Quantitation of aortic regurgitation (AR) using two-dimensional (2D) echocardiography, including vena contracta width (VCW) measurement, is still challenging. Three-dimensional (3D) echocardiography can directly measure the vena contracta area (VCA), regardless of the rheological characteristics. We intended to assess the possibility of 3D vena contracta area (3DVCA) as well as 2D vena contracta area (2DVCA) in the assessment of AR severity.Sixty-one patients with AR  underwent 2D and 3D color Doppler echocardiography. Using conventional 2D color Doppler imaging, we measured VCW, 2DVCA, regurgitant volume (RV), and effective regurgitant orifice area (EROA). We also measured 3DVCA manually off-line from 3D full-volume color Doppler datasets for reference. Comprehensive 2D and 3D data on AR severity were successfully obtained from 52 of the 61 (85.2%) patients.r = 0.89; p < 0.001). The cut-off 2DVCA for grading severe AR was 34 mm2 . Significant correlations existed between 3DVCA and EROA . The cut-off 3DVCA for grading severe AR was 32 mm2 . Significant correlations existed between 2DVCA and 3DVCA .Significant correlations existed between 2DVCA and EROA (Two-dimensional, as well as three dimensional, vena contracta area measurement is a simple technique suitable for clinical use during comprehensive Doppler echocardiographic AR assessment. Accurate quantitation of aortic regurgitation (AR) severity is essential for effective clinical management and surgical intervention timing ,2. Convex, y, and z) were constructed. The reference lines of planes x and y were adjusted according to the largest point on the AR jet to obtain a plane exactly perpendicular to the AR jet (z plane). We measured the cross-sectional area at the narrowest region of the AR jet (3DVCA) in the z plane. Informed consent was obtained from all patients, and the protocol was approved by the hospital ethics committee.We enrolled 61 patients referred to our institution for AR evaluation by echocardiography. Conventional echo Doppler imaging was performed using an IE-33 ultrasound machine with S5-1 and X5-1 probes . Standard measurements were performed according to American Society of Echocardiography guidelines. We measured 2DVCA, EROA , VCW, and RV using conventional 2D color Doppler imaging and described 2DVCA as the smallest area of the AR jet using the short-axis view during 2D color Doppler imaging while referring to a simultaneous gray scale image  [Statistical analyses were performed using SPSS  and R . Data were expressed as mean ± standard deviation (SD), and p-values <0.05 were considered significant. To compare 2DVCA with 3DVCA and to correlate 2DVCA with EROA, we performed linear regression analyses and calculated Pearson’s correlation coefficients. To assess intraobserver variability, 2D echocardiographic data were analyzed twice off-line with a 1-week interval by the same operator. Similarly, a second observer, blinded to the results of preceding investigations, performed analyses to assess interobserver variability. Interobserver and intraobserver variability were evaluated via the intraclass correlation coefficient. Percentages of intraobserver and interobserver variability were calculated as the absolute difference divided by the average of the two measurements. Agreement was assessed using the Bland–Altman analysis. Comparison of mean values was performed using the paired 2). Table Comprehensive 2D and 3D data evaluating AR severity was successfully obtained in 52 of 61 patients (85.2%). Mean patient age was 74.0 ± 10.1 years, and 17 patients were female (32.7%). Six patients (11.5%) exhibited an eccentric AR jet with bicuspid aortic valve, infective endocarditis, or aortic valve prolapse. Left ventricular diastolic diameter was 52.0 ± 8.1 mm, left ventricular end-diastolic volume was 134.7 ± 53.0 mL, left ventricular ejection fraction was 52.9 ± 10.8 % and 11 of 52 patients (21%) were severe aortic regurgitation . Bland–Altman analysis revealed an average bias of −0.36 mm2, and a variance (1 SD) of 4.07 mm2 [95% confidence interval (CI): −8.53–7.75; Figure r = 0.89; p < 0.001). Bland–Altman analysis revealed an average bias of 4.04 mm2, and a variance (1 SD) of 7.12 mm2  as well as 2DVCA and VCW . 3DVCA correlated with EROA . Bland–Altman analysis revealed an average bias of 4.42 mm2, and a variance (1 SD) of 7.57 mm2 (95% CI: −10.72–19.56). The optimal 2DVCA cut-off for grading AR severity was 34 mm2  and the optimal 3DVCA cut-off for grading AR severity was 32 mm2 ; for reference, EROA was >30 mm2 [Strong correlation existed between 2DVCA and 3DVCA  of 4.16 mm2 (95% CI: −7.43–9.21).The intraclass correlation coefficient was 0.91, and intraobserver variability was 15.9 ± 13.2%. Bland–Altman analysis revealed an average bias of 0.89 mm2, and a variance (1 SD) of 3.84 mm2 (95% CI: −6.70–8.66).The intraclass correlation coefficient was 0.96, and intraobserver variability was 14.8 ± 13.5%. Bland–Altman analysis revealed an average bias of 0.98 mmWe demonstrated that 2DVCA correlates with 3DVCA and EROA. Three-dimensional echocardiographic imaging facilitates direct measurement of VCA -14. Usin2 to define severe AR [2 for predicting severe AR [2 corresponded to severe aortic regurgitation [2. The VCW of 6 mm correspond to VCA of 28.3 mm2, the VCW of 7 mm correspond to VCA of 38.5 mm2 and the VCW of 8 mm correspond to VCA of 50.2 mm2. The measurement of VCA can be affected the ultrasound machine, software and machine settings. In this study, we have reported that optimal cut-off value of 3DVCA was >32 mm2, 2DVCA was > 34 mm2, for reference, ERO was >30 mm2. These thresholds need to be confirmed in further studies. Figure 2). VCW was >6 mm in 3 of 6 (50%) patients with eccentric AR, 2DVCA was >34 mm2 in 5 of 6 (83%) patients with eccentric AR and 3DVCA was >32 mm2 in all 6 (100%) patients with eccentric AR. The measurement of the 2D or 3DVCA could be a useful and accurate method in patients with eccentric AR.Second, a 2D view exactly perpendicular to the AR jet is important for measuring 2DVCA. In patients with an eccentric AR jet, we cannot accurately measure 2DVCA using the conventional parasternal short-axis view Figure , bottom evere AR . Chin etevere AR . Nozaki gitation . In pati1) and minor (VCW2) axis of the regurgitant flow, two orthogonal VCW: VCA = π × (VCW1) × (VCW2)/4 = 0.785 × (VCW1) × (VCW2), and this method may have a potential role as simple semiquantitative assessment of AR severity in patients with eccentric AR.In this study, interobserver and intraobserver variability were low and the intraclass correlation coefficient was good. To our knowledge, this is the first study to show that 2DVCA can evaluate AR severity as well as 3DVCA. Measurement of 2DVCA is a simple technique, feasible for use clinically at patients’ bedsides or in the emergency room, during comprehensive Doppler echocardiographic AR severity assessment. VCA can be measured simply by calculating the shape of regurgitant orifice as an ellipse, using the major (VCWIn this study, no patient exhibited multiple regurgitant orifices or prosthetic paravalvular regurgitation; thus, we cannot extrapolate our results to such patients. In a recent study , 3DVCA wSignificant correlation existed between 2DVCA and 3DVCA as well as between 2DVCA and ERO, RV, and VCW. Measurement of 2DVCA is a simple technique for clinical use during comprehensive Doppler echocardiographic assessment of AR severity.


The androgen receptor (AR) remains an important contributor to the neoplastic evolution of prostate cancer (CaP). CaP progression is linked to several somatic AR mutational changes that endow upon the AR dramatic gain-of-function properties. One of the most common somatic mutations identified is Thr877-to-Ala (T877A), located in the ligand-binding domain, that results in a receptor capable of promiscuous binding and activation by a variety of steroid hormones and ligands including estrogens, progestins, glucocorticoids, and several anti-androgens. In an attempt to further define somatic mutated AR gain-of-function properties, as a consequence of its promiscuous ligand binding, we undertook a proteomic/network analysis approach to characterize the protein interactome of the mutant T877A-AR in LNCaP cells under eight different ligand-specific treatments . In extending the analysis of our multi-ligand complexes of the mutant T877A-AR we observed significant enrichment of specific complexes between normal and primary prostatic tumors, which were furthermore correlated with known clinical outcomes. Further analysis of certain mutant T877A-AR complexes showed specific population preferences distinguishing primary prostatic disease between white (non-Hispanic) vs. African-American males. Moreover, these cancer-related AR-protein complexes demonstrated predictive survival outcomes specific to CaP, and not for breast, lung, lymphoma or medulloblastoma cancers. Our study, by coupling data generated by our proteomics to network analysis of clinical samples, has helped to define real and novel biological pathways in complicated gain-of-function AR complex systems. Significant advances in genomic sequencing methodology have allowed a better assessment of the extent of somatic mutations accrued in common neoplasms AR) gene. It is not unexpected that AR mutations can add to the protein’s repertoire of powerful new functions In keeping with the newly defined mutational landscape of tumors, prostate cancer (CaP) also has extensive genetic alterations that range from single missense mutations, copy number variation, splicing variants, genetic rearrangements and short DNA alterations in a large number of genes Recently, our own work strongly suggests that the AR function extends beyond its classical role as a transcription factor and includes the novel properties of RNA splicing, DNA methylation, proteasomal interaction and protein translation at the polyribosomes themselves In an attempt to describe novel gain-of-function properties associated with mutant CaP ARs, a proteomic-coupled network analysis was performed. Multiple proteomics-mass spectroscopy investigations were carried out in order to fully characterize the protein composition of T877A-AR “complexes” (interactome) under different classes of hormone/ligand conditions reflecting the promiscuity of ligand binding associated with T877A. Critically, mutant CaP ARs may have their own unique ability to undergo and define new interactions. The coupling of the data generated by our proteomics screen to system biology analysis has been helpful in defining real and novel biological endpoints in AR complex systems, within a clinical disease perspective.The LNCaP prostate cancer cell lines was obtained from the American Type Culture Collection (ATCC), Rockville MD.2 in T-75 plastic culture flasks. Once confluent, the medium was changed to RMPI supplemented with 10% charcoal–dextran stripped FBS and incubated for an additional 24 h. The following day, the medium was changed to fresh RMPI/charcoal–dextran stripped FBS for overnight hormone/ligand stimulation studies for an 18 hour period. Steroids hormones were used at the following final concentrations, 10 nM dihydrotestosterone (DHT), 10 nM mibolerone (MB), 10 nM R1881, 10 nM testosterone+10 µM finasteride, 10 nM 17β-estradiol, 10 nM progesterone, 10 nM dexamethasone, and 100 nM cyproterone acetate (CPA).LNCaP cell line was cultured in RPMI 1640 media supplemented with FBS (10%), at 37°C, 5% COLNCaP whole cell lysates were prepared by freeze-thaw method with 1X PDG buffer containing the appropriate hormone/ligand TCEP (tris(2-carboxyethyl)phosphine) was added to the protein samples to reach the concentration of 5 mM. Samples were incubated at 37°C for 30 min. One µg of trypsin was added and the samples digested overnight at 37°C, then dried down in a SpeedVac and resolubilized in 50 µl of ACN 5%/formic acid (FA) 0.2%.All MS analyses were performed using an LTQ-Orbitrap hybrid mass spectrometer with a nanoelectrospray ion source  coupled with an Eksigent nano-LC 2D pump  equipped with a Finnigan AS autosampler . Twenty µl of each sample was injected on a C18 precolumn (0.3 mm i.d.×5 mm) and samples separated on a C18 analytical column (150 µm i.d.×100 mm) using an Eksigent nanoLC-2D system. A 76-min gradient from (A/B) 10–60%  was used to elute peptides with a flow rate set at 600 nL/min. The conventional MS spectra (survey scan) were acquired in profile mode at a resolution of 60,000 at m/z 400. Each full MS spectrum was followed by three MS/MS spectra (four scan events), where the three most abundant multiply charged ions were selected for MS/MS sequencing. Tandem MS experiments were performed using collision-induced dissociation in the linear ion trap.t-test on sample replicates to identify peptide clusters with p-values <0.1 with fold changes greater than 7 standard deviations. Peptide clusters fulfilling these selection criteria was inspected manually to validate identification and changes in abundance. Expression analyses were performed on proteins identified by at least two different peptide sequences. Expression values and relative standard deviation were gained by averaging the intensity differences and standard deviations of the four most intense peptide triplets after removing outlying peptide clusters. Normalized proteomic data can be found in Table S1.The comparisons of peptide abundance across the different experimental paradigms were achieved using label-free quantitative proteomics All AR interactors were given NCBI gene IDs. Human protein interaction information was compiled from diverse data resources and annotation databases such as Biomolecular Interaction Network Database (BIND), the Database of Interacting Proteins (DIP), Human Protein Reference Database (HPRD), IntAct, and Molecular INTeraction database (MINT), most of which contain curated interaction data and high-throughput data. We generated a metadata of protein interactions by merging these data with our own manually curated human signaling network containing 4,000 proteins and 22,000 signaling relations A protein interaction network was constructed for each hormone using our manually curated human signaling network and protein-to-protein interaction network. We only considered proteins that were ≥2.5 times the hormone condition abundance  vs. the vehicle control abundance from the MS dataset, to be considered significantly present. In the network, a node and link represent the protein and interaction, respectively. To find the highly interconnected regions of the network, for each hormone, we scored each pair of interaction based on the number of neighboring nodes they have in common. This gave us a matrix with all the scores between all the interaction pairs. Hierarchical clustering was applied to the matrix and a threshold score calculated based on the partition density allowing us to identify the highly interconnected regions (clusters) for each of the hormone networks.http://www.geneontology.org/) that are significantly associated with each of the protein clusters . We also used Gene Set Enrichment Analysis (GSEA) defined pathways and performed GSEA if 25% of the protein cluster’s genes were in the pathway. For the pathways and GO-terms that were significant from the GSEA results , we also did survival analysis. We used genes associated with these GO-terms and performed GSEA using GSE21034 Next, we used these protein clusters and identified GO-terms . RNA samples were then processed by Quebec Genome Innovation Centre , for microarray analysis with Illumina Human HT-12 Expression Beadchip v4 . Raw data was processed using R Two global heatmaps were made. The first heatmap includes the most differentially expressed gene for each hormone using t-test , where each hormone is compared against all others. The second heatmap includes the top 10, 20, 50 and 100 genes with the highest variance. The t-test finds the most differentially expressed genes that are specific to each hormone, whereas the variance helps us observe genes that are differentially expressed across multiple hormones. GSEA analysis was performed using GSE21034 http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi) Gene expression profiles, patient survival data, and demographic information for the 267 clinical prostate samples  were obtained from GSE21034 −) were added to maintain electroneutrality of the system. Each system was minimized, first by applying harmonic restraints with force constants of 10 kcal/mol/Å2 to all solute atoms; second, by heating from 100 to 300 K over 25 ps in the canonical ensemble (NVT); and lastly by equilibrating to adjust the solvent density under 1 atm pressure over 25 ps in the isothermal–isobaric ensemble (NPT) simulation. The harmonic restraints were then gradually reduced to zero with four rounds of 25-ps NPT simulations. After additional 25-ps simulation, a 15-ns production run was obtained with snapshots collected every 1 ps. For all simulations, 2 fs time step and 9 Å non-bonded cut-off were used. The particle mesh Ewald method The Crystal structure of DHT (1I38) and CPA (2OZ7) bound to T877A mutant AR-LBD and testosterone (2AM9) and R1881 (1E3K) bound to wild type AR-LBD are available in the Protein Data Bank (PDB). The complexes of different ligands bound to the T877A mutant AR-LBDs were further prepared for MD simulations using Xleap Our ability to capture both ligand-bound and unliganded full-length wild-type AR by affinity chromatography under physiological conditions has previously allowed us to pursue a proteomics approach in order to characterize the components of wild-type AR complexes. This was done by subjecting such complexes to tryptic digestion followed by mass spectrometry (MS) to assign protein identification, in effect creating AR interactomes initiated by ligand binding The aim of differential hormone stimulation conditions will allow us to determine whether disease etiology of the T877A-AR mutation is dependent upon ligand and co-factor status. Therefore, we used LNCaP cells that endogenously express the T877A-AR mutation to characterize the ligand promiscuous protein interactome complexes under different hormonal conditions, in order to highlight the possible distinct complexes that may be linked to disease progression. LNCaP cells were stimulated with the following hormones, four androgens: DHT, mibolerone (MB), R1881, or testosterone (in the presence of finasteride (to prevent the conversion of testosterone to DHT), or 17β-Estradiol, progesterone, dexamethasone, or the anti-androgen cyproterone actetate (CPA), alone or with no-ligand/alcohol-vehicle control. Hormone stimulated T877A-AR complexes were immunopurified with an N-terminal specific AR antibody, which would not interfere with hormone binding to the AR ligand-binding domain. Eluates from all experimental conditions were analyzed by LC-MS/MS. In order to increase our sample frequency for peptide detection in our MS analysis, each experimental condition was performed four times. Our proteomics data is a compilation of only fully characterized proteins, with full gene ontology and function.Quantitative MS data, for each of the eight hormone stimulation conditions, was used to create a protein interaction map . Using the protein interaction data from all the ligand stimulation conditions, the major ontological functions are: RNA pol II-dependent transcription, protein biosynthesis, with components of the translational machinery , RNA metabolism , DNA repair (through an interaction with members of the DNA repair complex), and the proteasome/ubiquitination pathways (see Table S2). In contrast, the ligand-dependent gene expression pattern ontological classes included pathways involved in DNA replication, steroid/sterol biosynthesis, and apoptosis (see Table S3). Thus, although the AR is classically described as a transcription factor, its proteome profile would suggest that the AR is capable of functions beyond what has initially been described as a gene activator.To establish statistically significant biological functions, we implemented the incorporation of Gene Ontological (GO)/pathways terms using DAVID  simulation studies  of the eight different ligands used. Using the docking program WILMA Figure 2Mutant AR MD simulations were performed over 15 ns production runs. To inspect the local flexibility of each protein/hormone complex, we calculated the root-mean-squared deviation (RSMD) fluctuations of backbone atoms of each amino acid residue for each mutant AR complex. In the study of globular protein conformations, one customarily measures the similarity in the three-dimensional structure by the RMSD of the central carbon atoms in amino acids, after optimal rigid body superposition. The most significant fluctuations correspond to loop regions between α3/α4, α9, α10/α11 and α11/α12 and the helices α11 and α12 themselves of the LBD of AR Figure 3The other major differences observed between the mutant AR complexes are the positions of α11 and 12, which are known to be critical for dictating hormone binding and co-activator interactions. Residues of α11 and α12 and the loop between them showed higher flexibility  of proteins is an important tool to measure potential interaction propensity with neighboring proteins. We calculated the average SASA of each AR complex from the 15 ns MD trajectory. No large differences were observed among the calculated SASAs of different AR mutant complexes, which ranged from 12,124 to 12,390 Åmatrices Table 1.Computational modeling has also been carried out for a number of other AR somatic mutations in the ligand-binding domain (LBD), and show sensitivity to a broad range of hormone ligands, including, AR-W741LFrom each of the ligand mutant AR protein interaction network, we identified specific sub-network modules. These sub-networks suggest hormone-specific activated pathways involved in either tumor initiation or progression. The characterization of ontological functions across the stimulation conditions is important as these differences or similarities in interacting proteins within the T877A-AR mutant complex may account for unique or shared cellular properties contributing to disease progression and outcomes. Therefore, we annotated significant GO-terms directly on sub-network modules extracted from the eight different hormone-protein interaction networks to highlight functions that may be unique to each of stimulation condition, and extracted the list of genes corresponding to those GO-terms.vs. African-American populations. From these results, we identified 138 T877A-AR -interacting protein sub-network modules (gene-sets) that show significant (p≤0.05) enrichment of the T877A-AR -interacting partners that discerned CaP primary tumors vs. normal samples. Two gene-set examples are shown in Using the lists of genes from the annotated GO-terms (now to be referred to as “gene-sets”), we determined whether these gene-sets are enriched in the publicly available clinical prostatic tumor microarray dataset, by applying Gene Set Enrichment Analysis (GSEA). We used the clinical data set GSE21034 Table S4), a single gene-set from the White (non-Hispanic) population (gene-set 2) was able to predict disease outcomes across all scoring criteria, . Furthermore, these predictive clusters were shared between all the hormones, supporting the results from our dynamic modeling study, where the T877A mutation accommodates all steroid hormones to and exhibits very subtle structural differences, although the overall structures appear to elicit the same functional interaction platform.We next analyzed whether or not these distinct population T877A-AR gene-sets were predictive of 10-year survival outcomes, available from the GSE21034 data set vs. tumor in White (non-Hispanic) men, but not African-American men. There appears to be no predictive value associated with each different class of hormone stimulation, irrespective of whether the hormones act through T877A-AR or through their cognate receptor. It is also apparently clear that the two different data-sets (protein interactome vs. gene expression), result in two different capabilities of predicting disease outcomes. This is a direct result of linking ontological function to a specific protein sub-network vs. arbitrarily selecting a defined number of genes linked solely to expression profiles.A similar analysis to that performed with our proteomic data was performed using our LNCaP multi-panel hormone microarray gene expression data across our hormone stimulation conditions. We selected 10, 20, 50 and 100 of the most variably expressed genes from our microarray data set to assess the ability to predict disease progression and outcome between White (non-Hispanic) or African-American men. We identified two gene-sets of 10 and 50 genes respectively that were able to distinguish between normal in vitro, as LNCaP, and would also predict disease survival in vivo from clinical population data.Finally, although LNCaP cells are derived from a metastatic CaP lymph-node biopsy from a White (non-Hispanic) male, other cell lines also possessing the T877A-AR mutation, MDA-PCa-2a and MDA-PCa-2b, from bone metastatic CaP from African-American also exist. However, extensive genome-wide gene expression characterization between these cell lines and LNCaP, have found them to be most similar to one another vs. other androgen sensitive cell lines, LAPC4 (possessing a wild-type AR) or 22Rv1 (H874Y-AR) or vs. AR-null cell lines PC3 and DU145 Figure S2). In hormone-dependent breast cancer, certain ethnic population differences have been observed, with higher incidences of breast cancer occurring in African-American woman vs. other groups We subsequently determined whether these predictive gene-sets were cancer specific and extracted gene expression datasets with available clinical outcome profiles for breast, lymphoma, lung and medulloblastoma clinical samples Finally, to deduce a molecular mechanism to account for differential gene expression patterns for each gene-set, we analyzed copy number variation of a CGH array dataset (GSE21035) Knowledge of various molecular mechanisms of action contribute to our understanding of wild type AR function. Most mechanisms require the involvement of ligand binding and interacting partners. Examples of this include AR interactors involved in gene transactivation, including HSP70, HSP90, p300 and components of the RNA polII complex In this study we characterized whole mutant AR protein complexes with several classes of steroids and ligands known to bind T877A-AR. The specific interactomes were dependent on the ligand utilized; so too were the specific gene expression profiles associated with each ligand. Thus mutant AR gain-of-function properties are not singular but multiple, dictated by the class of steroid hormones used. Further exploration of other adrenal androgens such as DHEA or androstenedione or other anti-androgens such as flutamide and bicultamide were not examined in this study, however, we did select a diverse class of ligands known to bind to the T877A-AR variant.High-throughput gene expression microarray approaches described in CaP cells have identified hundreds of androgen-regulated genes and also characterized genome-wide AR recruitment sites ERCC1, ERCC2, ERCC3, ERCC5 and FEN1. The function of these proteins is required for mediating DNA damage excision repair. However, they are known components of the RNA polymerase II transcriptional complex From our data, there are several clusters of AR-interacting proteins that are worth exploring to understand their role in disease progression. The first cluster of AR-interacting proteins is unique to the African-American population group and consists of the following proteins: KDM1A, KDM4C, SIRT1, CTBP1, NR2C1 and SMARCD3. NR2C1 has been previously described as an AR interactor The second cluster of AR-interacting proteins is unique for the White (non-Hispanic) population and is involved in chromatin remodeling and histone deacetylation activity. From this cluster we identified the following proteins: BCL2, RELA, FAS, EEF1A2 and NR4A2. The role of these proteins have been well described in apoptotic pathways, and RELA (NFκB p65) is a well characterized interactor of AR A final pathway for further investigation, and unique to the White (non-Hispanic) group of men, is the role of the AR in participating in the negative regulation of apoptosis via its interaction with AR CAG repeat lengths which code for the polyglutamine tract of the AR Using the T877A-AR hormone-specific interaction complexes as a basis for a novel systems biology network analysis exercise established gene-sets with clear predictive CaP clinical outcome value. In doing so, we confirmed the critical importance of the genetic backgrounds of the CaP individuals in the clinical dataset. Without segregating the microarray expression data of CaP patients along White (non-Hispanic) and African-American datasets, no defined gene-sets with predictive clinical values could be identified. Once the data had been segregated, gene-sets with very powerful clinical outcome parameters were discovered. African–American men have long been considered to have clinically different CaP from White (non-Hispanic) men, based on their genetic background In conclusion, our unique approach of using an important gain-of-function AR mutation, has generated gene-sets along functional organization lines showing that we can distinguish prostatic disease between White (non-Hispanic) and African-American men. Moreover, in our study for classification of AR function based on interaction profiles was a much more powerful tool predicting disease and survival outcomes than analyzing androgen-dependent gene expression patterns. The identification of these new functional properties of the AR and somatic mutations of the AR, further suggests that there is a role for the AR beyond that as a transcription factor and also implicates the ability of non-androgenic hormones to activate CaP disease-linked pathways.Figure S1CaP 10-year survival outcomes forT877A-AR Gene-sets 3–10.(PDF)Click here for additional data file.Figure S2Disease-free survival outcomes of 4 non-CaP cancers for Gene-sets 3–10.(PDF)Click here for additional data file.Table S1Normalized Proteomic Data.(XLS)Click here for additional data file.Table S2GO-Term definitions of Proteomic data.(XLS)Click here for additional data file.Table S3GO-Term definitions of microarray data.(XLS)Click here for additional data file.Table S4GO-Term definitions of T877A-AR Gene-sets 1–10.(XLS)Click here for additional data file.


High androgen receptor (AR) level in primary tumour predicts increased prostate cancer (PCa)-specific mortality. Furthermore, activations of the AR, PI3K, mTOR, NFκB and Hedgehog (Hh) signaling pathways are involved in the fatal development of castration-resistant prostate cancer during androgen ablation therapy. MID1, a negative regulator of the tumor-suppressor PP2A, is known to promote PI3K, mTOR, NFκB and Hh signaling. Here we investigate the interaction of MID1 and AR.AR and MID1 mRNA and protein levels were measured by qPCR, Western blot and immunohistochemistry. Co-immunoprecipitation followed by PCR and RNA-pull-down followed by Western blot was used to investigate protein-mRNA interaction, chromatin-immunoprecipitation followed by next-generation sequencing for identification of AR chromatin binding sites. AR transcriptional activity and activity of promoter binding sites for AR were analyzed by reporter gene assays. For knockdown or overexpression of proteins of interest prostate cancer cells were transfected with siRNA or expression plasmids, respectively.The microtubule-associated MID1 protein complex associates with AR mRNA via purine-rich trinucleotide repeats, expansions of which are known to correlate with ataxia and cancer. The level of MID1 directly correlates with the AR protein level in PCa cells. Overexpression of MID1 results in a several fold increase in AR protein and activity without major changes in mRNA-levels, whereas siRNA-triggered knockdown of MID1 mRNA reduces AR-protein levels significantly. Upregulation of AR protein by MID1 occurs via increased translation as no major changes in AR protein stability could be observed. AR on the other hand, regulates MID1 via several functional AR binding sites in the MID1 gene, and, in the presence of androgens, exerts a negative feedback loop on MID1 transcription. Thus, androgen withdrawal increases MID1 and concomitantly AR-protein levels. In line with this, MID1 is significantly over-expressed in PCa in a stage-dependent manner.Promotion of AR, in addition to enhancement of the Akt-, NFκB-, and Hh-pathways by sustained MID1-upregulation during androgen deprivation therapy provides a powerful proliferative scenario for PCa progression into castration resistance. Thus MID1 represents a novel, multi-faceted player in PCa and a promising target to treat castration resistant prostate cancer. The androgen receptor (AR) is the key transcription factor regulating androgen-dependent gene expression and is critical for the development and maintenance of male sexual organs like the prostate. In the adult prostate, survival and function of the secretory epithelia is dependent on continuous androgen stimulation and this cell type is thought to be transformed in prostate adenocarcinoma .Prostate cancer (PCa) is the most common malignancy diagnosed in male humans and the second leading cause of male cancer deaths in Western countries . The twoMID1 is a microtubule-associated ubiquitin E3 ligase, which is mutated in the X-linked inherited disorder Opitz BBB/G syndrome (OS). This syndrome is characterized by mild intellectual disability and malformations of the ventral midline along with ocular hypertelorism (widely spaced eyes) and hypospadias . These urogenital malformations resemble those of patients with partial androgen insensitivity syndrome , suggestA main function of MID1 and its binding partner alpha4 (α4) is to trigger the degradation of the catalytic subunit of the tumor suppressor phosphatase 2A (PP2A) via the ubiquitin proteasome pathway ,7. PP2A Loss of function mutations of MID1 results in the accumulation of PP2A and the hypophosphorylation of its targets, which in turn results in altered protein functions ,14. As aIn addition to its function in the regulation of PP2A, MID1, together with the PP2A subunit α4, represent the core of a large microtubule-bound multiprotein complex that associates with active polyribosomes and mRNAs. This complex associates to microtubules and targets specific mRNAs to MID1 mediated translation ,17. The Moreover, PDPK-1, an important player of PI3K/Akt and mTOR/PP2A signaling is also translationally up-regulated by the MID1 complex . PDPK-1 The androgen receptor mRNA harbors two trinucleotide repeats in the region encoding its N-terminal domain. Indeed, in a screen of MID1 complex-associated mRNAs we have identified the AR mRNA. In the current study, we examined the role of MID1 in AR signaling and the potential mechanisms by which MID1 may contribute to prostate cancer initiation and/or progression.Previously, we have shown that the MID1-protein complex binds mRNAs via purine-rich RNA motifs, which form stable secondary structures ,20 incluTo map putative binding sites of the AR mRNA to the MID1 complex, we performed RNA-protein pull-down assays. As the analysis of respective deletion constructs in cells turned out to be difficult due to altered protein- and RNA-expression/stabilities and activities -24, we uNext, the question arose, if the MID1 protein is involved in translational control of the AR mRNA. In order to assess the importance of MID1 for AR activity, we performed AR dependent CAT reporter assays. Plasmids incorporating the CAT gene under the control of an AR responsive promoter and wild-type AR were co-expressed with either empty vector, wild-type MID1 or mutant MID1 expression vectors in the AR-negative prostate cancer cell line PC-3. AR activity increased up to 5-fold in the presence of MID1 and androgen (R1881). By contrast, expression of the MID1 mutant del1313TGAC did not enhance AR activity beyond basal levels Figure A.To findSince MID1 overexpression increased the protein level of AR and because MID1 also binds specifically to the purine-rich repeats regions in AR mRNA . To specify these findings, MID1 protein levels were measured at several time points in the AR-positive prostate cancer cell line DuCaP after treatment with synthetic androgen R1881. MID1 protein levels clearly decreased after 24 h, 48 h, and 72 h Figure A. Similain silico using the online web-based tool Math Inspector. One consensus sequence in the 105 kb distal promoter and two consensus sequences in the intronic region were determined. These consensus sites were mutated using site-directed mutagenesis. Reporter gene activities of these constructs were tested in DuCaP cells with or without androgen stimulation. Introduction of the putative AR binding sites into the reporter gene promoter slightly increased basal luciferase activities and significantly increased androgen-induced reporter gene activities whereas mutation of MID1-AREs abrogated the effect of androgens  in prostate cancer specimens. A statistically significant positive correlation between MID1 and AR expression levels was found Figure A R = 0.B. NotablIn this study, we identified the MID1 complex as a physiologically important mediator of AR signaling, which acts through the newly identified mechanism of enhancing AR mRNA translation. Many studies have shown that the AR pathway is crucial for prostate cancer progression and that this pathway maintains its role even after androgen depletion and development of resistance to therapy. Several mechanisms were proposed to explain the role of AR in this transition to therapy resistance. Increased expression of AR was reported for a vast majority of prostate cancer tissue samples, achieved through mechanisms such as AR gene amplification, which was found approximately in one third of patient samples , or postPost-transcriptional regulation of AR mRNA is a mechanism not well described in the literature. The AR mRNA 3′UTR has UC rich regions, which are targeted by RNA binding proteins (RBPs). HuR and poly-C binding protein 2 (PCBP2) were reported to bind the 3′UTR of AR and regulate it posttranscriptionally ,28. In aMechanisms that regulate AR levels are intensively studied due to the crucial role of AR in prostate development and maintenance. One mechanism reported in the literature is auto-regulation of AR by androgens. In this setting, AR down-regulates its own transcription by binding putative AREs inside the AR gene when it is bound to androgen -35. In aWe identified several AR binding sites in the MID1 gene by ChIP-Seq analysis. Consistent with current understanding , AREs ofThe regulatory impact of MID1 on AR and its possible implications in PCa suggested a role of MID1 expression in development and progression of PCa. Our IHC results with samples of prostate cancer tissues revealed that MID1 is mainly stromal in benign tissue areas. Interestingly, in cancerous tissue the MID1 expression shifted to the epithelial compartment and was excessive in higher-grade tumors and metastatic lesions of prostate cancer. This shift may be due to alterations of the regulatory loop between AR and MID1 which could be correlated with the reported stage-dependent reductions in androgen levels in PCa tissues  and in tIn addition, MID1 seems to positively modulate other important oncogenic players, like PDPK1, which is a crucial kinase in the PI3K pathway and a novel promising target for anticancer drugs – its mRNA harbors a MIDAS motif in its 3′UTR, which triggers MID1-dependent enhanced translation . NotablyAn interesting compound in this context is the anti-diabetic drug metformin that was identified as a disruptor of the MID1 protein complex . Very reInterestingly, the benefits seen with metformin in treatments of polycystic ovary syndrome (PCOS), a condition partially caused by increased androgen signalling , could aFinally, it was recently reported that metformin negatively regulates signalling via NFκB  which isThe awareness of MID1 as an essential regulator of a pool of specific mRNAs involved in cancer development and progression is a novel concept. Three translational studies support such a role of the MID1/α4/PP2A complex: (i) a study on colorectal cancer, pointing towards MID1 as a metastatic gene and marker for poor survival , (ii) a The ubiquitin ligase MID1, which is over-expressed in prostate cancer tissue in a stage-dependent manner, enhances androgen receptor protein levels. This upregulation occurs at the translational level and is reciprocally controlled by an androgen-dependent repression of MID1 expression by androgen receptor at the transcriptional level. The disruption of this feedback-loop by androgen withdrawal could play a role in the development of castration resistant prostate cancer.2. The concentration of reagents used for in vitro experiments was: synthetic androgen R1881: 1 nM, anti-androgen Bicalutamide: 2.5 μM, Cycloheximide: 80 μg/ml and ActinomycinD: 5 g/ml. For transfection of expression plasmids Attractene (Qiagen) was used for PC-3 cells, Lipofectamine (Invitrogen) for LNCaP and Nanofectin (PAA) for DuCaP and PC-3 cells. Antibodies for Western blot, immunoprecipitation and ChIP were obtained from Biogenex , Chemicon , Cell Signaling  and from Millipore , Stratagene (anti-FLAG) Abcam  and from Atlas Antibodies .DuCaP and PC-3 cell lines were cultured in RPMI 1640 medium supplemented with 5-10% FBS 1% Glutamax and 1% penicillin/streptomycin (P/S) LNCaP cells were cultured in MCDB-131 medium supplemented with 10% FBS, 10 mM Hepes, 0.45% glucose, 1% sodium-pyruvate and 1% P/S. Fibroblast cells harbouring mutant MID1 (OS17/98) and control fibroblasts (C18/98) were established from a patient and a healthy control subject, respectively, after written informed consent. Cells were maintained at 37°C and 5% COSeveral MID1 variants  were inserted into the multiple cloning site of the pCMV-Tag2C vector (Clontech) using EcoRI and HindIII. For investigating the influence of the polyQ and polyG regions of AR mRNA on mRNA translation these region were PCR amplified and inserted into the 3′ UTR of the luciferase reporter gene in a pGL3 luciferase reporter plamid (Promega).For CAT reporter gene assays AR expression vector AR-pSG5, reporter plasmid (ARE)2-TATA-CAT and fill-up plasmid pHRL-Null were used, for Dual-Luciferase Reporter Assays the reporter plasmid was replaced by plasmids constructs of the pGL3 luciferase promoter vector (Promega) and the renilla firefly expression plasmids pGL4.70 or pGL4.73 were added for normalizing transfection efficiency. Luciferase reporter plasmids containing MID1 105 kb promoter and intronic AR binding sites were constructed by inserting the PCR-amplified AR binding site fragments into the pGL3-promoter vector. The consensuses AREs in these AR binding fragments were mutated using the QuickChange II Site-directed Mutagenesis kit (Agilent).Primers used for amplification of AR binding site fragments and primers for site-directed mutagenesis105 kb promoter binding site:F: 5′ AGGAGTGACAGCAATGATTTCAGGG′3,R: 5′ AGTGCACACATGTGCCAGACCC′3;ARE Mutagenesis:GAGGTTAGGCCTTGATGTTGCCCTAGGC′3,F: 5′CCTTAAGTATGGAGATCCTAACCTCATCTCCATACTTAAGG′3.R: 5′ GCCTAGGGCAACATCAAGGIntronic binding site:F: 5′TCTCCAGCCTCCTGGCTCACCTA′3,R: 5′ AGTTGACAAAGCCAGGGTGCCC′3;ARE1 mutatenesis:GATGCTAGGCCTTCTGCATGGA′3,F: 5′TCTCTGGGCTTTTGCCCCTAGCATCGGCAAAAGCCCAGAGA′3,5′TCCATGCAGAAGGARE2mutagenesis:GACTGTAGGCTCACATCCCCTGCTCC′3F: 5′TTCTGCATGGAACCTACAGTCTTCCATGCAGAA′3R: 5′ GGAGCAGGGGATGTGAG6 HeLa cells overexpressing FLAG-MID1 were lysed in TKM buffer , supplemented with proteinase inhibitors and 1% NP40, incubated for 15 min on ice and passed 6 times through a 27¾ gauge needle and centrifuged for 15 min at 12000 × g at 4°C. 4 mg of cytosolic extract were precleared with 50 μl of protein-A/G agarose (Roche) and 10 μg of mouse IgG for 2 h at 4°C on a rocking platform. Beads were pelleted and discarded. The supernatant was immunoprecipitated with 50 μl of anti-FLAG M2 affinity gel (Sigma-Aldrich) over night. As control, the IP was carried out with mouse IgG. Agarose beads were pelleted and washed 3 times with 500 μl TKM buffer supplemented with 0.2% NP40 for 10 min at 4°C. Bound proteins were finally eluted for 1 h with 30 μl of 3× FLAG peptide (5 mg/ml), diluted with 200 μl TKM-buffer and 1 μl RNasin. 50 μl were directly analyzed by Western blotting. From the remaining elution fraction bound RNA was isolated by phenol/chloroform extraction followed by EtOH precipitation. cDNA was synthesized using random primers and subsequently used for RT-PCR with AR , PFDN5 and TMSL8 (negative control) specific primers.4×10To quantify mRNA levels, a one-step real-time PCR method was used, which combines reverse transcription with real-time PCR (Qiagen). Taqman probe-set containing both gene-specific primers and probe for MID1 and housekeeping gene GAPDH were used for amplification and detection on an ABI 7500 Fast PCR machine (Applied Biosystem).2O up to 20 μl. Transcribed RNA was purified and products were kept in nuclease free TE.Amplification of different AR mRNA fragments: All forward primers contained the T7 promoter sequence (5′-CCAAGCTTCTAATACGACTCACTATAGGGAGA-3′) to allow subsequent in vitro transcription of the PCR product. PCRs were performed under standard conditions using pSG5-AR plasmid as template and gene region-specific primers for the PCR reactions. Amplified transcripts were in vitro transcribed with the RiboMAXTM Large scale RNA production system-T7 (Promega), following the manufacturer’s instructions with some modifications. Briefly, 4 μg of purified PCR product was transcribed for 4 h at 37°C in the following reaction mixture: 4 μl T7 transcription buffer, 6 μl rNTPs , 4 μg PCR template, 2 μl RNA polymerase enzyme mix, DEPC-HRNA-PROTEIN binding assay: 2 μg of biotinylated and purified RNA were incubated with 150 μg of cytosolic protein extract from HeLa cells overexpressing FLAG-MID1 in 450 μl of TKM buffer for 1 hour at 4°C. Subsequently, the mixture was incubated for 2 h at 4°C with 40 μl of 50% slurry of M280 streptavidin coated magnetic beads. Beads were washed 3 times with TKM buffer for 10 min at 4°C. Bound proteins were eluted by boiling the beads in magic mix  for 10 min at 95°C and analyzed on Western blots using the respective antibodies.Reporter gene assays were accomplished according to the Promega Dual Luciferase Reporter gene assay protocol and luciferase activity was measured in a Chameleon V plate reader (Hidex). Cells were transfected with 0.25 μg DNA/well in 24-well format and 0.08 μg DNA/well in 96-well format respectively and kept in medium with 10% charcoal stripped FCS for 24 hours after transfection and treated with 1 nM R1881 for additional 24 hours before luciferase activity was measured. For polyQ/polyG Luciferase Reporter gene assays 0.15 μg reporter vector DNA/well were used to transfect 11,000 PC-3 cells/well in a 96-well format. Cells were kept in RPMI medium with 5% charcoal stripped FCS for 24 hours after transfection and treated with 1 nM R1881 for additional 24 hours.The polyQ and polyG regions of AR mRNA inserted into the 3′ UTR of the luciferase reporter gene were: gb/M23263.1 nt633-nt816 (polyQ) and gb/HM010955.1 nt2448-nt2616 (polyG).Cells were transfected with 20 to 40 nM final concentration of MID1, α4, AR or control siRNA oligonucleotides using Lipofectamin 2000 or Nanofectin. AR siRNA: 5′-GAAAGCUCCUCGGUAGGUC-3′; α4-4 siRNA: 5′-UUGAGAUGCCAUAGCAACGAG-3′; MID1-3 siRNA: 5′-GUGUGAUACUAGGAUGCGG-3′; ns siRNA: 5′-AAGAGGCUUGCACAGUGCA-3′ (Dharmacon or MWG). After 48 h of incubation the medium was replaced and after 72–120 h cells were harvested and analyzed by Western blotting or real-time PCR.Paraffin-embedded primary tumor and lymph-node metastasis specimens were obtained from previously untreated patients who had undergone radical prostatectomy at the Department of Urology, Innsbruck Medical University. Use of patients’ samples was approved by the ethics committee at the Innsbruck Medical University and written informed consent was obtained from all patients. Tissue microarrays (TMAs) were produced from the tumor specimens using a manual tissue arrayer. Immunohistochemistry (IHC) analysis was performed with 5 μm TMA sections employing the Ventana Discovery - XT staining automat . Standard CC1  pre-treatment for antigen retrieval was followed by incubation for 1 h with a polyclonal anti-MID1 antibody or a monoclonal anti-AR antibody diluted in antibody diluent, followed by secondary universal antibody solution for 30 min, staining with DAP map kit and counter staining for 4 min with haematoxylin II bluing reagent . Specificity of staining was controlled by including a control antibody (DAKO). Consecutive sections were HE stained or stained for the tumor marker AMACR and the non-malignant benign gland specific marker P63 for correct assignment of non-malignant and cancer areas. MID1 staining was evaluated and scored by a pathologist (G.S) according to the quick score method and AR staining was evaluated by an automated IHC image acquisition and analysis system . This analysis system uses nuclear counterstaining as a reference and calculates intensity and percentage of specific staining of the target antigen as described previously .ChIP was performed as previously described . In brieAR antibody precipitated DNA samples were used either for deep sequencing or PCR amplification. Preparation of the libraries and sequencing were performed using the Solexa sequencing platform (Illumina) following the manufacturer’s instructions. DNAs were also subjected to amplification by primers listed above.http://promotion.molgen.mpg.de/gb2/gbrowse/Human/).Sequencing data were analyzed as previously described . Illumint-test was used to compare two groups. For statistical analysis of AR-IHC, samples were categorized based on histological and pathological parameters. One-way Anova with Posthoc Benforoni or Duncan for categories containing more than 2 groups  and Mann Whitney U test for categories containing 2 groups (benign and cancer) were used for statistical analysis. Pearson and Spearmen correlation analysis were used for studying MID1 and AR correlation. Statistical significance is indicated as *p < 0.05, **p < 0.01,***p < 0.001, Error bars denote standard deviations except for box blots where they denote minima and maxima.Student The authors state that they have no competing interests.RS, HK and SS designed and supervised the study; AK performed knockdowns, reporter gene assays, overexpressions and protein stability experiments and statistics. ÜD performed knockdowns, reporter gene assays and statistics. ÜD and HB performed ChIP experiments. EK, JA, SK and BA performed the Co-IP, RNA-protein binding and mRNA level/stability experiments. AIK and CA provided expert guidance in cell culture methods. HB AW and MRS performed ChIP-Seq analysis. GS performed IHC analysis. AK prepared the manuscript, AK and ÜD prepared figures, ÜD, SS, RS and HK reviewed and revised the manuscript. All authors approved the final manuscript.(A) Westernblot as knockdown control for MID1-3 and MID1-9 siRNAs shown by reduction of FLAG-tagged MID1 overexpressed in LNCaP cells,  Westernblots as knockdown-controls for the α 4-3 and α 4-4 siRNAs using an antibody against endogenous α 4 in LNCaP cells (D) Real-Time PCR analysis of AR mRNA levels in LNCaP cells after knockdown of MID1 or α 4 relative to the non-silencing controls. (E) Real-Time PCR analysis of AR mRNA levels in LNCaP cells after over-expression of MID1 or α 4 relative to the control (empty vector).Click here for file


ATP) channels modulate the tocolytic effect of β2-AR agonists  in early-pregnant (day 6) and late-pregnant (day 22) rat uterus in vitro, in order to examine the relation between the KATP channel sulphonylurea-binding regulatory subunit (SUR) expression and pharmacological reactivity of β2-AR agonists.To investigate whether ATP-sensitive potassium (K-10-10-5 M) on spontaneous rhythmic contractions were investigated cumulatively, alone, or in the presence of the KATP channel blocker glibenclamide (10-6 M) and the KATP channel opener pinacidil (10-9-10-7 M) after 5-min preincubation.The tocolytic effects of ritodrine and salmeterol (102-AR agonist induced myometrial relaxation was inhibited by glibenclamide and enhanced by pinacidil on day 6, when SUR1 expression levels were high. Neither glibenclamide nor pinacidil mediated tocolytic effect was measured on day 22.βATP channels at the end of gestation may facilitate enhanced excitability and contractility in the rat myometrium. The combination of a betamimetic and a KATP channel opener will therefore not be of therapeutic relevance in the treatment of preterm delivery.Low expression of the K We earlit (SUR)  that SURATP channel in β2-AR agonist-induced myometrial relaxation. We studied the tocolytic effects of β2-AR agonists  in the presence of glibenclamide (KATP channel blocker) and pinacidil (KATP channel opener) in early pregnant (day 6) and late pregnant (day 22) rats in vitro, in order to clarify the relation between SUR1 expression and pharmacological reactivity of β2-AR agonists.The aim of the present study was to investigate the role of the Kad libitum.The study was approved by the Hungarian Ethics Committee for Animal Research (registration number: IV/01758-2/2008) and the animals were treated in accordance with the European Communities Council Directives (86/609/ECC) and the Hungarian Act for the Protection of Animals in Research (XXVIII.tv.32.§). Sprague-Dawley rats  were kept at 22 ± 3°C with relative humidity of 30%-70% and 12/12 h light/dark cycle. They were fed a standard rodent pellet diet (Charles-River Laboratories) with tap water available Mature female (180-200 g) and male (240-260 g) rats were mated in a special mating cage with a metal door separating the rats of different sex, which is open when the rats are allowed to mate. Four to five hours after the potential mating, vaginal smears were taken from the female rats. The female rats in which sperm cells were microscopically detected (magnification of 1200 times) and those in whom smears could not have been taken because of a vaginal sperm plug were regarded as first-day pregnant animals .2 inhalation and the uteri were removed on the 6th and 22nd day of pregnancy. Muscle rings 5 mm long were sliced from the uterine horns and mounted vertically in an organ bath containing 10 mL of de Jongh solution . The organ bath was maintained at 37°C, and carbogen (95% O2 + 5% CO2) was bubbled through it. After mounting, the rings were equilibrated for about 1 h before the experiments, with a solution change every 15 min. The initial tension of the preparation was set to about 1.25 g, which was relaxed to about 0.5 g at the end of equilibration. The tension of the myometrial rings was measured with a gauge transducer  and recorded with a SPEL Advanced ISOSYS Data Acquisition System (Experimetria Ltd)  on spontaneous rhythmic contractions was investigated cumulatively, alone, or in the presence of KATP channel blocker glibenclamide (10−6 M) or KATP channel opener pinacidil (10−9-10−7 M). Following the addition of each dose of β2-AR agonist, the changes were recorded for 300 s. Concentration-response curves were fitted and areas under curves (AUCs) were determined. Statistical analysis was carried out with the Prism 5.0 . From the AUC values, maximum inhibitory effects (Emax) of β2-AR agonists on a given day of pregnancy were calculated and the concentrations eliciting 50% of the maximum inhibition of uterine contraction (EC50) were calculated. Data were analyzed with the ANOVA Neuman-Keuls test. The alpha value was 0.05. The variances were constant and the distribution was normal.The tissue samples were incubated for 5 min and the tocolytic effect of β2-AR agonists; the dose-response curves shifted to the right, and the EC50 values of β2-AR agonists significantly increased. Pinacidil enhanced the tocolytic effects of β2-AR agonists; the dose-response curves shifted to the left and the EC50 values of β2-AR agonists significantly decreased (Glibenclamide blocked the tocolytic effects of βecreased  and 2.−10-10−5 M) on the 22-day-pregnant rat uterus were investigated in the presence of glibenclamide (10−6 M) or different doses of pinacidil    and 4.2-ARs affect the contractility of the pregnant uterus which is why they are used for the treatment of premature labor.Preterm delivery is one of the greatest challenges in obstetrical practice. The factors regulating myometrial function during pregnancy and labor are poorly understood. Understanding of these processes at cellular and molecular levels is essential for development of new therapeutic strategies. βATP channels are large hetero-octameric complexes containing four subunits from the inwardly rectifying K+ channel family (Kir6.x: either Kir6.1 or Kir6.2) and four SUR subunits from the ABC transporter family: ABCC8 (SUR1) and ABCC9 (SUR2). SUR2 has two different isoforms, SUR2A and SUR2B, which are splicing variants. Both types of subunits, SURs and Kir6.x are necessary for the channel function. Kir6.x comprises the channel component of the KATP, while the SURs are responsible for the ATP sensitivity, pharmacological properties, and trafficking of this channel  compared to early gestation .We found earlier  that botestation . This phas shown  that SUR


Areas with high frequency activity within the atrium are thought to be ‘drivers’ of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. Clinical groups have applied the traditional FFT-based approach to generate the three-dimensional dominant frequency (3D DF) maps during electrophysiology (EP) procedures but literature is restricted on using alternative spectral estimation techniques that can have a better frequency resolution that FFT-based spectral estimation.Autoregressive (AR) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and AR model order, were implemented to generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478 s-long segments in the left atrium (LA) and exported for analysis, together with their anatomical locations. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach.3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23 %). There were no significant differences between AR techniques (p = 0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5 Hz).We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF computation in human persAF studies. Atrial fibrillation (AF) is the commonest heart rhythm disturbance seen in clinical practice, affecting almost 1 % of the worldwide population. It is more prevalent in older patients, affecting over 10 % of those above 80 years old . It is rThe majority of the AF studies that studied the DF  were impTherefore, the objectives of this paper were (1) to generate 3D DF maps using different autoregressive (AR) spectral estimation methods [with emphasis on selection of appropriate sampling rate and AR model order to estimate the DF for each of those 2048 simultaneous unipolar noncontact intracardiac atrium electrograms (AEGs)], (2) to compare the DF between those AR spectral estimation techniques and (3) to compare them with the maps produced using Fourier-based approach .Noncontact mapping (NCM) is performed with a multielectrode array catheter (MEA) introduced into the cardiac chamber to record endocardial electrical activity without touching the heart walls. This technology uses an array catheter with 64 electrodes and an analysis system  that generates anatomic mapping and electrogram reconstruction. The simultaneous potentials are obtained using inverse solution mathematics and up to 3600 AEGs sampled at 1200 Hz are projected onto 3D representation of the cardiac chamber in real-time .The technique of using NCM with the MEA has previously been described and validated in the context of sinus rhythm as well as arrhythmia in humans , 18. EstAn Ensite array balloon was introduced trans-septally into the LA of eight patients with persAF with no previous history of heart diseases  of an autoregressive process n by Eq.  20–22],–22,PAR [x[n] and the output of the all-pole filter as given later by Eq. To estimate the AR coefficients The verification that an autoregressive model can describe the AEG signals was performed by fitting an AR model to the AEG data and testing the residuals. The difference between the predicted and the true signal sequence values resulted in random residuals (uncorrelated in time) normally distributed. The autocorrelation function of the residuals fell inside the confidence interval of 95 % and was close to zero for all non-zero lags.Different AR spectral estimation methods represent a trade-off between spectral estimation and computational efficiency , 20, 22.xx  rather than 1/N), where N is the number of samples [k = 2 to the desired model order  for each window location k and the variance are calculated using the following equations [In the Covariance method, the data are windowed and the points within the interval are used to compute the variance of the white noise. The estimated autocorrelation function quations –22: 11\dIn this method the AR parameters are estimated by minimizing the average Eq.  of the eThe autocorrelation is estimated as akk coefficient is estimated after the akk-1 model order prediction error filter coefficients had been estimated by minimizing the akk-1 model order prediction error power. First it is necessary to estimate the autocorrelation at lag zero with the forward and backward prediction errors , a method suggested by Parzen , for ideFor each patient, the model order value for each of the 2048 AEGs, with segment length of 20.478 s and re-sampling frequency were estimated using CAT method. The model order value can be different for each AEG. Hence, a model order value that would mathematically attend the majority of the AEGs’ population is needed for generation of the 3D AR DF maps. The authors have defined this order, as the optimum order, identifying on the cumulative histogram the order that satisfies mathematically at least 95 % of the AEGs of the entire segment  patient, (2) the interaction patient and down sampling (DS) in the time domain of the AEGs with different sampling frequencies  and (3) the interaction between patient, down sampled signal, and DF estimation using each of the AR spectral techniques . As fixed effects we included: (1) DF estimation using each of the AR spectral techniques, (2) DS of the AEGs, and (3) the interaction between AR spectral estimation techniques and DS of the AEGs. These analyses tested whether any differences between the DF using the AR spectral estimation techniques varied by sampling frequency, while properly allowing for the nested structure of the data . The results were compared with those obtained using the FFT-based approach by the percentage of DF agreement. The analysis was performed using the nlme  package nlme [R . Mixed mEight male patients with symptomatic drug-refractory persAF were included in this study (mean age of 47 ± 4 years). Patients presented a history of persAF episodes of 34 ± 9 months with a moderated dilated LA (48 ± 2 mm) and left ventricle ejection fraction above 55 % (5 out of 8). Patient characteristics are summarized in Table Figure Model orders were estimated for different Fs and the results are illustrated in Fig. Three sequential 3D DF maps were generated with different Fs values and the results were concentrated on the impact of downsampling on the DF maps.For the AR spectral analysis, the mixed model ANOVA of the entire AF segment revealed that the interaction between downsampling factor and DF estimation with AR spectral techniques had a non-significant statistical effect (p = 1). In other words, our result showed that any differences between AR techniques did not depend on the re-sampling factor on the AEG. The main effect of the AR spectral estimation techniques (i.e. the effect of technique averaged over sampling frequency) was also not statistically significant (p = 0.62) as detailed in Fig. Figure DF estimation using AR Yule-Walker method has the advantage of being computationally efficient. Aiming to observe the impact of the sampling frequencies to generate the 3D DF maps, the processing times of FFT and AR Yule-Walker were measured for estimation of 3D DF maps. The processing time for the FFT at Fs = 1200 Hz was 7.65 s and the processing time for the AR Yule-Walker, as expected, decreased with sampling frequency to: 5.44 s (Fs = 100 Hz), 5.35 s (Fs = 75 Hz), 5.32 s (Fs = 60 Hz), 5.27 s (Fs = 50 Hz), 5.15 s (Fs = 40 Hz) and 5.05 s (Fs = 37.5 Hz).Spectral analysis has been used as a tool to provide information about the behaviour of the electrical activity of the heart. Areas with high frequency activity are thought to be ‘driving’ the rhythm in patients with AF and hence are possible targets for catheter ablation. 3D DF mapping using FFT-based approach has been shown to enable the location of high-frequency areas –28 and aFor tracking the DF, two different definitions for DF are commonly presented in the literature , 32. We It has been argued that Levinson-Durbin Yule-Walker produces poorer spectral resolution than other AR-based methods , howeverWe have tested several methods (not presented here) for estimation of AR model order and observed good agreement between them . The CATWe have studied the three main issues that have made AR-based spectral analysis difficult for this application, namely the sampling frequency to be used, the AR model order to be chosen and the technique to find the AR coefficients. Our recommendations are that, for this application, the sampling frequency should be around 37.5 Hz, the order of the model should be 24 or slightly higher, and that the Yule-Walker Levinson-Durbin approach should be chosen to find the AR coefficients. We have shown that the AR-based DF maps of AEGs from persAF patients are very similar (more than 90 % of similarity) to those obtained using FFT-based maps. No statistically significant differences between the four AR techniques we tested were found, but AR Levinson-Durbin Yule-Walker has greater computational efficiency compared to the other three AR methods. Using the values and approach recommended above, the processing time  compares well with that of FFT, allowing for real-time implementation .Finally, concerning the limitations of the technology, AEGs acquired from remote areas (>4.0 cm) from the centre of the MEA have been shown to be significantly attenuated making the technology not suitable for large cardiac chambers. Movements of the balloon will produce distorted AEGs and as consequence, a new 3D geometry needs to be re-done if the balloon moves. The use of NCM to determine electrical activation in tubular structures  is a challenge due to the “line-of-sight issue”.This study showed the feasibility of AR spectral estimation techniques for producing 3D DF maps with appropriate sampling rate and AR model order, offering an alternative approach for 3D DF computation in human persAF studies that might contribute as an auxiliary tool for the study of AF ablation.


The hyperglycemia groups were administered glucose solution (10-40 %), while the normoglycemia groups were administered saline. Alterations of the mRNA expressions of inflammatory cytokines and CD4Bacteroidetes class, and altered the population sizes of the Clostridium perfringens and the Bacteroides fragilis subgroup. Hyperglycemia did not enhance these alterations of the microbiota evoked by LPS, although it did modify the bacterial populations of the L. reuteri subgroup and staphylococci in healthy condition without endotoxemia.Hyperglycemia was kept approximately 250–350 mg/dL during the 24 hr study period. At the end of 24 hr, hyperglycemia augmented the mRNA expressions of interleukin (IL)-1β and IL-6 in the MLNs, while both the helper T (Th) 2 and regulatory-T (Treg) transcriptional factors were simultaneously up-regulated under non-endotoxemic condition. LPS injection significantly modulated the obligate anaerobe bacterial populations of the The present study indicates that both gut immune function and microbiota are significantly modulated by persistent hyperglycemia.The online version of this article (doi:10.1186/s40560-015-0101-8) contains supplementary material, which is available to authorized users. Critically ill patients with persistent hyperglycemia are well known to show higher mortality rates, possibly due to the deterioration of a variety of cell functions , 2. Alth12 commensal bacteria covering over 1,000 species, inhabit and together constitute independent bacterial ecosystems, known as microbiota, in the gut [Maintenance of the intestinal homeostasis and function is very important issue to improve the prognosis of critically ill patients, since intestinal barrier dysfunction has been shown to contribute to the deterioration of the host immune function. Among the gut immune systems, gut-associated lymphoid tissue (GALT) is of great consequence, not only in relation to regulation of the intestinal homeostasis, but also to the maintenance of systemic immunity . In part the gut . Althoug the gut , its alt the gut , and areWe previously demonstrated that excessive hyperglycemia at levels of over 400 mg/dL for short-term periods, like 3 hr, did not evoke alterations of the gut microbiota, but apparently induced gut barrier dysfunction through TNF-α-dependent pathways . However+ lymphocytes to Th1 and Th17, which posses the ability of inducing inflammatory responses, in MLNs. Besides, we examined whether these alterations of the immune status in MLNs were followed by the disruption of the microbiota homeostasis; that is, increase of pathogens and decrease of probiotics. Furthermore, we proposed the 2x2 study design; control and hyperglycemia group with/without eodotoxemia, to evaluate the effect of disease condition on the change induced by hyperglycemia. We expected that endotoxemia augmented the effect of hyperglycemia.In the present study, we therefore hypothesized that persistent hyperglycemia for 24 hrs up-regulated the pro-inflammatory cytokines, and thereby promoted the differentiation of CD4This study protocol was approved by the Animal Care and Committee of Keio University School of Medicine (KI002001) in accordance with the National Institute of Health guideline.Male Wistar rats (6–7 weeks), weighting 350–400 g, were fed standard chow and studied after an acclimation period of 3 to 7 days at our laboratory. Under isoflurane anesthesia in oxygen, the jugular vein and carotid artery were cannulated . Jugular vein was used for continuous infusion of glucose solution, whereas arterial line was used for continuous monitoring and blood sampling described below. After the preparatory surgery, the animals were placed in a metabolic cage that allowed for awakening and stabilization for 1 hr.n = 10) or the Endotoxemia (E: n = 10) group, the animals in the latter of which were administered LPS (1 mg/kg) by intravenous injection. Based on our pilot study data, we chose this dose of LPS in which the mortality rate at 48 hrs was approximately 10-15 % without hyperglycemia and 20-30 % with hyperglycemia. In other words, we intended to make our endotoxemia model not lethal but moderately damaged to mimic disease condition. Both groups were further divided into 2 subgroups : the Normoglycemia group (N: 80–150 mg/dL), administered normal saline, and the Hyperglycemia group (H: 250–350 mg/dL), administered glucose solution, resulting in four different groups . All solutions were infused at the rate of 10 ml/kg/hr throughout the 24 hr study period. In our pilot study, we confirmed that such infusion rate did not alter arterial pH and electrolytes significantly at least for 24 hrs. Blood sample (10 μL) was obtained at 0, 3, 6, 12, and 24 hr study period for measurement of blood glucose. In the Hyperglycemia group, all rats received 20 % of glucose infusion at base line, and then, the concentration was adjusted according to the blood glucose levels measured at 3, 6, and 12 hr time-points during the study period. If blood glucose level was more than 350 or 500 mg/dL, the infusion concentration was reduced by 5 % or 10 % respectively. On the other hand, if blood glucose was less than 200 or 150 mg/dL, the infusion concentration was increased by 5 % or 10 % respectively . Since one animal in the EH group died before the end of the study period, we added one rat in the EH group so that the number of animals in each group was equal (n = 5). At 24 hr, the MLNs were excised thorough laparotomy and the adipose tissue around it was removed for the measurement of inflammatory cytokine  mRNAs and levels of transcriptional factors of CD4+ T lymphocyte subsets . Then, all tissues were incubated in RNAlater solution  overnight at 4 °C and stored at −80 °C until the mRNA measurement by semi-quantitative real time-polymerase chain reaction (Semi-quantitative RT-PCR).After the preparatory surgery and 1-hr stabilization period, 20 rats were randomly assigned to either the Control group  and received the same treatment during the next 24 hr study period. One rat was added to the EH group, since one rat in this group died before the end of the study period. After laparotomy under anesthesia, the contents of colon were harvested from the whole colon, and sampled and weighed before suspension in RNAlater solution  and then stored at 4 °C until the analyses described below.To clarify the alterations of the gut microbiota, another series of experiments were performed: Following the same methods described above, 26 rats were randomly divided into the same 4 groups . Briefly, 50 mg of minced MLNs was transferred to 1.5-ml sterile tubes containing microbeads. Then, 1-ml TRI regent , a monophasic solution containing phenol and guanidine thiocyanate, was added to the tubes, followed by homogenization using a tissue homogenizer  at 2,000 x g for 2 min at 4 °C. After the homogenization, each sample was centrifuged at 4,500 x g for 10 min at 4 °C and the supernatants were transferred to 1.5-ml sterile tubes. Then, 100 μL of 1-Bromo-3-Chloropropane, a phase separation reagent, was added to the supernatants, followed by incubation and centrifugation at 4,500 x g for 10 min at 4 °C, and transfer of the aqueous phase to 1.5-mL sterile tubes, to which 200 μL of 100 % ethanol was added. Thereafter, the samples were transferred into 1.5-mL sterile tubes with a filter cartridge, and centrifuged at 8,000 rpm for 10 min at 4 °C. The filter cartridges containing total RNA were washed twice with wash solution, followed by addition of 100 μL of elution buffer and centrifugation for 30 sec. The quantity and quality of the eluted total RNA were measured using the NanoDrop 1000A spectrophotometer , followed by dilution of the RNA samples with nuclease-free water . To remove contaminating DNA from the isolated RNA, DNase treatment was performed using a DNA-free kit . The complementary DNA (cDNA) synthesis from the total RNA was performed using the ReverTra Ace qPCR RT kit  with random primers, in accordance with the instructions supplied by the manufacturer.We performed semi-quantitative RT-PCR to examine the mRNA level of each of the inflammatory cytokines and CD4Tnf) for TNF-α, Rn00580432_m1 (Il1b) for IL-1β, Rn01410330_m1 (Il6) for IL-6, Rn01461633_m1 (Tbx21) for Th1 transcriptional factors, Rn00484683_m1 (Gata3) for Th2 transcriptional factors, Rn01533717_g1 (Rorc) for Th17 transcriptional factors, Rn01525092_m1 (Foxp3) for Treg transcriptional factors, and Rn00607869_m1 (Actb) for β-actin. Quantification was normalized to the endogenous expression of the β-actin gene. The reaction mixture and PCR cycles were adjusted according to the manufacturer’s instructions . The data were analyzed using the OneStep Software . After determining the comparative threshold cycle (CT) values for β-actin as reference, the relative quantifications were carried out using the 2Ct-ΔΔ method and standardized by the CT value for β-actin. The data were expressed as mean ± standard deviation.The target gene expressions were analyzed by semi-quantitative RT-PCR using TaqMan-based Applied Biosystem gene expression assays . The primers and probes designed for this system were as follows; Rn9999017_m1   staining methods, of the dilution series of the standard strains. In the present study, we measured the various types of bacteria in the gut microbiota by using specific primer sets, including major types of bacteria in the intestine, such as Enterococcus, and Enterobacteriaceae to which E.coli belong. To determine the number of bacteria present in the samples, 3 serial 10-fold dilutions of the extracted RNA sample  were used for the quantitative RT-PCR, and the CT values in the linear range of the assay were applied to the standard curve generated in the same experiment to obtain the corresponding bacterial cell counts in each nucleic acid sample. The specificity of the quantitative-RT-PCR assay using group- or species-specific primers was determined as described previously [After the intestinal contents of the colon were homogenized using a mortar on ice, an aliquot of fecal homogenate (20 μL) was added to 1 mL sterilized PBS solution and centrifuged at 5,000 x g for 10 min. The supernatant was discarded and RNA was isolated using a modification of the guanidinium thiocyanate-phenol-chloroform extraction method , 15. To eviously , 15Post-hoc test was performed using Tukey’s test. Other data, such as the mRNA levels detected by RT-PCR were analyzed by two-way ANOVA for interaction between two main effects of endotoxemia  and hyperglycemia . Ninety five percent of confidence interval for each four factors  was also presented. When the interaction between two factors didn’t exist, independent effects of these two factors were presented. If the interaction were detected, comparison of two groups were performed using Student’s t-test separately in the Control group (CN vs CH) and the Endotoxemia group (EN vs EH) to elucidate the effect of hyperglycemia in both healthy and endotoxemic condition. The statistical analyses for mRNA were performed using SPSS/17.0 J . If mRNA was not detectable by RT-PCR, the value was regarded as zero. Differences at two-sided p-values of less than 0.05 were considered to be statistically significant.All data were expressed as mean ± SD after confirmation of normal distribution. The repeated-measures data of blood glucose obtained at various time-points were analyzed by mixed-effects analysis of variance (ANOVA) using the statistical software package, EXCEL-Longitudinal Data Analysis, Ver1.0 for Windows . Then, Figure per se, regardless of the presence/absence of concurrent endotoxemic insult, depressed the expression of TNF-α mRNA, but augmented the IL-1β and IL-6 mRNA expression in MLNs  and Fox3 mRNA  in the Control group only, suggesting that hyperglycemia could induce the transcription of Gata3 and Foxp3 as representatives of Th2 and Treg in the MLNs in the healthy condition, not the endotoxemic condition.In the analysis of the transcriptional factors of CD4x21 Fig.  or Rorc x21 Fig. , but wasx21 Fig.  and Foxpx21 Fig. . There wTo clarify whether persistent hyperglycemia evoked by exogenous glucose infusion for 24 hrs modulates the intestinal microbiota differently in the presence or absence of endotoxemia, we evaluated the changes in the populations of the obiligate/facultative anaerobes and aerobes by quantitative RT-PCR. The classification of bacteria we evaluated in this study is shown in the Additional file Bacteroidetes class (decrease), even though hyperglycemia had no independent effects  was found on the Lactobacillus reuteri subgroup, staphylococci, enterococci and Enterobacteriaceae .There were no significant differences in the total populations of obligate/facultative anaerobes and aerobes in the colon contents among the groups Fig. . In the cts Fig. . Table 1Collectively, hyperglycemia and endotoxemia didn’t alter the gut microbiota synergically, even though both hyperglycemia and endotoxemia independently modified the proportions of the obligate and facultative anaerobes in the gut microbiota. In some type of bacteria, the effect of hyperglycemia differed between healthy and endotoxemic condition.per se could independently alter the mRNA expression levels of inflammatory cytokines in the MLNs. In particular, the mRNA expressions of IL-1β and IL-6 were augmented regardless of endotoxemia condition, and those of CD4+ T lymphocyte master genes, such as Gata3 and Foxp3, were increased under healthy condition without endotoxemia in the MLNs, following exposure to prolonged state of hyperglycemic insult for 24 hours. While endotoxemia is known to modulate the distribution of some obligate and facultative anaerobes, including both pathogenic and non-pathogenic bacteria, the present study indicates that hyperglycemia also evoked alterations of the gut microbiota, such as changes in the populations of the Lactobacillus reuteri subgroup and staphylococci in the colon under healthy condition.The present study demonstrated that persistent hyperglycemia induced by exogenous glucose infusion altered the gut immune functions and microbiota in the colon. Another important finding was that hyperglycemia per se can activate the MLNs to modulate the host immune responses. The discharges of TNF-α secretion might have been peaked-out at this moment in this experimental model. While several mechanisms underlying the activation of MLNs by continuous hyperglycemic state could be proposed, macrophages residing in the MLNs [via activation of macrophages, thereby modulating the functions of the immune system and organ functions.MLNs, the GALT tissue of greatest consequence, play a key role in the gut and host immune systems , and simthe MLNs  are consthe MLNs . In a prthe MLNs . Collect+ lymphocytes also contribute to the preservation of immune homeostasis and inflammatory responses in the gut [+ lymphocytes by measuring the master genes, including the Tbx21 for Th1, the Rorc for Th17, Gata3 for Th2, and Foxp3 for Treg to evaluate whether hyperglycemia and endotoxemia synergistically modulate the balance between pro- and anti-inflammatory responses. Previous study showed that both Th1 and Th17 cells were responsible for the release of inflammatory cytokines [Gata3) and Treg (Foxp3) master genes in favor of Th1 (Tbx21) and Th17 (Rorc) in healthy condition, which was not found in the endotoxemic condition. Recent evidence has indicated that T cell function and differentiation, including increased Th1 (inflammatory) to Th2 (anti-inflammatory) differentiation and increased Treg cells were associated with the development of immunosuppression in the late phase of sepsis [+ lymphocyte differentiation to Th2 and Treg was not clarified in our study, endogenous insulin could affect the differentiation of activated CD4+ lymphocytes, leading to the promotion of Th1 to Th2 differentiation [CD4 the gut . We examytokines , whereasytokines , 20. Conf sepsis . Thus, hntiation , and simntiation . Since ontiation , 25, theClostridia and Bacteroidetes appear to play pivotal roles in the prevention of dysbiosis, the population of facultative anaerobes, including pathogenic Enterobacteriaceae, increases when that of obligate anaerobes is reduced [Bacteroidetes classe in the colon, while the exact reason for this change remains to be elucidated. In lower-level analyses of the gut microbiota, endotoxemia up-regulated the Clostridium perfringens while decreasing the Bacteroides fragilis subgroups irrespective of hyperglycemic condition. It should be noted that hyperglycemia per se evoked alterations of the gut microbiota of the colon even in lower-level analysis, e.g., decrease of the Lactobacillus reuteri subgroup and increase of Staphylococci in the healthy condition, and decrease of Enterococcus and Enterobacteriaceae in the endotoxemic condition. While we did not evaluate how these alterations in the colon microbiota were induced, it is plausible to consider that these changes could affect the gut immune function.Disruption of the bacterial community in the gut may be associated with overgrowth of and colonization by pathogenic bacteria, which usually inhabit the intestinal tract in small numbers . While o reduced . Indeed,+ T cells differentiation in MNLs. Although the true mechanism was not elucidated in this study, some possibilities should be considered. One mg/kg LPS, which was less than the dose of 4 mg/kg we used in the previous study [+ T cells differentiation, even though the survival rate of the EH group at 24 hours was about 80 % in our preliminary study, which implies the severity of disease condition induced by this dose of LPS. Otherwise, concentration of cytokine had already peaked out and the alteration of CD4+ T cells differentiation had already finished at 24 hours. Further study is warranted to elucidate the mechanism how these changes interact and modulate the gut immune function. While the true mechanism was not elucidated in this study, the alteration of the gut microbiota, which was induced by hyperglycemia and endotoxemia in this study, could evoke the deteriorated gut immune function and bacterial translocation into systemic circulation [Lactobacillus reuteri subgroup considered as one of probiotic bacterial strains, which was reduced by hyperglycemia in this study, may be able to depress growth, adherence and virulence of Staphylococci [Although we demonstrated that inflammatory mediators and differentiation of lymphocytes in MNLs, and microbiological constitute in the intestine were modulated by persistent hyperglycemia and endotoxin in this study independently or dependently, it is mandatory to discuss how these changes interact with each other and consider clinically relevant implications of this study. Hyperglycemia evoked inflammatory cytokines while promoting differentiation to Th2 and Treg which are responsible for the release of anti-inflammatory cytokines in MNLs. Although these changes seem to be contradictory, it is often the case that hyper-inflammatory and anti-inflammatory responses coexist in the disease condition such as sepsis in which hyper-inflammatory condition is followed by anti-inflammatory response at the almost same time . Thus, ius study , might nculation . Consideylococci , 30. It First, some may argue that we should have measured the respective protein concentrations directly caused by the alterations of cytokine mRNA. Although previous study showed that protein level was remarkably elevated as well as cytokine mRNA in an endotoxemic model [Second, persistent hyperglycemia observed in critically ill patients is basically not caused by continuous glucose administration but by endogenous alterations such as resistance to insulin [Third, the values of CD4+T cells, evaluated by the change of transcriptional factors through RT-PCR, could not reflect the actual number of each lymphocyte since extraction of m-RNA was performed from the contents of MNLs without cell sorting by FACS, and the alteration of transcriptional factors could not always be consistent with the number of lymphocytes. Although these values of CD4+T cells could not represent the actual number, the trend of CD4+ T cells differentiations could be detected by this measurement method. Finally, we performed lots of multiple comparisons to evaluate the alteration of bacteria in colon microbiota, which might lead to the false positive. However, the possibility of false positive could be a minimum, since we performed multiple comparisons after detection of interaction between two factors, hyperglycemia and endotoxemia.Some limitations of this study must be borne in mind while interpreting the data herein. ic model , the pro insulin . However+ T cells differentiation in the MLNs, and induces the alterations of the colon microbiota. Further study is warranted to elucidate the causal relationship between persistent hyperglycemia and altered gut immunity as well as clinical implications of these changes in gut.In conclusion, the present study showed that persistent hyperglycemia evoked by exogenous glucose infusion for 24 hours modulates both inflammatory responses and CD4