Moreover, these cell models exhibit partly re-activated AR signaling despite presence of enzalutamide. In addition, we show that enzalutamide resistant cells are insensitive to bicalutamide but retain considerable sensitivity to abiraterone. Mechanistically, enzalutamide resistance was accompanied by increased AR and AR-V7 mRNA and protein expression as well as AR gene amplification, while no additional AR mutations have been identified.Enzalutamide is an androgen receptor (AR) inhibitor approved for therapy of metastatic castration resistant prostate cancer. However, clinical application revealed that 30 to 40% of patients acquire resistance after a short period of treatment. Currently, the molecular mechanisms underlying such resistances are not completely understood, partly due to a lack of model systems. In the present study we established three different cellular models of enzalutamide resistance including a cell line with wild type AR (LAPC4), DuCaP cells which overexpress wild-type AR, as well as a cell which has been adapted to long term androgen ablation (LNCaP Abl) and harbors the AR T878A mutation. After 10 months of cultivation, sustained growth in the presence of enzalutamide was achieved. When compared to controls, resistant cells exhibit significantly decreased sensitivity to enzalutamide as measured with  Enzalutamide (Xtandi®) is an oral androgen receptor (AR) inhibitor which has been developed for the treatment of prostate cancer (PCa). Enzalutamide blocks binding of androgens to AR, nuclear translocation of AR as well as AR-mediated DNA binding . AccordiDespite obvious benefits, application of the drug in a clinical setting revealed that not all patients respond to new generation AR modulators including enzalutamide . NumerouIn the present study we aimed to uncover a potential involvement of AR expression in enzalutamide resistance. We established three different cellular models of enzalutamide resistance including a cell line with wild type AR (LAPC4), a cell line overexpressing wild-type AR (DuCaP) as well as a cell line (LNCaP Abl) which has been generated by long term androgen ablation from LNCaP, which harbor a mutated AR T878A) H thymidine incorporation assay following treatment with different doses of vehicle or enzalutamide. Given the expected insensitivity of the EnzaR sub cell-lines, enzalutamide doses above the starting concentration of the selection process were selected . As expected, in all three control (vehicle) cell lines we observed maximal inhibition of proliferation  and vehicle treated (Veh) cell lines exhibit a comparable morphology Figure . In orden Figure  and viabn Figure  already n Figure  and 2C. n Figure . Of the Next, we aimed to evaluate possible cross-resistances of the newly generated enzalutamide resistant cell lines with other drugs that target the AR pathway, such as bicalutamide or abiraterone. Briefly, bicalutamide is an older, non-steroidal anti-androgen which is clinically still used as an adjuvant agent in patients receiving radiation therapy due to intermediate or high risk PCa. Moreover, bicalutamide prevents the flare up phenomena in LHRH agonist therapy . AbirateWhile we observed complete resistance to bicalutamide in both DuCaP sub-cell lines, LAPC4 and LNCaP Abl control cell lines are responsive to the drug. Notably, we detected decreased bicalutamide sensitivity in LNCaP Abl EnzaR compared to controls while LAPC4 EnzaR not only lost their sensitivity to bicalutamide, but exhibited even enhanced proliferation in the presence of the drug Figure . MeasureIn order to uncover the mechanisms leading to enzalutamide resistance, we first hypothesized that mutations in the AR gene may lead to decreased sensitivity to the drug. However, sequencing of AR coding cDNA revealed that LAPC4 and DuCaP EnzaR sub-lines did not gain any AR mutations compared to the vehicle treated controls. Similarly, LNCaP Abl EnzaR did not harbor additional AR mutations other than the known LNCaP mutation T878A .per se do not exhibit relevant levels of V7 mRNA or protein, acquired V7 mRNA and protein expression with development of enzalutamide resistance. In DuCaP on the other hand, V7 was present even in the control cell line and further increased in DuCaP EnzaR. In contrast, neither LNCaP Abl vehicle nor EnzaR exhibited detectable amounts of truncated AR variants  in relation to a POLG Exon 3 fragment (Chr 15q25). AR/POLG copy number ratios were calculated as fold change of normal male DNA which harbors 1 copy of AR. As expected, parental as well as vehicle treated LAPC4 cells exhibit a normal AR copy number. Strikingly, we detected a ~8-fold amplification of AR gene in enzalutamide resistant LAPC4 Figure  which waThe AR is one of the major drivers of PCa progression and various changes in AR status such as AR amplifications or mutations lead to persistence of AR signaling in CRPC . TherefoReviewing the literature, several possible mechanisms of enzalutamide resistance are discussed including AR splice variants, mutations or amplifications. One study showed that enzalutamide treatment of LNCaP causes selection of a F876L missense mutation in the AR ligand binding domain which leads to partial agonism and thus resistance to the drug , 21. MorRecently, it has been shown that presence of AR splice variant 7 (ARV7) in bone marrow biopsies from patients under enzalutamide therapy was associated with insensitivity to the drug . In addiGiven that in enzalutamide resistant LNCaP Abl cells we did not detect relevant changes in AR full length or V7 levels, we conclude that also other molecules apart from the AR may be involved in enzalutamide resistance. In this context, it has been shown that enzalutamide induces autophagy in CRPC cell lines by AMPK activation and mTOR suppression . In addiVan Soest et al. showed that enzalutamide resistant PC346C cells exhibit an almost complete cross resistance to abiraterone  which maInterestingly, enzalutamide resistant LAPC4 cells proliferate less under control conditions than under enzalutamide treatment. Moreover, enzalutamide resistant LAPC4 exhibit higher levels of nuclear AR in the presence of enzalutamide than without the drug. This observation may point to a switch from antagonistic to agonistic action of enzalutamide as described already for bicalutamide and may explain an enzalutamide withdrawal syndrome which was evident in a subset of patients . In lineIn the present study we show for the first time the successful establishment of three different enzalutamide resistant cell lines, which provide an important tool for investigation of resistance mechanisms in cells with different AR statuses. We found that in these models enzalutamide resistance is not accompanied by AR mutations, while increased expression of full length and truncated AR as well as AR gene amplification might be involved in development of resistance. Furthermore, our results suggest that abiraterone treatment might be feasible in a subset of patients resistant to enzalutamide.The human prostate cell lines LAPC4 (lymph node metastasis) were a gift from Dr. A. Cato . DuCaP (brain metastasis) were a gift from from Dr. J Schalken . The subline LNCaP Abl was previously established by our group after long term cultivation of LNCaP (lymph node metastasis) in steroid free medium as described earlier . The ideCell lines were cultured in the presence of increasing doses of enzalutamide or vehicle (EtOH). Drug treatment was started using 0.2μM enzalutamide. Drug-containing medium was changed every third day. Cells were splitted when 80% confluency was reached. Enzalutamide concentration was increased when cells started to regrow in the presence of the drug at a growth rate similar to that of control cells until final concentrations  were reached. Cells were then cultured for another 8 weeks in these conditions.Total RNA was isolated using the RNeasy mini kit  and cDNA synthesis was performed using iScript Select cDNA synthesis kit (Biorad). Taqman qRT-PCR was performed as described elsewhere . ExpressWestern blot was performed as previously described . The folImmunofluorescence was performed as described previously . Briefly3H]thymidine (1 μCi/well) were added for 24 h. DNA was harvested on 96-well filter plates (Perkin-Elmer). Scintillation fluid (50 μL) was added and radioactivity was quantified using a Chameleon 5025 liquid scintillation counter (HVD Life Sciences).Cells were seeded in triplicates onto 96-well plates. On the next day, cells were treated with enzalutamide, abiraterone, bicalutamide or vehicle (EtOH) as indicated. On day 5, 25 μL/well of diluted [Viability was assessed using WST reagent (Roche) according to the manufacturer's protocol.2TATA) in framework of nanoluc reporter vector pNL1.1 [Nluc] (Promega). A PGK-promoter driven luciferase control vector  was included for normalization. Per well, 30 ng of each, reporter and normalization control vector and 4 ng of AR-wild-type expression vector pSG5AR were mixed and complexed for 15 min with 0.2 μL of transfection reagent  in 3 μL of transfection buffer and thereafter mixed with 50 μL of PC3 suspended in medium supplemented with 3% charcoal-stripped FCS (2×105 cells/mL). After an incubation for 30 min at room temperature cell suspension was transferred to the wells, R1881 (1 nM), enzalutamide (10 μM) and vehicle were added and cells were incubated for 36 h before NanoLuc and firefly luciferase activities were measured using Chameleon 5025 instrument (HVD Life Sciences) and Nano-Glo Dual-Luciferase Assay (Promega) according to manufacturer's recommendations. Quadruplicate samples were measured for each treatment and nanoluc reporter gene activity was normalized to control luciferase activity.AR reporter gene assay was performed with AR-negative PC-3 cells in 96-well plate format employing a synthetic promoter composed of two androgen responsive elements and a TATA-box ((ARE)cDNA, prepared as described above, was used to PCR-amplify 3 overlapping AR cDNA fragments covering the entire AR coding sequence , 41. cDN−Δct and normalized to male control DNA, harboring 1 copy of AR gene.Amplification of AR gene in parental, vehicle and EnzaR cell lines was assessed by duplex qPCR quantifiying AR  in relation to POLG gene  in genomic DNA. Genomic DNA was isolated using QIAquick DNA isolation kit (Qiagen) and a duplex qPCR was performed with 20 ng of DNA using primer/probe sets AR_fw 5′-GACCTTAAAGACATCCTGAG-3′, AR_rev 5′CCCTAAGTAATTGTCCTTGG-3′, AR_probe FAM-5′-CAACTCCTTCAGCAACAGCAGC-3′_BHQ1 and POLG_fw 5′-AGAGCGTTACTCTTGGAC-3′, POLG_rev 5′-TCGGTCAAAGGAAACATTG-3′, POLG_probe YYE-5′-CACCACTAACTGCTCCTGCCA-3′BHQ1 on ABI 7500 FAST real-time PCR instrument (Life Technologies). Primers and probe were used at a concentration of 800 and 100 nM, respectively. PCR efficiency was calculated as 105%. The ratio AR/POLG DNA was calculated using the formula 2


BMI1, a polycomb group (PcG) protein, plays a critical role in epigenetic regulation of cell differentiation and proliferation, and cancer stem cell self-renewal. BMI1 is upregulated in multiple types of cancer, including prostate cancer. As a key component of polycomb repressive complex 1 (PRC1), BMI1 exerts its oncogenic functions by enhancing the enzymatic activities of RING1B to ubiquitinate histone H2A at lysine 119 and repress gene transcription. Here, we report a PRC1-independent role of BMI1 that is critical for castration-resistant prostate cancer (CRPC) progression. BMI1 binds the androgen receptor (AR) and prevents MDM2-mediated AR protein degradation, resulting in sustained AR signaling in prostate cancer cells. More importantly, we demonstrate that targeting BMI1 effectively inhibits tumor growth of xenografts that have developed resistance to surgical castration and enzalutamide treatment. These results suggest that blocking BMI1 alone or in combination with anti-AR therapy can be more efficient to suppress prostate tumor growth. The polycomb group protein BMI1 is highly expressed in prostate cancer. Here, the authors demonstrate that BMI1 directly interacts with AR leading to increased AR signaling independently of PRC1 complex and that targeting BMI1 inhibits tumor growth of castration-resistant prostate cancer tumors. PcG proteins are known to exert their functions by forming multimeric chromatin-associated protein complexes and repressing downstream targets. The two polycomb repressive complexes (PRC1 and PRC2) are major epigenetic regulators for monoubiquitination of histone H2A at lysine 119 and methylation of histone H3 at lysine 27. The major components of mammalian PRC1 include an E3 ubiquitin ligase ring finger protein 2 , ring finger protein 1 , chromo box proteins (CBXs), and either B lymphoma Mo-MLV insertion region 1  or the paralogs of BMI1 . Although BMI1 contains a ring motif, it does not have E3 ubiquitin ligase activities and has to form a complex with RING1B to ubiquitinate their substrate H2AK119 and then repress the expression levels of PRC1 targets3. Mammalian PRC2 consists of a histone methyltransferase, enhancer of zeste homolog 2 (EZH2), and its known binding partners, embryonic ectoderm development (EED) and suppressor of zeste 12 (SUZ12)4.Polycomb group (PcG) proteins are essential for determining cell differentiation, maintaining stem cell self-renewal, and regulating cellular memories and proliferation5. These findings suggest that BMI1 may have functions other than stem cell renewal capacity that has not been fully characterized. AR plays key roles in prostate epithelial cell differentiation and proliferation. Blocking the AR signaling is the mainstay in prostate cancer therapy, evidenced by the next-generation antiandrogens, e.g., abiraterone and enzalutamide that potently inhibit AR functions can suppress castration-resistant prostate cancer (CRPC) tumor growth. However, prostate cells can generate AR splice variants, gain-of-function mutations, or alter its functional mode independently of androgens to become therapy resistant7. Therefore, therapies that can fully block AR protein expression have been actively investigated. Since both BMI1 and AR are abundantly expressed in prostate cancer cells, whether BMI1 modulates AR protein expression and transcriptional activity remains unclear. In this study, we discovered that BMI1, independently of the PRC1 complex, binds and stabilizes AR proteins to regulate the AR pathway in prostate cancer. This discovery conceptually advances our understanding of a novel, PRC1-independent role of BMI1 in prostate cancer progression through the AR pathway. Further, our results demonstrate that BMI1 is not only a transcriptional repressor, but also a transcriptional activator through its binding partners . Most importantly, here, we show that for CRPC, especially therapy -resistant CRPC, targeting BMI1 alone or in combination with anti-AR therapy effectively kills tumor cells.BMI1 is abundantly expressed in prostatic luminal epithelial cells and its levels are associated with poor prognosis of prostate cancer patients8, to dramatically suppress cell growth, and found that AR or PSA levels were not downregulated , a well-known transcriptional target of AR Fig. . RNA lev9 indicated an interaction between BMI1 and AR , DNA-binding domain (DBD), and ligand-binding domain (LBD)gth Fig. , which s15. We expected that BMI1 might play a similar role to stabilize AR proteins. To test this hypothesis, we knocked down BMI1 in C4-2 cells, treated cells with cycloheximide (CHX) to block de novo protein synthesis, and measured the half-life of AR protein. BMI1 knockdown dramatically shortens the half-life of AR  of BMI1-occupied genes Fig.  and they30. Similar to BMI1 siRNA knockdown, PTC209 treatment also decreased AR, AR-V7, as well as BMI1 expression levels  among these mice were transplanted to the next few passages of castrated male SCID mice treated with enzalutamide to maintain the enzalutamide resistance. These mice were randomly divided into two groups when the tumors reached 100 mm3. One group was treated with PTC209 plus enzalutamide for 21 days, whereas the other group was treated with enzalutamide alone to serve as a control  have also been extensively investigated37. The leading dogma suggests that BMI1 is a key component of PRC1 and a transcriptional repressor , and neoplastic progressionIn this study, we made the unexpected observation that AR protein levels, but not transcript levels, as well as the protein and transcript levels of AR-activated targets PSA and TMPRSS2, were decreased by depletion of BMI1 in AR-positive prostate cancer cells. These results suggest that BMI1 loss-mediated AR downregulation is through a novel BMI1-mediated protein posttranslational function and not through its canonical role as a transcriptional repressor. We also demonstrated that BMI1 directly binds to full-length AR and AR variants through NTD in vitro and in vivo, and loss of BMI1 induces proteasome-mediated full-length AR and AR variants protein degradation.low/RING1Blow CRPC cells decreases AR protein and targets, and overexpressing siRNA-resistant BMI1 could completely restore the AR protein and its functions. To further investigate the PRC1-independent BMI1 function in prostate cancer, we demonstrated that the BMI1-truncated mutant, BMI1ΔRING, which does not contain the RING domain and cannot interact with RING1B, still directly interacts with AR NTD. Overexpression of BMI1ΔRING completely rescues the BMI1 loss-mediated decrease of AR protein and its targets, but could not rescue BMI1 loss-mediated decrease of H2A ubiquitination (which needs PRC1 activities). Importantly, our studies further demonstrate that targeting BMI1 by PTC209 significantly inhibits CRPC tumor growth, and combinational targeting of BMI1 and AR achieves better efficacy than single agents alone.Here, we show that BMI1 disassociates the interaction between AR and MDM2, thereby inhibiting MDM2-mediated AR ubiquitination and degradation. Intriguingly, the knockdown of key PRC1 proteins RING1B or RING1A, the H2A E3 ubiquitin ligases, or treatment with the PRC1 ubiquitin E3 ligase inhibitor PRT4165 have no effect on AR or AR target expression levels. However, a further knockdown of BMI1 in RING1A38. Two recent studies demonstrated that Rb1, Trp53, EZH2, and SOX2 were involved in the prostate cancer lineage plasticity40. Here, we show that targeting BMI1 to regulate AR and its degradation of variants is effective for enzalutamide-resistant CRPC. This suggests that BMI1 may be another novel therapeutic target for enzalutamide-resistant CRPC patients.Inherent or acquired resistance to contemporary AR inhibition, such as enzalutamide, remains a major clinical obstacle. Based on our current understanding, there are three general mechanisms: restored AR signaling (AR-LBD mutation and splice variants), bypass of AR, and complete AR independence (lineage plasticity)41. However, the mechanism by which EZH2 regulates AR pathway is yet unclear. Here, we demonstrate that, unlike the canonical transcriptional repressor and its functions in cancer stem cells, BMI1 performs its noncanonical function as a transcriptional activator and an AR cofactor. Here, we show that BMI1 directly binds to AR protein and regulates AR targets in a PRC1-independent manner in AR-positive CRPC , chloroquine diphosphate salt , and cycloheximide , and all were commercially obtained. Serial dilutions of enzalutamide were made using a vehicle of 0.5% CMC  and 0.25% Tween-80 . PTC209 was made using a vehicle of 14% DMSO, 36% polyethylene glycol 400, and 50% polypropylene glycol 400. Antibodies used for immunoblot assays are listed in Supplementary Table Drug concentrations (unless otherwise indicated) were PTC209 , enzalutamide , MDM2 inhibitor 373225 , MDM2 inhibitor 373226 , PRT4165 , MG132 , ammonium chloride  containing 10% bovine growth serum (BGS), 1% l-glutamine, and 1% penicillin and streptomycin and cultured at 37 °C in 5% CO2. These cell lines were recently authenticated by the University of Arizona Genetics Core using short tandem repeat (STR) profiling. Cell lines were mycoplasma negative during routine tests.LNCaP, VCaP, and 22Rv1 were purchased from ATCC. C4-2 cells were provided by Dr. Arul M. Chinnaiyan. All cells used in this study were within 20 passages after receipt. LNCaP, C4-2, and 22Rv1 were cultured in 5% CO5 cells were plated in six-well plates, grown without antibiotics to 80% confluence, and then transfected with siRNA sequences or their corresponding mock sequences using a Lipofectamine 2000 kit  with the procedure provided by the manufacturer.All the siRNA sequences were purchased from Thermo Scientific, RING1B siRNA: #1 s12067, #2 s12068; MDM2 siRNA: #1 s58628 or #2 s58629 or BMI1  siRNA . Briefly, 3×10Lentivirus was packaged by co-transfection of constructs with third-generation packaging plasmids pMD2.G, pRRE, and pRSV/REV with Fugene HD (Roche) into 10-cm plates with HEK293T cells. After the first 24 h of transfection , the medium was changed to DMEM, and the supernatants 72 and 96 h after transfection were pooled, filtered through a 0.45-µm filter, and stored at −80 °C. Pellets were resuspended in Opti-MEM (Gibco) and titers were determined by p24 assay, in addition to functional titration to determine an MOI of 1 for each initial batch of virus. Expression was verified by western blotting.Whole-cell lysate IP was performed by lysing cells in the NP-40 lysis buffer (2×)  or Pierce RIPA Buffer  with a protease and phosphatase inhibitor . The lysate was kept on ice for 20 min and sonicated for 2 s on and 2 s off for 40 s and insoluble material was removed by centrifugation. Lysates were precleared using Dynabeads protein A  or protein G . Antibodies were added to lysates and incubated at 4 °C overnight. The immune complexes were collected using Dynabeads protein A  or protein G , and beads were washed three times extensively with the lysis buffer. To denature proteins, lysates were added to 2× reducing SDS-sample buffer or 4× reducing buffer  and heated to 95 °C for 10 min. Protein levels were assessed by standard SDS–polyacrylamide gel electrophoresis and transferred to PVDF membranes . Images were captured using the ChemiDoc XRS+ Molecular Imager system (BIO-RAD). Antibodies used in western blotting were described above. Blots were incubated overnight with primary antibodies at 4 °C, followed by detection with Clean-Blot IP Detection Reagent (HRP) , goat anti-mouse IgG (H+L)-HRP , or goat anti-rabbit IgG (H+L)-HRP  secondary antibody.14. The cell lysates were added to 2% SDS and heated to 95 °C for 10 min. The lysates were then diluted using lysis buffer to reduce SDS concentration to 0.2% before anti-AR or anti-Ub antibody was added. Immunoblotting was performed as described above.AR ubiquitination was detected under immunoprecipitation as previously describedAs with in vitro immunoprecipitation, purified tagged proteins (dose as indicated) were added to chilled PBS (1 ml) with a protease and phosphatase inhibitor. A 50-μl aliquot was removed, rotated at 4 °C overnight, and used as the input. Proteins were denatured using 2× loading buffer and boiled for 5 min. The immune complexes were then washed three times with NP-40 lysis buffer, collected using the corresponding beads, and rotated at 4 °C for 2 h. The beads were then washed three times extensively with NP-40 lysis buffer. To denature proteins, the lysates were added to 2× reducing SDS-sample buffer and boiled for 10 min. Immunoblotting was performed as described above (whole-cell lysate IP). Unprocessed blot images for western blot analysis are shown in Supplementary Figs. 12. This was then applied to a Sephacryl S-300 HR column  precalibrated using the Gel Filtration HMW Calibration Kit . A volume of 2000 μl of elute was collected for each fraction at a flow rate of 0.25 ml min−1, and eluted fractions were subjected to western blotting and co-IP.C4-2 nuclear extracts were obtained using NE-PER nuclear extraction kit (Thermo Scientific), and 5 mg of nuclear protein was concentrated to 3000 μl using Microcon centrifugal filter (Millipore)Total RNA was isolated from cells to generate cDNA using the RNA MiniPrep kit  and amfiRivert cDNA Synthesis Platinum Master Mix . Each cDNA sample was amplified using Power SYBR Green PCR Master Mix  on the QuantStudio 6 Flex Real-time PCR System . Briefly, the reaction conditions consisted of 2 μl of cDNA and 0.2 μM primers in a 10-μl final volume of super mix. Each cycle consisted of denaturation at 95 °C for 15 s, annealing at 58.5 °C for 5 s, and extension at 72 °C for 10 s, respectively. β-actin was used as an endogenous control to normalize each sample. The experiment was performed in triplicate with three independent experiments. The primers are listed in Supplementary Table Escherichia coli strain DH5α that harbors the expression plasmid for GST ± YKR079C or GST ± ELAC1 was incubated at 37 °C in 250 ml of LB medium containing 50 mg ml−1 ampicillin until the A600 of the culture reached 0.642. At this point, the fusion protein was induced by adding IPTG (500 mM). After further incubation at 37 °C for 4 h, the cells were harvested by centrifugation. Cell pellets were resuspended in 10 ml of lysis buffer , 23 mM AEBSF, 100 mM EDTA, 2 mM bestatin, 0.3 mM E-64, and 0.3 mM pepstatin A). The cells were then sonicated and centrifuged at 100,000 × g for 1 h. The cleared lysate was incubated with 0.5 ml of glutathione ± sepharose beads at 4 °C for 12 h. After exhaustive washing, the retained proteins were eluted from the beads with 0.1 ml of buffer  containing 20 mM glutathione. All the purification steps were carried out at 4 °C.43. We downloaded the human reference gene set (Refseq gene) from https://www.ncbi.nlm.nih.gov/refseq/rsg/. The number of fragments that originated from each gene in each sample is estimated using cuffdiff in the software Cufflinks (version 2.2.1)44 with classic-fpkm normalization method. The differentially expressed genes based on read counts are inferred using edgeR R package v3.12 after library-size factor normalization. MEV version 4.8.145 was used to plot heat maps.The RNA-seq reads were mapped to the human reference genome version hg19 using TopHat (version 2.1.0)46 was employed for KEGG pathway analysis. Each pathway returned by DAVID with a P value smaller than 1 × 10−2 was defined as significantly enriched. GSEA (version 2.2.0)48 was used to analyze the enrichment level of AR-induced genes collected from literature28.DAVID (version 6.8)50, PROGgene (version 2)51, or GraphPad Prism (version 7.0b). Statistical significance of the difference between the survival curves for different groups of patients was assessed using log-rank test.The Kaplan–Meier survival analysis was carried out using Cbioportal (version 1.4.3)52. The Dregion function in DANPOS (version 2.2.3)53 was used to calculate read density and define enrichment peaks. Briefly, we extended each read at the 3′ end to be 200-bp long and then calculated read density as the number of reads covering each base pair in the genome. For each sample, the total number of mapped reads was normalized to 25 million. For AR and BMI1 data, we used Poisson test P value 1 × 10−35 for read density cutoff to define seed peaks. We then used Poisson test P value 1 × 10−15 for read density cutoff to extend each seed peak. DANPOS subtracted input background signal from the ChIP signal. To further reduce the input background effect, we removed any ChIP-seq peaks that overlap with a peak in the input data. For input data, we used Poisson test P value 1 × 10−15 for read density cutoff to define seed peaks. We then used Poisson test P value 1 × 10−10 for read density cutoff to extend each seed peak. The selector function in DANPOS (version 2.2.3) was used to map the peak regions to TSSs. The overlap function in DANPOS (version 2.2.3) was used to calculate the number of overlapped peak regions.All ChIP-seq sequencing reads were mapped to version hg19 of the human genome using Bowtie (version 1.1.2)Chromatin immunoprecipitation (ChIP) was performed using the ChIP Assay kit  with the procedure provided by the manufacturer. For PCR analysis of enrichment of target gene promoters, 2 μl each of input DNA, AR-enriched, or IgG-enriched DNA were subjected to PCR using Platinum PCR Super mix (Invitrogen) and primers specific for target gene promoters or enhancers (Supplementary Table ® Luminescent Cell Viability Assay Kit and was read on Synergy 2 Multi-Mode Reader. The bioluminescence results were quantitated into cell viability. And drug effects (one-cell viability) were analyzed by Calcusyn . Combination index (CI) values were then calculated to determine the synergy of the two drugs. Optimal concentration ratio of the two drugs was selected by CI values that showed best synergistic effects.Cells were seeded in 96-well plates and treated with the two single drugs, and the combination of both at concentration gradients for 96 h. Bioluminescence was measured to quantify cell survival by using CellTiter-Glo32. Mice were anesthetized using 2% isoflurane  and 2 × 106 VCaP or 22RV1 prostate cancer cells suspended in 100 μl of PBS with 50% Matrigel were implanted subcutaneously into the dorsal flank on both sides of the mice. Tumor volumes were measured by length (a), width (b), and calculated as tumor volume = MIN(a)2 × MAX(b)/2. For VCaP castration-resistant prostate tumor model, VCaP tumor-bearing mice were castrated when the tumors were approximately 200–300 mm3 in size and once tumors started to grow back, mice were randomized and treated with vehicle or enzalutamide  (10 mg kg−1) and/or PTC209 (60 mg kg−1) daily (5 days per week), and terminated about 28 days later. Loss of body weight during the course of the study was also monitored. For the antitumor agent combination experiment, 22RV1 castration-resistant prostate tumors were allowed to grow after castration for 3–4 weeks (to tumor volume 100 mm3), treated with enzalutamide   for about 28 days, and then the biggest tumor (~1500 mm3) was transplanted to nude mice. The mice were kept until tumor volume reached 100 mm3, and then randomized to two groups, treated with enzalutamide  or enzalutamide combined with PTC209  daily (5 days per week), and terminated about 28 days later.CB17SCID mice were purchased from Charles River. Animal care and use conditions were followed in accordance with institutional and National Institutes of Health protocols and guidelines, and all studies were approved by Houston Methodist Institution Animal Care and Use Committee. Tumor xenograft model was induced as previously described3 in size, mice were randomized and treated with enzalutamide (10 mg kg−1 per day) or PTC209 (60 mg kg−1 per day) + enzalutamide (10 mg kg−1 per day) (5 days per week), and terminated about 35 days later. Caliper measurements were taken every 4 days and loss of body weight during the course of the study was also carefully monitored.The LuCaP 35CR and LuCaP 86.2CR patient-derived xenografts (PDX) were kindly provided by Eva Corey (University of Washington). Castrated CB17SCID mice were implanted subcutaneously with tumor bits of LuCaP 35CR. When the tumors were approximately 200 mmP values were assessed using a two-tailed unpaired Student’s t test or a two-way analysis of variance (ANOVA), with P values considered to be significant as follows: *P < 0.05; **P < 0.01; and ***P < 0.001. For tumor-free mice frequency, statistics were done with log-rank (Mantel–Cox) test.No statistical method was used to predetermine sample size. Mice were assigned at random to treatment groups and, where possible, mixed among cages. There were no inclusion or exclusion criteria. Whenever possible, the investigators were blinded to group allocation during the experiments and when assessing outcomes. Experiments were repeated two to three times. Data were analyzed using Prism 5.0 software (GraphPad) and presented as means ± SEM. The The authors declare that all data that support the findings of this study are available within the article and its Supplementary Information


RFs) of retinal ganglion cells (GCs). However, the spatial antagonistic surround observed in classical experiments has proven more elusive. Tests for the antagonistic surround have heretofore relied on models that make questionable simplifying assumptions such as space–time separability and radial homogeneity/symmetry. We circumvented these, along with other common assumptions, and observed a linear antagonistic surround in 754 of 805 mouse GCs. By characterizing the RF's space–time structure, we found the overall linear RF's inseparability could be accounted for both by tuning differences between the center and surround and differences within the surround. Finally, we applied this approach to characterize spatial asymmetry in the RF surround. These results shed new light on the spatiotemporal organization of GC linear RFs and highlight a major contributor to its inseparability.Reverse correlation methods such as spike‐triggered averaging consistently identify the spatial center in the linear receptive fields ( Because of their genetic tractability, mice are an increasingly popular model to study retinal circuitry and its perturbation during disease  or offset (OFF) and an antagonistic polarity surround Barlow . The cenAnother factor that may contribute to difficulties characterizing the surround in the GC linear RFs is the way spatial filters are combined with temporal filters into space–time models. GCs of mice and other vertebrates have temporal filters with at least two phases – one fast with center polarity and the other slow and antagonistic  that minimizes complex space–time codependencies. When the SoSS model was applied to our sample of GC linear RFs it revealed five subcomponents with highly distinctive spatiotemporal filtering. Furthermore, the subfilter corresponding to the antagonistic surround was frequently asymmetrically organized supporting its hypothesized non‐Gaussian structure. These results clarify the rules that govern receptive field organization and demonstrate how improved parameterization can reveal its core underlying features.Mice were cared for and handled following approved protocols from the Animal Care and Use Committee of Baylor College of Medicine and in compliance with the National Institutes of Health guidelines for the care and use of experimental animals. All mice were euthanized by cervical dislocation after anesthetizing with isoflurane.Nineteen male C57Bl/6J mice were kept on a regular light/dark cycle and experiments were performed diurnally at 3–4 months of age. Prior to euthanization, mice were dark adapted for at least 90 min. Eyes were removed under infrared illumination using night vision  and their retinas were dissected in a dish containing carboxygenated recording solution. Retinas were placed ganglion cell side up onto nitrocellulose filter paper  and transferred onto an electrode array where the preparation was retained with a plastic ring.2, 5% CO2) recording medium  at pH 7.35 (Tian and Copenhagen The retina was kept at 35.6°C and perfused at 2 mL/min with prewarmed and carboxygenated (95% OE(λ), in microwatts cm−2) in the plane of the preparation . Photon flux in photoisomerizations/photoreceptor/second (ϕ) was calculated as ϕ=acλmax∑λNpλτλSrλ where acλmax is the effective collecting area of a photoreceptor at its peak wavelength (0.34 μm2 for cones and 0.67 μm2 for rods) optically reduced and presented from below the MEA onto the ganglion cell side of the retina. Similar to previous reports  as a function of the location of its center  and three composite variables a, b, and c.To characterize the location and extent of the relatively strong receptive field center, we combined a single two‐dimensional Gaussian with a biphasic temporal filter. The two‐dimensional Gaussian describes the sensitivity x and minor axes σy.where a, b, and c are dependent on an angle θ and standard deviations in the major σThe temporal filter was the difference in three low‐pass filter impulse responsesf(t) is the filter strength in time bin t before the spike, and p, τ, and n are all fit parameters that shape the temporal filter.where These two filters are combined and regressed against the raw STA ω.The spatiotemporal fit of the receptive field center was used to divide the spatial inputs into up to nine annular regions, each spanning one standard deviation (SD) of radial distance. Temporal STAs within these annuli were pooled by either summing or averaging, which generally improved signal‐to‐noise ratios. Initially this temporal filter was fit on a per‐annulus basis using a standard least squares regression. Appropriate temporal models were determined by starting with a simple line at zero and iteratively adding additional temporal subfilters, statistical improvement was assessed with an F‐test. Each temporal subfilter was an impulse response of a low‐pass filter as shown in Equation Singular value decomposition (SVD) was applied on the STAs that had been transformed into two‐dimensional space . SVD creates a set of separable subfilters, ordered by decreasing power, and has therefore found use as a test for space–time separability in the visual , we compared the temporal STA of each spatial input to the model prediction for that input (henceforth referred to as the probe) by calculating their zero‐lag cross‐correlation (x‐corr). These x‐corr values vary in magnitude based on the strength of the signal, the noise, and probe. To estimate the significance of the probe‐to‐signal interaction, we made a reference comparison of the probe to a simulated population of noise STAs. Our stimulus was binary, so noise STAs were generated by averaging a number of 1s and ‐1s equivalent to the number of spikes averaged in the original STA. The standard deviation of the x‐corrs from the probe to simulated noise comparison was used to normalize the probe to STA comparison. Any inputs that had a normalized STA to probe x‐corr of greater than 3 SD were classified as surround hotspots.h and in the full surround sThe ability of the hotspots to capture the temporal signal present in surround was estimated by linear regression of the β between the summed temporal STAs in the hotspots x‐axis and then normalized the major and minor axis by their respective sizes. We then calculated a center of mass for the set of hotspots. This gives a larger weight to distant inputs, but ignores the relative strength of the hotspots. A cell was designated as asymmetric if the distribution of its hotspot distances was significantly different from the centered origin as determined by a Wilcoxon signed‐rank test.To determine the strength and orientation of asymmetry, we rotated each cell so that its major axis aligned with the P‐values are indicated in the text and methods. For model comparisons we used an F‐test to compare a constrained and unconstrained model. For comparison of populations we used the Student's t‐test when normally distributed, otherwise we used a Wilcoxon signed‐rank test or Mann–Whitney U test. In all cases we applied a Bonferroni correction to account for multiple comparisons.Specific tests used and We used an electrode array to record ganglion cells (GCs) from flat‐mount mouse retinas during visual stimulation, as illustrated in Figure While previous studies in mice have observed antagonistic surrounds in response to stimuli with strong space–time correlations . To show that this result benefits from our more general approach we applied the difference‐of‐Gaussians fit to the spatial profile of the space–time receptive field, and only observed a linear surround in 29.8% of GCs.To assess the prevalence of the linear antagonistic surround, we first summated the temporal STAs of the spatial inputs from the noncenter region. Figure M is the data matrix, U is a spatial filter matrix in the radial distance domain, S is a diagonal matrix of weights, V is a temporal filter matrix, i is the index of the singular value, and Ai is the space–time filter for ith singular value.As a surround was observed in the majority of GCs, we proceeded to study its spatiotemporal properties. This analysis was carried out using the nine spatially collapsed temporal STAs shown in Figure 1‐9), which contain the outer product of a spatial filter  and a temporal filter , scaled by the ith singular value . A1–A9 are ordered by descending power and represent separable space–time components of the original receptive field (M). The rightmost segment of the equation is a parameterization of the spatiotemporal dependence, which will be discussed subsequently. Characteristic of inseparability, the signal‐derived variation in M does not appear to be captured by a single separable space–time filter (A1). Rather, significant power and structure is present in the second filter (A2).Figure n = 805). Three examples are shown to highlight the variation in the population in Figure 1 + A2 (fourth column) faithfully reflects the space–time structure of M, whereas in the separable cell A1 alone (second column) is sufficient.Using this approach, we characterized the degree of space–time inseparability in a population of retinal GCs (n = 805). A threshold value of 100 (meaning the inseparable model was 100 times more likely to be needed) was used to identify inseparability. While some mouse retinal GCs were approximately space–time separable, a substantial number were inseparable (24.1%). Separable cells  to a model including the first and second singular values (representing inseparability). The histogram in Figure eparable 4.1%. Sepf indexed by i and parameterized by magnitude p, time‐to‐peak τ, and the number of stages in the filter n . For the illustrated cell, the first subfilter (light brown line) was observed in all annuli, whereas a second (dark brown line) and third subfilter (orange line) were identified more frequently in the receptive field center (annuli 1–3). A fourth subfilter, observed at only chance rates across the population, was excluded. Notably, this is the first evidence for a three‐subfilter model in the receptive field center, which confers dual band‐pass frequency filtering (not illustrated). In total, this cell would have 13 subfilters. To determine if 13 distinct subfilters are indeed needed, as compared to a combination of fewer subfilters that change their size across space, we look at these subfilters across the population.Figure n = 778, cells with model fits) in Figure The subfilters from these fits are shown for a population of GCs , and filter order  are not dependent on radial distance. Conversely, subtypes in the bottom two plots showed vertical color progression (darker blue above lighter blue) signifying decreasing response magnitude  with increasing radial distance. In summary, the shape of the subfilters’ temporal profiles, as determined by τ and n, does not vary meaningfully with radial distance but their scale (magnitude) does. We therefore conclude that space–time separability can be applied to individual subfilters which are then combined to form an inseparable overall filter.Figure i subfilters, each with a dependence on radial distance r that is represented by a peak‐normalized Gaussian distribution g(s) and its dependence on time t is expressed by its temporal profile f(t)  and spatial g(s) functions eq. . In contEquation . The intF‐test at P < 0.01, post hoc corrected). We added subfilter type 2 to the preferred model from the first step and found its inclusion was justified in 96% of GCs. In subsequent steps, center 3, surround 1, and surround 2 were iteratively added to the preferred model to generate the distribution of subfilter combinations shown in Figure We used this model to examine 805 mouse GCs across 19 retinas. Up to five subfilters were included, each constrained to match the temporal profiles and spatial extents of the corresponding subfilter types from Figure To determine if the model accurately describes the signal in the STA, we evaluated the goodness of fit of the preferred model from Figure The model fits from two example cells are shown in Figure Y‐axis represent center/surround polarity rather than the ON/OFF polarity used in Figures Eii and Fii. Figure Figure As GC populations recorded on the MEA contain subtypes with different morphological and physiological properties . Among the surround‐associated subfilters, surround 1 was observed in 52.6% of ON‐dominated and 56.6% of OFF‐dominated GCs (n.s.), but surround 2 was observed in 38.8% of OFF‐dominated GCs but only 13.3% of ON GCs (P < 0.01).Figure P < 0.01) based on a post hoc corrected rank sum test on the parameters underlying the temporal model . The temporal filters of ON‐dominated cells were stronger than OFF‐dominated cells in centers 1 and 2, but weaker in center 3 and surround 1. Center 1/2 and surround 1/2 were tuned to higher frequencies in ON‐dominated cells, but center 3 had significantly higher tuning in OFF‐dominated cells. The normalized spatial profiles (third row) were statistically indistinguishable for all but surround 1, which was significantly wider in ON‐ than in OFF‐dominated GCs. If these subfilters were all driven by the same source we would not expect to see differences between ON‐ and OFF‐dominated groups . The differential shifts in specific subfilter types provide evidence that subfilter types are driven by distinct underlying processes.Figure Because inhomogeneity has been observed in the receptive field center at high mapping resolutions  as “hotspots”. The left side of Figure Figure y‐axis) and overall surround (x‐axis) STAs from the middle column. Each red dot in the scatter plot represents the relationship at a different time point and the slope of the black line  is an estimate of the percent signal in the overall surround contained in the hotspots. For example, the β of the bottom cell is 0.712, implying that hotspots contained 71.2% of the signal strength in the overall surround. As this process involves a circular relationship  we utilized cross‐validation to separate the hotspot identification and β calculation steps. Specifically, hotspots were identified using a subset of the data and determination of hotspot efficacy was determined using a different subset of the data.To quantify the amount of surround signal carried by the hotspots, we applied a linear regression between the average STA in the hotspots and the surround (including hotspots). We illustrate this process for three GCs with diverse spatial organization in Figure x‐axis) and β (y‐axis) for a population of GCs that had a model fit with an antagonistic surround . Hotspots were on average 11.5% of the inputs in the surround, and had an average beta of 24%. The ordering method used to identify hotspots (red trace) was validated by comparing to an alternative method: ordering the surround inputs by ascending distance from the center (blue trace). The two methods were comparable, with the hotspots outperforming by up to 5% initially. However, the hotspots achieved their performance while selecting inputs that were, on average, quite distant from the center . The hotspots of the remaining 129 GCs were not statistically asymmetrical (green dots) and had an average center of mass that was closer to the origin. Surround asymmetry did not show a significant preference for the major or minor axis of the receptive field center . We conclude that the surround is frequently non‐Gaussian due to both local (heterogeneity) and global (asymmetry) irregularities in its microstructure.We performed a similar analysis for the full GC population and found a diverse distribution of symmetries Fig. E, bottomIn this report we demonstrate that mouse GCs have an antagonistic surround which has not been previously described in isolated linear receptive fields maps   model concluded that an antagonistic surround was weak or not detectable in the linear receptive field maps of mouse GCs. To the contrary, when we applied more general approaches we identified an antagonistic surround in a majority of cells. This success implies that some of the assumptions underlying the DoG model are not valid. For example, the comparisons of a single Gaussian to a DoG spatial profile would be skewed away from the DoG when the surround's signal is focused in a small number of hotspots . These assumptions continue to be adopted in the context of white noise checkerboard mapping because of the lack of direct contradictory evidence and an absence of good alternatives.In light of this, the work presented here makes two contributions to our understanding of inseparability in the RF. First, we utilized spatial averaging to smooth non‐Gaussian imperfections and identified space–time inseparability in linear receptive fields mapped by white noise checkerboards. This corroborates past observations of space–time inseparability made using classical stimuli , and confirms separability cannot be generally assumed (Derrington and Lennie Our observation that the antagonistic surround preferentially localizes in hotspots that are organized inhomogenously extends recent work in the receptive field center. Specifically, GCs’ center was found to deviate from a Gaussian profile in high‐resolution receptive field maps due to the microstructure of its dendritic tree and inhomogeneity of bipolar cell inputs (Schwartz et al. Subtypes within the ganglion cell population are believed to encode different features in ~30 parallel channels (Roska and Werblin The distinct spatiotemporal properties and polarities of the subfilter types suggest their possible synaptic origins. For example, our center subfilter 1 is consistent with ON and OFF bipolar cell inputs driven directly by photoreceptors because of its high strength and spatial profile/polarity that match the receptive field center (Famiglietti and Kolb X‐cell responses (Enroth‐Cugell et al. Although recent investigations have identified nonlinear interactions in the center and surround, here we characterized only the linear interactions, which are a major component of the receptive field at the target resolution (Bolinger and Gollisch Our findings also validate other evidence from these studies. First, the finding of space–time inseparability within GC surrounds but not centers (Dawis et al. No conflicts to report for any authors.


Recently, a library of selective androgen receptor degraders (SARDs) that degrade the full length and splice variant ARs (AR-SVs) through proteasome pathway was discovered and characterized by our laboratory . These mUnderstanding that despite the typical initial efficacy of androgen-deprivation therapy that the AR continues to play an important role in advance castration-resistant prostate cancer (CRPC) led to development of the next- generation AR competitive antagonist enzalutamide and the CYP17A1 inhibitor abiraterone . This reNext-generation AR-targeting molecules that are in clinical development include ARN-509  [Although degraders to other nuclear receptors are available , AR degrLack of an antagonist-bound AR-LBD crystal structure makes it challenging to use structure-based drug design to develop AR antagonists or degraders. Utilizing a strategy similar to ER degraders with long side-chains to recruit ubiquitin failed to provide active AR degraders in our SARD program. Structural modifications of bicalutamide, an aryl propionamide with S-bridge, rendered modest AR degradation. However, a structure-activity relationship could not be deduced with the modified bicalutamide scaffold. Modification of the bridging atom in the arylpropionamide scaffold resulted in the first-generation molecules, UT-155 and UT-69 [A surprising feature of UT-69 and UT-155 is their ability to degrade the AR-SVs. In CRPC patients, the AR-SVs have been observed only in the presence of full length AR. This observation led to hypothesis that the AR-SV degradation with the SARDs could have been due to destabilization of the heterodimer as a result of AR degradation. However, results from engineered cells expressing only an AR-SV suggest that these molecules might have a second binding site. We narrowed this binding site down to the activation function-1 (AF-1) region in the N-terminal domain (NTD). Again, lack of a crystal structure of the NTD, an intrinsically disordered domain, makes it difficult to visualize the conformational changes in the presence of such binders. It is unclear whether the NTD possesses a binding pocket that can be utilized to design molecules or that the SARDs have a non-selective affinity to certain amino acid sequence in the NTD. Despite the inability to demonstrate a direct binding of SARDs to the AF-1 region due to the lack of a competitive-binding assay, interaction of the SARDs with the AF-1 was shown using three independent biophysical methods. The SARDs promoted degradation of the AR and AR-SV under multiple conditions, including under conditions of activated kinases and mutated AR-LBD [Therapeutic potential of molecules that potently degrade the AR can be extended beyond CRPC to other maladies, such as a subset of triple-negative breast cancer, Kennedy's disease, male-pattern baldness, and others. The degraders could also be used as the first-line therapy in early androgen-responsive prostate cancer, where gonadotrophins are the treatment of choice. Based upon structure alone, SARDs belonging to the UT-69 and UT- 155 scaffold may not elicit CNS side-effects, we believe that the toxicity is due to the hydantoin backbone of enzalutamide and apalutamide and due to 7-aminobutyric acid receptor cross-reactivity. Other side-effects such as muscle weakening and osteoporosis might be encountered and will be discerned as studies progress. These side- effects could be overcome with potential disease-directed therapies. A SARD could provide an effective therapy for CRPC patients and could become the treatment of choice in future.


The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA) integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at  Simple sequence repeats (SSRs), also called microsatellites (repeat unit length ≤6 bp) or minisatellites (unit length ≥6 bp), are relatively short tandem repeats (STRs) of DNA  Benson, , MISA T, and SciDespite the large number of available tools, none possesses multiple functions for the identification of SSRs, characterization of their distribution, design of SSR markers, and presentation of SSR information on a genome scale. Our previous software GMATo was developed for fast and accurate SSR discovery and statistical analyses at the genome level to overcome the limitations and challenges mentioned above  to an appropriate length to increase mining speeds. A short overlapping region (default 20 bp) at the end of each chunk provides for accurate SSR identification at the end of the sequence Figure . After mThe statistical classification and summarization are sorted into five classes as follows:motif type based on the unit length; the motif type (MT) is measured by the length (L) of the repeated motif unit,motif composition, which is composed of any combination of the nucleotides A, G, T, and C;grouped motif units, which are grouped if two motifs are complementary to each other; if the DNA orientation on the chromosome is unknown, these complementary motifs are considered the same, and the two motifs are grouped together;SSR distribution over each chromosome, which represents the frequency of SSRs per million bases (Mb) of DNA as calculated by the formulawhere “SSR” represents the SSR count and “Length” represents the length of the source DNA;distribution of SSR length (SL), which is calculated bywhere “l” represents the SSR count at a particular SSR length.Each classification result provides a class name, count and percentage in descending rank. Statistical graphics are also plotted as multiple chart types .To efficiently design primers for each SSR locus in a long genomic sequence, we used a new strategy to improve the speed of the primer design. First, all of the SSR loci and genome sequences are indexed. Then, a short flanking sequence to either side of each SSR locus is extracted at a user-controlled length (default 400 bp) instead of using the full-length DNA sequence, which is performed in other reported tools used for primer design. Primers are designed using the Primer3 algorithm from the flanking sequences, and one primer is located on each side of an SSR. Users can configure parameters such as a range of amplicon sizes (default 100–400 bp) and annealing temperatures (default 60°C). Primer pairs are clustered to a unique pair if 100% identical among all the designed primers, and then a unique marker ID is assigned. The primer information is saved in “.mk” and “.sts” files in the NCBI's STS format, containing the marker name, its primer sequences and amplicon size, ready for primer ordering.To determine the polymorphism of any designed markers in any given DNA sequence, simulated marker mapping (e-mapping) is performed based on a forward e-PCR algorithm Schuler, . A markeThe workflow of the GMATA software consists of six modules, including DNA pre-processing, SSR mining, SSR viewing, SSR statistics, marker design, and electronic mapping of the marker Figure . Each moGMATA was packed into a package that integrates all modules, which work together seamlessly regardless of whether the modules were written in Perl , R version 3.0) or Java (version 7). Each module can also be invoked independently. Perl scripts are used to perform formatting and chunking raw DNA sequences, identifying microsatellites, designing primers, and markers and e-mapping markers. Java scripts are used for developing the graphical user interface (GUI), which internally calls Perl scripts. Both Perl and R scripts are used to perform statistical analyses and plot statistical graphics. The GMATA software is compatible with multiple platforms and has been tested and shown to work in Linux, Mac OS 10 and Windows 7, 8, or 10 systems. Either a graphical GUI or command line can be invoked to run the GMATA software. The GMATA software provides user-friendly graphical and command line interfaces  in a flexible FASTA format is required for SSR mining, which is performed via mouse clicks in the graphical interface or typed via the command line. A second sequence file in the FASTA format may be required if the user wants to transfer the designed markers to other DNA sequences. GMATA can also receive DNA sequences or next generation sequence file in fastaq format if using our utility provided.Nicotiana genomes from the links in the previous review  by using the DNeasy Plant Mini Kit . DNA was diluted to 30–50 ng/μl in water before use.We mined SSR and designed markers from seven The tailed forward primer was synthesized with the tail sequence CGTTGTAAAACGACGGCCAGT added to the 5′ end of each NIX forward primer. The PCR analysis was performed in 20 μl volumes containing 30–50 ng DNA, 0.13 μM tailed forward primer, 0.27 μM 5′ FAM, or 5′ HEX florescent-labeled primer CGTTGTAAAACGACGGCCAGT, 0.53 μM reverse primer, and 10 μl Phusion Hot Start Flex 2 × Master Mix . PCR amplification and amplicon scoring were conducted according to our previous description for UGSW markers   can be generated providing SSR starting position, ending position, repetitions, and motif Table . A summaIn GMATA, a SSR statistical module uses the SSR loci file (.ssr) to generate the statistical “.sat2” file and perform graphical plotting of each statistic. The file provides statistical data for the five genomic classifications: motif type, motif composition, grouped complementary motifs, SSR distribution for each DNA molecule or chromosome, and SSR length    for directly input and display in genome browsers online or locally, such as GenomeView  Figure  and phytPoaceae) consists of the most important food crops, including rice and wheat, and SSR information from these plants can be used for crop improvement. However, a systematic knowledge of SSRs across grass genomes is largely unknown. We analyzed the SSRs over all 15 Poaceae genomic assemblies available to date  presented more monomer motifs than dimers followed by trimers. Although the grass genomes are known to be G/C rich, we found that the SSR motifs in the grasses did not display similar sequence enrichment. Most of the grass genomes (11 of 15) were rich in A/T monomers, whereas the remaining four genomes were rich in G/C monomers. For the dimers, GA/TC was the most abundant motif in 11 of 15 genomes, and it was followed by the AT/AT motif in three genomes. Among the trimers, GCG/CGC was the most abundant, and it was followed by CTC/GAG in several grasses. Most of the SSRs were located in intergenic regions, whereas only a few SSRs (7–32%) were located in genic regions. However, 42% of the SSRs in the assembly of hexaploid wheat were concentrated in genic regions, which may have been related to the current assembly being primarily obtained from low-copy sequence gene regions. A comparison of the SSR density (SSR count per megabase of sequence) revealed that the SSR density was higher in genic regions than intergenic regions. Long SSRs, which should be highly polymorphic, were 1–3% higher in the genic regions than all other genomic regions , TAIR (www.arabidopsis.org), Phytozome , and the human genome database at UCSC (http://genome.ucsc.edu). This feature enables a connection between SSRs and other genome features by clicking, sliding, or zooming in the genome browser. To achieve these results in GMATA, only a few clicks by the user are required. A one-step utility for all of the functions in GMATA is also provided to allow the package to be easily integrated into any pipeline. Therefore, GMATA facilitates analyses by all users, including those without a background in genomics and bioinformatics, and allows them to link SSRs with genome features through a network.GMATA is the first tool that generates results that enable viewing SSR loci and SSR marker information along with other genome features in a genome browser. The genome browser tools are widely used to graphically integrate data containing whole-genome sequences, gene models, SNPs, gene expression, and other features. GMATA's .gff3 and .gbf output formats can be directly imported into genome browser tools, such as GenomeView (Abeel et al., GMATA offers more comprehensive functioning for the statistical analysis of SSRs compared with available software/tools, which provide less or no statistical information about SSR distribution. The SciRoko tool (Kofler et al., GMATA software is a rapid and unique genome-wide marker design tool. Current software/tools, such as SSRLocator (da Maia et al., Brachypodium (Sonah et al., Triticum urartu (Ling et al., T. urartu (Ling et al., Sativa japonica (Zhang et al., The distribution of SSRs was previously reported in several grass genomes, including rice (Zhang et al., GMATA is the advanced multi-functional software capable of fast SSR mining, marker analyses, statistics calculations, and graphical presentations, especially at the whole-genome level. GMATA enables the display of SSR loci and SSR markers with other genic features; thus, it can easily connect a genomics network with SSR markers. We recommend that GMATA should be routinely used as a one-step tool for SSR analysis and SSR marker development immediately after a new genome sequence assembly is completed. GMATA is an advanced SSR application tool for a broad variety of users in many fields, from genomics to breeding.XW designed the study and programmed the software. XW and LW participated in the software evaluation, and wrote the main manuscript text. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


All of these compounds were tested for their in-vitro anti-mycobacterial activity. The influence of the functional group and position of substituent on anti-bacterial activity of compounds is investigated too. The preliminary results indicated that all of the tested compounds showed good activity against the test organism. The compounds 11 and 30 showed the highest anti-tubercular activity (0.39 μg/mL). This synthesis method has the advantages of high yields and good bioactivity.Thiosemicarbazides are potent intermediates for the synthesis of pharmaceutical and bioactive materials and thus, they are used extensively in the field of medicinal chemistry. The imine bond (-N=CH-) in this compounds are useful in organic synthesis, in particular for the preparation of heterocycles and non-natural β-aminoacids. In this paper the synthesis of some new thiosemicarbazide derivatives by condensation reaction of various aldehydes or ketones with 4-phenylthiosemicarbazide or thiosemicarbazide is reported. This synthesis method has the advantages of high yields and good bioactivity. The structures of these compounds were conﬁrmed by IR, mass,  Thiosemicarbazone derivatives are of special importance because of their versatile biological and pharmacological activities. Thiosemicarbazides are potent intermediates for the synthesis of pharmaceutical and bioactive materials and thus, they are used extensively in the field of medicinal chemistry. The imine bond (-N=CH-) in this compounds are useful in organic synthesis, in particular for the preparation of heterocycles and non-natural β-aminoacids -5. TheseMycobacterium tuberculosis. This notorious pathogen infects about one-third of the world’s population and is responsible for approximately 2 million deaths worldwide per year (Tuberculosis (TB) is one of the deadly infectious diseases caused mainly by per year -23. The per year . Mycobacterium bovis, a non-virulent tubercle bacillus very closely related to M. tuberculosis  . On the rculosis , 27. TheHence, it is clear that there is an urgent need to develop novel anti-mycobacterial drugs with improved properties such as enhanced activity against multidrug-resistance, reduced toxicity, shortened duration of therapy, rapid mycobactericidial mechanism of action, ability to penetrate host cells and exert anti-mycobacterial effect in the intracellular environment.In continuation of our research on the development of synthetic methods in heterocyclic chemistry -31. and 1H NMR) spectra and carbon nuclear magnetic resonance (13C NMR) spectra were determined on a Bruker Avance DRX 500 MHz spectrometer and chemical shifts are reported as δ (ppm) in CDCl3 solution (0.05% v/v TMS). Infrared spectra were determined with a Perkin-Elmer 843 spectrometer. Proton nuclear magnetic resonance To a magnetically stirred solution of substituted thiosemicarbazide (1.0 mmol) in MeOH (30 mL) in a round bottom flask was added a solution of benzaldehyde derivatives (1 mmol) at room temperature. The mixture was stirred for 24 h. After completion of the reaction, the precipitate product was filtered and washed with MeOH (20 mL) and dried at room temperature.(E)-1-(3-fluorobenzylidene)thiosemicarbazide (3)8H8FN3S: C, 48.72; H, 4.09; N, 21.30, Found: C, 48.68; H, 4.04; N, 21.37. IR : 3394, 3238, 3158, 1602, 1577. 1H-NMR : δH (ppm) 7.15-7.48 , 7.79 , 7.99 , 8.10 and 8.19 , 11.45 . It was obtained as a white solid; yield: 30%, mp: 185-6 °C (dec.). Anal. Calcd for C(E)-1-(3-chlorobenzylidene)thiosemicarbazide (4)8H8ClN3S: C, 44.97; H, 3.77; N, 19.66, Found: C, 45.06; H, 3.75; N, 19.61. IR : 3391, 3232, 3152, 1602, 1427. 1H-NMR : δH (ppm) 7.35-7.97 , 7.01 , 8.14 and 8.20 , 11.45 . It was obtained as a white solid; yield: 40%, mp: 195-6 °C (dec.). Anal. Calcd for C(E)-1-(3-bromobenzylidene)thiosemicarbazide ( 5)8H8BrN3S: C, 37.22; H, 3.12; N, 16.28, Found: C, 37.26; H, 3.05; N, 16.31. IR : 3386, 3233, 3153, 1602, 1533. 1H-NMR : δH (ppm) 7.33-7.70 , 8.18-8.19 , 8.00 , 8.21 and 8.26 , 11.51 . 13C NMR : δ 178.1 (C=S), 140.4 (CH=N), 136.6 (Cq Ar), 132.2 (CH Ar), 130.6 (CH Ar), 128.8 (CH Ar), 126.9 (CH Ar), 122.3 (Cq Ar). It was obtained as a white solid; yield: 20%, mp: 204.5-205 °C (dec.). Anal. Calcd for C(E)-1-(4-fluorobenzylidene)thiosemicarbazide ( 6)8H8FN3S: C, 48.72; H, 4.09; N, 21.30, Found: C, 48.68; H, 4.07; N, 21.34. IR : 3391, 3235, 3157, 1601, 1506. 1H-NMR : δH (ppm) 7.22-7.25 , 7.85-7.88 , 8.02 , 8.04 and 8.19 , 11.43 . 13C NMR : δ 177.1 (C=S), 164.6 and 161.3 (CF), 141.0 (CH=N), 135.6 (Cq Ar), 140.6 (CH Ar), 130.8 (Cq Ar), 115.8 (CH Ar). It was obtained as a white solid; yield: 50%, mp: 189.5-191 °C (dec.). Anal. Calcd for C(E)-1-(4-chlorobenzylidene)thiosemicarbazide ( 7)8H8ClN3S: C, 44.97; H, 3.77; N, 19.66, Found: C, 44.95; H, 3.76; N, 19.69. IR : 3435, 3281, 3057, 1599, 1466. 1H NMR : δH (ppm) 7.44-7.84 , 8.01 , 8.07 and 8.23 , 11.48 . It was obtained as a white solid; yield: 13%, mp: 207-208 °C (dec.). Anal. Calcd for C(E)-1-(4-bromobenzylidene)thiosemicarbazide ( 8)8H8BrN3S: C, 37.22; H, 3.12; N, 16.28, Found: C, 37.27; H, 3.10; N, 16.25. IR : 3323, 3153, 1593, 1493. 1H-NMR : δH (ppm) 7.59-7.78 , 8.01 , 8.08 and 8.24 , 11.49 . 13C NMR : δ 178.0 (C=S), 140.9 (CH=N), 133.5 (Cq Ar), 131.6 (CH Ar), 129.2 (CH Ar), 123.0 (Cq Ar). It was obtained as a white solid; yield: 30%, mp: 194-195 °C (dec.). Anal. Calcd for C(E)-1-(2-nitrobenzylidene)thiosemicarbazide (9)8H8N4O2S: C, 42.85; H, 3.60; N, 24.99, Found: C, 42.89; H, 3.58; N, 24.94. IR : 1602 (C=N). 1H-NMR : δH (ppm) 7.61-7.75 , 8.02-8.04 , 8.43-8.45 , 8.14 and 8.40 , 8.46 , 11.74 . It was obtained as a white solid; yield: 82%, mp: 240-241 °C (dec.). Anal. Calcd for C(E)-1-(3-nitrobenzylidene)thiosemicarbazide ( 10)8H8N4O2S: C, 42.85; H, 3.60; N, 24.99 Found: C, 42.75; H, 3.67; N, 25.09. IR : 3424, 3157, 2971, 1604, 1515. 1H-NMR : δH (ppm) 7.67 , 8.18-8.24 , 8.65 , 8.13 , 8.31 and 8.33 , 11.62 . 13C NMR : δ 178.3 (C=S), 148.3 (Cq Ar), 139.8 (CH=N), 136.1 (Cq Ar), 133.5 (CH Ar), 130.1 (CH Ar), 123.9 (CH Ar), 121.3 (CH Ar). It was obtained as a white solid; yield: 61%, mp: 230-231 °C (dec.). Anal. Calcd for CE)-1-(4-nitrobenzylidene)thiosemicarbazide(11) : 3365, 3141, 1611, 1588. 1H-NMR : δH (ppm) 8.10-8.22 , 8.23 , 8.28 and 8.42 , 11.73 . It was obtained as a white solid; yield: 33%, mp: 236-237 °C (dec.). Anal. Calcd for C(E)-1-(4-methylbenzylidene)thiosemicarbazide (12)9H11N3S: C, 55.93; H, 5.74; N, 21.74, Found: C, 55.88; H, 5.72; N, 21.76. IR : 3240, 3156, 3025, 1598, 1539. 1H-NMR : δH (ppm) 2.32 , 7.22 , ), 7.68 , 8.01 , 7.95 and 8.16 , 11.38 . 13C NMR : δ 177.8 (C=S), 142.3 (CH=N), 139.6 (Cq Ar), 131.4 (Cq Ar), 129.2 (CH Ar), 127.2 (CH Ar), 21.0 (CH3). It was obtained as a white solid; yield: 40%, mp: 163-164 °C (dec.). Anal. Calcd for C(E)-1-(3-methoxybenzylidene)thiosemicarbazide (13)9H11N3OS: C, 51.65; H, 5.30; N, 20.08, Found: C, 51.62; H, 5.31; N, 20.04. IR : 3394, 3275, 3109, 1592, 1534. 1H-NMR : δH (ppm) 3.79 , 6.94-6.96 , 7.24-7.43 , 8.00 , 8.06 and 8.22 , 11.43 . 13C NMR : δ 177.9 (C=S), 159.5 (Cq Ar), 142.1 (CH=N), 135.6 (Cq Ar), 129.6 (CH Ar), 120.5 (CH Ar), 116.3 (CH Ar), 110.9 (CH Ar), 55.2 (CH3). It was obtained as a white solid; yield: 37%, mp: 194-195 °C (dec.). Anal. Calcd for C(E)-1-(4-methoxybenzylidene)thiosemicarbazide(14)9H11N3OS: C, 51.65; H, 5.30; N, 20.08, Found: C, 51.62; H, 5.27; N, 20.01. IR : 3404, 3153, 2970, 1653, 1597. 1H-NMR : δH (ppm) 3.78 , 6.94-6.96 , 7.71-7.74 , 7.98 , 7.91 and 8.11 , 11.31 . 13C NMR : δ 177.5 (C=S), 160.6 (Cq Ar), 142.2 (CH=N), 128.9 (CH Ar), 126.7 (Cq Ar), 114.1 (CH Ar), 55.2 (OCH3). It was obtained as a white solid; yield: 30%, mp: 168-169 °C (dec.). Anal. Calcd for C(E)-1-thiosemicarbazide (15)10H13N3O2S: C, 50.19; H, 5.48; N, 17.56, Found: C, 50.22; H, 5.49; N, 17.57. IR : 3443, 3239, 3183, 1590, 1531. 1H-NMR : δH (ppm) 3.81, 3.82 , 6.54-660 , 8.02 , 8.31 , 7.85 and 8.05 , 11.30 . It was obtained as a white solid; yield: 60%, mp: 190-191 °C (dec.). Anal. Calcd for C(E)-1-((furan-2-yl)methylene)thiosemicarbazide (16) 6H7N3OS: C, 42.59; H, 4.17; N, 24.83, Found: C, 42.63; H, 4.17; N, 24.82. IR : 3411, 3218, 3016, 1609, 1474. 1H-NMR : δH (ppm) 6.57-6.92 , 7.94 , 7.76 , 7.56 and 8.15 , 11.36 . It was obtained as a brown powder; yield: 30%, mp: 135-136 °C (dec.). Anal. Calcd for C(E)-1-(3-fluorobenzylidene)-4-phenylthiosemicarbazide (17) 14H12FN3S: C, 61.52; H, 4.43; N, 15.37, Found: C, 61.54; H, 4.42; N, 15.39. IR : 3342, 3138, 2983, 1599, 1510. 1H-NMR : δH (ppm) 7.17-7.55 , 7.95 , 8.11 , 10.16 , 11.85 . It was obtained as a white solid; yield: 50%, mp: 199-200 °C (dec.). Anal. Calcd for C(E)-1-(3-chlorobenzylidene)-4-phenylthiosemicarbazide(18)14H12ClN3S: C, 58.03; H, 4.17; N, 14.50, Found: C, 58.00; H, 4.17; N, 14.55. IR : 3298, 3139, 3063, 1595, 1509. 1H-NMR : δH (ppm) 7.17-7.71 , 8.09 , 8.12 , 10.17 , 11.84 . 13C NMR : δ 176.4 (C=S), 141.2 (CH=N), 139.0 (Cq Ar), 136.3 (Cq Ar), 133.8 (Cq Ar), 130.5 (CH Ar), 129.6 (CH Ar), 128.1 (CH Ar), 127.0 (CH Ar), 126.4 (CH Ar), 126.2 (CH Ar), 125.6 (CH Ar). It was obtained as a white solid; yield: 30%, mp: 197-198 °C (dec.). Anal. Calcd for C(E)-1-(3-bromobenzylidene)-4-phenylthiosemicarbazide(19)14H12BrN3S: C, 50.31; H, 3.62; N, 12.57, Found: C, 50.27; H, 3.60; N, 12.60. IR : 3335, 3296, 3136, 1609, 1551. 1H-NMR : δH (ppm) 7.22-7.79 , 8.29 , 8.11 , 10.23 , 11.90 . 13C NMR : δ 176.3 (C=S), 141.1 (CH=N), 139.0 (Cq Ar), 136.5 (Cq Ar), 132.5 (CH Ar), 130.7 (CH Ar), 129.0 (CH Ar), 128.0 (CH Ar), 127.3 (CH Ar), 126.4 (CH Ar), 125.5 (CH Ar), 122.3 (Cq Ar). It was obtained as a white solid; yield: 75%, mp: 189-190 °C (dec.). Anal. Calcd for C(E)-1-(4-fluorobenzylidene)-4-phenylthiosemicarbazide(20) 14H12FN3S: C, 61.52; H, 4.43; N, 15.37, Found: C, 61.55; H, 4.41; N, 15.38. IR : 3315, 3134, 3046, 1601, 1544, 1505. 1H-NMR : δH (ppm) 7.20-7.55 , 7.97-8.00 , 8.14 , 10.15 , 11.84 . It was obtained as a white solid; yield: 25%, mp: 179-181 °C (dec.). Anal. Calcd for C(E)-1-(4-chlorobenzylidene)-4-phenylthiosemicarbazide (21) 14H12ClN3S: C, 58.03; H, 4.17; N, 14.50, Found: C, 57.93; H, 4.17; N, 14.55. IR : 3309, 3134, 2980, 1595, 1537. 1H-NMR : δH (ppm) 7.20-7.96 , 8.14 , 10.18 , 11.89 . It was obtained as a white solid; yield: 71%, mp: 200-201 °C (dec.). Anal. Calcd for C(E)-1-(4-bromobenzylidene)-4-phenylthiosemicarbazide(22)14H12BrN3S: C, 50.31; H, 3.62; N, 12.57, Found: C, 50.35; H, 3.61; N, 12.55. IR : 1602 (C=N). 1H-NMR : δH (ppm) 7.22-7.79 , 8.29 , 8.11 , 10.24 , 11.91 . 13C NMR : δ 176.1 (C=S), 141.5 (CH=N), 139.0 (Cq Ar), 133.3 (Cq Ar), 131.5 (CH Ar), 129.5 (CH Ar), 128.0 (CH Ar), 126.0 (CH Ar), 125.4 (CH Ar), 123.2 (Cq Ar). MS, m/z (%): 334 , 335 , 333 , 199 , 197 , 151 (90), 136 (35), 119 (40), 93 (100), 77 (70), 43 (30).It was obtained as a white solid; yield: 28%, mp: 191-192 °C (dec.). Anal. Calcd for C(E)-1-(2-nitrobenzylidene)-4-phenylthiosemicarbazide (23) 14H12N4O2S: C, 55.99; H, 4.03; N, 18.65, Found: C, 55.75; H, 4.09; N, 18.51. IR : 3341, 3127, 2973, 1692, 1507. 1H-NMR : δH (ppm) 7.22-7.90 , 8.05-8.07 , 8.57-8.58 , 8.59 , 10.23 , 12.14 . 13C NMR : δ 176.4 (C=S), 148.3 (Cq Ar), 138.9 (CH=N), 137.8 (Cq Ar), 133.3 (CH Ar), 130.5 (CH Ar), 128.6 (CH Ar), 128.3 (CH Ar), 126.0 (Cq Ar), 125.6 (CH Ar), 124.5 (CH Ar). It was obtained as a yellow solid; yield: 72%, mp: 199-201 °C (dec.). Anal. Calcd for C(E)-1-(3-nitrobenzylidene)-4-phenylthiosemicarbazide (24) 14H12N4O2S: C, 55.99; H, 4.03; N, 18.65, Found: C, 55.80; H, 4.00; N, 18.80. IR (KBr) (νmax /cm-1): 3228, 3179, 1688, 1525, 1507. 1H-NMR : δH (ppm) 7.23-8.25 , 8.26 , 8.35-8.37 , 8.74-8.75 , 10.35 , 12.03 . It was obtained as a yellow solid; yield: 57%, mp: 203-204 °C (dec.). Anal. Calcd for C(E)-1-(4-nitrobenzylidene)-4-phenylthiosemicarbazide (25) 14H12N4O2S: C, 55.99; H, 4.03; N, 18.65, Found: C, 55.16; H, 4.13; N, 18.80. IR : 3345, 3315, 2975, 1599, 1582, 1510. 1H NMR : δH (ppm) 7.23-7.54 , 8.20-8.26 , 10.35 , 12.13 . MS, m/z (%): 300 , 207 (30), 149 (35), 122 (50), 93 (100), 77 (90), 57 (90), 43 (80).It was obtained as a white solid; yield: 33%, mp: 209 °C (dec.). Anal. Calcd for C(E)-1-(4-methylbenzylidene)-4-phenylthiosemicarbazide (26) 15H15N3S: C, 66.88; H, 5.61; N, 15.60, Found: C, 66.18; H, 5.71; N, 15.43. IR : 3322, 3298, 2990, 1600, 1551, 1504. 1H NMR : δH (ppm) 7.19-7.38 , 7.56 , 7.80 , 8.13 , 10.08 , 11.77 . 13C NMR : δ 175.8 (C=S), 143.00 (CH=N), 139.9 (Cq Ar), 138.9 (Cq Ar), 131.3 (Cq Ar), 129.3 (CH Ar), 128.0 (CH Ar), 127.6 (CH Ar), 125.9 (CH Ar), 125.3 (CH Ar), 21.1 (CH3). It was obtained as a white solid; yield: 60%, mp: 186 °C (dec.). Anal. Calcd for C(E)-1-(3-methoxybenzylidene)-4-phenylthiosemicarbazide (27) 15H15N3OS: C, 63.13; H, 5.30; N, 14.73, Found: C, 63.43; H, 5.36; N, 14.53. IR : 3432, 3286, 3164, 1611, 1599, 1589. 1H NMR : δH (ppm) 3.81 , 6.99-7.56 , 8.13 , 10.13 , 11.84 . It was obtained as a white solid; yield: 82%, mp: 155-156 °C (dec.). Anal. Calcd for CE)-1-(4-methoxybenzylidene)-4-phenylthiosemicarbazide (28): 3325, 3147, 1607, 1542, 1504. 1H NMR : δH (ppm) 3.81 , 6.97-7.87 , 8.11 , 10.05 , 11.73 .It was obtained as a white solid; yield: 57%, mp: 182-183 °C (dec.). Anal. Calcd for C(E)-1--4-phenylthiosemicarbazide (29) 16H17N3O2S: C, 60.93; H, 5.43; N, 13.32, Found: C, 61.13; H, 5.43; N, 13.41. IR : 3310, 3135, 2942, 1601, 1542. 1H NMR : δH (ppm) 3.82 , 3.85 , 6.56-8.20 , 8.43 , 9.99 , 11.70 . 13C NMR : δ 175.3 (C=S), 162.5 (Cq Ar), 159.3 (Cq Ar), 139.1 (CH=N), 138.7 (Cq Ar), 128.0 (CH Ar), 127.7 (CH Ar), 125.7 (CH Ar), 125.1 (CH Ar), 114.7 (Cq Ar), 106.3 (CH Ar), 97.9 (CH Ar), 55.8 (OCH3), 55.4 (OCH3). It was obtained as a white solid; yield: 70%, mp: 194-195 °C (dec.). Anal. Calcd for C(E)-1-((furan-2-yl)methylene)-4-phenylthiosemicarbazide (30) 12H11N3OS: C, 58.76; H, 4.52; N, 17.13, Found: C, 58.66; H, 4.58; N, 17.03. IR : 3291, 3126, 2974, 1618, 1540. 1H NMR : δH (ppm) 6.66-7.86 , 8.08 , 9.87 , 11.84 .It was obtained as a yellow solid; yield: 60%, mp: 183-184 °C (dec.). Anal. Calcd for C(E)-1-(1-(3-bromophenyl)ethylidene)-4-phenylthiosemicarbazide (31) 15H14BrN3S: C, 51.73; H, 4.05; N, 12.07, Found: C, 51.57; H, 4.12; N, 11.77. IR : 3290, 3140, 2945, 1682, 1540. 1H NMR : δH (ppm) 2.37 , 7.23-8.18 , 8.20 , 10.15 , 10.62 . It was obtained as a white solid; yield: 70%, mp: 190-191°C (dec.). Anal. Calcd for C(E)-1-(1-(4-bromophenyl)ethylidene)-4-phenylthiosemicarbazide (32) 15H14BrN3S: C, 51.73; H, 4.05; N, 12.07, Found: C, 51.43; H, 3.95; N, 12.19. IR : 3297, 3261, 1682, 1557. 1H NMR : δH (ppm) 2.37 , 7.22-7.99 , 8.25 , 10.15 , 10.62 .It was obtained as a white solid; yield: 55%, mp: 190-191 °C (dec.). Anal. Calcd for C(E)-1-(1-(3-bromophenyl)ethylidene)-4-phenylthiosemicarbazide (33) 9H10BrN3S: C, 39.72; H, 3.70; N, 15.44, Found: C, 39.58; H, 3.74; N, 15.53. IR : 3410, 3232, 1685. 1H NMR : δH (ppm) 2.28 , 7.56 , 7.91 , 8.25 , 8.01 and 8.32 , 10.27 .It was obtained as a white solid; yield: 30%, mp: 193-194 °C (dec.). Anal. Calcd for CIn-vitro evaluation of anti-mycobacterial activity In-vitro anti-mycobacterial activity evaluations of the compounds were done by the broth microtiter dilution method) against BCG (1173P2) and ethambutol were used as standard controls. The test compounds were initially dissolved in DMSO to give a concentration of 1 or 2 mg/L. All wells of micro plates received 100 µL of freshly prepared Middle broke 7H9 medium , except first column. 200 µL of distilled water was added to the first column of 96 well plates to minimize evaporation of the medium in the test wells during incubation. Then 100 µL of test compounds with desired concentrations (1000 or 2000 µL) were added to the wells of the first row (each concentration was assayed in duplicate) and serial dilution was made from the first row to the last. Microbial suspension of BCG (1173P2) (100 µL), which had been prepared with standard concentration of 0.5 Mcfarland and diluted with 1:10 proportion by the distilled water, was added to all test wells. Plates were then sealed and incubated for 4 days at 37 °C. After that 12 µL Tween 80 10% and 20 µL Alamar blue 0.01%  were added to each test well. The results were assessed after 24 and 48 h. A blue color was interpreted as no bacterial growth, and color change to pink was scored as bacterial growth. Wells with a well-defined pink color were scored as positive for growth. The MIC  was defined as the lowest drug concentration, which prevented a color change from blue to pink. Ethambutol  were used as positive control and DMSO as negative control.Brine shrimp toxicity studyet al., 1992; Hartl et al. 2000; Favilla et al., 2006) was carried out to explore the toxicity of selected compounds with anti-mycobacterial potency. Brine shrimp lethality bioassay -37. (BorDried cysts (1 g cyst per liter) of brine shrimp  were hatched in a bottle containing artificial sea water (3.5% (w/v) marine salts/distilled water) at 28–30 °C with strong aeration (flow rate of 7 l/min), under a continuous light regime (1600lux) for 30-35 h. Consequently, the newly hatched brine shrimp larvae (nauplii) were separated from the remaining cysts and collected with a pipette from the lighted side and concentrated in Petri dishes to be immediately utilized for bioassay. Assays were carried out in 24-well flat test plates . Acetone100%  was utilized for the preparation of different concentrations  of tested compounds, in triplicates. Each well of treated groups exposed with several concentration of acetone dissolved compounds in the basic salt medium (3.5% (w/v) marine salts /distilled water in addition to poly ethylene glycol (PEG) 6000  1.2%, while control groups only received basic salt medium. Gallic acid  was utilized as positive control; respectively. Following evaporation of vehicle solvent, entire wells introduced with 10 fresh nauplii and put on a shaker with 40 rpm to be aerated at room temperature. After 24 h. the numbers of survivors  were counted by microscope AC 230V, 50 Hz  and recorded to determine the corrected mortality via following formula: ct)t - (mmct)c / 100 - mmct)c] * 100Corrected mortality (%) = [ = [NMm  / N0 ] * 100 mmct)t = calculated mmct for treated test wells(mmct)c = calculated mmct for control test wells(mm50)s with 95% confidence intervals were estimated by Graph Pad Prism 5.0 (2007) for each selected anti-mycobacterial compound , whilst in position 4 compounds 6-8 the changing of halogen has very different effect on Mycobacterium bovis activity . It is interesting to note that compound 7 more active than ethambutol which was used as standard controls (0.39 vs 0.75 μg/mL). In compounds 9-11 in which X is NO2 (very strong electron-withdrawing group), different location has different effect; in this case meta isomer of these compounds is characterized by higher anti-bacterial activity than their ortho and para isomers (15.6 vs 62.5 and 125 μg/mL).As can be seen from No previous literature results no comparison and contrast with previous works [similar molecules showing other activity vs. similar molecules showing similar activity (vs. different other molecules)]. 2), they relatively have more activity (12-15 vs 9-11).About electron-donating substitution (Me or OMe) at positions 3 or 4 and 2, 4 there is not logical trend, but in contrast to electron-withdrawing group  Fluorine in position 4 has low activity (500 μg/mL) and chlorine in position 4 more activity (0.39 μg/mL) than the other halogens with R=H or Ph. In position 3, all of the halogens almost have same activity. 2) NO2 group in positions ortho, meta and para with R=H or Ph no significantly activity differences. 3) In overall with R=H or Ph, electron-donating substitution (Me or OMe) relatively more active than electron-withdrawing group (NO2) on aromatic ring. 4) Another important point is that products 16 and 30 which is furan derivatives are active compounds and compound 30 was found to be the most active compound in-vitro with an MIC of 0.39 µg/ml against Mycobacterium bovis and was 2 times more potent than ethambutol which was used as standard controls (0.39 vs 0.75 μg/mL). 5) Finally products 31-33 in 2 = methyl no important effect on compounds activity in contrast to R2 = H.The important results of Mycobacterium bovis. Among these compounds which have electron-donating or electron-withdrawing substituents on phenyl ring, in general; NO2 group more active than the other substituents (39-41 in contrast to 34-38).Compounds 34-43 which are starting material, demonstrate different activity against Mycobacterium tuberculosis . 50s demonstrated based on analytical method non-linear regression (dose response inhabitation). Analyzed data showed selected derivatives exposed 50% lethality to nauplii The related LD50 to MIC that the range of calculated SIs was from 0.08 to 35.87. Based on obtained data, it would be obvious that 30, 16 and 28 with SIs 35.87, 6.75 and 6.38 have selective toxicity against Mycobacterium in comparison to eukaryotic organism. There would be no doubt that considering SIs for syntactic compounds may lead to introducing a set of novel anti-mycobacterial candidates with minor side effects for further investigations.To discover the selectivity of anti-mycobacterial property for each tested compounds, a selectivity index (SI) was calculated by dividing LDin-vitro anti-mycobacterial activity. The preliminary results indicated that most of the tested compounds showed good activity against the test organism. The compounds 7 and 30 showed the highest anti-mycobacterial activity (0.39 μg/mL) against Mycobacterium bovis (BCG).The new thiosemicarbazide derivatives were synthesized by the reaction of various aldehydes and or ketones with 4-phenylthiosemicarbazide or thiosemicarbazide at room temperature. All of these compounds were tested for their


The median survival time of patients with glioblastoma is still poor (14.6 month), partly due to a lack of effective treatment.in vitro and in vivo, as well as of genetic silencing of the receptor in glioblastoma cell lines. AR antagonists, induced concentration-dependent death in three glioblastoma cell lines, as well as in two glioma initiating cell lines. Silencing of AR expression by siRNA induced cell death in the three tested glioblastoma cell lines. Enzalutamide given orally to nude mice bearing subcutaneous human glioma xenografts resulted in a 72% reduction in tumor volume (p=0.0027).We have observed that androgen receptor (AR) is amplified in glioblastomas at the DNA, RNA and protein levels. The AR gene was amplified in 27% of glioblastoma specimens from men (n=22) and of 38.2% from women (n=21). AR-RNA was overexpressed (>2.5 fold) in 93% (n=30), and AR-protein was induced (>two fold) in 56% of the glioblastomas samples (n=16). Thirty percent of the glioblastomas (n=21) also expressed a constitutively active AR-splice-variant (AR-V7/AR3) lacking the Ligand-Binding-Domain. Following these findings, we examined the effect of pharmacological inhibition of androgen receptor in vivo and in vitro, point to the important role of AR in GBM survival and render a potential therapeutic target for this devastating disease.The presence of AR-V7/AR3 in glioblastoma, together with the present data showing that genetic silencing of the full length AR in cell lines and pharmacological inhibition of AR, induce GBM cell death  Glioblastoma (GBM) is the most common and the most aggressive primary brain tumor, with a very poor prognosis. The most common genetic aberrations associated with malignant glioma are amplification or activating mutations of the epidermal growth factor receptor (EGFR) or both –3. ActivThe association between sex steroid receptors and brain tumors was first described in 1983 , but in The AR gene is mapped to Xq12. It functions as a steroid-hormone-activated-transcription-factor and is composed of the N-Terminal-Regulatory-Domain, the DNA-Binding-Domain and the Ligand-Binding-Domain (LBD). In the presence of ligand, the ligand-binding domain is released from heat shock protein and AR is translocated into the nucleus, where it binds to the androgen-response-element in the promoter and stimulates transcription of androgen- responsive genes . It has AR splice variants lacking the LBD, such as AR-V7/AR3, which arise primarily through exon skipping and cryptic exon inclusion, are activated by a ligand-independent mechanism such as the ones mentioned above. . AlthougAR signaling inhibitors such as enzalutamide target the AR signaling pathway at three key stages: by blocking binding of androgens to AR, by inhibiting nuclear translocation of activated AR, and by impairing binding of activated AR to DNA .in vitro and in the subcutaneous in vivo model, as well as the effect of genetic silencing of the receptor in GBM cell lines.The findings from our laboratory, some of which were previously presented , show amFocal CNV analysis of the AR chromosomal region with Droplet Digital™ PCR (ddPCR™) showed AR amplification in 27% of GBM specimens from men (n=22) and 38.2% of women n=21) (Table  Table 1. To deteQuantitative-PCR (qPCR) showed that AR-RNA was overexpressed  in 93% of 30 glioblastoma samples of both men and women patients Figure ; Table 2Detailed correlation analysis between AR expression and clinical parameters was adapted from the analysis performed on 24,519 genes by the Broad Institute TCGA Genome Data Analysis Center in 519 GBM tumors . The anaWestern blot analysis revealed AR protein induction 2-106-fold) in 56% of the glioblastomas samples (n=16) of both men and women patients Figure ; Table 2-106-foldThe AR-V7/AR3, known to be upregulated only in tumors and activated via a ligand-independent mechanism, was found in 30% of the GBM surgical specimens, in addition to the wild type AR (n=21) Figure .The finding of AR overexpression in GBM in the present study and based on the documented benefit of AR inhibitors in prostate cancer, the effect of AR inhibitors bicalutamide and enzalutamide on the survival of glioma cell lines A172, U87MG and T98G was explored. Prostate carcinoma cell line PC3, which does not express AR , served In view of the results obtained with the standard cell lines A172, U87MG and T98G, the efficacy of enzalutamide was tested also on two glioma-initiating cell (GIC) lines, ZH-161 and ZH-305 , 14. BotTo test whether AR has a specific effect on GBM cell viability, the three GBM cell lines were transfected with siRNA targeted to the full length AR or with non-targeted siRNA. Real time RT-PCR, confirmed a reduction in AR transcription in cells transfected with siRNA targeted to the full length AR compared with that in cells transfected with control siRNA. In all the three cell lines there was a correlation between tumor cell death and the extent of reduction of AR expression Figure .Cell cycle analysis of the three glioma cell lines treated with enzalutamide revealed a dose-dependent number of cells in the sub-G1 phase, suggesting that apoptosis was one of the mechanisms responsible for cell death Figure . The finin vivo in subcutaneous xenografts of U87MG human glioblastoma. On day 7 when the tumors reached an average volume of about 50 mm3, the mice were randomly assigned to two treatment groups, based on caliper measurements. All the mice were treated three times weekly by oral gavage of 20 mg/kg XTANDI®  or vehicle. The study was terminated on day 31 post tumor inoculation when the tumor volume of half of the mice in the vehicle group reached 800–1000 mm3. At the endpoint of the experiment, there was a 72% reduction in tumor volume in the group treated with XTANDI vs that in mice treated with vehicle (p=0.0027) , whereas AR RNA and protein overexpression was found in all cell lines (with higher overexpression in the T98G cell line). Furthermore, only about 1/3 of the specimens showed AR DNA amplification, whereas 93% of the patients demonstrated AR RNA overexpression and 56% showed AR protein overexpression. A possible other mechanism may include activation of the enhancer in the AR second intron contributing to increased AR expression at low androgen levels, as in castration-resistant prostate cancer .In the present investigation, pharmacological inhibition of AR with AR antagonists in three glioma cell lines, as well as in two GICs, induced dose concentration-dependent death. Cell cycle analysis of the AR antagonist- treated cells, as well as staining with anti- cleaved caspase 3, suggest that the mechanism responsible for cell death is at least in part, apoptosis. Genetic silencing of AR expression by siRNA resulted in reduced GBM cell viability. These finding validate the results of the pharmacological AR inhibition and indicate that AR has a specific role in GBM cell survival.In addition, we now demonstrate that 30% of GBM samples expressed constitutively-activated AR-V7/AR3 lacking the LBD. This variant is a major androgen-independent driver of AR-regulated gene expression in advanced prostate cancer . The exiThe AR-V7/AR3 variant was not detected by Bao et al.  in U87MGT98G, as well as the GIC cell lines, show high expression of the full length AR and of the constitutive active AR variant. These cell lines showed reduced but still significant susceptibility to AR inhibitors. This residual sensitivity to AR inhibitors could be related to the fact that the mechanism of action of inhibitors such as enzalutamide , 18. In It has been shown in prostate and breast cancers that AR activation might be achieved through ligand-independent mechanisms. These may include increased coactivator expression and activation of several kinases that may directly or indirectly activate AR. . This isin vitro and in vivo.Since EGFR amplification or activating mutations –3 are thin vitro results to the clinic, an attempt was now made to find in vivo proof of concept. The in vivo experiment described here shows that the AR antagonist enzalutamide efficiently reduces the growth of human glioblastoma. This was recently also demonstrated by Bao et al. , and prfound in . The mict test.The statistical significance between the studied groups vs the control groups for each experiment was calculated according to Student's 2-tailed