapy include overexpression of tumor suppressor miRNAs and targeting oncogenic miRNAs using their antagonists. Based on the miRNA signature that we have identified in glioblastoma stem cells, we may be able to develop targeted glioblastoma therapies by inhibiting the upregulated miR-10a or miR-10b function using miR-10 antogonists or overexpressing the down-regulated miR-124 or miR-874. Of note,16 miRNAs that are up-regulated in glioblastoma stem cells (.1.5-fold, Table S1) in our study, including miR-10a and miR-10b, are also up-regulated in malignant astrocytomas (glioblastomas and anaplastic astrocytomas) in a genome-wide miRNA expression profiling between malignant astrocytomas and normal brain samples [START_REF] Rao | Genome-wide expression profiling identifies deregulated miRNAs in malignant astrocytoma[END_REF]. Eleven miRNAs that are down-regulated in glioblastoma stem cells (.1.5-fold, Table S1) in our study, including miR-124, are also down-regulated in malignant astrocytomas.

Moreover, the prognosis of glioblastoma patients remains poor. Biomarkers for this disease are needed for early detection of tumor progression [START_REF] Roth | A specific miRNA signature in the peripheral blood of glioblastoma patients[END_REF]. The miRNA signature that we identified here may be used as biomarkers to differentiate glioblastoma stem cells from normal neural stem cells. Recently, a miRNA signature was gene downstream of 39 UTR of CSMD1. WT or MT CSMD1 39 UTR luciferase reporter was transfected into HEK 293 cells along with control, miR-10b RNA duplexes, or the combination of miR-10b RNA duplexes and a miR-10b inhibitor. * p,0.005 by student's t-test. E. Expression of CSMD1 in glioblastoma stem cell line 1 (GSC) and neural stem cell line 1 (NSC) determined by real-time RT-PCR analysis. F. Expression of HOXD10 in GSC and NSC determined by real-time RT-PCR analysis. For all panels, data shown are mean 6 standard deviation of three replicates. ** p,0.01 by student's ttest for both panels E and F. doi:10.1371/journal.pone.0036248.g004 identified in the peripheral blood of glioblastoma patients [START_REF] Roth | A specific miRNA signature in the peripheral blood of glioblastoma patients[END_REF]. Interestingly, several of the miRNAs that showed elevated expression in the blood samples of glioblastoma patients (vs healthy control) also exhibited increased expression in glioblastoma stem cells (vs normal neural stem cells) in our study (Table S1), including miR-424, miR-148a, miR-362-3p, miR-30d, miR-128. These miRNAs may therefore easily accessible biomarkers that can be used for diagnostic purposes in glioblastoma patients.

Materials and Methods

Ethics statement

The derivation of PBT003 (GSC1) and PBT017 (GSC2) has been described by Brown et al [START_REF] Brown | Recognition and killing of brain tumor stem-like initiating cells by CD8+ cytolytic T cells[END_REF]. PBT707 (GSC3) is a de novo cell line derived from anonymized leftover tissues with the approval of the City of Hope Institutional Review Board. The study involves the use of completely anonymized specimens. No informed consent is involved. NOD-scid IL2Rgamma null (NSG) mice (6-8weeks) were used for glioblastoma stem cell transplantation. Tumor cell transplantation was performed under the

Glioblastoma stem cell and neural stem cell culture

Glioblastoma stem cells were derived from newly diagnosed WHO grade IV glioblastoma tissues. Specifically, freshly isolated glioblastoma tissues were minced with sterile scissors and dissociated into single cells using 400 units/ml of collagenase III in DMEM/F12 medium supplemented with 5 mg/ml heparin, 16 B27 (GIBCO/BRL), and 2 mM L-glutamine. Dissociated cells were then centrifuged at 1,200 rpm for 5 min and the supernatant was discarded. To eliminate red blood cells, the resultant cells were incubated in 10 ml red blood cell lysis buffer (Invitrogen) for 10 min. Cells were centrifuged again at 1,200 rpm for 5 min and supernatant was discarded. The resultant cells were resuspended in DMEM/F12 medium supplemented with 20 ng/ml EGF, 20 ng/ml FGF, 5 mg/ml heparin, 16 B27 (GIBCO/BRL), and 2 mM L-glutamine and cultured in this medium thereafter. Tumor spheres appeared around one week in culture. Normal human neural stem cells were derived from primary human brain tissues and maintained in the same culture media. Specifically, human fetal brain tissues (Biosciences Resources) were dissociated in cold Hanks balance salt solution (HBSS) using polished glass pipette. The resultant cells were centrifuged and resuspended in DMEM/F12 medium supplemented with 0.56 B27, 25 mg/ml insulin, 20 mg/ml apo-transferrin, 30 nM sodium selenite, 20 nM progesterone, 100 mM putrescine, 20 ng/ml FGF and 10 ng/ml LIF. The initial culture was split at 1:2 each day for 4 days, followed by media change every other day till day 21. Human neurospheres started to appear around day 14. The spheres were split around day 21 with Accutase (Sigma) and cultured in DMEM/F12 medium supplemented with 20 ng/ml EGF, 20 ng/ ml FGF, 5 mg/ml heparin, 16 B27 (GIBCO/BRL), and 2 mM Lglutamine thereafter. Both tumor spheres and normal neurospheres were characterized for their self-renewal and multipotency. Glioblastoma stem cell spheres were also characterized for their ability to derive brain tumors.

For differentiation, both glioblastoma stem cells and neural stem cells were induced into differentiation using 0.5% fetal bovine serum and 1 mM all-trans retinoic acid. For in vivo tumor formation assays, 2610 5 dissociated glioblastoma stem cells were injected into cerebral cortex of NSG mice by stereotaxic injection. The coordinates for the injection were AP 0.6 mm, ML +1.6 mm and DV 22.6 mm. Brains were harvested 5 weeks after cell transplantation. Frozen brains were cut into 20 mm coronal sections, followed by Hematoxylin & Eosin (H&E) staining.

Glioblastoma stem cell transfection using a dendrimerbased delivery system

Spheres of glioblastoma stem cells were dissociated and seeded into 24-well plates at 2610 5 cells per well in 300 ml of medium. The generation-5 (G5) dendrimers and Opti-MEM solution were mixed by vortex for 10 seconds, and incubated at room temperature (RT) for 10 min. The miRNA duplexes or the combination of miRNAs and their short hairpin RNA inhibitors were added into dendrimer/Opti-MEM solution in a total volume of 100 ml, mixed gently for 10 sec, and incubated at RT for 25 min. The nitrogen-to-phosphorus (N/P) ratio of the dendrimer/RNA complex is 5. The 100 ml dendrimer/RNA complex was added into 300 ml cell suspension in each well of 24-well plates, shake gently and put back to CO 2 incubator. Forty-eight hr after transfection, cells were collected and subjected to Western blot analysis.

Western blot analysis

Whole cell extracts of glioblastoma stem cells were prepared using RIPA buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% NP40, 0.5% deoxycholate and 0.1% SDS) containing protease inhibitor cocktail (Roche). Western blotting was performed with anti-NRAS (sc-31, 1:100) and anti-PIM3 (sc-98959, 1:100) antibodies from Santa Cruz.

Glioblastoma stem cell transplantation

NSG mice (6-8weeks) were used for glioblastoma stem cell transplantation. Tumor cell transplantation was performed under the IACUC protocol 05050 approved by the City of Hope Institutional Animal Care and Use Committee. 5610 4 dissociated glioblastoma stem cells were injected into the front lobe of forebrains by stereotaxic injection. The coordinates for the injection were AP 0.6 mm, ML +1.6 mm and DV 22.6 mm.

Reporter construct preparation

DNA fragments encoding the 39 UTR of putative miRNA targets were cloned into psiCHECK 2 (Promega), downstream of a Renilla luciferase reporter gene. The PCR primers that were used for 39 UTR cloning of each gene are as follows: CSMD1 forward: 59 GAT CCT CGA GCT GTT CTG TCG CAG AAT G 39 and CSMD1 reverse: 59 GAT CGC GGC CGC GTC AGC ATT TTG CAC CTA G39; PIM3 forward: 59 GAT CCT CGA GGC TTG TGA GGA GCT GCA C 39 and PIM3 reverse: 59 GAT CGC GGC CGC GGA AAC TTG TCA GGT CAC C 39; NRAS forward: 59 GAT CCT CGA GCT GGA GGA GAA GTA TTC CTG 39 and NRAS reverse: 59 GAT CGC GGC CGC TGC AAA TGT AGA GCT TTC TGG 39. Corresponding miRNA binding sites on the 39 UTRs were mutated by site-directed mutagenesis according to the manufacturer's instructions (Stratagene). The binding site of hsa-miR-124 on the 39 UTR of PIM3 and NRAS was mutated from GTGCCTT to GTGGACA; the binding site of hsa-miR-10b on the 39 UTR of CSMD1 was mutated from ACAGGGT to ACAGTCC.

Transfection and reporter assay

We transfected plasmid DNA or DNA-miRNA mixture into HEK293 cells using Transfectin (Bio-Rad) as described [START_REF] Zhao | A feedback regulatory loop involving microRNA-9 and nuclear receptor TLX in neural stem cell fate determination[END_REF][START_REF] Zhao | MicroRNA let-7b regulates neural stem cell proliferation and differentiation by targeting nuclear receptor TLX signaling[END_REF][START_REF] Sun | miR-137 forms a regulatory loop with nuclear receptor TLX and LSD1 in neural stem cells[END_REF]. miR-10a, miR-10b or miR-124 RNA duplexes and/ or their correspondent RNA inhibitors (Dharmacon) were mixed in 50 ml serum free media with Transfectin, incubated at RT for 20 min. Negative controls for miRNA and their hairpin inhibitors were included. The final concentration of miRNAs or their inhibitors was 20 nM. The resultant mixture was added dropwise to HEK293 cells in a 24-well plate with 450 ml medium per well to a total volume of 500 ml per well. The transfected cells were harvested 48 h after transfection and subjected to subsequent reporter assays as described [START_REF] Sun | Orphan nuclear receptor TLX recruits histone deacetylases to repress transcription and regulate neural stem cell proliferation[END_REF]. Reporter Renilla luciferase activity was measured 48 hrs after transfection using Dual Luciferase Assay kit (Promega). The Renilla luciferase activity was normalized by firefly luciferase internal control and expressed as relative luciferase activity. The miR-10a RNA duplex sense sequence is 59 TAC CCT GTA GAT CCG AAT TTG TG 39. The miR-10b RNA duplex sense sequence is 59 TAC CCT GTA GAA CCG AAT TTG TG 39. The miR-124 RNA duplex sense sequence is 59 TAA GGC ACG CGG TGA ATG CC 39. And the control RNA duplex sense sequence is 59 UCA CAA CCU CCU AGA AAG AGU AGA 39.

Real-time RT-PCR analysis

For miRNA expression, total RNAs were reversely transcribed and quantified by real-time RT-PCR with TaqMan MicroRNA Assay kit (Applied Biosystems). The expression of specific miRNAs was normalized using human U18 snRNA. For mRNA expression, putative miRNA targets were quantified by iTaq SYBR Green Supermix with ROX (Bio-Rad). Primers used for RT-PCR include PIM3 forward: 59 AGC TCA AGC TCA TCG ACT TC 39 and PIM3 reverse: 59 TAG CGG TGG TAG CGG ATC 39; NRAS forward: 59 CCA TGA GAG ACC AAT ACA TGA G 39 and NRAS reverse: 59 GCT TAA TCT GCT CCC TGT AG 39; HOXD10 forward: 59 TTC CCG AAG AGA GGA GCT G 39 and HOXD10 reverse: 59 CTG CCA CTC TTT GCA GTG AG 39; CSMD1 forward: 59 GCA GAA ATG CTT ACT GAG GAT G 39 and CSMD1 reverse: 59 AGA ACC CTC AAA CTG CAA CTG 39; GAPDH forward: 59 ATC ACC ATC TTC CAG GAG C 39 and GAPDH reverse 59 CCT TCT CCA TGG TGG TGA AG 39.

miRNA microarray and deep sequencing analysis

Total RNAs were extracted from glioblastoma stem cells or human neural stem cells by TRIzol (Invitrogen) method according to manufacturer's protocol. Ten mg of RNA was used for miRNA microarray using Exiqon platform. One mg of RNA was used for deep sequencing using Illumina Genome Analyser II (GAII). All data are MIAME compliant.

Pathway analysis

Common putative targets of either the down-regulated or the up-regulated miRNAs were uploaded onto the Database for Annotation, Visualization and Integrated Discovery (DAVID) Functional Annotation Bioinformatics Microarray Analysis (http://david.abcc.ncifcrf.gov/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in DAVID was used to depict the biological meanings of the common miRNA targets.

Figure 1 .

 1 Figure 1. The morphology, differentiation and growth curve of glioblastoma stem cells (GSCs) and neural stem cells (NSCs). A. Representative images of neurospheres from normal human neural stem cell lines 1-3 (NSC1-3) and glioblastoma stem cell lines 1-3 (GSC1-3). B. The multipotency of NSCs and GSCs. When induced into differentiation, both NSCs and GSCs gave rise to Tuj1+ neurons (green) and GFAP+ astrocytes (red). Representative images of NSC1 and GSC1 differentiation were shown. Nuclear Dapi staining was shown in blue. C. H&E staining of coronal sections from GSC-transplanted brains. The tumor region was indicated by an arrow, shown in dark purple color. doi:10.1371/journal.pone.0036248.g001

Figure 2 .

 2 Figure 2. Real-time RT-PCR validation of miRNA expression in glioblastoma stem cells. The expression levels of miR-10a (A), miR-10b (B), miR-140-5p (C), miR-124 (D), and miR-874 (E) in three glioblastoma stem cell (GSC) lines were measured by real-time RT-PCR, and compared to their expression in three neural stem cell (NSC) lines. The expression shown in each cell line is relative to the expression in NSC1, with the expression in NSC1 as 1. Error bars are standard deviation of the mean. * p,0.001, ** p,0.005 by one way Anova test. doi:10.1371/journal.pone.0036248.g002

Figure 3 .

 3 Figure 3. Real-time RT-PCR analysis of miRNA expression in glioblastoma tissues. The expression levels of miR-10a (A), miR-10b (B), miR-124 (C), and miR-874 (D) in 9 glioblastoma tissues and 4 normal brain tissues were determine by real-time RT-PCR analysis, shown in scatted graph and bar graph. Error bars are standard error of the mean. p value was obtained by student's t-test. doi:10.1371/journal.pone.0036248.g003

Figure 4 .

 4 Figure 4. Expression of miR-10b targets in glioblastoma stem cells. A, B. The base-pairing of hsa-miR-10a and hsa-miR-10b with the 39 UTR of CSMD1 gene. C. miR-10a-mediated repression of luciferase reporter gene downstream of 39 UTR of CSMD1. Luciferase reporter gene under the control of wild type (WT) or mutant (MT) CSMD1 39 UTR was transfected into HEK 293 cells along with control, miR-10a RNA duplexes, or the combination of miR-10a RNA duplexes and a miR-10a inhibitor. * p,0.001 by student's t-test. D. miR-10b-mediated repression of luciferase reporter

Figure 5 .

 5 Figure 5. miR-124 targets NRAS and PIM3 expression. A. The base-pairing of hsa-miR-124 with the 39 UTR of NRAS gene. B. miR-124-mediated repression of luciferase reporter gene downstream of 39 UTR of NRAS. Luciferase reporter gene under the control of wild type (WT) or mutant (MT) NRAS 39 UTR was transfected into HEK 293 cells along with control, miR-124 RNA duplexes, or the combination of miR-124 RNA duplexes and a miR-124 inhibitor. *p,0.001 by student's t-test. C. Western blot analysis of NRAS expression in control RNA, miR-124 RNA duplexes, or the combination of miR-124 RNA duplexes and a miR-124 inhibitor-transfected GSC1 cells. D. Expression of NRAS in GSC1 and NSC1 determined by real-time RT-PCR analysis. *p,0.05 by student's t-test. E. The base-pairing of hsa-miR-124 with the 39 UTR of PIM3 gene. F. miR-124-mediated repression of luciferase reporter gene downstream of 39 UTR of PIM3. Luciferase reporter gene under the control of wild type (WT) or mutant (MT) PIM3 39 UTR was transfected into HEK 293 cells along with control, miR-124 RNA duplexes, or the combination of miR-124 RNA duplexes and a miR-124 inhibitor. *p,0.001 by student's t-test. G. Western blot analysis of PIM3 expression in control RNA, miR-124 RNA duplexes, or the combination of miR-124 RNA duplexes and a miR-124 inhibitor-transfected GSC1 cells. H. Expression of PIM3 in GSC1 and NSC1 determined by real-time RT-PCR analysis. **p,0.001 by student's t-test. For all panels, data shown are mean 6 standard deviation of three replicates. doi:10.1371/journal.pone.0036248.g005

Figure 6 .

 6 Figure 6. Pathways targeted by deregulated miRNAs in glioblastoma stem cells. Common miRNA targets were subjected to DAVID functional annotation with KEGG pathway analysis. A. The up-regulated miRNAs in glioblastoma stem cells were predicted to target the p53 pathway. The p53-centered pathway has been shown to regulate cell cycle, apoptosis, angiogenesis, metastasis, and genome stability. B. The downregulated miRNAs were predicted to target components of the IGF pathway. Various components of the IGF signaling pathways were targeted by down-regulated miRNAs. The IGF pathway has been shown to enhance cell growth, survival, and migration. doi:10.1371/journal.pone.0036248.g006

Table 1 .

 1 Up-regulated and down-regulated miRNAs in human glioblastoma stem cells, compared to human neural stem cells.

		Chromosomal
	miRNA	location	Fold-Change	p-value
	up-regulated
	hsa-miR-10a	17q21.32	93.65	3.28E-10
	hsa-miR-10b	2q31.1	90.38	2.06E-09
	hsa-miR-140-3p	16q22.1	14.10	1.62E-10
	hsa-miR-140-5p	16q22.1	12.19	5.56E-09
	hsa-miR-204	9q21.12	9.05	4.08E-08
	hsa-miR-424	Xq26.3	8.38	2.53E-08
	hsa-miR-455-5p	9q32	5.87	1.48E-05
	hsa-miR-455-3p	9q32	5.41	9.32E-05
	down-regulated
	hsa-miR-371-5p	19q13.42	215.27	8.52E-11
	hsa-miR-124-1*	8p23.1	213.37	8.33E-05
	hsa-miR-124-2 *	8q12.3
	hsa-miR-124-3 *	20q13.33
	hsa-miR-335	7q32.2	213.24	1.43E-09
	hsa-miR-492	12q22	27.77	5.38E-08
	hsa-miR-874	5q31.2	26.76	1.53E-08
	hsa-miR-30b*	8q24.22	26.54	2.73E-09
	hsa-miR-602	9q34.3	25.75	2.63E-08

*hsa-miR-124 is transcribed from three chromosomal locations, but the mature sequences are the same. doi:10.1371/journal.pone.0036248.t001

Table 2 .

 2 The miRNA signature of glioblastoma stem cells identified using both microarray and deep sequencing analyses.

				Deep
		Microarray		sequencing
	Up-regulated Fold change p value	Fold change	p value
	hsa-miR-10a	93.65	,1E-07	35,949	0.00E+00
	hsa-miR-10b	90.38	,1E-07	4,128	0.00E+00
	hsa-miR-140-5p 12.19	,1E-07	7.2	0.00E+00
	has-miR-204	9.05	,1E-07	5	0.00E+00
	hsa-miR-424	8.38	,1E-07	66	0.00E+00
	hsa-miR-34a	7.73	2.00E-07	2.5	4.00E-240
	hsa-miR-193a-3p 6.4	8.00E-06	93	9.00E-21
	hsa-miR-455-5p 5.87	1.00E-05	5.3	5.00E-214
	Down-regulated
	hsa-miR-124	213.37	8.00E-05	210	5.00E-80
	hsa-miR-874	26.76	,1E-07	233	1.50E-98
	doi:10.1371/journal.pone.0036248.t002

PLoS ONE | www.plosone.org

April 2012 | Volume 7 | Issue 4 | e36248

April | Volume Issue 4 | e36248

Acknowledgments

We thank members of the Shi laboratory for critical reading of the manuscript.

This work was supported by National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH NINDS) R01 NS059546, RC1 NS068370 (http://www.ninds.nih.gov/)and California Institute for Regenerative medicine TR2-01832 (http://www.cirm.ca.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supporting Information


Robert Laurini
email: robert.laurini@insa-lyon.fr

Pre-consensus Ontologies and Urban Databases

Keywords: Urban ontologies, Towntology project, consenus, pre-consensus ontologies, groupware

Facing the difficulties of interoperability and cooperation between several urban databases, a solution is based on ontologies which can help not only clarify the vocabulary used in urban planning, but also organize urban applications; indeed multiple definitions can be given to various urban objects. So this is the scope of the Towntology project which aims at defining ontologies for urban planning whose design is characterized by the multiplicity of definitions. After having presented some ways of using ontologies for various actors in urban applications, a definition of pre-consensus ontologies is given, together with some groupware tools to collect multiple textual and multimedia definitions in sub-ontologies, to check and consolidate the vocabulary in order to reach some consensus. We conclude this paper by giving some recommendations for the Towntology project for covering the whole urban field by integrating various sub-ontologies.

-Introduction

One of the main problems we have to face in urban information systems is the problems of interoperability and cooperation between several databases [START_REF] Laurini | Information Systems for Urban Planning: A Hypermedia Cooperative Approach[END_REF]: indeed, each database was created independently from others, i.e. with different entities and attributes with different meanings. Usually, two levels of interoperability are defined, the lower level called syntactic, and the upper level called semantic. As the syntactic level is solved through OpenGIS1 recommendations, a general solution of the semantic level is based on ontologies in order to deal with the meaning of vocabulary. But in reality, the vocabulary problem is not only a database problem, but more important is the clarification of the vocabulary used by all actors dealing with urban databases, and especially by urban planners.

For this reason, the Towntology project was launched in 2003 at INSA Lyon in order to create an urban ontology between urban planners and computer scientists (see [START_REF] Keita | Towards an Ontology for Urban Planning: The Towntology Project[END_REF] or [START_REF] Roussey | Le projet Towntology : Un retour d'expérience pour la construction d'une ontologie urbaine[END_REF] for details). Then facing the difficulty to cover the whole urban field, a COST group 2 was created and placed under the responsibility of Jacques Teller [START_REF] Teller | Urban Ontologies for an Improved Communication in Urban Civil Engineering Projects[END_REF]. Now, it regroups more than 15 laboratories in Europe.

The scope of this paper is to give an overview of problems we have to face in order to define urban sub-ontologies and to integrate them into an unique domain ontology. This paper is organized as follows. In the second section, we will address the necessity of ontogies in urban applications, and second the organization of groupware to create urban ontologies.

-Necessity of ontologies for urban applications

In this section, examples for interoperability in urban applications will be detailed in order to show how ontologies can be used to solve those problems.

Examples of interoperability

The main examples of interoperability in urban databases can be seen in street repairs in which different databases can be used, not only belonging to the municipality (sewerage, traffic light control) but also belonging to different companies such as for water supply, electricity, gas. Other examples can be found in environmental assessment (for instance dealing with pollution control of an international river such as the Rhine or the Danube), and for providing new pervasive services (Location-Based Services). Let us examine an example in the cooperation of several urban databases, linked to physical hypermedia [START_REF] Challiol | Designing Pervasive Services for Physical Hypermedia[END_REF]: find the roadmap for going from the Da Vinci Gioconda painting in the Paris Louvre Museum, to the Velasquez Meninas painting in Madrid Prado Museum. The solution must be found by means of the cooperation of several databases:

-from the Louvre database for exiting from the Gioconda to the next metro station, -from the Paris Transportation Company to go from the nearest metro station to Paris Airport, -from the Airlines database to fly from Paris Airport to Madrid Airport, -from the Madrid Transportation Company for going from the airport to the nearest metro station, -from Prado database for going from the nearest metro station to the Meninas painting.

Definition of ontologies

The word "ontology" comes from Greek "Οντος", Being and "Λογια", Discourse, so meaning the discourse about existing things. More precisely, ontology refers to the theory of objects and of their relations. Gruber [START_REF] Gruber | A Translation Approach to Portable Ontology Specifications[END_REF] defines an ontology as "an explicit specification of a conceptualization", and Guarino [START_REF] Guarino | Formal Ontology and Information Systems[END_REF] "An ontology is an engineering artifact, constituted by a specific vocabulary used to describe a certain reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary words". An important aspect is that the various actors must agree about the definition of objects and their relations; so we speak about ontological commitment between actors.

Pragmatically, a common ontology defines the vocabulary with which queries and assertions are exchanged among actors. Ontological commitments are agreements to use the shared vocabulary in a coherent and consistent manner. From a computing point of view, an ontology can be seen as a semantic network.

But in the case of urban planning, there exist many different definitions of keyobjects such as "city" or "road".

In the Wikipedia3 , one can find a dozen of definitions of the word "city", but none addresses the whole urban complexity. After Toynbee [START_REF] Toynbee | Cities on the Move[END_REF], a city can be defined as a human habitat which cannot provide all food they need, whereas other defines a city as petrified expression of power structures [START_REF]Programme de Recherche Urbaine du VIIème plan, Paragraphe sur la Poétique des Villes[END_REF]. How to combine those definitions into a single expression?

Let us consider another problem regarding the definitions of "streets". Let us consider three actors in the same city, street cleaners, postmen and gas men: they all can claim "we do have a street file". In reality those files are slightly different:

-street cleaners only clean public streets, so their file only is composed of public streets; -in theory postmen passes in all streets, but when a cul-de-sac has letter-boxes in a main street, they do not consider those cul-de-sac streets -for gas men, their file only consists only in streets in which residents have gas. As a conclusion, even if the concept of street can receive an acceptable definition from urban planners, analyzing several databases can reveal that definitions are different. Generalizing this, we can claim that in practice, even if two databases are using the same word (street), the probability is high that there exist some hidden differences in the definitions.

In other words, multiplicity of definitions is often hidden behind similar terms. To solve this problem, one solution is to define contextual ontologies (See [START_REF] Benslimane | Contextual Ontologies: Motivations, Challenges, and Solutions[END_REF] for details.

Ontology-based interoperability

To ensure interoperability, one way is to use ontologies. In the framework, each database is assigned its own local ontology perhaps written from its conceptual model. Moreover a domain ontology is used as a sort of bridge between both local ontologies (i.e. linked to a database) as illustrated in Figure 1. By means of those ontologies, a mediator is generated made in two parts, one for translating the initial query to be accepted by the second database, and the second to transform the results (See Figure 2).

Let us take a small example in demography, with two databases, (i) DB1 with one entity residents, and (ii) DB2 with two entities men and women. How can we get the number of men and women separately in DB1, and the total number of residents in DB2? The second case can be solved by an exact mediator, so giving: DB2.residents= DB2.men + DB2.women. However, for the first case, only approximate mediators can be generated, for instance: DB1.men = 0.48×DB1.residents DB1.women = 0.52×DB1.residents. From a language point of view, ontologies are generally written with languages such as OWL 4 which derives from description logics.

Specifications of pre-consensus urban ontologies

Ontologies are easy to define in applications where the vocabulary is well standardized [START_REF] Gómez-Pérez | Ontological Engineering[END_REF]. The topical example is chemistry.

As illustrated in the previous paragraphs, in urban planning, the context is totally different especially due to the variety of definitions. So before translating some textual definition into OWL, some consensus between actors must be found. Now, we can introduce two kinds of ontologies, pre-consensus and post-consensus ontologies as depicted Figure 3. As the majority of existing ontologies can be considered as postconsensus, in our case, our domain ontology in urban planning is a pre-consensus ontology whose main characteristics is the necessity of a repository to collect existing definitions. Then, when all definitions are accumulated, actors can convene to look for a consensus; and when the consensus is reached, translation into OWL can start. It is important not to forget cultural and linguistic problems in this task.

Fig. 3. Differences between pre-consensus ontology, and post-consensus ontology.

So, a repository must be design to collect multiple definitions and attributes. Since in some cases multimedia definitions must be considered, for instance for noise 4 Refer to http://www.w3.org/TR/owl-features/.

Post-consensus ontology

Tools for -Interoperability -cooperation -mediator generation -deduction, inference

Pre-consensus ontology

Consensus among actors

Tools for -concept identification -attribute identification -concept organization -actor's points of view -concept repository definitions or in architecture when drawings and sketches are necessary. Another important issue is lineage and traceability of definitions. Finally not only a repository must be defined, but also software tools to manage the various pre-consensus ontologies together with adapted human visual interfaces. Figure 4 illustrates those visual various access methods, (i) from graphs of concepts (semantic network), (ii) from photos illustrating various concepts. In addition to those visual methods, a third one was added based on the alphabetic list of concepts. Since the ontology is represented as a graph, a nice visual solution is to access directly from the graph and to navigate from concepts to concepts. Another interesting access method can be based on photos in which several zones can be activated, especially zones representing concepts; in other words, several photos of cities can be used as entry point into ontologies. The access methods based on photos is very important for us because the user is very familiar with urban scenes as given in photos. For integrating a photo into the system, one needs to find rectangular zones corresponding to concepts. Let's take the example of a rectangular zone surrounding a bus. According to the level of abstraction, this zone can correspond to several concepts:

-bus itself, -bus as a mean of public transportation, -public transportation, -transportation of passengers, -etc.

Finally, the main characteristics of our system are as follows:

-Semantic network, -Hypertext structure, -Multiple definitions, -Origin and lineage of definitions,

Urban ontology

Visual Access from photos Visual Access from concepts Visual Access from graphs -Possibility of updating, -Photos and drawings -Visual presentation.

All main objects of our pre-consensus ontology can be regrouped into a conceptual model given Figure 5.

-Groupware for pre-consensus urban ontologies

As said earlier, the scope of the Towntology project is to define a complete urban ontology. For that, each laboratory interested is developing its small sub-ontology. The role of the groupware system will be to help those laboratories define preconsensus sub-ontologies, i.e. collect the various multimedia definitions including lineage. For that, each group of actors can work independently on the definition of their important terms. In other words, they need frequently to add some fresh definitions or update them into the repository. When a sub-ontology is ready, it will be presented to the groupware system which will integrate it. Of course a subontology can refer a concept already present in another sub-ontology.

After having very rapidly presented the description language, the groupware system will be sketched.

Ontology

Sub-ontology Photo

Zone Relation Type Relation 1-n 1-1 1-n 1-1 1-1 1-n 1-n 0-n 1-n 1-n 1-n 1-1 Definition 1-n 1-1 1-n 2-2 1-n 1-n Concept

Language

Since OWL was not adequate to our problem, taking all those aspects into consideration, a new language was created to store all multimedia definitions into our repository. This language is an extension of XML, some excerpts of the structure of which are given Figure 6. The main divisions are HEAD and BODY. HEAD regroups some identification and metadata regarding this ontology, whereas BODY is really the core of the ontology: the reader can see that any concept can have various multimedia definitions, and every update can be traceable.

Groupware system

The groupware system consists of two parts, the first one for creating sub-ontologies, and the second one for integrating sub-ontologies.

For the creating of ontologies, based on the previous language, three modules were written:

-navigating and browsing a sub-ontology, essentially based on the three types of accesses as illustrated Figure 4, -updating a sub-ontology, especially by adding new concepts, new definitions and new multimedia resources, -and preparing an image that can be used as an entry into the sub-ontology; mainly this image is split into rectangular zones which addresses one or several concepts.

The second system is for integrating a new sub-ontology. It consists in several modules:

-validating the proposed sub-ontology, essentially by checking the grammar and some integrity constraints, -and validating cross-references of concepts with two main aspects: a relation can refer a concept located in another sub-ontology, or a new definition can be added to a concept already stored elsewhere. When all those tasks are made, the new sub-ontology is integrated into the system. Another module must be written for the seamless browsing of the sub-ontologies. Until now, when a sub-ontology refers to a concept which is located in another ontology, the system only show it; indeed, it could be of interest to continue the browsing without taking those divisions into account.

Several examples of sub-ontologies will be found in this book, together with examples of the navigating system.

Sub-ontology yet in the repository

SUB-ONTOLOGY 2

This additional definition must be added

-Final remarks

To conclude this paper, let me remind that the Towntology project was initiated with two scopes, (i) interoperability of urban databases, and (ii) clarification of the vocabulary used by urban planners. Presently, a first software tool exists that can be used to define sub-ontologies. For me the first goal is to populate the various subontologies in order to cover the whole urban field, and for that a first tool was created. When all definitions are collected, the next step is to consolidate those sub-ontologies and check completeness. The subsequent phase will to reach to some consensus; a special tool must be defined, and also a tool for helping the users transform those verbal or multimedia definitions into descriptive logics, so as to code with OWL.

Concerning the language problems, this is not so easy. A naïve way could be to collect terms and definitions in English, and after to translate everything into other languages. One of the first difficulties could be to translate all definitions of very important terms such as cities, towns, urban planning, etc., terms which have sometimes no EXACT counterparts in other languages, especially due to historical, cultural aspects. Similarly a difficulty arises for the translating of legislative terms.

Fig 1 .

 1 Fig 1. Using domain ontology to ensure interoperability between two databases.

Fig 2 .

 2 Fig 2. Generation of mediator to ensure interoperability.The previous formulae can be included into the query-part of the mediators. For the transformation of the results, let us mention an example in distances with different

Fig 4 .

 4 Fig 4. Various ways of accessing to the ontologies.

Fig. 5 .

 5 Fig. 5. Conceptual model of a pre-consensus ontology.

Fig 6 .

 6 Fig 6. Exerpts of the structure of the Towntology Language.

Fig. 7 .

 7 Fig. 7. Integrating a new sub-ontology into the ontology repository.

Refer to http://www.opengeospatial.org/standards.

Refer to http://www.towntology.net/.

Refer to http://en.wikipedia.org/wiki/City.


Bernard Castaing

Éléonore Rusaouën

Julien Salort

Francesca Chillà

E Rusaouën

Turbulent heat transport regimes in a channel

I. INTRODUCTION

In the past decade, scalar free convection in a tiltable channel appeared as an interesting model flow for evidencing the base mechanisms of convection [START_REF] Seon | Laser-induced fluorescence measurements of buoyancy driven mixing in tilted tubes[END_REF][START_REF] Znaien | Experimental and numerical investigations of flow structure and momentum transport in a turbulent buoyancy-driven flow inside a tilted tube[END_REF][START_REF] Znaien | Flow structure and momentum transport for buoyancy driven mixing flows in long tubes at different tilt angles[END_REF][START_REF] Delgado-Buscalioni | Flow and heat transfer regimes in inclined differentially heated cavity[END_REF][START_REF] Gibert | High-Rayleigh-Number Convection in a Vertical Channel[END_REF][START_REF] Gibert | Heat Convection in a Vertical Channel: Plumes versus Turbulent Diffusion[END_REF][START_REF] Tisserand | Convection in a vertical channel[END_REF][START_REF] Riedinger | Heat transport regimes in an inclined channel[END_REF][START_REF] Calzavarini | Rayleigh and Prandtl number scaling in the bulk of Rayleigh-Bénard turbulence[END_REF][START_REF] Schmidt | Axially homogeneous Rayleigh-Benard convection in a cylindrical cell[END_REF][START_REF] Cholemari | Axially homogeneous, zero mean flow buoyancy-driven turbulence in a vertical pipe[END_REF]. Different regimes have been evidenced depending on the angle ψ between the channel axis and the vertical one, the Prandtl (or Schmidt) number ν/κ, or the amplitude of density differences. ν is the kinematic viscosity of the fluid, and κ, the diffusion coefficient of the active scalar.

The typical geometry is schematized in figure 1. A channel connects two chambers. The chambers have different temperatures, in the case of heat transport, different scalar concentrations in the other cases. For the sake of clarity, we shall concentrate on heat transport, speaking of temperature instead of scalar concentration, of heat flux instead of scalar flux, but all what follows can be easily translated in the other language.

The Boussinesq equations write:

∂ t v i + v j ∂ j v i = - ∂ i p ρ -g i αϑ + ν∂ j ∂ j v i (1)
∂ t ϑ + v j ∂ j ϑ = κ∂ j ∂ j ϑ (2) ∂ j v j = 0 (3)
g is the gravitation acceleration, p the pressure and ρ the density of the fluid. ϑ is the temperature, but could as well be the scalar concentration. The origin of ϑ is such that its mean value on the channel is zero. α is the coefficient of thermal expansion.

We are interested in the turbulent case, where the mean diffusive heat flux is negligible compared to the convective one. Multiplying equation 1 by v i and averaging, it is easy to show that the kinetic energy input rate per unit mass, , is simply related to the heat flux

Q i [12]: = -g i α ϑv i = -g i α Q i C p (4
) * bernard.castaing627@laposte.net † Francesca.Chilla@ens-lyon.fr 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 000000000000000000000000 where C p is the heat capacity per unit volume. can also be expressed as:

111111111111111111111111
= ν ∂ i v j ∂ i v j (5)
which gives a useful evaluation for the typical velocity gradients:

∂ i v j ∂ i v j = ν 1/2 (6)
The characteristics of the inertial regimes is that the velocity, at constant inclination angle, scale with [START_REF] Gibert | Heat Convection in a Vertical Channel: Plumes versus Turbulent Diffusion[END_REF][START_REF] Tisserand | Convection in a vertical channel[END_REF]:

U t = Qdgα C p 1/3 ( 7
)
where d is the channel diameter.

In the same regimes, as can be seen from equation 1, temperature fluctuations scale with U 2 t . In spite of this great unity in the scaling behavior, three different regimes have been observed, concerning the relation between the temperature (or scalar concentration) gradient:

β = - d ϑ dz (8)
along the channel, and the heat flux Q, thus between the Nusselt number:

N u = Q C p κβ (9)
and the Grashof number:

Gr = gαβd 4 ν 2 (10)
At large Grashof number, every one [START_REF] Gibert | High-Rayleigh-Number Convection in a Vertical Channel[END_REF][START_REF] Tisserand | Convection in a vertical channel[END_REF][START_REF] Schmidt | Axially homogeneous Rayleigh-Benard convection in a cylindrical cell[END_REF][START_REF] Cholemari | Axially homogeneous, zero mean flow buoyancy-driven turbulence in a vertical pipe[END_REF]] agree with:

N u P r ∝ Gr 1/2 (11)
At lower Grashof number, Riedinger et al. [START_REF] Riedinger | Heat transport regimes in an inclined channel[END_REF] evidenced a regime:

N u ∝ Gr 2 (12)
without precision on the Prandtl number (P r) dependence. Recently, Pawar and Arakeri [START_REF] Pawar | Two regimes of flux scaling in axially homogeneous turbulent convection in vertical tube[END_REF] observed a new regime:

N u P r ∝ Gr 0.3 (13)
The goal of this paper is to explain these regimes, and to precise their P r dependence. We shall show that the Gr 0.3 regime can be intermediate between the two other ones, and disappears for P r < 1. We shall call this regime the Batchelor regime (for reasons which appear below). In a similar way, the Soft Turbulence regime (Gr 2 ) should disappear for P r >> 1. Following Riedinger et al [START_REF] Riedinger | Heat transport regimes in an inclined channel[END_REF], we shall call the N u ∝ Gr 1/2 regime the Hard Turbulence one. We develop our arguments in the following section.

II. EVALUATION OF β

Our evaluation of β will be based on two different expressions for the entropy production per unit volume Ṡ. On the one hand, with a macroscopic point of view,

Ṡ = - Q i ∂ i T T 2 Qβ T 2 (14)
where the absolute temperature T = T o + ϑ , and T o is the mean temperature of the channel. On the other hand, this entropy production can be related to the microscopic dissipative temperature gradients:

Ṡ = C p κ ∂ i ϑ∂ i ϑ T 2 (15)
This gives a formal expression for β:

β = C p κ ∂ i ϑ∂ i ϑ Q (16)
Calling θ = ϑϑ the temperature fluctuation, and neglecting the average gradients compared to the instantaneous ones, ∂ i ϑ∂ i ϑ can be evaluated as the mean squared fluctuation at the temperature dissipative scale θ 2 D , divided by this dissipative scale η θ squared:

∂ i θ∂ i θ θ 2 D η 2 θ ( 17
)
The evaluation of θ 2 D and η θ will differ, depending if the Prandtl number is larger or smaller than 1.

A. Small Prandtl

Let us first assume that the Reynolds number is sufficient to have a Kolmogorov velocity inertial range, between the velocity correlation scale and the velocity dissipation scale η. At small Prandtl number, the temperature dissipative scale is larger than the velocity one: η θ > η. η θ is such that the typical stirring frequency at this scale equals the dissipation frequency:

v(η θ ) η θ U t (η θ / ) 1/3 η θ κ η 2 θ ( 18
)
This qualitatively means that, at scales lower than η θ , due to the rapidity of diffusion, stirring is unable to enhance the temperature gradients. The temperature dissipative scale η θ can then be expressed as:

η θ κ U t 3/4 (19)
In a Kolmogorov 41 approach, the temperature fluctuation at the scale η θ writes [START_REF]Bolgiano range where θ 2 r depends on the scale r as r 2/5 , down to a Bolgiano scale B . Such a range has often be reported in convection flows[END_REF]:

θ 2 D θ 2 η θ 2/3
(20)

According to equation 1, the mean squared temperature fluctuation θ 2 in an inertial regime, is of order:

θ 2 U 4 t (gα ) 2 (21)
Using equation 17, we have thus the following estimate for

∂ i θ∂ i θ ∂ i θ∂ i θ U 4 t (gα ) 2 η θ 2/3 1 η 2 θ = U 4 t (gα 2 ) 2 η θ -4/3 (22) = U 4 t (gα 2 ) 2 U t κ = U 5 t κ(gα) 2 3 (23)
and for β, using equations 7,16:

β κdgα U 3 t U 5 t κ(gα) 2 3 = U 2 t d gα 3 (24)
The Nusselt number N u is given by:

N u P r = Q C p νβ = U 3 t dgανβ (25)
We now can write, using equation 24:

N u P r βgα 3 d 3/2 1 dgανβ = d 9/2 gαβd 4 ν 2 1/2 = AGr 1/2 (26)
where A = ( /d) 9/2 is a constant. We thus find, as expected the Hard Turbulence regime (Small Prandtl Hard Turbulence SPHT). At lower Reynolds number, the temperature dissipative scale η θ join [START_REF]Bolgiano range where θ 2 r depends on the scale r as r 2/5 , down to a Bolgiano scale B . Such a range has often be reported in convection flows[END_REF]. We simply have to make η θ = in equation 22:

∂ i θ∂ i θ U 4 t (gα 2 ) 2 (27)
Then:

β κdgα U 3 t U 4 t (gα 2 ) 2 = κU t d gα 4 (28)
and:

N u P r βgα 4 dκ 3 1 dgανβ = d 12 gαβd 4 ν 2 2 ν κ 3 = BP r 3 Gr 2 (29)
where B = ( /d) 12 is a constant. This is the Soft Turbulence scaling (Small Prandtl Soft Turbulence SPST). Note however that it does not coincide here with the absence of inertial range: as η θ > η, we yet have > η.

The two scalings above can be presented as:

N u = A(GrP r 2 ) 1/2 and N u = B(GrP r 2 ) 2 (30)
which shows that in a diagram N u versus GrP r 2 all the different (small) Prandtl numbers merge on a single curve (at constant inclination angle ψ).

B. Hight Prandtl

When the Prandtl number is large, the high Reynolds number situation is different. Indeed, the Kolmogorov inertial range is followed by a Batchelor range of scales [START_REF] Tennekes | A first course in Turbulence[END_REF] in which the temperature fluctuations remain approximately constant. The temperature dissipative scale η θ is now smaller than the velocity one η:

v(η θ ) η θ = v(η) η ν 1/2 = U 3 t dν 1/2 κ η 2 θ ( 31
)
On the other hand:

θ 2 D θ 2 η 2/3 U 4 t (gα ) 2 ν 3 d U 3 t 4 1/6
(32)

Using equations 16, 17, 31, and 32, we have:

β gαd U 3 t U 3 t dν 1/2 U 7/2 t ν 1/2 d 1/6 (gα ) 2 2/3 = U 2 t gαd 2 d 8/3 (33)
and using equation 25:

N u P r = U 3 t dgανβ 1 dgανβ gαβd 2 3/2 d 4 = CGr 1/2 ( 34
)
where C = ( /d) 4 is fixed at constant inclination angle ψ. This is the Hard Turbulence regime (High Prandtl Hard Turbulence HPHT). At lower Reynolds number, η reaches , there is no Kolmogorov inertial range, but the velocity gradients continue to smooth down, and the temperature dissipative scale η θ continues its growth as:

κ η 2 θ U 3 t dν 1/2 (35)
In the absence of inertial range, θ 2 D θ 2 and, using equations 16, 17:

β gαd U 3 t U 4 t (gα ) 2 U 3 t dν 1/2 = U 5/2 t gαν 1/2 d 3/2 d 2 (36)
As in the previous cases, we can extract the expression of U t versus β:

U t gαβν 1/2 d 3/2 2/5 d 4/5 (37)
and use it in the expression of N u/P r:

N u P r = U 3 t dgανβ gαβd 4 ν 2 1/5 d 12/5 = EGr 1/5 (38)
where E = ( /d) 12/5 is fixed at constant inclination angle ψ. While the resulting exponent (0.2) differs from the Pawar et al. [START_REF] Pawar | Two regimes of flux scaling in axially homogeneous turbulent convection in vertical tube[END_REF] one (0.3), these regimes nicely correspond in other aspects. Indeed, plotting N u/P r versus Gr, all Prandtl number merge on a single curve, as observed by Pawar et al.. Moreover, as this regime occurs between a Gr 1/2 and a Gr 2 (see below) regimes, it appears as an inflexion point, whose slope hardly reaches the asymptotic one. We shall call this regime the Batchelor regime (High Prandtl Batchelor Regime HPBR). Finally, if the Prandtl number is not too high, in such a way that η θ reaches [START_REF]Bolgiano range where θ 2 r depends on the scale r as r 2/5 , down to a Bolgiano scale B . Such a range has often be reported in convection flows[END_REF] while the flow is always in a turbulent inertial regime, then η θ cannot grow further. Then:

∂ i θ∂ i θ θ 2 2 (39)
and, using equation 16:

β gαd U 3 t U 4 t (gα ) 2 κ 2 = U t κ gαd 4 d 4 (40)
Then, using equation 25:

N u P r = U 3 t dgανβ gαβd 4 κ 2 2 κ ν d 12 = B Gr 2 P r 3 (41)
where B = ( /d) 12 is a constant at constant inclination angle ψ. This is the Moderate Prandtl Soft Turbulence scaling, which can also be written N u = B (GrP r 2 ) 2 , and is identical to the Small Prandtl Soft Turbulence (B = B).

III. DISCUSSION AND CONCLUSION

Let us thus resume the succession of inertial regimes, starting from the highest Grashof numbers and going down. When the Prandtl number is low, the Hard Turbulence regime (SPHT) corresponds to the relation: [START_REF]Échanges turbulents en convection thermique[END_REF], in reasonable agreement despite the square cross section of the channel. Green triangle down correspond to an inclined channel (5 degrees, reference [START_REF] Riedinger | Heat transport regimes in an inclined channel[END_REF]), and show the same succession of regimes, while with other values for the constants A, B, and E.

N u = A(GrP r 2 )
At lower Grashof number, but sufficient to have an inertial regime, we have the Soft Turbulence regime (SPST), where:

N u = B(GrP r 2 ) 2 (43)
The transition corresponds to the temperature dissipative scale reaching the large correlation scale [START_REF]Bolgiano range where θ 2 r depends on the scale r as r 2/5 , down to a Bolgiano scale B . Such a range has often be reported in convection flows[END_REF]. On a N u versus GrP r 2 diagram, all the (small) Prandtl number merge on the same curve.

When the Prandtl number is large, the highest Grashof numbers again correspond to the Hard Turbulence regime (HPHT):

N u P r = CGr 1/2 (44)
At lower Grashof number, but sufficient to have an inertial regime, we have here the Batchelor regime (HPBR):

N u P r = EGr 1/5 (45)
The transition corresponds to the velocity dissipative scale reaching the large correlation scale, thus to the Kolmogorov inertial range vanishing. On a (N u/P r) versus Gr diagram, all the (high) Prandtl number merge on the same curve.

At even lower Grashof number, but always sufficient to have an inertial regime (thus for moderate Prandtl numbers), we find back the Soft Turbulence regime, equation 43. Again, the transition corresponds to the temperature dissipative scale reaching the large correlation scale [START_REF]Bolgiano range where θ 2 r depends on the scale r as r 2/5 , down to a Bolgiano scale B . Such a range has often be reported in convection flows[END_REF].

We find thus all the observed regimes [START_REF] Riedinger | Heat transport regimes in an inclined channel[END_REF][START_REF] Pawar | Two regimes of flux scaling in axially homogeneous turbulent convection in vertical tube[END_REF], if we identify the predicted exponent for the Batchelor regime (0.2) to the observed one [START_REF] Pawar | Two regimes of flux scaling in axially homogeneous turbulent convection in vertical tube[END_REF] (0.3). Moreover, these regimes nicely underline the existence of the Kolmogorov or Batchelor ranges of scales, and their clearcut transitions.

FIG. 1 .

 1 FIG. 1. A sketch of the typical cell showing the definition of the coordinates, and of the inclination angle ψ.

  FIG. 2. Open and full circles, squares, stars and crosses are data from Pawar and Arakeri [15], corresponding to high or moderate Prandtl numbers in a circular cross section vertical channel. With this compensated plot, the visible plateau in the open circle and crosses data is the Batchelor regime. The high Gr regime is the Hard Turbulence one. The lowest Grashof open circle points probably shows the Soft Turbulence regime (moderate Prandtl). Also shown are data (gray triangles up) from reference

		-1/5 N u/P r	10 2
		Gr					(8.3 ± 3)Gr 0.3 (0.75 ± 0.1)Gr 1/2 25Gr 1/5
			10 3 10 1	10 4	10 5	10 6	10 7	10 8	10 9
						Gr
	1/2	(42)