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Reprogramming Antagonizes the Oncogenicity of HOXA13‐Long Noncoding RNA HOTTIP Axis in Gastric Cancer Cells

Reprogramming of cancer cells into induced pluripotent stem cells (iPSCs) is a compelling idea for inhibiting oncogenesis, especially through modulation of homeobox proteins in this reprogramming process. We examined the role of various long noncoding RNAs (lncRNAs)‐homeobox protein HOXA13 axis on the switching of the oncogenic function of bone morphogenetic protein 7 (BMP7), which is significantly lost in the gastric cancer cell derived iPS‐like cells (iPSLCs). BMP7 promoter activation occurred through the corecruitment of HOXA13, mixed‐lineage leukemia 1 lysine N‐methyltransferase, WD repeat‐containing protein 5, and lncRNA HoxA transcript at the distal tip (HOTTIP) to commit the epigenetic changes to the trimethylation of lysine 4 on histone H3 in cancer cells. By contrast, HOXA13 inhibited BMP7 expression in iPSLCs via the corecruitment of HOXA13, enhancer of zeste homolog 2, Jumonji and AT rich interactive domain 2, and lncRNA HoxA transcript antisense RNA (HOTAIR) to various cis‐element of the BMP7 promoter. Knockdown experiments demonstrated that HOTTIP contributed positively, but HOTAIR regulated negatively to HOXA13‐mediated BMP7 expression in cancer cells and iPSLCs, respectively. These findings indicate that the recruitment of HOXA13–HOTTIP and HOXA13–HOTAIR to different sites in the BMP7 promoter is crucial for the oncogenic fate of human gastric cells. Reprogramming with octamer‐binding protein 4 and Jun dimerization protein 2 can inhibit tumorigenesis by switching off BMP7. Stem Cells 2017;35:2115–2128

INTRODUCTION
The gene for homeobox protein HOXA13 (HOXA13) is located as the most-posterior gene of the HOXA cluster in 7p15.2 [1]. Expression of HOXA13 is important for homeostasis in morphogenesis [1]. HOXA13 has a role in cancer promotion of liver stem-like cell lines [2] and the growth of esophageal cancer [3], and is involved in hepatocarcinogenesis [4] and leukemogenesis [5]. Moreover, HOXA13 is a poor prognostic marker for gastric cancer patients [6]. Recently we found that upregulation of HOXA13 plays a critical role in the a Division of Gastroenterology, tumorigenesis of gastric cancer by comparing the nontumorigenic gastric cell line CSN and its derivative cancerous subline CS12 [7].
HOXA13 is reported to directly control the expression of bone morphogenetic protein 7 (BMP7) via binding to its enhancer element [8,9]. BMP7 is a multifunctional growth factor belonging to the transforming growth factor beta superfamily. In addition to its function in bone formation and cartilage development, BMP7 is relevant in cell proliferation, apoptosis, and organ regulation and repair. In cancer, BMP7 behaves as an oncogene [10] or anti-oncogene [11] depending on ligands and cell types [12].
The long noncoding RNA (lncRNA) HOXA transcript at the distal tip (HOTTIP), located at the 5 0 -end of the HOXA cluster, is expressed significantly in anatomically distal human fibroblasts [13]. The interaction of the HOTTIP and WD repeatcontaining protein 5 (WDR5)/mixed-lineage leukemia 1 lysine N-methyltransferase (MLL1 also known as histone-lysine Nmethyltransferase 2A or KMT2A) in Tithorax complex enhances histone H3 lysine 4 trimethylation (H3K4me3) to activate the expression of 5 0 -HOXA genes including HOXA13 [14,15]. Increased HOTTIP expression is a negative prognostic factor in some cancers [16] and promotes tumor progression and drug resistance by regulating HOXA13 in pancreatic cancer [17]. Another lncRNA HOTAIR is expressed from the posterior region of the HOXC11-12 cluster and is implicated in cancer development. HOTAIR interacts with enhancer of zeste homolog 2 (EZH2), suppressor of zeste 12 homolog subunit (SUZ12) in Polycomb repressive complex 2, and lysine demethylase 1A (LSD1 or KDM1A) to mediate repressive histone modifications at H3K27 and H3K4 at posterior HOXD locus [18,19]. Thus, the actions of HOTTIP and HOTAIR to HOXA13 and HOXD10 loci are cis and trans, respectively [14,15,18,19]. However, to our knowledge no common targets, which are regulated by both lncRNAs are present previously and the mechanistic regulation of the lncRNAs-HOX axis on their target remains to be clarified [20].
We have shown that the Jun dimerization protein 2 (JDP2) is a modulator of wingless-related integration site signaling pathway [21] and works together with octamer-binding protein 4 (OCT4) for the reprogramming of medulloblastoma to generate induced pluripotent stem cell (iPSC)-like cells (iPSLCs) [22]. Liu et al. [23] also reported that JDP2 reprograms the somatic cells into the iPSCs through the five non-Yamanaka factors (Id1, Jhdm1b, Lrh1, Sall4, and Glis1). Thus, JDP2 is a critical factor for the determination of stemness. Here, we used iPSC technology [24] with JDP2 and OCT4 to reprogram CS12 gastric cancer cells to generate induced pluripotent cancer cells [25,26], and found a reduced tumorigenicity in CS12-derived iPSLCs, suggesting that reprogramming of CS12 cells seems to abrogate the oncogenic function of HOXA13 in CS12 cells [7]. Mechanically, although the expression of HOXA13 was increased in both CS12 cells and iPSLCs, HOXA13 behaved as an oncogene in CS12 but acted as a tumor suppressor in iPSLCs through working with HOTTIP and HOTAIR to up-and down-regulate BMP7 expression, respectively. Knockdown of HOTTIP and BMP7 in CS12 cells decreased cancerous features but downregulation of HOTAIR in iPLSCs promoted cell proliferation. These results suggest that networking between HOXA13 and lncRNAs can manipulate the oncogenic expression of BMP7 in human gastric cancer cells.

Cell Lines, Animals, Antibodies
Human gastric normal cells CSN and cancerous CS12 cells were cultured as described elsewhere [27]. 293T, and mouse embryonic fibroblasts (MEFs) were obtained from the RIKEN Cell Bank (Tsukuba, Ibaraki, Japan, http://cell.brc.riken.jp/en/) and cultured in Dulbecco's modified Eagle's minimal essential medium (DMEM; Gibco, Carlsbad, CA, https://www.thermofisher.com) supplemented with 10% charcoal-stripped fetal bovine serum (FBS; Gibco) with or without 1% penicillin and streptomycin (Gibco) as described elsewhere [7]. The animal welfare guidelines for the care and use of laboratory animals were approved by the Animal Care Committee of the RIKEN BioResource Center in Japan, the National Laboratory of Animal Center and the Kaohsiung Medical University in Taiwan. All antibodies used in this work are listed in Supporting Information Table S1.

Cell Proliferation, BrdU-Incorporation, and Colony Assay
The numbers of living cells were quantitated using a trypan blue dye-exclusion assay [21,29]. To analyze the cell cycle, serum-starved cells were stimulated with 15% FBS and then labeled with 10 lM BrdU (Sigma-Aldrich) for 1 hours, trypsinized and fixed overnight with 70% ethanol at 48C. The fixed cells were incubated in 2M HCl for 1 hours at room temperature, neutralized with 0.1 M Na 2 B 4 O 5 (OH) 4 .8H 2 O, stained by BrdU antibody (BD Bioscience, San Jones, CA, http://www. bdbiosciences.com) at room temperature for 2 hours, followed by Alexa 488-conjugated secondary antibody at room temperature for 30 minutes. DNA was stained with propidium iodide (PI; 5 mg/ml). Cell samples were analyzed with EPICS XL-MCL flow cytometer (Beckman Coulter, Miami, FL, https://www. beckmancoulter.com). To quantitated the cells in S phase, the BrdU Labeling and Detection Kit (Sigma-Aldrich) was used [29]. For the colony-formation assay, cells were plated in duplicate at 5 3 10 2 or 5 3 10 3 cells/10 cm gelatin-coated dish. Two weeks later, colonies with a diameter > 2 mm were counted after staining with Giemsa staining solution (Wako Chemicals, Tokyo, Japan, http://www.wako-chem.co.jp/english/).

Teratoma, Tumor Formation, and Immunohistochemistry
CS12 and CS12iPSLCs were injected subcutaneously into the dorsal flank of SCID mice as described [7,29]. Tumor size was measured once or twice a week using a caliper. Tumor volume was estimated according to the formula: volume (cm 3 ) 5 (L 3 W 2 )/2, where L and W are the length and width of the tumor, respectively. The teratomas were fixed in 4% paraformaldehyde overnight and embedded in paraffin. Sections were stained with hematoxylin and eosin.

Alkaline Phosphatase, Immunocytochemistry, and Karyotyping
Alkaline phosphatase activity measurement and immunocytochemistry were performed as described elsewhere [29,30]. Chromosome spreads for karyotyping were prepared using a conventional air-drying technique. GTG (G-banding) staining was performed as described elswewhere [32].

Migration, Invasion, and Chemoresistance Assays
Cells (1 3 10 4 cells) resuspended in DMEM without FBS were seeded in the upper Transwell with or without Matrigel (1 mg/ ml, Corning, NY, https://www.corning.com) as described elsewhere [29]. The lower plate contained DMEM plus 10% FBS. Three days later, the cells moved to downside of Transwell were fixed with 4% formaldehyde, stained with 1% crystal violet, and counted under a microscope. Regarding chemoresistance assay, cells were seeded in 96-well plates for 24 hours and then treated with a serial mixture of cisplatin, 5-fluorouracil, and doxorubicin for additional 48 hours. Cell viability was examined using the MTT assay as described elsewhere [21,29].

Reverse Transcriptase PCR and Quantitative Polymerase Chain Reaction
Total RNA was extracted from cells using the TRIzol reagent (ThermoFisher Scientific). RNA was reverse transcribed to cDNA using a reverse transcription kit (Promega). PCR was performed using the GoTaq Green Master Mix (Promega). Quantitative polymerase chain reaction (qPCR) was performed using the QuantiFast SYBR Green PCR kit (Qiagen, Gaithersburg, MD, https://www.qiagen.com) as described elsewhere [7,21]. bactin and GAPDH were used as internal controls. Primer sequences are listed in Supporting Information Tables S2-S4.

Transient Transfection and Luciferase Assay
Transient transfection and the luciferase assay were performed as described elsewhere [7,21,[33][34][35]. Cells were plated into each well of a 12-well plate and cultured for 24 hours. The cells were then cotransfected with indicated amounts of various effector constructs and BMP7-promoterluciferase reporters using Lipofectamine 2000 (Invitrogen). The total amount of transfected DNA was kept constant at 1 lg per well by the addition of pBluescript. After 48 hours or the indicated period of incubation, cells were harvested for luciferase activity assay using Dual-Luciferase Reporter Assay System (Promega) and an illuminometer (Berthold Technologies, Bad Wildbad, Germany, https://www.berthold.com).

Chromatin Immunoprecipitation Assay
Chromatin immunoprecipitation (ChIP) assay was performed as described elsewhere [7,29,33]. The immunoprecipitated protein-DNA complexes were washed twice with binding buffer (10 mM HEPES, pH 7.9, 10 mM Tris-HCl, pH 7.9, 12.5% glycerol, 0.25% NP-40, 0.5% Triton X-100, 0.24 M NaCl, Wu, Wang, Liu et al. 2117 www.StemCells.com V C 2017 The Authors STEM CELLS published by Wiley Periodicals, Inc. on behalf of AlphaMed Press 0.75 mM MgCl 2 , 1.1 mM EDTA, and protease inhibitor mixture) and then washed twice with Tris-EDTA buffer (10 mM Tris-HCl, pH 7.9, and 1 mM EDTA). The protein-DNA complexes were disrupted with proteinase K (Sigma-Aldrich) at pH 6.8. DNA was extracted with phenol and chloroform, precipitated in ethanol, and analyzed by real-time PCR using the Power SYBR Green Master Mix (Invitrogen). The PCR conditions consisted of 1 cycle of 2 minutes at 508C and 1 cycle of 10 minutes at 958C followed by 40 cycles of 958C for 15 seconds, and 558C2608C for 60 seconds. The primers used in these experiments are shown in Supporting Information Table S5.

RNA Immunoprecipiotation Assay
RNA immunoprecipiotation (RIP) assay was performed as described elsewhere [36]. Cells were lysed in RIP lysis buffer (10 mM HEPES, pH 8.0, 40 mM KCl, 3 mM MgCl2, 5% glycerol, 2 mM DTT, 0.5% sodium deoxycholate, 100 unit/ml RNase inhibitor and protease inhibitors) and the resulted cell lysates (1 mg) were incubated with HOXA13 antibody for 2 hours, followed by 1 mg/ml tRNA-precleared protein A/G beads for another 2 hours. The immunoprecipitates were washed by RIP lysis buffer and resuspended in TRIzol reagent (ThermoFisher Scientific) to extract the immunoprecipitated RNA for reverse transcription and qPCR.

Statistical Analyses
The data are presented as the mean 6 SEM from 3-6 experiments. Difference between two and multiple experimental groups was assessed using two-tailed Student's t tests and one-way ANOVA, respectively. Survival analysis was performed using the Kaplan-Meier method with log-rank test. p < .05 was considered statistically significant.

Comparative Expression of Stemness Genes in iPSLCs and CS12 Cells
We used transcription factors, JDP2 and OCT4, to induce reprogramming and generate iPSCs as described previously [22], because it was not feasible to use the standard four Yamanaka factors [24] to generate stem cell-like colonies (data not shown) in the normal human gastric mucosa cell line CSN and its derived CS12 cancer cells [7]. Three weeks after electroporation of JDP2 and OCT4, small, packed, domed iPSC-like colonies were detected (Fig. 1A). The number of colonies with a typical iPSC phenotype increased over time by repetitive passage. When iPSLCs were cultured for more than 4 weeks, the CS12iPSLCs displayed strong alkaline phosphatase activity (Fig. 1A). qPCR revealed that the endogenous expression of OCT4 and KLF4 in CS12iPSLCs increased about 3.4-and 5.3-fold, respectively, but NANOG and ESRRB were repressed by 50% compared with CS12 cells (Fig. 1B). Endogenous expression of JDP2 and p53 was also reduced by about 50% in CS12iPSLCs (Fig. 1B). RNA sequence analysis showed that the primitive germ layer markers such as SOX1 (ectoderm), SOX17 (endoderm), and T (mesoderm) were not changed between CS12 and CS12iPLSCs (Supporting Information Fig. S1A); by contrast, the pluripotent markers ESRRB and KLF2, but not REX1 and SALL4, were reduced by 30%-45% in CS12iPSLCs (Supporting Information Fig. S1A). Immunofluorescence staining confirmed the expression of stemness markers (OCT4, SOX2, NANOG, SSEA4, Tra-1-60, Tra-1-81) in CS12iPSLCs (Fig. 1C). To confirm the pluripotency [29], we transferred CS12iPSLCs into SCID mice to generate teratomas, which showed two germ layers such as endoderm and ectoderm (Fig. 1D). However, three germ layers were not observed in any instance. These results suggest that CS12iPSLCs were not fully pluripotent. The karyotype analysis showed no changes of chromosomes in CS12iPSLCs (Supporting Information Fig. S1B).

CS12iPSLCs Exhibited Reduced Tumorigenicity In Vivo
To compare the tumorigenicity of CS12 and CS12iPSLCs, we injected these cells subcutaneously into SCID mice. One month after injection, tumors generated by CS12 cells expanded to 2.5 cm 3 , which was 10 times the size of those generated by CS12iPSLCs (Fig. 1E). Both sizes and weights of tumors generated by CS12iPSLCs were smaller than those of CS12 cells (Fig. 1F, 1G). CS12iPSLCs-derived xenografts exhibited reduced numbers of necrotic cells and the cells with large nucleus and mitosis (Fig. 1E). Thus, CS12iPSLCs exhibited reduced tumorigenicity in vivo when compared with CS12 cells.

Decreased Cell Proliferation, Colony Formation, Cell Migration and Invasion, and Drug Resistance in CS12iPSLCs
To verify the difference in oncogenic activity between CS12 and CS12iPSLCs, cell proliferation, colony formation, cell migration, invasion, and drug resistance were examined. The CS12 cells proliferated more rapidly than CS12iPSLCs and CSN cells (Fig. 2A). The colony-forming ability of CS12 cells was twofold higher than that of CS12iPSLCs (Fig. 2B). Cell cycle analysis of bromodeoxyuridine (BrdU)-labled cells and BrdU ELISA assay confirmed that cell proliferation of CS12iPSLCs was reduced (Fig. 2C). No increase in apoptosis was found in CS12iPSLCs (Supporting Information Fig. S1C). Transwell assays demonstrated that both the migration and invasion efficacies of CS12 cells were higher than those of CS12iPSLCs and CSN cells (Fig. 2D), which were consistent with the increased expression of Snail 1 family transcriptional repressor 1/2 (Snail1/2), Matrix metalloproteinase 2 (MMP2), Vimentin, and with the decrease of E-CADHERIN in CS12 cells (Supporting Information Fig. S1D, S1E). To examine the drug resistance, cell viability was measured by incubating cells with a mixture of three conventional drugs for chemotherapy: 5-fluorouracil, cisplatin, and doxorubicin. CS12 cells showed greater resistance to these drugs than CS12iPSLC and CSN cells (Fig. 2E), demonstrating the increased chemoresistance of CS12 cells.

Enhanced Expression of HOXA13 in CS12iPSLCs and CS12 Cells
We previously reported that several HOXA genes were upregulated in CS12 when compared with CSN cells [37] and upregulated HOXA13 contributed to the tumorigenicity of CS12 cells [7]. Here, we found that the expressions of HOXA4, 5, 7, and 9 were decreased in CS12iPSLCs when compared with   To clarify the effects of HOXA13 on the different tumorigenicity between CS12 and CS12iPSLCs, HOXA13-knockdown CS12 cells (Fig. 3C), and CS12iPSLCs (Fig. 3E) were inoculated into SCID mice to compare xenograft formation ability. Knockdown of HOXA13 in CS12 cells decreased tumor   (Fig. 3D, 3G, 3I). In contrast, HOXA13-knockdown CS12iPSLCs increased tumor size and tumor weight (Fig. 3F, 3H, 3J). These data support the notion that HOXA13 in conjunction with some unspecified factors determined the tumorigenicity of CS12 cells and CS12iPSLCs.
Differential Expressions of BMP7 and lncRNAs were Critical to Determine the Tumorigenicity of CS12 Cells and CS12iPSLCs To further characterize the differences between CS12 and CS12iPSLCs, we examined the expression profiles of CS12 and CS12iPSLCs using RNA sequencing and confirmed that BMP7, a known HOXA13 target [8,9], was upregulated in CS12 compared with that in CS12iPSLCs ( Fig. 4A; Supporting Information Fig. S3B). Expressions of BMP7 protein and phosphorylated SMAD1 protein were indeed upregulated in CS12, suggesting the crosstalk of BMP7-SMAD axis [9] (Supporting Information Fig. S2B). BMP7 might be critical because it was altered significantly in several gene ontology categories, such as stem cancer stem, pluripotent stem progenitors, epithelial-mesenchymal transition (EMT)/mesenchymal-epithelial transition (MET), cytokines, and growth factors (Supporting Information Fig. S3B). Knockdown of HOXA13 but not c-JUN (as a control) in CS12 cells decreased BMP7 protein level (Supporting Information Fig.  S3A). Expressions of other HOXA13 targets including BMP2, insulin-like growth factor-binding protein 3 and annexin A2 as well as FGF8 were not changed (Fig. 4A).
In CS12 cells, siHOTTIP reduced the expression of HOXA13 as reported [6,12,14], but siHOTAIR did not affect the expression of HOXA13. By contrast, in CS12iPSLCs, siHOTTIP did not affect the expression of HOXA13, but siHOTAIR decreased the expression of HOXA13 (Supporting Information Fig. S4A). In CS12 cells, knockdown of BMP7 and HOTTIP, but not HOTAIR, decreased cell growth, colony formation, cell migration and invasion activity (Fig. 4E-4H), suggesting that HOTTIP and BMP7 contribute to the tumorigenicity of CS12 cells. Conversely, knockdown of HOTAIR, but not BMP7, in CS12iPSLCs, increased cell growth, colony formation, cell migration, and invasion activity (Fig. 4E-4H), indicating that HOTAIR played a tumor suppressor role in CS12iPSLCs. Knockdown of HOXA13 resulted in a reduced cell growth in CS12 cells but caused an increase of cell proliferation in CS12iPSLCs (Fig. 4E), which was consistent with the results of mouse xenograft model (Fig. 3). The reduced tumorigenicity of CS12iPSLCs was also associated with restored expressions of p53 and several CDK inhibitors including p16 Ink4a , p15 Ink4b , p18 Inc4c , p21 Cip1 , p27 Kip1 , and p57 Kip2 , together with decreased E2F-1 expression in CS12iPSLCs compared with that in CS12 cells (Supporting Information Fig. S4B).
On the other hand, overexpression of BMP7 in CS12iPSLCs increased cell proliferation, colony formation, and cell migration and invasion (Supporting Information Fig. S5A-S5E). The in vivo tumorigenicity of BMP7-transduced CS12iPSLCs showed increased tumor incidence and mortality compared with that of control cells (Supporting Information Fig. S5F,  S5G). Blood vessels, giant cells, mitotic cells, and necrosis were also detected significantly in the tumors derived from BMP7-transformed CS12iPSLCs (data not shown).
Differential Recruitment of HOXA13 to the BMP7 Promoter at S1 and S2 Sites in CS12 and CS12iPSLCs, Respectively Given that BMP7 was differentially expressed in CS12 and CS12iPSLCs (Fig. 4A, Supporting Information Fig. S2B, S3B), and was critical for determining the tumorigenicity of both cells (Fig. 4E-4H), and forced expression of BMP7 enhanced tumorigenic features in CS12iPSLCs (Supporting Information Fig. S5), we investigated how BMP7 expression was regulated in both cells. It is known that HOXA13 directly binds to the BMP7 promoter and induces BMP7 expression [9,38,39]. We conducted ChIP assays to examine the binding of HOXA13 to the BMP7 promoter in CS12iPSLCs and CS12 cells. Computational sequence analysis revealed three putative HOX-binding sites: S1 (-412/-416), S2 (-1,113/-1,117), and S3 (-1,613/-1,617) in the BMP7 promoter (Fig. 5A) (PROMO3; TRANSFAC v8.3) [40]. The results of ChIP showed that recruitment of HOXA13 to the S1 site was increased in CS12 cells but not in CS12iPSLCs. By contrast, binding of HOXA13 to the S2 site was increased in CS12iPSLCs but not in CS12 cells (Fig. 5A). No significant recruitment of HOXA13, HOXA9, or HOXA7 was found at the S3 site and nonspecific NS site. In addition, ChIP assays demonstrated an elevated trimethylation of lysine 4 on histone H3 (H3K4me3) and an increased recruitment of MLL1/ WDR5 at the S1 site in CS12 cells, but not in CS12iPSLCs (Fig.  5B), indicative of activated BMP7 promoter in CS12 cells. By contrast, increased H3K27me3 and recruitments of EZH2 and jumonji AT-rich interactive domain 2 (JARID2) [41] were found at the S2 site in CS12iPSLCs, but not in CS12 cells (Fig. 5B), explaining the reduced BMP7 expression in CS12iPSLCs. No significant difference in these epigenetic marks was observed at the S3 and NS sites in both cells (Fig. 5B). The expression levels of Polycomb group RING finger complex protein B lymphoma Mo-MLV insertion region 1 homolog (BMI1), EZH2, SUZ12, and JARID2 were significantly higher in CS12iPSLCs than in CS12 cells (Supporting Information Fig. S6). Moreover, an RNA immunoprecipitation experiment [36] demonstrated that the association between HOXA13 and HOTTIP was predominant in CS12; by contrast, HOTAIR-HOXA13 interactioon is evident in CS12iPSLCs (Fig. 5C). These findings suggested that the differential recruitment of HOXA13 was correlated with contrasting epigenetic marks at the S1 and S2 sites of the BMP7 promoter and conversed expression patterns of BMP7 in CS12 cells and CS12iPSLCs.

HOXA13 Differentially Upregulates and Downregulates
BMP7 Promoter Activity Through the S1 and S2 Sites, Respectively To further examine the roles of S1 and S2 sites in HOXA13mediated BMP7 expression, we cloned BMP7 promoters, both wild-type and HOX-binding site mutants, into a luciferase reporter plasmid (Fig. 6A) and then cotransfected them with a HOXA13-expressing plasmid into CS12 cells. The results showed that HOXA13 transactivated wild-type and S2-and S3- mutated BMP7 promoters comparably, but just marginally induced S1-mutated BMP7 promoter (Fig. 6B), supporting that HOXA13 induced BMP7 expression through S1 site in CS12 cells. To examine the effect of HOXA13 on BMP7 promoter in iPSLCs, we used a feasible promoter (pPyCAG) to drive HOXA13 expression in stem cells [35]. The results showed that

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Wiley Periodicals, Inc. on behalf of AlphaMed Press only S2 mutant displayed increased activity but all other mutants and wild type BMP7 promoter constructs showed significant repression in CS12iPSLCs (Fig. 6C), suggesting that HOXA13 inhibited BMP7 expression through S2 site.

HOTTIP-and HOTAIR-Differentially Regulated BMP7
Promoter Activity and the Epigenetic Configurations at S1 and S2 Sites Because HOTTIP and HOTAIR regulate gene expression through MLL-WDR5 and PRC2-EZH2 complexes, respectively [12,17,42], we examined the effects of siHOTTIP and siHO-TAIR on the recruitments of MLL-WDR5 and SUZ-EZH2-JARID2 to the BMP7 promoter in CS12 and CS12iPSLCs. The results showed that knockdown of HOTTIP, but not HOTAIR, reduced H3K4me3 and decreased the recruitment of MLL/ WDR5 to the S1 site in CS12 cells (Fig. 7A), suggesting that HOTTIP knockdown might decrease BMP7 promoter activity. Knockdown of HOTAIR, but not HOTTIP, reduced H3K27me3 and decreased the recruitment of EZH2 and JARID2 to the S2 site of the BMP7 promoter in CS12iPSLCs (Fig. 7B), indicating that HOTAIR contributed to the suppressive configuration at the S2 site. The knockdown of HOXA13 also showed similar patterns of HOTTIP-MLL-WDR5 axis in CS12 cells and HOTAIR-EZH2-JARID2 axis in CS12iPSLCs (Fig. 7A, 7B). Indeed, knockdown of HOTTIP or HOXA13 decreased BMP7 promoter activity in CS12 cells (Fig. 7C), while HOTAIR knockdown increased BMP7 promoter activity in CS12iPSLCs (Fig. 7D). These findings demonstrated that HOTTIP upregulated BMP7 expression in CS12, but HOTAIR suppressed BMP7 expression in CS12iPSLCs, both were involved with HOXA13 (Fig. 7E).

DISCUSSION
In this study, we reprogrammed the human gastric cancer cell line CS12 using OCT4 and JDP2 to generate CS12iPSLCs (Fig.  1). This technique is useful for cellular reprogramming of somatic cells and cancer cells into stem cells or stem-like cells, which can serve as disease model or a platform for drug screening. Notably, the reprogrammed CS12iPSLCs exhibited less tumor formation capacity in SCID mice when compared with the parental CS12 cells (Fig. 1E-1G), suggesting that reprogramming of cancer cells may be a strategy that could be developed for the future therapy.
Accumulating evidence reveals that high-level expression of BMP7 correlates with increased invasion and metastasis  [43], cartilage and bone repair [44], programmed cell death [45], and various cancers including gastric cancer [43,46]. Our knockdown experiments also supported an essential role of BMP7 in promoting cell proliferation, migration, invasion, and colony formation in CS12 cells (Fig. 4E-4H). Moreover, we found that forced expression of BMP7 in CS12iPSLCs, whose BMP7 level was low, increased oncogenic phenotypes such as xenograft tumor formation in SCID mice, invasive and migratory activity, colony formation and cell proliferation (Supporting Information Fig. S5). These results indicate that, at least in gastric cancer, BMP7 behaves as an oncogene.
Our results demonstrated the importance of the HOTTIP/ HOTAIR-BMP7 axis in gastric cancer (Fig. 4E-4H). In particular, the increase of tumorigenicity in CS12iPSLCs by HOTAIR knockdown suggests that HOTAIR is a tumor suppressor gene as previously reported in glioblastoma and pediatric ependymoma [47,48]. However, some studies suggest that HOTAIR is more likely to be an oncogene [42,49,50]. Thus, the precise role of HOTAIR may be context-dependent.
The differential recruitment of HOXA13 at positive and negative regulatory sites of the BMP7 promoter is a new finding, which demonstrates that HOXA13 can play either a positive or a negative role in expression of the same gene. Most previous studies only show the oncogenic role of HOXA13. Our study reveals that HOXA13 has an anti-oncogenic activity in CS12iPSLCs possibly through suppressing BMP7 expression (Figs. 3F, 3H, 3J, 6C, 7D). This study also demonstrates a novel, opposing regulation of the BMP7 expression pattern, which is regulated by HOTTIP-and HOTAIR-mediated epigenetic changes at the S1 and S2 sites of BMP7 promoter, respectively (Fig. 7). These epigenetic changes are differentially regulated in CS12 and CS12iPSLCs, which represent different gastric cancer stages. Thus, the present study reveals the complexity of epigenetic regulation of BMP7 expression during the development of gastric cancer.
This study has identified the differential effects of HOTTIP and HOTAIR on the S1 and S2 sites of BMP7 promoter that is correlated with upregulated and downregulated BMP7 expression, respectively. However, this finding does not fully explain the differential HOXA13 binding of S1 and S2 sites that is also correlated with BMP7 expression. Several issues need further investigation. First, the recruitment of HOXA13 at the S1 and S2 sites after knockdown of HOTTIP and HOTAIR can be examined to show the effect of lncRNAs on HOXA13 recruitment to the BMP7 promoter. Second, whether HOTTIP and HOTAIR directly target to S1 and S2 sites, respectively, or HOTTIP and HOTAIR regulate epigenetic changes at S1 and S2 sites indirectly is still unclear. Although our data showed the increased recruitment of WDR5/MLL to the HOTTIP-regulated S1 site and EZH2/JARID2 to the HOTAIR-regulated S2 site (Fig. 5), we did not yet check the presence of HOTTIP and HOTAIR at these sites. Third, if HOTTIP and HOTAIR are recruited to the S1 and S2 sites, is HOXA13 involved in this recruitment? Moreover, is HOXA13 required for HOTTIP and HOTAIR recruitment? The interactions between HOXA13 and HOTTIP-HOTAIR, either directly or through other factors such as WDR5 and EZH2/SUZ12, remain to be examined, although they are associated each other (Fig. 5C). We are exploring these questions to elucidate more comprehensively the mechanism underlying BMP7 expression regulated by HOXA13-HOTTIP-HOTAIR in different settings.

CONCLUSION
Here, we have identified BMP7 as the key target of HOXA13 to control cell growth, invasion, and migration of human gastric cancer cells. In addition to BMP7, the finding of involvement of the histone methyltransferase (HMT) MLL and EZH2 in regulating BMP7 expression has revealed potential targets for cancer therapy. Several HMT inhibitors are under development to inhibit MLL and EZH2-SUZ12 for therapeutic purpose. Thus, the MLL-WDR5-HOTTIP-HOXA13 axis in CS12 cells can be used to test the therapeutic efficacy of HMT inhibitors. Furthermore, the EZH2-JARID2-HOTAIR-HOXA13 axis in CS12iPSLCs provides a platform to screen carcinogens that increase BMP7 expression.


Signaling Pathway Activities Improve Prognosis for Breast Cancer

With the advent of high-throughput technologies for genome-wide expression profiling, a large number of methods have been proposed to discover gene-based signatures as biomarkers to guide cancer prognosis. However, it is often difficult to interpret the list of genes in a prognostic signature regarding the underlying biological processes responsible for disease progression or therapeutic response. A particularly interesting alternative to gene-based biomarkers is mechanistic biomarkers, derived from signaling pathway activities, which are known to play a key role in cancer progression and thus provide more informative insights into cellular functions involved in cancer mechanism. In this study, we demonstrate that pathway-level features, such as the activity of signaling circuits, outperform conventional gene-level features in prediction performance in breast cancer prognosis. We also show that the proposed classification scheme can even suggest, in addition to relevant signaling circuits related to disease outcome, a list of genes that do not code for signaling proteins whose contribution to cancer prognosis potentially supplements the mechanisms detected by pathway analysis.

Introduction
Over the past decades, many efforts have been addressed to the identification of gene-based signatures to predict patient prognosis using gene expression data (Paik, et al., 2004;Sotiriou and Pusztai, 2009;van 't Veer, et al., 2002;Wang, et al., 2005). Despite the success of its use, gene expression signatures have not been exempt of problems (Ein-Dor, et al., 2006;Iwamoto and Pusztai, 2010). Specifically, one major drawback of multi-gene biomarkers is that they often lack proper interpretation in terms of mechanistic link to the fundamental cell processes responsible for disease progression or therapeutic response (Dopazo, 2010;van't Veer and Bernards, 2008). Actually, it is increasingly recognized that complex traits, such as disease or drug response, are better understood as alterations in the operation of functional modules caused by different combinations of gene perturbations Barabasi and Oltvai, 2004;Oti and Brunner, 2007). To address this inherent complexity different methodologies have tried to exploit several functional module conceptual representations, such as protein interaction networks or pathways, to interpret gene expression data within a systems biology context Fryburg, et al., 2014;Hood, 2013;Vidal, et al., 2011). Actually, it has recently been shown that the pathway-level representation generates clinically relevant stratifications and outcome predictors for glioblastoma and colorectal cancer (Drier, et al., 2013) and also breast cancer (Livshits, et al., 2015). Moreover, mathematical models of the activity of a pathway have demonstrated a significantly better association to poor prognosis in neuroblastoma patients than the activity of their constituent genes, including MICN, a conventional biomarker (Fey, et al., 2015). This observation has recently been extended to other cancers (Hidalgo, et al., 2017) and to the prediction of drug effects (Amadoz, et al., 2015). Given that the inferred activity of the pathway should be closely related to its cellular mechanism for disease progression, its use to guide cancer prognosis seems promising. Recently, a number of pathway activity inference methods have been proposed (Hidalgo, et al., 2017;Jacob, et al., 2012;Li, et al., 2015;Martini, et al., 2013). Here, we use the canonical circuit activity analysis method, which has demonstrated to have a superior performance (Hidalgo, et al., 2017) finding significant associations of specific circuit activities, directly responsible for triggering the prominent cancer hallmarks (Hanahan and Weinberg, 2011), to patient survival. This method recodes gene expression values into measurements of signaling circuit activities that ultimately account for cell responses to specific stimuli. Such activity values can be considered multigenic mechanistic biomarkers that can be used as features for cancer prognosis. We demonstrate that the activity of signaling circuits yields comparable or even better prediction in breast cancer prognosis than the expression of individual genes, while detected mechanistic biomarkers enjoy the compelling advantage of readily available interpretation in terms of the corresponding cellular functions they trigger. Moreover, we show that the proposed prediction scheme can even suggest, in addition to interesting signaling circuits related to disease outcome, a list of prognostic genes that do not code for signaling proteins whose contribution to cancer prognosis potentially supplements the mechanism included in the pathways modeled.

Data source and processing
The breast cancer gene expression and survival data used here was downloaded from The Cancer Genome Atlas (TCGA), release No. 20 of the International Cancer Genome Consortium (ICGC) data portal (https://dcc.icgc.org/releases/release_20/Projects/BRCA-US). This dataset provides the RNA-seq counts of 18,708 genes for 879 tumor samples, in which we also have records of the vital status of corresponding donors, namely the overall survival outcome of the cancer patients being alive or deceased at the end of clinical treatment (Table 1). Since TCGA cancer data are from different origins and underwent different management processes, non-biological experimental variations, commonly known as batch effect, associated to Genome Characterization Center (GCC) and plate ID must be removed from the RNA-seq data. The COMBAT method (Johnson, et al., 2007) was used for this purpose. We then applied the trimmed mean of M-values normalization method (TMM) method (Robinson and Oshlack, 2010) for data normalization which is essential in applying the CCAA method. The resulting normalized values were finally entered to the pathway analysis method. A total of 60 KEGG pathways (Supplementary Table 1) were downloaded from the KEGG repository (Kanehisa, et al., 2012), including 2,212 gene products that participate in 3,379 nodes.

Modeling framework for signaling pathways
We applied the canonical circuit activity analysis method (Hidalgo, et al., 2017), as implemented in the hipathia R package available at https://github.com/babelomics/hipathia, in pursuit of modeling signaling activity. Within the modeling context, a circuit is defined as all possible routes the signal can traverse to be transmitted from a particular input node to a particular output node (see Supplementary Figure 1A). A total of 6,101 circuits are identified and modeled in this study. The transmission of the signal depends on the integrity of the chain of nodes that connect the receptor to the effector and briefly, it is estimated as follows. The presence of the mRNA (the normalized RNA-seq counts rescaled between 0 and 1) is taken as a proxy for the presence of the corresponding protein in each pathway node (Bhardwaj and Lu, 2005;Efroni, et al., 2007;Montaner, et al., 2009;Sebastian-Leon, et al., 2014). Then, the degree of integrity of the circuit is estimated by modeling the signal flow across it. Specifically, the input node (receptor) is initialized by an incoming signal of intensity value of 1, and then for each node n of the circuit, the signal value s n is updated by the following rule: where An denotes the set of signals arriving to the node from activation edges, In denotes the set of signals arriving to the node from inhibition edges, and vn is the (normalized) value of the current node n.
Finally, the activity value for the circuit is defined by the signal intensity transmitted through the last (effector) protein of the circuit which quantifies the cell function ultimately activated by the circuit.
Since output nodes at the end of circuits are the ultimate triggers of specific cellular actions, an effector circuit is defined from a functional viewpoint as a higher-level signaling entity that composes all circuits ending at the same output node. When applied to an effector circuit, the method returns the joint intensity of the signal arriving to the corresponding effector node (see Supplementary Figure 1B). Furthermore, the known functions triggered by each effector protein in cell can be derived from their functional annotations. Here we use UniProt (UniProt_Consortium, 2015) and Gene Ontology (Ashburner, et al., 2000) (GO) annotations. Finally, inferred signaling activity values of those effector circuits ending at proteins with the same annotated functions are averaged to quantify the activity of the function realized in cell. This way we obtain estimated activity values directly connected to a list of cellular functions (Supplementary Figure  1C). Supplementary Figure 1 depicts the different levels of abstraction from circuits, to effector circuits and finally functions. Eventually, a subset of curated functions can be used for a specific scenario in which the relevant functions are known. Here we use cancer hallmarks (Hanahan and Weinberg, 2011) .

Cancer prognosis with inferred signaling pathway activity
In this study, we are interested in evaluating the prognostic power of pathway-level mechanistic features and gene-based features alone and in combination. Using the hipathia method we recoded the list of gene expression values of each tumor sample into the corresponding lists of signaling activity values for the three levels of abstraction: circuits, effector circuits and functions, as described in UniProt and GO annotations. Therefore for each tumor sample we end up with a profile of gene expression, a profile of circuit signaling activity, a profile of effector circuit signaling activity, a profile of UniProt-based cellular function activity and a profile of GO-based cellular function activity. These profiles are sample-specific profiles that can be straightforwardly used as prognostic features using any classification algorithm. Note that pathway-level profiles are derived with no regard to any information provided by the genes whose products do not participate in cell signaling, and the prognostic power of pathway-level profiles may thus be limited by the coverage of genes in known biological pathways. In order to understand the relative contribution to the pathway-level profiles and gene-level profiles to the accurate separation between good vs poor prognosis, we devised four artificial profiles: path-gene expression profile containing only genes that are involved in the KEGG signaling pathways, other-gene expression profile containing only genes that are absent from the KEGG pathways, a combined profile consisting of signaling activity of effector circuits and expression of other-genes, and a combined profile consisting of signaling activity of circuits and expression of other-genes. Thus, we use a total of 9 types of profiles (detailed in Table 2) From the viewpoint of machine learning, this study is formulated as a typical binary classification problem where we determine a positive or negative pseudo label for each sample. Based on the data available in this study (Table 1) we perform a 5-fold cross-validation repeated 10 times on the dataset and report the mean performance over the 5 × 10 = 50 splits to assess the prognostic power for each type of profile. The performance is evaluated by the Area Under the ROC Curve (AUROC) criteria (Sing, et al., 2005). Note that usually a classifier returns a continuous prediction between 0 and 1 for each sample denoting the probability of that sample being in the positive class rather than in the negative class, and then assigns either label to the sample according to some cutoff value thresholding the prediction. In fact, AUROC is a cutoff-free score that measures the probability that the classifier will score a randomly drawn positive sample higher than a randomly drawn negative sample. In this study we consider 12 classification algorithms as candidate classifiers, most of which are state-ofthe-art (Table 3). When we assess the prognosis performance for a specific type of profile on a specific (external) cross-validation split of the data, we perform an internal 5-fold cross-validation on the training set to determine which classifier returns the highest cross-validated performance and the best classifier is then used on the test set to obtain the performance score. This procedure guarantees that the performance on each (external) cross-validation split is evaluated impartially for each profile with its best suited algorithm.  (Ripley, 2007;Venables and Ripley, 2002) LogitLasso L1-regularized logistic regression (Friedman, et al., 2010) LinearSVM Support Vector Machine with linear kernel (Chang and Lin, 2011) RadialSVM Support Vector Machine with Gaussian RBF kernel (Chang and Lin, 2011) KendallSVM Support Vector Machine with Kendall kernel (Jiao and Vert, 2016;Zeileis, et al., 2004) KNN k-nearest neighbor classifier (Ripley, 2007;Venables and Ripley, 2002) NB Naive Bayes classifier (Ripley, 2007) GBM Gradient Boosting Machine (Friedman, 2001) RF Random Forest (Breiman, 2001;Liaw and Wiener, 2002) SparseSVM L1-regularized L2-loss Support Vector Machine (Fan, et al., 2008) PAM Nearest shrunken centroid classifier (Tibshirani, et al., 2002) Constant Majority voting classifier -

Signaling pathway activity leads to improved prognosis for breast tumor samples
The performance of using different types of profiles (Table 2) as predictive features to classify survival outcome for breast cancer patients is shown in Figure 1 Under either criterion of AUROC to evaluate the classification performance, we observe that the activity values of signaling circuits, denoted by path.vals, yield the best performance overall. In particular, they outperform the profiles based solely on gene expression values, denoted by path.genes.vals, other.genes.vals and genes.vals. In other words, we are able to integrate the expression values of path-genes into the a priori knowledge of cell signaling to obtain pathway-level features that achieve improved prognosis. Interestingly, these pathway-level features relate to biological processes and cellular functions per se. Although the pathway-level features are derived from the expression of path-genes and thus agnostic to the expression of other-genes, the inclusion of other-genes to the signaling circuits activity values, denoted by eff.and.other.genes.vals and path.and.other.genes.vals profiles, does not significantly improve the performance (no significant differences after applying a two-sided t-test comparing differences between the cross-validation AUROC scores obtained by each pair of profiles, and adjusted for multiple testing (Benjamini and Hochberg, 1995), see Table 4). When comparing the prognostic power between pathway-level and gene-level profiles, we have also derived cellular function activity profiles, denoted by fun.vals and go.vals (Table 2), and observed that the performance of these profiles are slightly worse than other pathway-level profiles (Fig. 1). This is probably due to the excessively simplistic procedure that basically averages the signaling activity values of effector circuits ending at proteins with the same annotated keywords according to Uniprot or GO (Hidalgo, et al., 2017), annotations that can be incomplete and ambiguous to some extent. Table 4. FDR-adjusted p-values comparing the corresponding classification scores of feature s in columns versus features in files over 50 cross-validation splits. See Figure 1 for the performance values of each feature. Significant values are in boldface and marked with an asterisk..  Table 5 summarizes the best-performing classifiers for each type of prognostic profile in the sense that they are most frequently selected by internal cross-validation. Notably, it evidences that Support Vector Machines with various kernels are recurrently selected as the competent classifier in breast cancer prognosis that suits well for both gene-level and pathway-level features.

Signaling circuits selected as features relevant for cancer prognostic account for cancer hallmarks
From the clinical standpoint of cancer prognosis, we are interested in identifying a small set of biomarkers that can guide decision making in cancer prognosis. As our analysis is made at the level of pathways, we would like to detect a few signaling circuits whose activity, and thus the underlying cell functionality, has a significant impact on discriminating the prognosis classes of cancer patients. We opted for the Random Forest classifier to perform this analysis, since it simultaneously predicts the survival outcome of tumor samples and scores the importance of each feature that is ultimately used in the prediction. We focus on the feature importance measure returned by fitting a Random Forest which accounts for the mean decrease in classification performance if we randomly permute the data of the corresponding feature. Table 6 lists the five top-scored signaling circuits by fitting Random Forests with the profiles of circuit activities (denoted by path.vals). The role played by each signaling circuit in cancer progression can be inferred from the underlying cellular functions (taken from GO annotations) triggered by the last (effector) protein on the circuit. Thus, the first circuit, belonging to the HIF-1 signaling pathway, starts with the TLR4 receptor, which is known to be related to progression of several cancers (breast, ovarian, prostate and head and neck) via Lipopolysaccharide Stimulation (Yang, et al., 2014) and ends in the EDN1 effector, an hypoxia-inducible factor that mediates cancer progression (Semenza, 2012). Another relevant circuit belongs to the NF-kappa B signaling pathway and has the IL1B protein as receptor and the CXCL2 as effector. Polymorphisms in the receptor have been linked to several cancers in different populations (El-Omar, et al., 2000;Lu, et al., 2005) and it has been demonstrated the role of CXCL2 in tumor growth and angiogenesis (Keane, et al., 2004). Similarly, polymorphisms in the LEP protein, the receptor of another circuit in the Adipocytokine signaling pathway, have been linked to cancer (Cleveland, et al., 2010), and its effector, the tyrosine phosphatase Shp2 (PTPN11), contributes to the pathogenesis of many cancers and other human diseases (Chan, et al., 2008). The Cell cycle signaling pathway contains another relevant circuit whose receptor TTK transmits the signal until the cohesin complex. This four proteins complex is essential for chromosome segregation and DNA repair and mutations in its component genes have recently been identified in several types of tumors (Losada, 2014). Finally, the fifth most relevant circuit, belonging to the Tight junction pathway, contains the AKT3 serine/threonine kinase with a known role in tumorigenesis (Testa and Bellacosa, 2001), is signaled by the receptor ACTN4, a protein which has been related to cell invasion and metastasis (Honda, 2015). Supplementary Table 2 shows an expanded list of top-scored 50 circuits.  Table 7 lists the top-scored effector circuits by fitting Random Forests with the profiles of effector circuit activities (denoted by eff.vals). Although the cohesion complex effector is again selected, the effector circuit level analysis provided a slightly different perspective of relevant aspects of signaling in cancer patient survival. Thus, two effector circuits with effector proteins LEPR and PPARα, from the AMPK and the Adipocytokine signaling pathways, respectively, are activators of the fatty acid metabolism. Two more effector pathways ending in the Interleukin 6 (IL6), related to inflammatory processes and immune response in the Toll-like receptor pathway, seem more likely to be involved in blocking the cell differentiation through the Pathways in cancer (KEGG id hsa05200). Actually, it has been described that IL6 blocks apoptosis in cells during the inflammatory process, keeping them alive in toxic environments, but the same process protects cells from apoptosis and chemotherapeutic drugs during neoplastic growth (Hodge, et al., 2005). Supplementary Table 3 shows an expanded list of top-scored 50 effector circuits.
Beyond the top scored signaling circuits (Table 6) and effector circuits (Table 7), other relevant circuits are listed in Supplementary Tables 2 and 3. Although an exhaustive list of the consequences that processes differentially activated can have in tumorigenesis is beyond the scope of this work, it is worth noticing that cancer a hallmark such as apoptosis inhibition is represented by inhibition of signaling circuits IL6-BCL2 in the HIF-1 signaling pathway and IL10-BNIP3 in the FoxO signaling pathway (eighth and ninth in Supplementary Table 2, respectively), both containing the protein STAT3, known to mediate apoptosis inhibition in breast cancer (Gritsko, et al., 2006) (see Figure 2). The graphic representation of the complete effector circuits containing the two signaling circuits in the HIF-1 signaling pathway and the FoxO signaling pathway has been obtained with the hipathia web tool (Hidalgo, et al., 2017), using the path.genes.vals gene expression profiles (that are converted to eff.vals and path.vals profiles by the program).

The classification algorithm suggests additional prognostic genes that do not code for signaling proteins
In order to find genes that could be relevant for patient survival that are not in the signal pathways, we build a profile by combining signaling circuit activity profiles and gene expression profiles corresponding to genes outside signaling pathways (the other-genes profile), denoted by path.and.other.genes.vals. A feature selection procedure in breast cancer prognosis based on such a profile can select signaling circuits along with genes unrelated to signaling, whose activity is related to patient survival. Thus, Random Forest was again used to assess feature importance when fit with the path.and.other.genes.vals profile to classify survival outcome. Table 8 lists the top 5 most important gene features (the other-genes part of the path.and.other.genes.vals composed profile). These genes are of particular interest given that they might represent relevant cancer processes not included in cell signaling. Notably, the gene ABCB5 belongs to the ATP-binding cassette subfamily B which is well known to be involved in multiple drug resistance in cancer therapy (Dean, et al., 2001), probably because its functionality of efflux transmembrane transporter. It has also been reported that ABCB5 could mediate cell-to-cell fusion and contribute to breast cancer chemoresistance in expressing breast tumors (Frank, et al., 2005;Frank, et al., 2003). In addition, ABCB5, as a "pro-survival" gene, has been suggested to be a potential target against drug resistant breast cancer cells (Yang, et al., 2010). Besides, ABCB5 has been linked to melanoma (Wilson, et al., 2014). LMO4 encodes a LIM-domain protein that has been reported as an essential mediator of cell cycle progression in ErbB2/HER2/Neu-induced breast cancer which is characterized by poor survival due to high proliferation and metastasis rates (Matthews, et al., 2013;Montañez-Wiscovich, et al., 2009). It has been reported that LMO4 interacts with the renowned tumor suppressor BRCA1 and inhibits BRCA1 activity (Sum, et al., 2002;Sutherland, et al., 2003). OPA1 encodes a mitochondrial fusion protein which might be a target for mitochondrial apoptotic effectors (Olichon, et al., 2003), such as sorafenib (Zhao, et al., 2013). The role in cancer survival played by two most important genes according to the predictor, VPS72 and CHADL, is not as clear from the literature. It is worth mentioning that a mutation in VPS72 in cervix cancer with a high FATHMM pathogenicity score (Shihab, et al., 2015) is described in the COSMIC database (entry COSM458603). Regarding CHADL, it has been related to chondrocyte differentiation (Tillgren, et al., 2015) and extracellular matrix remodeling ( Barallobre-Barreiro, et al., 2012). Therefore, both genes are potentially involved in cancer processes, which suggest that further investigation of the complete list of top-ranked other-genes could render new cancer drivers and potential therapeutic targets. An expanded list containing the top 50 most important features among the other-genes can be found in Supplementary Table 4, in which many genes with cancer-related functions can be seen. Functions for the genes have been taken from their Uniprot (UniProt_Consortium, 2015) annotations and, when absent, from GeneCards annotations (Stelzer, et al., 2016).

Availability of data and results
All experiments are produced with R and codes are available via https://github.com/YunlongJiao/hipathiaCancerPrognosis. There is an R package available at https://github.com/babelomics/hipathia. Additionally, there is a web interface to the hipathia methodology that includes prediction functionalities, which is freely available at: http://hipathia.babelomics.org/.

Conclusions
In this study we have proposed a novel scheme to classify survival outcome for breast cancer patients based on mechanistic features consisting of signaling pathway activity profiles. We applied a pathway activity analysis method (Hidalgo, et al., 2017) to recode gene expression profiles into activity values of signaling circuits and demonstrated that, making use of the state-of-the-art computational tools, signaling circuit activity yields better prediction in breast cancer prognosis than gene expression. An additional advantage is that the identified pathway-level biomarkers are mechanistic signatures whose contribution to cancer progression can be readily interpreted in terms of the underlying cellular functions and biological processes. The three feature sets path.genes.vals, eff.vals and path.vals are composed by the same genes (those present in the pathways). However, the prediction performance of the genes recoded into circuits activity values with the hipathia method (eff.vals and path.vals) clearly outperforms (see Table 4) to those of the original genes (path.genes.vals). Moreover, predictors based on circuits (eff.vals and path.vals) have similar performance (see Table 4) to predictors based on all the genes (genes.vals), which include more information than the subset of genes. It is worth noting that genes in the circuits represent only 12% of the total number of genes, but have the same predictive performance, which suggests that combining the genes into circuits provides a real added value for prediction purposes. Although a significant improvement of the performance was not observed when the expression values of other-genes were concatenated to the activity values of signaling circuits, the analysis based on the combination of both data provides an interesting perspective regarding the interpretation of the biomarkers detected. In fact, the selected genes from the category of other-genes represent other aspects of the mechanism of the disease not explained by cell signaling. This approach allows expanding the scope of the analysis beyond the processes included in the pathways modeled.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
Frequency of bacetrial content finding in persistant periapical lesions

Objectives To determine the percentage of persistant apical lesions positive for bacterial nucleic acids, to detect microorganisms difficult to cultivate in persistant apical lesions by PCR and relate them to endodontic failure, clinical symptoms and diabetes mellitus. Materials and methods The samples of persistent apical lesions were collected during apicoectomy. Bacterial ubiquitous primer 16S rRNA was used to detect 16S ribosomal RNA in 36 samples. A species–specific PCR was performed with primers targeted to the bacterial 16S rRNA genes of Prevotella Nigrescens, Pseudoramibacter alactolyticus, and Propionobacterium propionicum. Results Six samples (16.67%) were positive for bacterial ribosomal RNA. Pseudoramibacter alactolyticus was detected in three samples. Propionibacterium propionicum and Prevotella nigrescens were detected in one sample each. The prevalence of infection of such lesions with P. intermedia, P. propionicum and P. alactolyticus is low. Conslusion The study we conducted gave insufficient data about extraradicular infection and its connection with diabetes mellitus and clinical symptoms. Conclusions Apical lesions persisting after endodontic treatment could harbor microorganisms other than Actinomyces and Propionicum species.

Introduction
The persistence of apical lesions after endodontic treatment is associated with intraradicular infection, extraradicular infection, foreign body reaction, cyst formation and fibrous scar tissue healing (1,2).
A number of studies dealt with the detection and identification of microorganisams in the root canal of root-filled teeth (3,4,5,6,7), and the persistence of microorganisms in the apical part of the root canal was recognized as the major cause of endodontic treatment failures, even after lege artis performed endodontic procedures (1). This may occur due to inability of endodontic instruments and irrigants to reach all parts of the root canal system and effectively remove microorganisms. Root canal microflora between primary endodon-Uvod Perzistencija apikalnih lezija nakon endodontskog liječenja povezana je s intraradikularnom infekcijom, ekstraradikularnom infekcijom, reakcijom na strano tijelo, stvaranjem ciste i cijeljenjem vezivnim ožiljkom (1,2).
Intraradicular microorganisms can overcome periapical defense barrier and establish extraradicular infection, which may cause acute apical abscess. Microorganisms that prevail in extraradicular infection are anaerobic bacteria such as Actinomyces spp., Propionibacterium propionicum, Treponema spp., Porphyromonas endodontalis, Porphyromonas gingivalis, Treponema forsythia, Prevotella spp. and Fusobacterium nucleatum (10,11). In classic histology studies, micro-organisms were consistently present in the periapical tissue of cases with clinical signs and symptoms such as acute abscessi and draining sinus tracts, but asymptomatic persistant periapical lesions were generally not infected (1). There are, however, reports on extraradicular biofilms in asymptomatic periapical periodontitis and chronic apical abscesses with sinus tracts (12,13). There are, also, reports on extraradicular infection of asymptomatic periapical lesions persisting after proper endodontics using microbial culture and molecular methods, where strict aseptic sampling procedures were used (10,11).
A clear understanding of the etiology and pathogenesis of the microbes causing persistent endodontic lesion helps in deciding on the use of intracanal medicaments and irrigants in endodontic retreatment cases with periapical lesions, or whether apical surgery should be preferred. The aim of this study was to detect microorganisms difficult to cultivate in periapical lesions by PCR, and to relate them to endodontic failure, clinical symptoms, and diabetes mellitus.
The exclusion critera were: (i) teeth with periodontal pockets, (ii) treatment with antibiotics within the last three months, (iii) serious systemic diseases, (iv) pregnant or breast feeding female patients, (v) participation in other clinical studies within the last three months.

Tissue samples
The samples of persistent apical lesions were collected during apicoectomy procedures. The patients washed their mouth with 0.2% chlorhexidine gluconate solution for 30 seconds prior to surgery. The Nowak-Peter incision was applied and full-thickness mucoperiosteal flap was reflected. The operative field was subsequently washed with sterile saline and the apex was approached using sterile carbide bur. The entire lesion was enucleated and apicectomy was performed using Lindeman carbide bur. Periapical tissue samples were transferred to lysis buffer from the extraction kit QiaAmp † DNA Mini Kit (Qiagen, GmbH, Germany) according to the manufacturer instruction, and subsequently stored in liquid nitrogen,and then in criotubes at -80°C prior to DNA extraction.

DNA extraction
DNA extraction was performed using commercial kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The samples were taken out of the criotubes, warmed to room temperature (20-25°C), and cut to smaller pieces using sterile scissors. Less than 25 mg of each sample was placed into 1,5 ml tube (Eppendorf Safe-Lock Tubes, Eppendorf, Garmany) with previously prepared emzymatic solution (20 mg/ml lysosime; 29mM TrisHCl, 2 mM ED-TA; 1,2% tritione) for the degradation of gram positive bacteria wall. Subsequently, the samples were incubated for 1 h at 37°C.

DNA amplification
At the beginning, the bacterial ubiquitous 16S rRNA primer was used to detect the 16S ribosomal RNA in all 36 samples. Subsequently, a PCR mixture with oligonucleotide primers specific for rDNAs was used.
The quality of extractracted DNA was checked spectrophotometrically (NanoDrop 1000 Spectrophotometer, Thermo Fisher Scientific, Wilmington, USA). A species-specific PCR was performed with primers targeted to the bacterial 16S rRNA genes of Prevotella Nigrescens All PCR essays were performed in a 50 μL reaction mixture containing 1 μM of each specific primer, 2 mM Mg-Cl2, 0.2 mM dNTP, the 0.25 μL AmpliTaq Gold DNA polymerase, and 10 μL extracted DNA (all reagents from Applied Biosystems, Lewinsville. USA).
Amplification was carried out in a thermal cycler (Applied Biosystems 2720 Thermal Cycler, Singapore) with an initial denaturation at 95°C for 5 min followed by 36 cycles of denaturation at 95°C for 30 s, primer annealing step at 55°C for 1 min, an extension step at 72°C for 1 min (apart from 2 min for Prevotella nigrescens), and a final step at 72°C for 2 min (except 10 min for Prevotella nigrescens).
PCR products were examined by 2% agarose gel electrophoresis performed at 5 V/cm. Agarose gel was made by melting agarose in Tris-acetate EDTA buffer in the microwave owen until boiling. After that, it cooled down to the temperature of approximately 50°C, and ethidium bromide of 0.5 μg/ml concentration was added. The gel was then solidified at room temperature and placed in 1X TAE buffer. Amplification products were mixed with a buffer with bromphenole-blue (BlueJuice, invitrogen, California, USA) in a ratio 6 to 1. The gel was photographed using Kodak DC 290 Zoom Digital Camera (Kodak corp, Rochester, New York, United States) under UV light (λ=302 nm). For determinig the length of bacterial DNA strand, a commercial standard with DNA strands ranging in length from 67 to 1114 bp was used (Roche, Indianapolis, USA).
One sample was positive for Pseudoramibacter alactolyticus and Propionibacterium propionicum, one sample was positive for Prevotella nigrescens and Pseudoramibacter alactolyticus, while one sample was positive only for Pseudoramibacter alactolyticus.
There was no statisticaly significant correlation between either of three microorganisms and diabetes mellitus ( Table  2) or symptms (Table 3). However, considering a low number of infected tissue samples, it is hard to consider the results of this particular statistical analysis as highly relevant. Due to this fact, this study did not give sufficient data about extraradicular infection and its connection with diabetes mellitus and symptoms.

Discussion
The role of microorganisms in primary endodontic infections was early established (16). Also, residual intraradicular infection was recognized as the major cause of persistant apical periodontitis (1). Apart from intraradicular infection, the absence of post-treatment healing can be caused by: aloplastic material in periapex (e.g. extruded root canal filling materials), cholesterol crystals accumulations, and true cysts formation. Also, periapical radiolucency can be caused by scar tissue healing and extraradicular infection that is according to Nair (1) generally in the form of periapical actinomycosis. Nevertheless, there are studies that have shown the presence of biofilms on the outer-extraradicular side of the root using scanning electron microscope, and studies reporting that Actinomyces species are not the only infective agents found in unresolved periapical lesions (13). Wang et al. (13) investigated extraradicular bacterial flora in persistent apical periodontitis, and although the prevalences of Actinomyces sp. and Propionibacterium were the highest, bacterial species were multiple and included Prevotella sp., Streptococcus, Porphyromonas endodontalis, and Burkholderia. In cases with vital and necrotic pulps there were no microorganisms on extraradicular surfaces, which suggested that extraradicular biofilm is associated with persistent infection after failed endodontics (13).
We chose to evaluate the tissue samples of closed periapical lesions for the presence of 3 microorganisms that were reported to be isolated from root canals with necrotic pulp and/ or from the root canals of the teeth with failed endodontic procedure. Prevotella Nigrescens was chosen as a representative of bacterial species that are highly prevalent in root canals with necrotic pulp, and frequently isolated in symptomat-
Mi smo odabrali ispitati uzorke tkiva zatvorenih periapikalnih lezija na prisutnost triju mikroorganizama izoliranih iz korijenskih kanala s nekrotičnom pulpom i/ili korijenskih kanala neuspješno endodontski liječenih zuba. Prevotella nigrescens odabrana je kao reprezentativna bakterija visoko prevalentnih bakterija u korijenskih kanalima s nekrotičnom ic endodontic infections (17). Pseudoramibacter alactolyticus and Propionobacterium propionicum were detected in root canals of patients with failed endodontic treatment (4). Exclusion criteria were applied to the patients that had been taking antibiotic therapy during the previous three months, due to a possible decrease in the number of anaerobic bacteria caused by antibiotic therapy. In this research, the intention was to study microbiological aspects of periapical tranparencies persisting after endodontic treatment that are "closed" i.e. not communicating with periodontium or oral cavity through caries, not adequately condensed root canal fillings, unfilled root canals, and leaking coronar restoration. Also, root fracture, sinus tract, communication with maxillary sinus, and tooth mobility indicate communication of periapical lesion with oral ecosystem, and similar cases were also exculded from our research before or during operative procedure.
It must be pointed out that we took great care about the samples in order to prevent contamination with oral bacteria. For this purpose, the rules of aseptic surgical sampling were applied. Marginal incision was performed to reflect mucoperiostal flap and reach periapical region, because it was reported that the risk of contamination was not significantly higher when compared to submarginal incision, and significantly smaller then with Partsch incision (18). Mucosa was rinsed with 0.2% chlorhexidin-gluconate, and microbiological samples were cultivated, and although with marginal incision, 100% of the samples from bone were positive, as compared to only 20% with submarginal incision, the ratio of periapical samples positive for microorganisms did not significantly differ between the two groups, and the microorganisms cultivated from mucosa, bone and periapical tissues of the same patient differed. The authors concluded that mucoperiostal flap reflection was not responsible for positive findings. Still, even if care is taken to avoid contamination from the mouth, it is still possible that the lesion gets infected by the intraradicular bacteria during sampling, and the positive finding of microorganisms in such samples can be reporeted as extraradicular infection (1). Apart from the mentioned contamination of the samples, molecular techniques such as PCR in detecting microorganisms in the samples of periapical tissue are questionable as well. Is not possible to differentiate between viable and non-viable microorganisms nor is it possible to distinguish microbes and their structural elements in phagocytes from extracellular microorganisms using the PCR method (1, 4). Therefore, although molecular methods are quite sophisticated, they do not solve the primary issue of avoiding contamination of periapical lesion during sampling.
The use of universal bactreial primer in our research revealed bacterial infection in 16.67% of the samples. Our results also suggest that there was a low incidence of periapical infection with the three bacterial species examined. In our study Prevotella nigrescens and Pripionibacterium propionicum were detected in one sample each. Low prevalence of extraradicular infection with these two species was expected and is in agreement with the reported low prevalence of extraradicular biofilms (found in 6%) in the teeth with either untreated or treated root canals (14). In the cited study, pulpom i često izolirana u simptomatskim endodontskim infekcijama (17). Pseudoramibacter alactolyticus i Propionobacterium propionicum detektirani su u korijenskim kanalima pacijenata nakon neuspješnog endodontskog liječenja (4).
In our research, Pseudoramibacter alactolyticus and Propionibacterium propionicum were detected in 3 samples and 1 sample, respectively. In the studies dealing with endodontic failures, these species were detected in significant percentages 11%-50% (4, 23) and were, apart from E. fecalis, the most prevalent species detected in the root canals of the teeth with persistant periapical lesion (4). In these reports, the samples were taken from root canals. Hence, these results cannot be compared to ours. As already mentioned, we detected Pseudoramibacter alactolyticus in 3 tissue samples of persistant apical lesions. Although the percentage of the infected samples is low it, such a result has been expected and it is in accordance with the findings of Siqueira and Rôças (4). They found that E. faecalis, P. alactolyticus and P. propionicum are the most prevalent species in the root canal samples of the teeth with failed endodontics, and that all of the examined samples harbored at least 1 of the gram-positive bacterial species: E. faecalis, P. alactolyticus, or P. propionicum. It must be pointed out that that we analysed periapical tissues rather than root canal samples. Propionibacterium propionicum is a facultative anaerobe formerly known as Arachnia propionica. This microorganism shares similar invasive characteristics as actinomyces (3). Genera Actinomyces and Propionibacterium can cause a chronic, granulomatous, infectious disease called actinomycosis. These bacteria are Grampositive filaments that end in hyphae and they are non-acid, non-motile, and form intertwining filamentous colonies called "sulphur granules" (1). The endodontic infections of actinomyces are caused by Actinomyces israelii and Propionibacterium propionicum, commensals of the oral cavity. These microorganisms can establish cohesive extraradicular colonies resistant to the immune system. They are consistently isolated from the periapical lesions which did not heal after an adequate endodontic treatment (1).
The role of Enterococcus faecalis as a key-stone pathogen in posttreatment disease was questioned (24,25) and the species belonging to other genera including Prevotella, Propionibacterium, and Pseudoramybacter have also been detected in root canals of treated teeth (7,26,27). Rôças and Siqueira (27) found that Propionibacterium (52%) and Pseudoramibacter alactolyticus (14%) were more prevalent in root canal samples of the teeth with posttreatment apical periodontitis undergoing retreatment than Enterococcus faecalis (12%). Our findings are in line with recent studies that have questioned the status of E. faecalis as the main pathogen in posttreatment apical periodontitis (24,25). Moreover, although ceratin bacteria can be recognized as key-stone pathogens, it is the synergistic activity of the whole bacterial community that interferes with host immune defence and causes tissue destruction (14,13). Biofilm is a form of microbial community attached to a solid surface in a nutrient-containing fluid, where the microbial cells embedded in an extracellular matrix, interact with each other. The microorganisms living in a biofilm are able to self-organize, resist environmental changes, act synergistically and respond to the changes in the environment as a community (12,13,14). Bearing in mind the tendency of microorganisms to form intraradicular and extraradicular biofilms, the future metagenomic studies should be oriented on the pathologic potential of bacterial biofilms rather than on a single microorganism, despite relatively few bacterial species involved in persistant periapical periodontitis. Study designs using cultivation methods and PCR cannot reveal interaction potentials between different microbial species since it is hard to co-cultivate many species, and PCR does not differentiate between viable and non viable microorganisms.

Conclusions
We can conclude that lesions of apical periodontitis associated with failed endodontic treatment could harbor microorganisms other than Actinomyces and Propionicum species. The prevalence of infection of closed lesions of persistant apical periodontitis with P. intermedia, P. propionicum and P. alactolyticus is low. We have insufficient data for clearly determining extraradicular infection and its connection with diabetes mellitus and clinical symptoms.


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Dexamethasone Inhibits S. aureus-Induced Neutrophil Extracellular Pathogen-Killing Mechanism, Possibly through Toll-Like Receptor Regulation

Neutrophils release neutrophil extracellular traps (NETs) in a pathogen-killing process called NETosis. Excessive NETs formation, however, is implicated in disease pathogenesis. Therefore, to understand how NETosis is regulated, we examined the effect of dexamethasone (DXM), an anti-inflammatory drug, on this process and the role of toll-like receptors (TLRs). We stimulated human neutrophils with phorbol 12-myristate 13-acetate (PMA) or Staphylococcus aureus (S. aureus) and quantified NETs formation. We also examined the effect of DXM on the bactericidal effect of NETs and the role of reactive oxygen species (ROS) and nuclear factor (NF)-κB in DXM-regulated NETosis. DXM significantly inhibited S. aureus-induced NETosis and extracellular bacterial killing. ROS production and NF-κB activation were not involved in DXM-regulated NETosis. TLR2 and TLR4, but not TLR5 or TLR6, modified S. aureus-induced NETs formation. Neither DXM nor TLRs were involved in PMA-induced NETosis. Furthermore, TLR2 and TLR4 agonists rescued DXM-inhibited NETosis, and neither TLR2 nor TLR4 antagonists could further inhibit NETosis reduction induced by DXM, indicating that DXM may inhibit NETosis by regulating TLR2 and TLR4. In conclusion, the mechanisms of S. aureus- and PMA-induced NETosis are different. DXM decreases NETs formation independently of oxidant production and NF-κB phosphorylation and possibly via a TLR-dependent mechanism.

Although NETosis contributes to pathogen control, it is essential for the balance between the formation and removal of NETs to be regulated to ensure tissue homeostasis, because large amounts of NETs may contribute to collateral damage within inflamed tissues. Excessive amounts of NETs are associated with the pathogenesis of inflammatory and autoimmune diseases, including preeclampsia (6), cystic fibrosis (7), and lupus (8). Moreover, NETs have been observed to act as a scaffold for thrombus formation (9,10), which is increasingly being recognized as a critical phenomenon linking inflammation with venous thrombosis. Therefore, NETosis is a double-edged sword: while it is an effective first-line antimicrobial mechanism, it might also lead to organ failure and death if it is unregulated. Hence, it is important to understand the mechanism of NETs regulation, but little information is available about this topic thus far.
Since an inflammatory microenvironment is essential for NETs formation, we believed that using glucocorticoids, which are potent anti-inflammatory drugs, can help elucidate how NETs formation is regulated. They are commonly used to resolve inflammation and are closely related to neutrophil function. They have been shown to inhibit neutrophil apoptosis and cytokine release during inflammation (11) and are also associated with many neutrophil functions, including chemotaxis, migration, and phagocytosis (12). Therefore, we examined the effect of a commonly used glucocorticoid drug, dexamethasone (DXM), on NETs formation. On the other hand, toll-like receptors (TLRs), which are essential pattern-recognizing receptors (PRRs) that mediate the recognition of microbial structures, have been reported to activate neutrophil extracellular traps to ensnare bacteria in septic blood (13). Moreover, most of the TLRs were reported to be expressed in neutrophils and were involved in neutrophils activation (14). So, we also investigated the role of different TLRs in NETs formation.
We found that DXM significantly inhibited NETs formation induced by Staphylococcus aureus (S. aureus) but not that induced by phorbol 12-myristate 13-acetate (PMA), which suggested that DXM can serve as a potential drug to regulate NETosis. In addition, the modulation of TLR-2 and TLR4 had an effect on NETs production, thus indicating the involvement of TLRs in this process.

isolation of human neutrophils
Neutrophils were isolated from the peripheral blood of fasting healthy donors by Percoll gradient centrifugation, as previously described (7). For those donors, comprehensive history and physical examination were performed, basic laboratory tests were used to exclude occult disease. This study was conducted according to the principles expressed in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Affiliated Second Hospital, School of Medicine, Zhejiang University, China. All participants provided written informed consent for the collection of samples and subsequent analyses. For each donor, 10-30 ml blood was drawn according to the need of different assays. Bloods from at least three donors were used to repeat the same assay. Cell suspensions contained >96% neutrophils, as determined by Wright-Giemsa staining, with 98% cell viability as determined by Trypan blue staining. The cells (4 × 10 5 /ml) were re-suspended in RPMI 1640 medium supplemented with bovine serum albumin (2%).

neTs Formation assay
After stimulation, cells were fixed with 4% PFA, blocked with 3% normal donkey serum and 0.05% Tween 20 in phosphate-buffered saline (PBS), and incubated with the primary antibodies anti-H2B and anti-neutrophil elastase, which were detected with secondary antibodies coupled to AF488 or AF555. Isotype-matched controls were used. For DNA detection, 4′, 6′-diamidino-2-phenylindole (DAPI) was used. Specimens were mounted and analyzed under a confocal microscope (Olympus IX-50).
Neutrophil extracellular traps were also examined using the membrane-impermeable DNA-binding dye SYTOX green (Molecular Probes, Invitrogen Life Technologies). SYTOX green (5 µM) was added to the cultures after specific periods of incubation, and the cultures observed 5 min later. In one case, DNase I (100 U/ml) was added for 10 min to degrade the NETs structure as control. To visualize NETs, live-cell cultures were imaged with an inverted fluorescence microscope (Olympus IX-50).

Quantification of extracellular Dna
The levels of extracellular DNA in supernatants were quantified using Quant-iT PicoGreen dsDNA assay kit according to the manufacturer's instructions. PicoGreen is a cell-impermeable dye that binds to extracellular dsDNA without staining live cells. Fluorescence intensity was measured on a SpectraMax M3 (Molecular Devices) fluorescent plate reader at an excitation wavelength of 480 nm and an emission wavelength of 520 nm, with a 515-nm emission cutoff filter. The calibration curve was constructed using a standard dsDNA of a known concentration.

Bacterial survival assay of neTs
A bacterial survival assay was performed as described in earlier studies (15). Neutrophils (1 × 10 6 cells/well in 200 µl) were preincubated with or without DXM for 2 h and then treated with 50 nM PMA or left untreated for another 2 h at 37°C and 5% CO2. NETs killing was examined by inhibiting phagocytic killing by the addition of 100 µg/ml cytochalasin D for 15 min before the addition of bacteria. After 1 h at 37°C, neutrophils and clumped NETs were disrupted by the addition of 0.01% Triton X-100 and three passes through a 25-gauge needle. Following serial dilution, bacteria were plated on LB plates for colony counting. After overnight incubation at 37°C, the number of colony-forming units (CFU) was determined. Zero killing was defined by control samples consisting of RPMI 1640. Killing efficacy was determined by subtracting the CFU of indicated treatment from control group.

rOs Production
Neutrophils were incubated in PBS (Ca 2+ -and Mg 2+ -free) with 10 µM DCF-DA (Sigma) at 37°C for 20 min. Subsequently, they were pelleted, washed in PBS three times, and transferred to a 96-well plate (1 × 10 6 cells/well in 100 µl). They were then stimulated with S. aureus for 1 h (some cells were pretreated with DXM for 120 min), and fluorescence was measured using SpectraMax M3 fluorescent plate reader at an excitation wavelength of 480 nm and an emission wavelength of 520 nm.
immunoblotting The neutrophils (3 × 10 6 cells/well in 500 µl) were pre-incubated with or without DXM for 2 h and then stimulated for another 2 h with S. aureus. Cell lysates were prepared using 1× loading buffer and boiled. Samples were then frozen at −80°C until use. Equal amounts of proteins were run on 12% sodium dodecyl sulphate-polyacrylamide gel and then electrotransferred onto polyvinylidenefluoride membranes. After blocking with 5% bovine serum albumin, membranes were incubated with phospho-NF-κBp65 and anti-GAPDH antibody overnight at 4°C, and then with HRP-conjugated secondary antibody for 2 h at room temperature. Protein bands were visualized by enhanced chemiluminescence. The gray degree of protein bands was detected by image J, and the value of p-NF-κB p65/GAPDH was calculated.

statistical analysis
Statistical analyses were performed using GraphPad Prism 6.1. Data are expressed as mean ± SE of individual samples. For two-group comparison, Student's t-test was applied for normally distributed data. The comparisons between multiple groups were performed using one-way ANOVA, followed by a Bonferroni's post-test. The significance threshold was set at 0.05.

resUlTs neTs Formation in response to PMa and Bacterial stimulation
Microscopic observation clearly showed NETs structure, including neutrophil-derived proteins. Neutrophils were labeled with DAPI to identify DNA (blue) and with antibodies to identify neutrophil histone (green) and elastase (red) ( Figure 1A). This confirmed PMA-or S. aureus-triggered NETs formation. SYTOX green staining further showed that bacteria were trapped in the web like structure and could be released when this NETs formation was degraded by Dnase I (Figure 1B).

DXM inhibits neTs Formation induced by S. aureus But not That induced by PMa
Fluorescence microscopy showed that DXM did not have any effect on the NETs formation induced by PMA but markedly inhibited that induced by S. aureus (Figure 2A). To further corroborate these, NETs formation was measured by quantifying the extracellular DNA in the supernatants. This experiment confirmed that S. aureus-induced formation of extracellular traps was significantly decreased by DXM (p < 0.05). In contrast, the amount of NETs formed after PMA induction was similar in controls and in DXM-treated neutrophils (p > 0.05) ( Figure 2B). In addition, DMSO (0.1% v/v) in the solution of stimulates had no effect on NETs formation.

DXM Decreases the Bactericidal efficacy of neTs
Dexamethasone significantly decreased the bactericidal efficacy of NETs, following abrogating phagocytic killing by the addition of cytochalasin D (p < 0.05; Figure 3). However, if neutrophils were activated to form NETs by PMA, DXM treatment had no effect on the killing efficacy of NETs (p > 0.05).
rOs and nF-κB activation are not involved in DXM-regulated neTosis     ROS-dependent, ROS production by activated neutrophils with or without DXM stimulation was measured. S. aureus infection elicited significant neutrophil oxidative burst, but DXM treatment neither increased nor decreased this response noticeably ( Figure 4A). The transcription factor NF-κB is a key regulator of inflammation and therefore plays a pivotal role in a wide range of inflammatory diseases (18). The phosphorylation of NF-κB has been believed to be involved in NETs generation (19). Therefore, we explored the role of DXM in the activation of NF-κB induced by S. aureus. The expression of p-NF-κB (p65) was significantly higher when the cells were stimulated with S. aureus, but this effect was not modified by DXM (Figures 4B,C).

Tlrs are involved in neTs Formation induced by S. aureus But not That induced by PMa
Toll-like receptors are key PRRs, which are important in innate immune responses. Thus, we explored the role of TLRs in the formation of NETs. None of TLR2 agonist, TLR4 agonist, TLR5 agonist, and TLR6 agonist could induce NETs formation. However, TLR2 and TLR4 agonists significantly enhanced NETs formation induced by S. aureus but not that induced by PMA, as shown by the quantification of extracellular DNA. Moreover, blocking TLR2 and TLR4 with neutralizing antibodies significantly reduced the NETs formation induced by S. aureus but not that induced by PMA, as shown by quantification of extracellular DNA (Figure 5). Furthermore, neither the TLR5/ TLR6 agonist nor the antagonist could modulate the formation of NETs.

DXM May Modulate S. aureus-induced neTs Formation through Tlr2 and Tlr4
To explore the mechanism of DXM-modulated NETs formation, we first pre-incubated the cells with TLR agonists to examine the effect of TLRs on DXM-inhibited NETs formation. As expected, both HKLM (TLR2 agonist) and LPS (TLR4 agonist) rescued DXM-reduced NETs formation ( Figure 6A). Moreover, neither TLR2 nor TLR4 antagonist could further decrease DXM-induced NETosis reduction (Figure 6B). While these findings suggested that DXM may modulate S. aureus-induced NETs formation through TLR2 and TLR4, further research is required to understand the precise mechanism.

DiscUssiOn
NETosis, a recently identified mechanism of pathogen killing, helps in isolating and preventing the spread of invading bacteria, but the persistent formation or insufficient degradation of NETs can also cause injury to the host (8,20). Since regulation of NETs formation is essential for tissue homeostasis, we aimed to determine the mechanisms and molecules underlying the regulation of this process.
A variety of stimuli promote NETs formation. In our study, NETs formation could be induced in neutrophils by both pharmacologic (PMA) and pathogenic (bacterial) stimuli, a finding that is in agreement with those of previous studies (21,22). Although several signaling mechanisms responsible for NETs formation have been reported, critical regulatory elements remain unidentified. Since the findings from different studies often vary, it is possible that more than one mechanism exists. In this study, we observed that DXM-inhibited NETs formation induced by bacteria but not that induced by PMA. In addition, it markedly decreased the bactericidal ability of NETs. Thus far, DXM has not been reported to affect NETs formation induced by S. aureus. Lapponi reported that treatment of neutrophils with DXM had no effect on NETs formation induced by PMA or TNFα (19). This is consistent with our observation that DXM was not required for the regulation of PMA-induced NETs formation. Other studies have suggested that NETs formation induced by different stimuli have distinct mechanisms. For example, Riyapa et al. (23) reported that when compared to the neutrophils of diabetic patients, those of normal individuals produced less PMAinduced NETs but the same amount of S. aureus-induced NETs. Parker et al. (24) hypothesized that whether NADPH oxidase and myeloperoxidase are required in NETs formation depends on the stimulus. These results prompted us to investigate whether different stimuli indeed have different underlying mechanisms. Our findings strongly suggested that bacteria and PMA regulate NETs formation through different pathways and that DXM may | activation of reactive oxygen species (rOs) or nuclear factor (nF)-κB was not involved in dexamethasone-regulated neTosis. Neutrophils were pretreated with or without dexamethasone (DXM) for 2 h and then stimulated with Staphylococcus aureus (S. aureus) for 1 h. ROS production were determined by dichlorofluorescein diacetate fluorescence and NF-κB activation were determined by Western blot. (a) S. aureus infection elicited significant neutrophil oxidative burst, but DXM treatment neither increased nor decreased this response. Data represent mean ± SEM of triplicate experiments. (B) NF-κB was activated when stimulated with S. aureus but not modified by dexamethasone. (c) Quantification showed that p-NF-κB (p65) expression was significantly higher when the cells were stimulated with S. aureus, but this effect was not modified by dexamethasone. Data represent mean ± SEM of triplicate experiments, *p < 0.05 by Student's t-test. have an effect on NETs formation induced by bacteria but not on that induced by PMA. Neutrophil extracellular traps formation has been shown to require NADPH oxidase activity as well as NF-κB activation. Our results verified the involvement of NADPH oxidase activity and NF-κB activation in the process of NETs formation. However, in contrast to our expectation, no change in ROS or pNF-κB levels was observed in DXM-treated neutrophils stimulated by S. aureus, which indicated that ROS and NF-κB signaling pathways were not involved in DXM-regulated NETs formation. NETosis was previously reported to be of two types: ROS dependent and ROS independent. Our study shows that DXM may modulate ROSindependent NETosis. Interestingly, DXM has been reported to inhibit calcium mobilization, which was shown to increase in LPS-treated cells (25). Therefore, DXM may regulate NETosis by modulating calcium mobilization, which is ROS independent. Moreover, our study showed that the phosphorylation of NF-κB, which has been shown to participate in NETs formation (19), is not involved in DXM-modulated NETosis. It may be because different stimuli were used, with bacteria in ours and PMA in others. Nevertheless, as we only detected the phosphorylation of NF-κB in whole cell, it could not be excluded that there were NF-κB shifting from plasma to nucleus.
The specific detection of microorganisms by innate cells is mediated by PRRs-germline-encoded receptors that recognize microbial structures referred to as pathogen-associated molecular patterns (26). TLRs are essential PRRs that mediate the recognition of microbial structures, such as those of bacteria, as well as the subsequent inflammatory and adaptive responses (27)(28)(29)(30). Because neutrophils and TLRs are, respectively, the prototypical cells and receptors involved in innate immune responses, the effect of TLRs on NETosis was investigated. Our findings suggested that TLRs involved in inflammatory response could be key regulatory factors in NETs formation. Our results  showed that S. aureus-induced NETosis was markedly inhibited by TLR2 and TLR4 antagonists and enhanced by TLR2 and TLR4 agonists. This strongly supports the role of TLR2 and TLR4 in the biogenesis of NETs, but these effects were not observed in PMA-induced NETosis. Furthermore, neither TLR5 nor TLR6 agonists/antagonists had any effect on bacteria-induced NETosis. As TLR2 is the main receptor for Gram positive, and TLR4 is for Gram-negative bacteria, respectively, it is reasonable that both of them may directly or indirectly participate in the process of NETosis triggered by S. aureus through the whole inflammatory network. It is further confirmed by the following results. The addition of TLR2 and TLR4 agonists (HKLM and LPS) rescued DXM-inhibited NETs formation induced by S. aureus, but to a lower extent than in the control group stimulated by S. aureus. Therefore, we believe that both TLR2 and TLR4 were involved in DXM-modulated NETosis, which is consistent with the observation in other studies that multiple receptors may together regulate NETs formation (31). Besides, we were unable to conclude whether other TLRs that mediated the interaction of neutrophils and other pathogens like viruses could also be involved.
In addition, we aimed to determine the relationship between DXM and TLRs. Both HKLM and LPS rescued DXM-reduced NETs formation. Moreover, neither TLR2 nor TLR4 antagonist could further decrease DXM induced NETosis reduction. This indicated the involvement of TLRs in DXM-reduced NETosis. A previous study showed that DXM down-regulates TLR4 mRNA expression in neutrophils (32), which implies that it may regulate NETosis by modulating TLR expression (33).
Our study has a limitation: we examined neutrophil function only in vitro; further in vivo studies are needed to characterize the fate of neutrophils. It is also not clear how DXM and TLRs cooperatively modulate NETs formation. Further research is needed to clarify these points.
In conclusion, we have demonstrated that NETs formation can be induced in neutrophils by different stimuli but not by a common mechanism. The mechanism of how DXM modulates bacteria-induced NETs formation was found to be unrelated to oxidant production and phosphorylation of NF-κB. TLR2 and TLR4 are involved in the formation of NETs. Although the specific mechanisms of how DXM regulates (a) Neutrophils were pretreated with HKLM (TLR2 agonist) and LPS (TLR4 agonist), followed by DXM treatment and S. aureus stimulation. NETs formation was quantified by Quant-iTPicoGreen double-stranded deoxyribonucleic acid (dsDNA) assay kit. Both HKLM and LPS rescued dexamethasone-reduced NETs formation. (B) Neutrophils were pretreated with TLR2 and TLR4 antagonist, followed by DXM treatment and S. aureus stimulation. NETs formation was quantified by Quant-iTPicoGreen dsDNA assay kit. Neither TLR2 nor TLR4 antagonist could further decrease DXM induced NETosis reduction. The assay was repeated for three times, each case in five wells, error bars represent SEM. *p < 0.05 by Student's t-test.