Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing in steps of 10% from 10 to 90%, and using the remaining lines from the same year as well as lines from other years in a training set, ranged from 0.23 to 0.55. The predictive ability estimated for a new year using the other years ranged from 0.17 to 0.28. Further, we tracked lines advanced based on phenotype from each of the four F3:6 nurseries. Lines with both above average genomic estimated breeding value (GEBV) and phenotypic value (BLUP) were retained for more years compared to lines with either above average GEBV or BLUP alone. The number of lines selected for advancement was substantially greater when predictions were made with 50% of the lines from the testing year added to the training set. Hence, evaluation of only 50% of the lines yearly seems possible. This study provides insights to assess and integrate genomic selection in breeding programs of autogamous crops.Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F Triticum aestivum) is a cereal crop with the highest hectarage in the world. In contrast to maize (Zea mays) and soybean (Glycine max), wheat varieties developed by the public sector account for a majority (∼75–90%) of wheat acreage in the United States. Public sector breeding programs previously relied primarily on phenotypic data or a combination of phenotype and molecular markers associated with traits controlled by one or a few genes to make selections . Similarly, Recent literature on genomic prediction (GP) has focused on estimating predictive ability (PA) of various traits using different GP models and cross-validation schemes. Although testing PA is critical information for GS, a large gap exists between findings in these studies and their application in breeding programs . A limit2 and F3 nurseries are grown in bulk populations and nearly 45,000 heads selected from the F3 bulks are planted as headrows (F3:4). The selections made in F2, F3, and F3:4 nurseries are primarily based on disease resistance, winterhardiness, and plant type. Experimental lines are evaluated for yield along with other traits such as agronomic performance and end-use quality starting from F3:5 onwards. The observation yield trial  is grown in plots at one location for grain yield and in rows at a second location for winterhardiness and disease resistance due to the limited amount of seed. The preliminary yield trial , the first multi-location yield trial, is then grown in one or two replicates as augmented trials with replicated check cultivars. The advanced yield trial  and elite yield trials  are grown in a replicated alpha lattice design in multiple locations, and lines are tested for more than one year. Subsequently, a few elite lines are advanced to regional nurseries such as Southern Regional Performance Nursery (SRPN), state cultivar testing, and foundation seed increase. The SRPN are replicated trials comprising 50 entries contributed by various breeding programs in the Great Plains and is grown at more than 30 locations, with sites in Texas, Oklahoma, Kansas, Colorado, Nebraska, and South Dakota. The SRPN trials are coordinated by the USDA-ARS at Lincoln, NE. Phenotypic selection for yield is most accurate in the AYT and EYTs due to the extensive replications and testing of the lines at multiple environments for multiple years. However, in the early generation (mainly F3:5) and PYT, phenotypic selection for yield is less effective either due to the absence of replication or testing done primarily in few environments, which do not represent the diverse range of environments possible when wheat is grown in Nebraska. The inaccuracy of phenotypic selection for yield in trials can be overcome or at least reduced by using GS, which has the potential to increase selection accuracy over phenotypic selection.The University of Nebraska winter wheat breeding program makes over 1,000 unique crosses annually and the progenies from these crosses are tested systematically over the subsequent 11 years to identify a cultivar for release  onwards take into account agronomic performance, end-use quality, and disease resistance among other factors in the breeding program, yield is the most important trait considered for selections . Hence, making accurate selections in the PYT is important to advance lines that have the greatest expectation to subsequently perform well. In addition, selection of top-performing lines from the PYT allows for recycling of elite lines sooner to the subsequent breeding cycle as parents before testing them in the AYT and EYTs. Increasing selection accuracy and reducing the breeding cycle time will increase genetic gain over time (3:6) of the University of Nebraska winter wheat breeding program grown in 2012-2015 were utilized for GP (File S2). Each year ∼270 lines are tested in the PYT nursery. Two hundred and eighty experimental lines were grown in 2012 and 2013 and 270 experimental lines in 2014 and 2015 thus a total of 1,100 unique lines were used in this study. There was no overlap between experimental lines tested across years. The experimental design was an augmented design with 10 incomplete blocks. Each incomplete block in 2012 and 2013 had 28 lines and two different check cultivars and in 2014 and 2015 there were 27 lines and three different check cultivars. Thus, 270 or 280 lines along with 20 or 30 check plots (each check cultivar was replicated 10 times) were grown in a single trial. Trials were grown as a single replicate, or sometimes two replicates, at 8 to 10 locations annually (File S2). Testing locations include Mead, Lincoln, Clay Center, North Platte, McCook, Grant, Sidney, and Alliance in Nebraska, and Hutchinson and Mount Hope in Kansas (File S2). Yield measured on the lines in the PYT nurseries (33 environments) was used for GS.Four independent PYT nurseries (F3:7) grown in 2013-2016 to test the effectiveness of GS for making selections using GEBVs for yield from the PYT nurseries grown in 2012-2015. The AYT nursery was grown in an alpha lattice experimental design with 57 lines and three different check cultivars each year and had either two or three replicates at each location (File S2). The 57 lines in AYT are selected from PYT primarily based on yield, and also end-use quality and disease resistance, and represent ∼21% of top performing lines in the PYT. There was no overlap between the lines tested across years and a total of 228 unique lines were tested across four years. Each year the nursery was tested at six to nine locations with a total of 29 environments across four years. The locations were the same Nebraska locations as described earlier for the PYT nursery (File S2).We utilized four AYT nurseries .Grain yield at each location within a year was analyzed separately to account for spatial variation in the field and obtain the best estimates of the phenotype. Each PYT nursery was analyzed using mixed models that accounted for either experimental design features such as incomplete blocks, rows, and columns or spatial variation in the field  is the incomplete block nested within a replicate, The AYT nursery was also analyzed separately at each location within a year and one linear model was used to generate the BLUP value for each line at each location. The model used to analyze yield data of the AYT nursery is as follows:r is the number of replicates within the location. Heritability was estimated across locations in both the PYT and the AYT nurseries (Broad-sense heritability (H) was estimated for the PYT nurseries at each of the locations grown in two replicates (File S2). The PYT nurseries that were not replicated, but had two to three different check cultivars grown in each of the augmented trials, separated the genetic and residual variances and were used to calculate the H. The variance components were generated using the mixed model that performed the best for each location. The H was also estimated at each of the locations for the AYT nurseries (File S2). The variance components were generated using the equation (1) and the H was estimated using the following equation:All models were fit using ASreml v3.0 . The R sPstI (CTGCAG), and a frequent-cutter MspI (CCGG), and barcoded adapters were ligated to individual samples. The GBS library was prepared by pooling samples from two 96 well plates and amplified in a thermocycler. Each library was then sequenced in a single flow cell lane of Illumina HiSeq. Detailed protocols and updates on the GBS protocol can be found on the WGGIL website (http://www.wheatgenetics.org).Five seeds of each line in the PYT nursery were grown annually in the University of Nebraska-Lincoln (UNL) greenhouse and young leaves were bulked and harvested from 2-week old seedlings in 96-well plates. Two randomly chosen wells were kept blank in each plate for quality control purposes. The leaves were dried and desiccated by placing the plates in boxes filled with silica beads. DNA was extracted using BioSprint 96 DNA Plant Kit from QIAGEN per the manufacturer’s protocol. The genotyping-by-sequencing (GBS) was performed at the Wheat Genetics and Germplasm Improvement Laboratory (WGGIL) at Kansas State University. Samples were quantified using PicoGreen and GBS libraries constructed in 188-plex using the P384A adaptor set (http://hcc.unl.edu) at UNL. The sequence quality was inspected using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). SNP calling was done using the TASSEL GBS pipeline v4.0  over the years for assessing the SNP calling accuracy (File S5). SNP markers with missing information in more than 80% (2 estimated using Beagle less than 0.5 (SNPs that could be difficult to impute accurately) were excluded  to the AYT nursery (2013-2016) were used to evaluate GS and phenotype to make selections . The GEBWe also evaluated GS and phenotypic selection by skipping the PYT nursery tested in the AYT year in the training set . For exaLastly, genomic and phenotypic selections are evaluated using the 57 lines advanced from the PYT to the AYT nursery and not all of the ∼270 lines in the PYT. These 57 lines generally represent high-yielding lines and belong to one tail of the distribution containing all lines in the PYT nursery . Nevertheless, using PYT and AYT provides an opportunity to compare GS and phenotypic selection using existing datasets.To evaluate the prospects of implementing GS in the PYT, we tracked the lines advanced until 2016 primarily based on phenotype from the PYT nurseries grown in 2012-2015 and compared it with BLUP and GEBV values estimated on the lines in the PYT nursery. For instance, lines advanced from the 2012 PYT nursery until 2016 were selected and retained in the breeding program for five years. Similarly, lines are retained for four years starting from 2013 PYT nursery, three years from 2014, and two years from 2015. The GEBVs of the lines grown in the PYT each year were estimated using the lines from the rest of the years as training dataset (NA00 scenario), which is equivalent to predicting new lines in a new year. Further, studying the frequency of lines with above average GEBV or BLUP or both, selected from the PYT nursery together with the information on the number of years a line was retained in the breeding program will allow for evaluation of the prospects of implementing GS in the PYTs in the breeding program. We repeated this entire process by making predictions on 50% of the lines randomly selected in each year and using the remaining 50% of the lines along with the lines from other years as training dataset (NA50 scenario). The NA50 scenario is expected to have higher PA compared to NA100 scenario. Thus, this comparison using the GEBVs from NA50 scenario will test the prospects of implementing GS when PA is higher.Puccinia graminis Pers.:Pers. f. sp. tritici Eriks. E. Henn.), agronomic performance, and end-use quality . The PCA was performed in TASSEL (vs. PC2 was made using the ggplot2 package (ijU) and ijU is the realized kinship coefficient between line i in the test set and line j in the training set. The MRKC value represents the kinship between a specific line in the test set and all lines in the training set. Further, a mean of maximum kinship coefficient values across all lines in the test set was calculated.Population structure among the 1,110 F package  in R. Th package . The MRK3:6) and advanced yield trials (F3:7) grown in 2012-2015 and 2013-2016. The experimental design, number of locations, number of replicates per location, heritability for yield within and across locations is provided. File S3 contains the R script developed for analyzing preliminary yield trials (F3:6) grown in an augmented design. Mixed models incorporated both experimental design and spatial variation. File S4 provides an example phenotypic dataset for utilizing the R script provided in File S3. File S5 shows the SNP calling accuracy of 20 lines genotyped by genotyping-by-sequencing at least twice  over the years. The accuracy is presented as sequential pairwise comparisons of 20 unique samples. File S6 contains the yield data (best linear unbiased predictors) of the preliminary yield trials (F3:6) grown in 2012-2015 used for genomic selection. File S7 contains the SNP marker data of 26,925 SNPs on the 1,110 F3:6 lines grown in 2012-2015. The homozygous major, homozygous minor, and heterozygous genotypes are coded as 1, 0, and 0.5 in the genotype matrix. File S8 shows the Akaike Information Criterion (AIC) values and the residual plots for each of the mixed models tested for the yield data collected on the preliminary yield trial (F3:6) grown in McCook, NE in 2014. The rest of the analyses described in this study  were performed using publicly available software and R packages. Supplemental material available at Figshare: https://doi.org/10.25387/g3.6249410.File S1 contains supplementary Tables S1-S2 and Figures S1-S7. File S2 has details of the preliminary yield trials . The H across locations within a year ranged from 0.63 (2013 year) to 0.81 . In the AYT nursery, the H was generally higher than the PYT nursery with values up to 0.94 and the H across locations within a year ranged from 0.58 (2014 year) to 0.79 . The AYT nursery’s smaller sample size (57 lines) and increased replication (two or three replicates) improved heritability relative to the PYT nursery with ∼270 lines and one or two replicates. The 2013 PYT (except at Lincoln and Clay Center) and 2014 AYT  had lower heritability trials at other locations. Sequential addition of locations  within a year in the AYT nursery led to a similar increase in H as was found in the PYT nursery. The H for the AYT nursery ranged from ∼0.20 (across two distant locations) to ∼0.79 .2 generated in BEAGLE was used as a filtering criterion to exclude the likely low quality imputed SNPs  SNPs prior to imputation looks random compared to the left-skewed distribution of all SNPs used for imputation . Subsequently, the imputed marker data set was subdivided to include just the 1,100 lines of the 2012-2015 PYT nurseries and filtered using two criteria: (1) MAF > 0.05; (2) MAF > 0.05 and allelic R2 > 0.5. Filtering using only MAF criterion resulted in 41,913 SNPs and both MAF and allelic R2 provided 26,925 SNPs. The Pearson’s correlation coefficient estimated between kinship coefficients estimated using 41,913 and 26,925 SNP markers was 0.99. This is not unexpected and previous studies have shown the method of imputation has little effect on the kinship and predictive abilities . The 206,622 SNPs were filtered to exclude markers that had missing information in more than 80% of the samples, and this resulted in 79,118 SNPs. The distribution of missing sites across SNPs was left-skewed, and across lines, it was relatively normally distributed . These SNPs were then processed using BEAGLE and the missing genotypes were imputed. The use of GBS results in low coverage and high missing information, which precludes the accurate estimation of imputation accuracy. Therefore, allelic Rvs. PC2 did not indicate the presence of prominent clusters suggesting the absence of strong population structure in the PYT nurseries grown in 2012-2015 . The PC1 and PC2 explained 4.3% and 3.2% of the variation in the dataset .The 26,925 SNPs were distributed on all 21 chromosomes. The number of markers on a chromosome ranged from 114 (4D) to 3,323 (3B) with an average of 1,282 per chromosome . Nearly 19,707 of the 26,925 SNPs were placed in CSS contigs anchored and ordered on chromosomes using population sequencing . The distribution of CSS contigs containing the 19,707 SNPs across each chromosome showed good coverage of the wheat genome except for a few D genome chromosomes, previously reported to be less polymorphic  to 90% (NA90) of the lines grown annually in steps of 10% and repeated the random sampling of lines for the test set 10 times and estimated the average predictive abilities. In three of the four years , the average predictive abilities for NA10 ranged from 0.42 to 0.52, NA50 from 0.37 to 0.44, and NA90 from 0.23 to 0.30 . In 2013vs. 0.36 in 2012-2013 and 0.36 vs. 0.11 in 2015-2016  of the PYT nursery and BLUP of the AYT nursery was higher than the correlation coefficient between BLUP of the PYT and AYT nursery, 0.48 015-2016 . When th015-2016 . This suning set . And in vs. 0.37 . This inTo examine the prospects of using GS in the PYT for making selections in the breeding program, we compared the advancements made primarily based on phenotype for five years 2012-2016 with the BLUP and GEBV values estimated on the lines in the PYT nurseries grown in 2012-2015 . Clearly3:8 nursery; Nebraska Interstate Nursery). Three lines from 2013 PYT  are also tested in the Southern Regional Performance Nursery. A few of these lines are included in the state variety testing program and they have performed well (data not shown).In 2017, one (NE12561) of the two lines from the 2012 PYT nursery and five  of the 13 lines from 2013 PYT are still retained and are tested in the elite yield trails  indicate predictive abilities are high for NA10, modest for NA50, and low for NA100. Also, the variability in the predictive abilities is higher from NA10 to NA40 compared to NA50 to NA90. Higher predictive abilities obtained on a smaller number of lines (NA10) and relatively lower predictive abilities on a larger number of lines (NA90) in the test set is probably due to the sample size differences and kinship between lines in the training and test sets. Predictive ability is a function of heritability of the trait, the size of the training population, and an effective number of chromosome segments affecting the trait , GS was similar to phenotypic selection in 2012 , GS did better than phenotypic selection in 2015, and phenotypic selection outperformed GS in 2013 and 2014. The lower PA of GS compared to phenotypic selection after excluding the AYT year in the training set is likely either due to the smaller training set or the fact that the AYT year was not observed in the training set. Overall, GS did better than phenotypic selection in 2012 and 2015, and the phenotype selection was better than GS in 2013 and 2014.Puccinia striiformis f. sp. tritici) and Fusarium head blight  Petch]) . This result is expected because each experimental line in the University of Nebraska wheat breeding program is derived from a cross made with one or more lines adapted to the Nebraska region, and thus strong population structure is not expected among the breeding program lines. The possible reasons that may have affected the PA values are: (1) the phenotype of lines grown in 2013 was influenced by the quality of seeds harvested in 2012 coupled with the soil and field conditions affected following severe drought stress in 2012 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).The results obtained in 2013 were quite different from all other years. The GS abilities were quite low compared to phenotypic selection. Also, the predictive abilities obtained for NA10 to NA90 were relatively lower. This is despite the fact that the kinship of lines grown in PYT 2013 with lines grown in PYTs in other years was similar to the kinship among other PYTs. In addition to kinship, we specifically looked at the clustering of the lines advanced from PYT 2013 to AYT 2014 with the rest of the PYT lines . A plot of PC1 In the PYT, lines with both above average BLUP and GEBV values were retained for more years compared to lines with either just GEBV or BLUP alone. A limitation of using phenotype data alone for making selections is that they are based on the performance of lines only in the growing year. But, the expectation is that the selected line performs well in multiple years with potentially varying environmental conditions. The GEBV estimates based on lines grown in multiple years and multiple environmental conditions leveraged information across years and environments and overcomes the limitation of phenotype-based single-year selection. Thus, using both BLUP and GEBV independently and selecting lines that are having both their GEBV and BLUP above the respective means and excluding lines with just the GEBV or BLUP alone above the mean provided an increased opportunity to select lines likely to perform well across environments and years compared to lines selected based on the phenotype alone in one year.At present, with the current training dataset, the PA is approximately 0.22 for predicting all lines in the PYT nursery in a new year. Hence, with the current selection intensity  for the subsequent nursery (AYT nursery), we can make highly accurate selections by using both phenotype and GEBV values. Another possibility would be to lower the selection intensity by selecting all lines above the average GEBV for the subsequent nursery. This would allow skipping the PYT, but the downside is the cost of many more lines advanced to the elite trials replicated and grown in many locations, which makes this approach practically challenging and potentially cost ineffective in a breeding program.3:5), and thus enough seed is available to skip the PYT and test lines in the AYT. In addition, we now genotype the lines in OYT and the marker data will be available for lines selected for PYT prior to testing them in the field.The percentage of lines with above average GEBV and BLUP retained for more years in the breeding program increased when 50% of the lines tested in the prediction year are added to the training dataset and predictions are made on the remaining 50% of the lines. The predictive abilities for this NA50 scenario are higher than the NA100. In 2014, the predictive ability was greater than 0.37, and very few lines were in the quadrant with above average GEBV and lower than average BLUP. Our data suggest that growing only a subset of PYT and combining the information from previous years will effectively utilize GEBV for making selections for yield. Also, these results indicate when the predictive ability surpasses the average predictive abilities observed in the NA50 scenario (∼0.40), we may be able to use just GEBV alone for making selections with the current selection intensity for the subsequent nursery and skip the PYT. We have more than ∼1,000 g of seed for most of the lines in the OYT , is to either use both the GEBV and BLUP values to make better selections or grow only a selected subset of lines  advanced to the PYT nursery and use GS to predict the rest and make selections in the PYT nursery. The subset of lines whose phenotype is difficult to predict can be determined by using the reliability criterion recently described by 3:5) containing nearly 2,000 lines mostly grown at one location in 2016. We used a training dataset comprising PYT nurseries plus 50% of the lines from the OYT nursery that were less hail damaged to predict the phenotype of the remaining 50% of the lines in the OYT nursery damaged due to hail and recovered lines that had the potential for high-yielding based on GS and advanced these to the PYT nursery for harvest in 2017.The results obtained in this study are encouraging, and this has motivated implementation of GS in the breeding program for grain yield starting in 2016. Here, we describe three examples of the current use of GS. First, in 2016, we used both GEBV and BLUP to make more accurate selections from the PYT nursery. Preliminary observations indicate in 2017 more lines with both above average GEBV and BLUP were advanced to 2018 and lines with either just above average GEBV or BLUP alone were dropped. Second, lines with both above average GEBVs and BLUPs were shortlisted as top performing lines in the PYT nursery and used as parental lines for the subsequent year’s crossing block. Recycling elite lines sooner saved one to two years in the breeding cycle. Third, hailstorms damaged more than half the OYT nursery (FOverall, integration of GS for grain yield in the University of Nebraska wheat breeding program is positioned to successfully improve the efficiency of the breeding program. We currently are testing PAs of other traits and investigating approaches to improve the use of GS in the breeding program.


Cidofovir (CDV) is an antiviral agent with antiproliferative properties. The aim of our study was to investigate the efficacy of CDV in HPV-positive and -negative head and neck squamous cell carcinoma (HNSCC) cell lines and whether it is caused by a difference in response to DNA damage. Upon CDV treatment of HNSCC and normal oral keratinocyte cell lines, we carried out MTT analysis (cell viability), flow cytometry , (immuno) fluorescence and western blotting . The growth of the cell lines was inhibited by CDV treatment and resulted in γ-H2AX accumulation and upregulation of DNA repair proteins. CDV did not activate apoptosis but induced S- and G2/M phase arrest. Phospho-Aurora Kinase immunostaining showed a decrease in the amount of mitoses but an increase in aberrant mitoses suggesting mitotic catastrophe. In conclusion, CDV inhibits cell growth in HPV-positive and -negative HNSCC cell lines and was more profound in the HPV-positive cell lines. CDV treated cells show accumulation of DNA DSBs and DNA damage response activation, but apoptosis does not seem to occur. Rather our data indicate the occurrence of mitotic catastrophe.  Each year ~600,000 people worldwide are diagnosed with head and neck squamous cell carcinoma (HNSCC), making HNSCC the sixth most common cancer in the world . ImportaCidofovir (CDV) is an acyclic nucleoside phosphonate which targets DNA viruses that encode for their own DNA polymerase, because the active diphosphate metabolite (CDVpp) has a higher affinity for viral DNA polymerase compared to cellular DNA polymerase. CDVpp competitively inhibits the incorporation of deoxycytidine triphosphate (dCTP) into viral DNA by viral DNA polymerase, which results in reduction in the rate of viral DNA synthesis ,7. Curre50 of the HPV-positive HNSCC and UCC cell lines versus the HPV-negative HNSCC cell lines after 6 days of treatment (p = 0.0102). The IC50 values of day 6 and 9 varied considerably between the different cell lines , the DNA damage response pathway was investigated at protein level. In response to DNA damage, cells normally activate the DNA damage response pathway, which causes G1/S arrest via the p53 pathway and G2/M arrest via checkpoint kinases Chk1 and Chk2. We performed both western blotting of DNA damage response proteins and p53 mutation analysis on the cell lines. In 93-VU-147T, starting from day 3 a strongly increased expression of the phosphorylated checkpoint kinases Chk1 (p-Chk1) and Chk2 (p-Chk2), phosphorylated BRCA1 (p-BRCA1) and a moderately increased expression of phosphorylated p53 at ser15 (ser15p53) was observed upon CDV treatment compared to the control. In addition, cdc2 was phosphorylated at Tyr15 (p-cdc2), which is one of the two inhibition sites for the activation of the cdc2-cyclin B complex. P53 and p21 were upregulated in the treated and untreated cells A. This mA consequence of the activation of the DNA damage response pathway may be cell cycle arrest followed by apoptosis. For this purpose, we first analyzed the cell cycle distribution by Flow Cytometry analysis after 3 and 6 days of CDV treatment. In the four cell lines there was a decrease of cells in the G1 phase and an increase of cells in the S-phase compared to the control. Furthermore, in the UM-SCC-47, UPCI-SCC-72 and NOK also after 6 days an increase in cells in the G2/M phase was observed. These results indicate that under CDV treatment cells accumulate in S- and G2/M-phase . This was further confirmed by cyclin B1 immunostaining in CDV treated cell lines, showing an increase in intensity as well as the number of cyclin B1 positive cells after 6 days of CDV treatment . The mosIn order to assess if cells go into apoptosis under CDV treatment, we performed an Annexin-V assay. First, all cell lines were treated with 1 µM Staurosporine for 1 day, a known inducer of apoptosis. In the three HNSCC cell lines there was a strong increase of apoptotic cells observed, whereas only a slight increase was observed in the NOK cell line. In contrast, after CDV treatment there was no increase in apoptotic cells observed in the HNSCC cell lines, except for the 93-VU-147T, showing a significant increase of apoptotic cells after CDV treatment, but this was an increase of 2.7%. The NOK cell line showed a strong increase in apoptotic cells. Taken together, CDV induced apoptosis in the NOK cell line, but not in the HNSCC cell lines .50 value), we also wanted to investigate if mitotic catastrophes could explain the differences in sensitivity. Indeed, Cyclin B1 accumulation in the nucleus indicates that a part of the cells enter mitosis and with an inactive apoptosis machinery, this may lead to mitotic catastrophe. To visualize this process, we used immunofluorescence detection of phospho-Aurora Kinase, which is detected at the centrosomes along mitotic spindle microtubules and plays a role in the mitotic chromatid segregation. The first observation in these experiments were an increase in cell nuclei size after CDV treatment in comparison with the control cells . CDV treThe antiproliferative effects of CDV were studied in three HPV-positive, two HPV-negative HNSCC cell lines, two HPV-positive UCC cell lines and the immortalized NOK cell line. In all the cell lines the cell growth was inhibited by CDV with differences in response between the cell lines. Treatment with CDV caused DNA damage by means of DNA DSBs and as a result the DNA damage response pathway became activated. There was an accumulation of cells in the S- and G2/M phase and with an inappropriate apoptosis machinery, the cells appeared to undergo mitotic catastrophe. 50 values of the cell lines SiHa, CaSki, UM-SCC-47 and UD-SCC-2 were in accordance to those found by Mertens et al. [CDV targets DNA viruses that encode for their own DNA polymerase. In addition, CDV has been shown to have antiproliferative properties against HPV-positive and HPV-negative malignancies in vitro and vivo ,11,12. Ts et al. . They reIn order to further investigate the mechanism of action of CDV, we examined the activation of the DNA damage response pathway, the cell cycle and the induction of apoptosis. After treatment with CDV, the DNA damage response pathway became activated by means of phosphorylation of the DNA repair proteins  in the two HPV-positive HNSCC cell lines. This effect was seen to a lesser extent in the HPV-negative cell line and NOK cell line. In the HPV-positive cell lines only a slight upregulation of phosphorylated p53 would be expected, because of inactivation by E6, which in turn is not influenced by CDV ,18. ThisWe found a S-phase arrest after 3 and 6 days CDV treatment and after 6 days there was also a G2/M arrest visible. The expression of cyclin B1 in the nucleus after treatment with CDV was also increased after 6 days. Additionally, the phosphorylation of cdc-2 on Tyr15 increased, also suggesting G2/M arrest. However, there was still a significant amount of DNA damage visible in the treated cells after 6 days, which implies that DNA repair does not occur efficiently in the HPV-positive cell lines. Similar results were found in HPV-positive UCC cells  by De Schutter et al. . They foWe noted that CDV treatment did not lead to an increase in Annexin-V staining. Abdulkarim et al. also did not detect apoptosis after CDV treatment in HPV-positive UCC and HNSCC cells and proposed cell cycle arrest to occur . These rImmunofluorescence of phospho-Aurora Kinase revealed nuclei increased in size and the presence of multiple centrosomes in CDV treated cells. Combined with the suggested G2/M arrest, this finding indicates the development of mitotic catastrophe being the predominant cause leading to cell death. Indeed, more mitotic catastrophes were observed with increasing sensitivity for CDV. Radiation as well as various antitumor drugs have been described to induce mitotic catastrophe ,25,26. PIn contrast to the HNSCC cell lines that do not show an evident increase in apoptosis due to DNA damage caused by CDV, already substantial apoptosis was detectable at baseline in the NOK cell line which increased under CDV treatment. Assuming that NOK cells contain a least one wild-type allele of TP53, one would expect less DNA damage at baseline and induction of apoptosis under CDV treatment because of functional p53. An alternative explanation of the observed results could be that this cell line is polyclonal, with subclones having homozygous wild-type TP53 or homozygous mutated TP53. This would explain the baseline DNA damage (in the mutated p53 cells) and detection of apoptosis under CDV treatment (occurring in the wild-type p53 cells). Hence, the question is whether or not the NOK cell line is a good normal keratinocyte control. Rather, the observed features, including the presence of a TP53 mutation, more resemble features seen in the HNSCC cell lines. The fact that normal keratinocytes cell lines that are not immortalized do not show DNA damage after CDV treatment, as has been reported by Mertens et al., further underscores this suggestion . In conclusion, we found that CDV inhibits the cell growth of HPV-positive and -negative HNSCC cell lines, and was more profound in HPV-positive cell lines. CDV treated cells showed accumulation of DNA DSBs and DNA damage activation, but apoptosis did not seem to occur. Rather our data indicate the occurrence of mitotic catastrophe. Three HPV16-positive head and neck squamous cell carcinoma (HNSCC) cell lines: UD-SCC-2 , 93-VU-147T , and UM-SCC-47  were used. Two HPV16-negative HNSCC cell lines: UPCI-SCC-72 and UPCI-SCC-003  were used. Two HPV16-positive uterine cervical carcinoma cell lines, SiHa and CaSki, were purchased from the American Type Culture Collection (ATCC). The normal oral keratinocyte (NOK) cell line , which is immortalized by activation of h-TERT  is a cel2. All HNSCC cell lines used in this study were cultured in Dulbecco’s modified Eagle medium (DMEM) containing 10% fetal calf serum (FCS). CaSki was cultured in Roswell Park Memorial Institute (RPMI) with 10% FCS. SiHa was cultured in Minimum Essential Medium (MEM) with 10% FCS, supplemented with L-glutamine and non-essential amino acids. The NOK cell line was cultured in keratinocyte serum-free medium (KSFM) supplemented with (2.6 µg/mL) bovine pituitary extract (BPE) and (0.16 ng/mL) recombinant epidermal growth factor (rEGF). All the cell lines were regularly tested and found to be mycoplasma-free. All cell lines were confirmed to have unique genotypes, as tested using the ProfilerPlus assay [Cells were cultured at 37 °C in a humidified atmosphere with 5% COus assay . The preus assay .Cells were seeded in 96-well flat bottom plates at densities that allowed exponential growth for the duration of the experiment. They were placed in the cell culture incubator overnight at 37 °C allowing the cells to attach, after which they were treated with concentrations of Cidofovir  of 10, 100, 200 and 300 µM or PBS (control). At indicated time points post-treatment , the MTT -2,5-diphenyl tetrazolium bromide) assay  was performed as previously described . The expThe cells were irradiated at room temperature with 4 Gray (Gy). After 4 and 24 h of incubation the irradiated cells and the no irradiated control cells were fixed with methanol for 15 min at −20 °C and analyzed for γ-H2AX expression by immunofluorescence (see below). 50) or PBS. After 3 and 6 days, cells were washed with PBS and trypsinized to form a cell pellet. Ice-cold 70% ethanol was added to the cell pellet while vortexing, assuring fixation of the cells and minimizing cell clumping. Cells in 70% ethanol were stored at −20 °C for a minimal duration of 30 min. Cells were washed with PBS and resuspended in 0.5 mL propidium iodide(PI)/RNAse staining solution (100 μg/mL PI and 1 mg/mL RNAse in PBS). Cells were incubated on ice for 30 min and analyzed by flow cytometry using a FACScanto II . Data analysis was performed using FACSdiva software (BD Biosciences). The different cell cycle regions were set to those defined by the untreated control cells for each cell line individually.Cells were seeded in T25 culture flasks and placed in the cell culture incubator at 37 °C and allowed to attach overnight. Culture medium was added containing CDV (IC50) or PBS for 3 and 6 days. Cells were stained with Hoechst 33,342  in culture medium for 15 min at 37 °C. Cells were washed with Annexin-V binding buffer  and stained with Annexin-V-FITC (2.5 μg/mL in Annexin-V binding buffer) for 15 min at 37 °C. Staining intensities of cells were measured in High-Content Imaging. Data was acquired using a BDpathway855 High-Content Bioimager (BD Biosciences). Digitalization and segmentation of acquired data was done with Attovision software (BD Biosciences). Processed data was evaluated by DIVAsoftware (BD Biosciences). As a positive control for apoptosis, the cells were treated with 1 µM Staurosporine (Sigma-Aldrich). For the Annexin-V assay cells were seeded in 96-wells plates and allowed to attach overnight at 37 °C. Cells were treated with CDV (IC50) or PBS was added, and cells were incubated at 37 °C. After 3 and 6 days, cells were washed with PBS followed by fixation in CytoRich Red for 20 min at RT (γ-H2AX) or methanol for 15 min at −20 °C (cyclin B1 and phospho-Aurora Kinase A/B/C). After washing with PBS, the cells were permeabilized with 0.1% Triton in TBS/T (0.1% Tween20 in TBS) for 20 min and then blocked with 5% bovine serum albumin (BSA) in TBS/T for 30 min at RT. Cells were incubated with the primary antibody (Cells were grown in 96-well plates (γ-H2AX) or on coverslips (cyclin B1 and phospho-Aurora Kinase A/B/C) and allowed to attach overnight at 37 °C. Culture medium containing CDV  Hoechst 33,342 for 10 min at 37 °C. Staining intensities of cells were measured in High-Content Imaging. Data was acquired using a BDpathway855 High-Content Bioimager (BD Biosciences). Digitalization and segmentation of acquired data was done with Attovision software (BD Biosciences). Processed data was evaluated by DIVAsoftware (BD Biosciences). For cyclin B1, phospho-Aurora Kinase A/B/C, and for γ-H2AX expression in the radiotherapy experiment, nuclear morphology was visualized with 4′6-diadomidino-2-phenylindole (DAPI). Cell images were obtained using a Leica DM5000B microscope  with filters for DAPI and fluorescein and Leica Qwin Software (Leica Microsystems). For further analysis of cyclin B1 and phospho-Aurora Kinase A/B/C, Cell Profiler image analysis software  was used .For cyclin B1 and γ-H2AX analysis, the ‘IdentifyPrimaryObjects’ module has been run on the DAPI image to identify the cell nuclei and ‘MeasureObjectSizeShape’ to determine the nucleus diameter. This was followed by the ‘MeasureObjectIntensity’ to measure the antibody intensity inside the nuclei. The intensity in each nucleus was normalized to the fluorescence background intensity measured in a cell-free area of the image. Nuclei were considered positive if the intensity was higher than the average intensity plus two times standard deviation of the negative control. Phospho-Aurora Kinase A/B/C was analyzed using the ‘IdentifyPrimaryObjects’ and ‘MeasureObjectSizeShape’ module. Mitosis and mitotic catastrophes were counted manually. Cells treated with CDV or PBS were lysed by incubation with RIPA buffer  containing Protease/Phosphatase Inhibitor Cocktail for 5 min on ice, followed by brief sonication. After centrifugation, the pellet was discarded and the protein extracts were quantified using the Pierce BCA Protein Assay Kit  as per manufacturers’ instructions. Equal amounts of the extracts (10 µg for UM-SCC-47 and 93-VU-147T versus 30 µg for UPCI-SCC-72 and NOK) were separated on 8–12% SDS-PAGE and electrotransferred to nitrocellulose membranes according to the manufacturers’ instructions using Mini-Protean Tetra System . Membranes were blocked with non-fat dry milk (NFDM) and incubated with primary antibodies diluted in blocking buffer (5% NFDM or BSA diluted in TBS). For detection, secondary antibodies labeled with Horseradish Peroxidase (HRP)  were incubated on membranes during 1 h at RT. Bands were visualized with enhanced chemiluminescence  on the Image reader LAS-3000 . DNA was extracted using Maxwell FFPE LEV Automated DNA Extraction Kit . DNA concentration was measured using the QuantiFluor dsDNA Dye System (Promega Corporation) . DNA wast-test. Levels of p < 0.05 were considered to be of statistical significance. GraphPad Prism  was used to conduct all statistical analyses. All results were expressed as the mean ± standard error of the mean. Independent experiments were analyzed by an unpaired Student’s CDV inhibits the cell growth of HPV-positive and -negative HNSCC cell lines and was more profound in the HPV-positive cell lines. CDV treated cells showed accumulation of DNA DSBs and DNA damage response activation, but apoptosis did not occur. Instead, our data indicate the occurrence of mitotic catastrophe.


Human embryonic stem (ES) cell lines have opened great potential and expectation for cell therapy and regenerative medicine. Monkey and human ES cell lines, which are very similar to each other, have been established from monkey blastocysts and surplus human blastocysts from fertility clinics.Nonhuman primate ES cell lines provide important research tools for basic and applicative research. Firstly, they provide wider aspects of investigation of the regulative mechanisms of stem cells and cell differentiation among primate species. Secondly, their usage does not need clearance or permission from the regulative rules in many countries that are associated with the ethical aspects of human ES cells, although human and nonhuman embryos and fetuses are very similar to each other. Lastly and most importantly, they are indispensable for animal models of cell therapy to test effectiveness, safety, and immunological reaction of the allogenic transplantation in a setting similar to the treatment of human diseases.So far, ES cell lines have been established from rhesus monkey (Macaca mulatta), common marmoset , and cynomolgus monkey (Macaca fascicularis), using blastocysts produced naturally or by in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). These cell lines seem to have very similar characteristics. They express alkaline phosphatase activity and stage-specific embryonic antigen (SSEA)-4 and, in most cases, SSEA-3. Their pluripotency was confirmed by the formation of embryoid bodies and differentiation into various cell types in culture and also by the formation of teratomas that contained many types of differentiated tissues including derivatives of three germ layers after transplantation into the severe combined immunodeficiency (SCID) mice.The noneffectiveness of the leukemia inhibitory factor (LIF) signal makes culture of primate and human ES cell lines prone to undergo spontaneous differentiation and thus it is difficult to maintain these stem cell colonies. Also, these ES cells are more susceptible to various stresses, causing difficulty with subculturing using enzymatic treatment and cloning from single cells. However, with various improvements in culture methods, it is now possible to maintain stable colonies of monkey ES cells using a serum-free medium and subculturing with trypsin treatment. Under such conditions, cynomolgus monkey ES cell lines can be maintained in an undifferentiated state with a normal karyotype and pluripotency even after prolonged periods of culture over 1 year. Such progress should facilitate many aspects of stem cell research using both nonhuman primate and human ES cell lines.


RBFOX2 is a master regulator of alternative splicing1. This RNA-binding protein (RBP) is expressed in the brain2, muscle3, and embryonic stem cells4. RBFOX2 is required not only for the proper splicing of target RNAs, but also for cerebellar development2, myogenesis3, and for survival of human embryonic stem cells4.RBFOX2 in hematopoetic tissues. In an early report, RBFOX2 had been shown to be capable of promoting inclusion of exon16 in protein 4.1R5. This splicing event is important for erythropoiesis because it increases the affinity of 4.1R for target genes5. In a recent study, expression of RBFOX2 was detected in the human T-cell line JURKAT and a functional antagonism of the RBPs RBFOX2 and CELF2 was demonstrated6. We wanted to describe the expression patterns of RBFOX2 in hematopoetic malignancies, to discover target genes and to unravel the consequence of RBFOX2 repression for target gene splicing and isoform expression.Hitherto, not much is known about the expression and function of RBFOX2 negative or positive  cell lines are ion Fig. .Fig. 1RBRBFOX2 and expression of the full-length forms of all four candidate target genes, MALT1, CLSTN1, FMNL3, and MYO9B  had already been described in the context of RBFOX2-mediated splicing7. The RBFOX2 target sequence “UGCAUG” was present in all introns following the retained exons, indicating that high RBFOX2 levels might be the cause of the full-length forms in the RBFOX2 positive cell lines. Supporting the notion that RBFOX2 was important for splicing of these genes was also the finding that RBFOX2 was the sole gene that was significantly overexpressed in cell lines expressing full-length MALT1 when compared to cell lines expressing MALT1 without exon 7 , MYO9B (w/o exon 37), and FMNL3 (w/o exon 26) . In sum, our data showed that isoforms of MALT1, MYO9B, and FMNL3 in B-NHL cell lines are controlled by RBFOX2: the full-length RNAs were expressed when RBFOX2 was high, the short variants prevailed when RBFOX2 was repressed.We performed knockdown experiments to test whether 50% Fig. . Repress26) Fig. . The lonRBFOX2 is part of a mesenchymal splicing network7. The gene is also essential for the viability of human embryonic stem cells and for normal cerebellar development of mice4. Thus, RBFOX2 appears to have different functions in cells of different origin. To check whether RBFOX2 induced splicing of MALT1, CLSTN1, FMNL3, and MYO9B in hematopoetic cells other than B-cells, we tested RBFOX2 expression and expression of the splice variants of the putative RBFOX2 targets in T- and myeloid cell lines.RBFOX2 mRNA expression level of the B-cell line HT. Nevertheless, the T- and myeloid cell lines with highest RBFOX2 mRNA levels also expressed the protein  appear to be targets of RBFOX2 in the B-lineage only. These results suggest that tissue-specific factors might contribute to the splicing process mediated by RBFOX2.Neither T-cell lines nor myeloid cell lines reached the RBFOX2 target genes in B-NHL cell lines. As shown in cell lines, also primary tumor cells of patients with diffuse large B-cell lymphoma (DLBCL) show differential RBFOX2 gene expression , Burkitt lymphoma (BL) (n = 21), follicular lymphoma (FL) (n = 87), and FL-DLBCL (n = 15). Germinal center (GC) B-cells (n = 5) and naive B-cells (n = 5) were included as controls.We limited our further studies to B-NHL, because we had identified the ion Fig. . We analRBFOX2 expression and MALT1 exon 7 inclusion were positively correlated in BL, FL, activated B-cell (ABC), and GC DLBCL (p < 0.05) , we found a statistically significant positive correlation between expression of RBFOX2 and inclusion of target exons in FL, BL, and in at least one subtype of DLBCL  in regulated exons or in flanking introns8. To analyze whether RBFOX1 and RBFOX3 might also contribute to the splicing of our four target genes, we tested for correlation between expression of these RBFOX family members and inclusion of exons in target genes. We did not find a statistically significant correlation between RBFOX1 or RBFOX3 expression and inclusion of exons in CLSTN1 and FMNL3  in BL, FL, and DLBCL.RBFOX2 is a regulator of splicing in B-NHL. This notion is based on (i) the positive correlation between RBFOX2 expression and expression of the full-length variants of the putative RBFOX2 target genes in B-NHL cell lines and in primary B-NHL samples, and (ii) results of knockdown experiments demonstrating that RBFOX2 is responsible for inclusion of exons in MALT1 and other target genes.These data suggest that MALT1 appears to be of special interest as it encodes a protease that activates the IKK complex9. In lymphocytes, MALT1 cleaves RelB, which also leads to the activation of NFkB10. Both MALT1 isoforms (with and w/o exon 7) are expressed in T-lymphocytes and expression of the individual variants has consequences for T-cell receptor triggered signal transduction11. As part of the CARMA1–BCL10–MALT1 complex, MALT1 is also a central regulator of the B-cell receptor (BCR) / NFkB pathway. ABC-type DLBCL cells rely on the constitutive activation of this pathway to block apoptosis12. Recurrent mutations in CD79A/B, CARD11, and other BCR/NFkB pathway genes have been described13. Like Brutons Tyrosine Kinase, upstream to MALT1 in the BCR/NFkB pathway, also MALT1 is a potential target for precision therapy14. Future studies shall elucidate whether the two MALT1 isoforms display different capacities to activate NFkB in B-NHL, which might be of importance for the clinical application of MALT1 inhibitors.RBFOX2 is expressed in hematopoetic cell lines of different origin; (ii) expression of RBFOX2 correlates with isoforms of potential target genes in B-NHL cell lines and in primary B-NHL cells; and (iii) knockdown experiments suggest that RBFOX2—directly or indirectly—contributes to the splicing of target genes including MALT1, a protease in the BCR/NFkB pathway.In summary, (i) Supplemental MethodsSupplemental Figure


These data support further analysis of published data on clonogenic assays using SF2 and D10 as survival endpoints, which facilitates robust identification of biological profiles representative of cancer cell sensitivity to photons.Photon radiation therapy is a major curative treatment for cancer. However, the lack of robust predictive biomarkers for radiosensitivity precludes personalized radiation therapy. Clonogenic assays are the gold standard method for measuring the radiosensitivity of cancer cells. Although a large number of publications describe the use of clonogenic assays to measure cancer cell radiosensitivity, the robustness of results from different studies is unclear. To address this, we conducted a comprehensive detailed literature search of 256 common cancer cell lines and identified the eight cell lines most-frequently examined for photon sensitivity using clonogenic assays. Survival endpoints and experimental parameters from all 620 relevant experiments were compiled and analyzed. We found that the coefficients of variation for SF XRCC family genes involving in DNA repair are significantly associated with radiotoxicity [Photon radiation therapy is a major curative treatment for cancer. The concept underlying precision medicine, i.e., stratification of treatment strategies according to the genetic profile of the individual cancer and/or patient, is common practice . From thtoxicity .2 (surviving fraction after 2 Gy irradiation) and D10 (dose that yields a surviving fraction of 10%) was <30%. Thus, the SF2 and D10 of clonogenic assays show acceptable inter-assay precision as a biomarker for radiosensitivity [Clonogenic cell survival, together with growth delay and tumor control, is one of the principal pre-clinical endpoints for assessing tumor responses to radiation ,6. Compasitivity . However2, SF4, SF6, SF8, D10, D50, and the mean inactivation dose  a. The usX. CVSF2 and CVD10 were <30% for all eight cell lines examined  at any combination of experimental setting and survival endpoint. These data indicate that the influence of differences in the timing of cell seeding, radiation type and dose rate on clonogenic survival is not evident, at least in our dataset.Finally, we investigated the influence of differences in experimental setting on clonogenic survival . p valueA large-scale database for in vitro radiosensitivity exists in the field of particle therapy ; Friedri2 for a given cell line is associated with 95% of the clinical control dose delivered to a tumor of the corresponding type. Daecon et al. analyzed data derived from 51 non-HeLa human tumor cell lines [2, is associated with the clinical response of a tumor to radiation therapy. Fertil and Malaise used 101 survival curves derived from 92 human cell lines to analyze the ability of clonogenic survival parameters . Secondly, the effect of the differences in radiation source, such as cobalt 60 and cesium 137, were not analyzed due to the small proportion for these settings. Future studies should focus on these issues. Lastly, this study lacks cell line authentication which is of great importance in inter-study comparison of in vitro experimental data. This was impossible unfortunately, due to a retrospective nature of this study.2 and D10 were below 30%, suggesting that the inter-study precision for these endpoints is acceptable as the indices for cancer cell sensitivities to photon irradiation under diverse experimental settings. These data support combined analysis of published clonogenic assay data, which will promote discovery of the biological profiles predictive of cancer cell responses to photon radiation therapy.In conclusion, we comprehensively analyzed published radiosensitivity data derived from clonogenic assays for 256 cancer cell lines. CVs for the clonogenic survival endpoints SFThe flow of the literature search is described in 2, SF4, SF6, SF8, D10, and D50) were acquired as described previously [X indicates the surviving fraction after exposure to X Gy, whereas DX indicates the dose that yields a surviving fraction of X% of cells. The values were recorded up to two decimal places. A surviving fraction <0.01 was not recorded. For studies in which SF2, SF4, and SF6 values were available, α, β, D10, D50, and Survival endpoints  or the Bonferroni method.The inter-assay precision of the clonogenic assays was determined by calculating the CV values. As recommended by the FDA, a CV < 30% was considered acceptable for assessment of potential biomarkers ,14. DiffBased on the results of the present study, we recommend that the following be considered in conducting in vitro assessment of cancer cell radiosensitivity using clonogenic assays and/or integrative analysis of the relevant radiosensitivity data.Standardization of the clonogenic assay protocol is of great importance. Referencing protocol articles  will beH1299, A549, H460 , HT-29, SW480, HCT-116 , DU145, and PC-3 (prostate cancer) are commonly used for clonogenic assays. The advantage in using these cell lines is that multi-layer omics data are available at CCLE [There is no solid evidence for the influence of timing of cell seeding (before vs. after irradiation), radiation type (X-rays vs. γ-rays) or dose rate on clonogenic survival. However, this issue needs to be addressed further by inter-laboratory comparison under controlled experimental settings.To investigate radiosensitizing or radioprotective effect, treatment with reagent, plasmid vector or siRNA may be used in combination with irradiation. For such cases, control cells used for “radiation alone” setting are often treated with vehicle, empty vector, or scramble-siRNA. Nuryadi et al. reported that dimethyl sulfoxide, a reagent commonly used for vehicle, does not affect clonogenic survival after irradiation . However2 or D10 is recommended because these indices show acceptable inter-study precision. From a different perspective, Anakura et al. reported that SF2 distinguishes the cell lines with different mutational status  greater than D10 [2 may be the clonogenic survival index most suitable for meta-analyses of clonogenic survival data with omics data counterparts.In integrative analyses of clonogenic survival after irradiation, usage of SFthan D10 . Taken t


Recent studies have reported that TUBB3 overexpression is involved in docetaxel (DTX) resistance in prostate cancer (PCa). The aim of this study was to clarify the role of TUBB3 in DTX and cabazitaxel (CBZ) resistance, and cross-resistance between DTX and CBZ in PCa. We analyzed the effect of TUBB3 knockdown on DTX and CBZ resistance and examined the interaction between TUBB3 and PTEN. We also investigated the role of phosphoinositide 3-kinases (PI3K) inhibitor (LY294002) in DTX and CBZ resistance. TUBB3 expression was upregulated in DTX-resistant and CBZ-resistant cells. TUBB3 knockdown re-sensitized DTX-resistant cells to DTX and CBZ-resistant cells to CBZ. Additionally, TUBB3 knockdown re-sensitized DTX-resistant cell lines to CBZ, indicating that TUBB3 mediates cross-resistance between DTX and CBZ. Knockdown of TUBB3 enhanced PTEN expression, and PTEN knockout enhanced TUBB3 expression. LY294002 suppressed TUBB3 expression in DTX-resistant and CBZ-resistant cell lines. LY294002 re-sensitized DTX-resistant cell lines to DTX and CBZ-resistant cell lines to CBZ. These results suggest that TUBB3 is involved in DTX resistance and CBZ resistance. A combination of LY294002/DTX and that of LY294002/CBZ could be potential strategies for PCa treatment. Prostate cancer (PCa) is the most common solid malignancy among men and the second leading cause of cancer-related death in developed countries . AlthougTUBB3 gene is a microtubule protein normally expressed in neuronal cells. A recent study has shown TUBB3 to be associated with the progression of CRPC [Taxanes block cell mitosis and induce apoptosis in tumor cells by targeting microtubule activity . Microtu of CRPC . What is of CRPC ,12,13. T of CRPC . Another of CRPC ,16. CollIn this study, we use DTX- and CBZ-resistant PCa cell lines to analyze the involvement of TUBB3 in, and the effect of TUBB3 knockdown on, DTX and CBZ resistance in PCa cell lines. We also investigate the effect of LY294002 in these DTX- and CBZ-resistant PCa cell lines.We used two DTX-resistant  ,18,19 anGiven the previous reports that TUBB3 is associated with cancer progression and DTX resistance in PCa ,14, we sWe next analyzed whether knockdown of TUBB3 improves DTX and CBZ sensitivity in DTX- and CBZ-resistant PCa cells. We measured cell viability in DU145-DR and C4-2-DR cells with knockdown of TUBB3 under various concentrations of DTX. Western blotting showed that the expression of TUBB3 was suppressed in DU145-DR and C4-2-DR cells transfected with small interfering RNAs (siRNAs) for TUBB3 compared with that in DU145-DR and C4-2-DR cells transfected with siRNAs for negative control A. We fouPTEN and TUBB3 in 28 PCa tissues by qRT-PCR. There was a significant inverse correlation between PTEN and TUBB3  (TUBB3 was significantly associated with a high Gleason score (p = 0.048) and high tumor stage (p < 0.001)  A. In thi< 0.001) B. We als< 0.001) C. Furthe< 0.001) D. PTEN k< 0.001) E. These It is often reported that PTEN loss or activation of the phosphoinositide 3-kinases (PI3K)/AKT pathway leads to progression of CRPC ,25. TherDTX is the first-line therapy for patients with metastatic CRPC. Recent clinical studies have shown that early DTX treatment with androgen deprivation therapy leads to better overall survival in comparison to androgen deprivation therapy alone in patients with metastatic hormone-sensitive PCa ,33. CBZ Recently, several studies reported that cross-resistance exists between DTX and CBZ treatment in PCa cell lines ,7,34. InPTEN loss is an early and stable event in the carcinogenesis process and is associated with poor prognosis in PCa. The expression of PTEN is regulated by mutations, epigenetic and transcriptional silencing, post-translational regulation, and protein–protein interactions . Some stLY294002 is a synthetic compound that was designed as a PI3K inhibitor . SeveralThere are some limitations in this study. We performed qRT-PCR analysis for PTEN and TUBB3 expression in a relatively small sample size, and the samples were not derived from CRPC patients. Therefore, a study with a larger number of CRPC patients will be necessary to verify the current findings. Second, we showed that the combination of LY294002 with DTX or CBZ had higher efficiency than DTX or CBZ alone chemotherapy. However, we did not clarify whether the combination is synergistic or additive. Third, so far, several PI3K inhibitors have been reported, and several clinical trials using PI3K inhibitors are ongoing . FurtherIn summary, we showed that the expression of TUBB3 was upregulated in DTX- and CBZ-resistant cells in PCa. TUBB3 was involved in both DTX resistance and CBZ resistance. There was a reciprocal interaction between TUBB3 and PTEN in PCa. TUBB3 mediated cross-resistance between DTX and CBZ. LY294002 re-sensitized cells resistant to DTX treatment and also re-sensitized cells resistant to CBZ treatment. The data presented here highlight the great potential of combination therapy of LY294002 with DTX and that of LY294002 with CBZ in chemotherapy for PCa.2 and 95% air at 37 °C. DTX-resistant PCa cell lines were cultured under DTX (DU145: 2 ng/mL and C4-2-DR: 5 ng/mL). CBZ-resistant cell lines were cultured under CBZ (LNCaP-CR: 2 ng/mL and 22Rv-1-CR: 5 ng/mL).Four PCa cell lines , two DTX-resistant PCa cell lines  and two CBZ-resistant PCa cell lines  were kindly provided by Masaki Shiota . DU145 cells were maintained in MEM . LNCaP, C4-2, and 22Rv-1 cells were maintained in RPMI 1640  containing 10% fetal bovine serum , 2 mM L-glutamine, 50 U/mL penicillin, and 50 g/mL streptomycin in a humidified atmosphere of 5% CODTX, CBZ, and LY294002 were obtained from Funakoshi . An MTT assay was performed to analyze drug sensitivities as described previously . At 24 hFor Western blotting analysis, cells were lysed as described previously . PrimaryExtraction of total RNA, synthesis of cDNA, and qRT-PCR were performed as described previously . Total RWe obtained 28 PCa tissue samples for qRT-PCR. The clinicopathologic features are summarized in Silencer® Select  against TUBB3 was used for RNA interference. Two independent oligonucleotides and negative control siRNA  were used .To knock out PTEN in DU145 and 22Rv-1 cells, we used CRISPR-Cas9 technology, which was performed as described previously [t-test or Mann–Whitney U test. A p-value of <0.05 was considered statistically significant. Statistical analyses were conducted primarily using GraphPad Prism software .Statistical differences were evaluated using the two-tailed Student’s


Our appreciation of the critical role of the genome’s 3D organization in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type-invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD, and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type-specific gene expression programs that are executed under basal and challenged conditions.Here, we carried out a large-scale meta-analysis that integrated Hi–C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrate a strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that A/B compartmentalization of cells under basal conditions significantly correlates not only with gene expression programs and TF binding profiles that are active under the basal condition but also with those induced in response to treatment. Yet, in pairwise comparisons between different cell lines, we find that a large portion of differential TF binding and gene induction events occur in genomic loci assigned to A compartment in both cell types, underscoring the role of additional critical factors in determining cell-type-specific transcriptional programs.Our results further indicate the role of dynamic genome organization in regulation of differential gene expression between different cell types and the impact of intra-TAD enhancer–promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.The online version of this article (10.1186/s13072-018-0220-2) contains supplementary material, which is available to authorized users. E) and promoters (P) that are located at high distance along the linear DNA sequence to close spatial proximity. Such E–P loops, a portion of which is cell type specific, mostly occur within TADs and unfrequently cross over TAD boundaries [3C-based methods measure the frequency of physical interactions between any pair of genomic loci as a proxy for their spatial proximity. These novel technologies are shedding light on the principles of 3D organization of the genome in the nucleus and its relation to gene regulation –3. A fouundaries , 10. TheOur understanding of the roles that the 3D organization of the genome plays in gene regulation has markedly increased in recent years. It emerges that TADs serve as fundamental structural and regulatory building blocks of chromosomes that constrain and largely exclude physical interactions between genes and regulatory elements located in different TADs, while providing sufficiently dynamic local environment that is required for the establishment of intra-TAD E–P links , 8. In lIntra-TAD E–P links are required for the implementation of transcriptional programs that establish and maintain cell identity and responses to environmental cues. How these regulatory interactions are modulated in response to transient perturbation is still not well understood. While some studies have shown that gene induction is accompanied by alterations of chromatin interactions and internal restructuring of TADs –14, unexHere, we carried out a large-scale meta-analysis, integrating Hi–C data from 13 different cell lines and dozens of ChIP-seq and RNA-seq datasets recorded in the same cellular systems at basal conditions and in response to various treatments, to further elucidate the intricate interplay between the hierarchical 3D organization of the genome and gene regulation.We first defined the higher-order organization of the genome into A/B compartments for 13 human cell lines for which Hi–C data are available  implies that binding sites are enriched for the A compartment and D < 1  implies that binding sites are enriched for B compartment; “As the A compartment is associated with open state of the chromatin we next systematically examined the association between A/B compartmentalization and TF binding. We analyzed 122 TF ChIP-seq datasets recorded by ENCODE for cell lines with Hi–C data  The strongest effect was observed for EP300, a transcriptional activator that marks active enhancers. In the analysis of other pairs of cell lines, we found a similar significant preference of cell-type-specific TF binding events for cell-type-specific A compartment . For each pair of cell lines, we divided the genes into four groups, based on RNA Pol-II ChIA-PET data: no promoter interactions in both cells (“00” group); promoter interactions detected only in cell line 1 (“10” genes); only in cell line 2 (“01” genes) and in both (“11” genes). Notably, differential gene expression between pairs of cell lines was strongly associated with differential engagement of promoters in chromatin interactions . Several experiments that examined responses to TNF-α and estradiol met this requirement. For each pair of cell lines treated by the same agent and profiled for the same TF, we again divided the induced TFBSs into three groups: binding sites induced upon treatment only in cell line 1, binding sites induced only in cell line 2, and binding sites induced in both. Induced TFBSs in each group were then divided into four categories—AA, AB, BA, and BB as defined above. In all comparisons, TFBSs induced only in cell line 1 showed a significant preference for AB regions over the BA ones, and vice versa for TFBSs induced only in cell line 2  and 32 out of 34 experiments had enrichment factor larger than 1.0 . For each cell line and treatment, we tested whether the set of genes that were induced upon treatment was over-represented in the A compartment . Indeed, in most conditions, we observed a significant preference of the induced genes to the A compartment  . We followed the same analysis that we applied above to TFBSs that were induced in a cell-type-specific manner Table , and appTo further explore links between the 3D organization of the genome and gene regulation, we have analyzed together Hi–C data from 13 different human cell lines and numerous ChIP-seq and RNA-seq experiments that were recorded in the same cell lines under basal conditions and in response to various treatments. We first confirmed the strong relationship between the partition of the genome into the A/B compartments and transcriptional activity. In all cell lines, expression level of genes located within the A compartment was significantly higher than expression level of genes located in B Fig. . SimilarUsing E–P links derived from Hi–C and ChIA-PET data, we found, for genes located within the A compartment, a significant correlation between gene expression levels and the extent to which promoters are engaged in chromatin interactions. This association was most pronounced in the comparison between genes with no interaction at all and those that have at least one Fig. . MoreoveWe then turned to analyze the association between the organization of the genome under basal conditions and transcriptional programs that are induced in response to various triggers. First, we showed that, typically, both induced TF binding events and induced genes are enriched in the A compartment Fig. a, c, sugCurrent techniques for determining the 3D organization of the genome are still limited in their resolution and sensitivity. Further development of these methods together with advances in their application to single cells will allow us to better understand how the genome organization in different cells is causally linked to cell-type-specific transcriptional programs both under basal conditions and in response to various challenges.Collectively, the large-scale meta-analysis that we carried out in this study further demonstrates the strong association between cell-type-specific A/B compartmentalization, modulation of landscape of TF-chromatin interactions, and differential gene expression. Moreover, our results further suggest a role for the 3D organization of the genome under basal conditions, at the layers of both A/B compartmentalization and intra-TAD enhancer–promoter interactions, in shaping TF binding events and the network of genes that are induced in response to treatment. Yet, our pairwise comparisons also show that most events of differential TF binding and gene induction occur in genomic loci assigned to A compartment in both cell types, underscoring the role of additional critical factors in determining transcriptional programs that are active in each cell type.We defined A/B compartments for 13 human cell lines for which Hi–C data are available  using TopHat2 . The numTo ensure analysis uniformity, we did not rely on peaks called by original studies, but downloaded raw sequence data and detected TF peaks ourselves. Briefly, for each ChIP-seq experiment, reads were aligned to the human genome (hg19) using Bowtie2  and peakAB density factor, D, defined as follows: Let the number of observed binding sites in region S be O(S) and number of expected binding sites in region S be E(S):D > 1 implies that binding sites are enriched for A compartment, and D < 1 implies that binding sites are enriched for B compartment. For TF binding sites, E(A)/E(B) is equal to the ratio between the genomic size of the two compartments. For induced genes, E(A)/E(B) is equal to the ratio between the number of genes located within these two compartments.For each transcription factor and cell line we computed the AB occupancy enrichment ratio R, as follows: Let the number of BSs in region S occurring only in cell line i be For pairwise comparisons between cells, to test whether cell-type-specific TFBSs occur more often in regions assigned as A compartment in the cell line where the binding event was detected and as B in the other cell line than the opposite assignment, we defined the Additional file 1: Table S1. Hi-C datasets. Table S2. Encode ChIP-seq data included in our analyses (122 TFs profiled in cell lines with Hi-C data). Table S3. Enrichment of CTCF binding sites for the A compartmentalization. Table S4A. Enrichment of cell-type-specific H3K9ac events for cell-type-specific A compartment over B compartment. Table S4B. Enrichment of cell-type-specific H3K27me3 events for cell-type-specific B compartment over A compartment. Table S5. Preference of induced TF binding sites and epigenetic marks to the A compartment. Table S6. Binding site induction and compartmentalization in two cell lines under the same treatment, for a particular TF. Table S7. Preference of induced genes to the A compartment. Table S8. Promoters of induced genes are involved, in basal condition, in higher numbers of chromatin interactions. Table S9. Preference of cell-type-specific induced genes to cell-type-specific A compartment.Additional file 2: Fig. S1. Correlation between the magnitude of the PC that represents A/B compartmentalization and gene expression level. Fig. S2. Association between changes in A/B compartmentalization and differential gene expression between cell types. A. For each pair of cell lines, we examined the difference (fold change) in expression level between genes assigned to the AB and BA sets . For 27 out of 28 pairwise comparisons , we observed a highly significant association (FDR ≪ 5%) between differential compartmentalization and expression.  B. Correlation between the change in the magnitude of PC1 and change in gene expression level in the comparison between GM12878 and four other cell lines. Fig. S3. Enrichment of TFBSs in the A compartment. ChIP-seq experiments are sorted by p value, and A-B density factors are represented by bars. Red line indicates p value = 0.01. Shown are experiments in the GM12878 cell line. Similar results were observed for all other cell lines (data not shown). Fig. S4. Preference of cell-type-specific TF binding for cell-type-specific compartments. Preference of cell-type-specific TF binding events to AB genomic regions over BA regions is measured by the AB occupancy enrichment ratio. The compared cell lines and the examined TF are indicated below each bar . Fig. S5. Enrichment of histone modifications in the A/B compartments. The analysis presented in Fig. S3 for TF binding events is applied here to histone modification peaks. Most modifications showed a significant enrichment for the A compartment. The repressive marks H3K27me3 and H3K9me3 showed markedly lower enrichment, and the latter was even significantly enriched in the B compartment (in HMEC and HUVEC cell lines). Fig. S6. Gene expression levels vs. promoter interactions in compartment A. The same analyses described in the legend of Fig. 4A-D are applied here to additional cell lines. Fig. S7. Enrichment of treatment-induced genes in the basal A compartment. The same analysis as in Fig. 6C, but calling differential genes based on FDR of 5% (rather than based on fold change criterion).


MPNSTs) are an aggressive form of soft tissue neoplasm with extremely poor prognosis and no effective medical options currently available. MPNSTs can occur either sporadically or in association with the neurofibromatosis type 1 (NF1) syndrome. Importantly, activation of RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, and WNT/CTNNB1 signaling pathways has been reported in both NF1‐related and late‐stage sporadic MPNSTs. In this study, we found that DAW22, a natural sesquiterpene coumarin compound isolated from Ferula ferulaeoides (Steud.) Korov., could inhibit cell proliferation and colony formation in five established human MPNST cancer cell lines. Further molecular mechanism exploration indicated that DAW22 could target the main components in the MPNST tumorigenic pathways: namely suppress phosphorylation of AKT and ERK, and reduce levels of non‐phospho (active) CTNNB1. Using the xenograft mouse model transplanted with human MPNST cancer cell line, daily treatment with DAW22 for 25 days was effective in reducing tumor growth. These results support DAW22 as an alternative therapeutic compound for MPNST treatment by affecting multiple signaling transduction pathways in its disease progression.Malignant peripheral nerve sheath tumors ( Cooperating genes enriched in the PI3K/AKT/mTOR, mitogen‐activated protein kinase (MAPK), and WNT/CTNNB1 transduction pathways have been implicated in MPNST disease initiation and progression, as well as the main regulators in mediating cell cycle, cell division, and cell death.Therapeutic drugs used in preclinical and clinical trials for the treatment of MPNSTs currently include mTOR inhibitors and its derivatives (such as everolimus and temsirolimus), with varied response on tumor growth inhibition when combined with other candidate drugs.Ferula ferulaeoides (Steud.) Korov., has been reported to trigger glioma cell apoptosis in vitro.DAW22, a natural sesquiterpene coumarin compound isolated from the 22.1Ferula ferulaeoides (Steud.) Korov. according to previous methods.DAW22 was isolated from the root of the 2.2AKT inhibitor AZD5363 was prepared as a 100 mmol/L stock solution in DMSO.2.32. The identity of these cell lines was verified by short tandem repeats profile comparison. All the cell lines were also tested and found to be negative for mycoplasma contamination using MycoFluor™ Mycoplasma Detection Kit (Thermo Fisher Scientific).MPNST cell lines including STS‐26T,2.4MPNST cell lines were plated onto 96‐well plates at a concentration of 6000 cells per well. After 24 hours, cells were treated with either vehicle control or designated doses of DAW22 for 48 hours. The cell viability was quantified by CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) . The cell viability assays were performed and analyzed using the Opera Phenix high‐content imaging system .2.5MPNST cell lines were seeded onto six‐well plates at a density of 1000 cells per well. After 24 hours, the medium was removed and replaced with either vehicle control or DAW22‐containing medium for 2 weeks to allow for colony growth. Following 2 weeks, media were removed and cells were fixed with 4% paraformaldehyde in phosphate‐buffered saline (PBS) for 15 minutes and stained with 0.1% crystal violet solution in PBS for 10 minutes, followed by repeated washing with PBS.2.6MPNST cell lines were seeded onto six‐well plates, followed by treatment with an indicated concentration of DAW22 for 24 hours. Next, cells were fixed with 70% ice‐cold ethanol in PBS, stained with propidium iodide , and analyzed by flow cytometer.2.7Protein was isolated from DAW22‐treated MPNST cell lines using the Qproteome Mammalian Protein Prep Kit  following the manufacturer's instructions. Concentrations of protein were determined by Bradford Protein Assay  followed by denaturation as described by the manufacturer. Protein was separated on a 12% SDS‐PAGE gel and transferred to polyvinylidene difluoride (PVDF) membrane . Protein on the membrane was first incubated with indicated primary antibodies at 4°C overnight, followed by corresponding secondary antibodies’ incubation at room temperature for 1 hour. Targeted proteins were detected using a horseradish peroxidase‐conjugated chemiluminescent kit (Millipore). AKT (#2920), phospho‐AKT (#4060), ERK (#4695), phospho‐ERK (#4370), poly(ADP‐ribose) polymerase 1 (PARP) (#9532), Non‐phospho (active) CTNNB1 (#8814), caspase 3 (CASP3), and ACTB (#4970), with the exception for CASP3 that was purchased from Santa Cruz Biotechnology , remaining primary antibodies were from Cell Signaling Technology . ACTB was used as the loading control.2.86 STS‐26T cells in 0.1 mL PBS containing 50% Matrigel . One week later, mice were randomly divided into two groups and treated with either vehicle or DAW22 at a dose of 60 mg/kg/d for 25 days by daily intraperitoneal injection. Body weights and tumor sizes were measured every 3 days. At the experimental end point (25 days) and upon necropsy, tumor sizes and weights were measured for each mouse. All animal studies were approved by the appropriate ethics committee and performed in accordance with the ethical standards stipulated by both The Hong Kong Polytechnic University and The Chinese University of Hong Kong.Six‐week‐old immunocompromised nude mice  were anesthetized with ketamine and each flank injected subcutaneously with 2 × 102.9Tissues were carefully removed from the sacrificed animal, weighed, washed, and placed in cold PBS. Formalin‐fixed paraffin‐embedded sections from various tissues were sectioned at 5 μm using a standard microtome , mounted, and heat‐fixed onto glass slides. Tissue section slides were either processed and stained with hematoxylin and eosin (HE) using standard protocols.2.10t test and ANOVA in Prism were used for statistical analyses. Value of P < 0.05 was considered as statistically significant.Raw data were analyzed using GraphPad Software Prism , and resulting values were expressed as mean ± standard error of the mean (SEM). Student's 33.1The therapeutic potential of DAW22, structure shown in Figure 50) in these five cell lines ranged from 30.42 to 46.73 μmol/L   Figure D. In ord) Figure E.4With a high rate of metastases and extremely poor prognosis, MPNSTs represents one of the most difficult‐to‐cure sarcoma. Currently, there are no effective drugs for the treatment of MPNST and surgical resection remains the most effective means of therapy, but this method is limited due to the close proximity of the affected peripheral nerves with other tissues. Better therapeutic regimes for MPNST require a greater understanding of the genetic mechanisms associated with the disease. As precision medicine becomes more important for cancer therapy, identification of accurate therapeutic targets and discovery of specific drugs to control cancer development are becoming ever more critical.Ferula ferulaeoides (Steud.) Korov., inhibited cell proliferation in both sporadic and NF1‐related MPNST cell lines at varied doses. ST8814 and T265 cell lines have a IC50 of about 45 μmol/L, compared with STS‐26T, S462, and S462‐TY cell lines, where the IC50s were around 30 μmol/L. The differences might be caused by their distinct genetic backgrounds  tumor suppressor gene,NF1 deficiency, and activated AKT‐mTOR signaling,50 concentration as S462 cells, as S462‐TY was derived from a xenograft passage of S462.TP53 expression in STS‐26T cells was completely absent,50 concentration. Cell cycle was not influenced by DAW22 based on our cell cycle assay results . This could be paraptosis‐like cell death, which could further contribute to the anti‐cancer effect. However, the molecular mechanism(s) associated with paraptosis remains to be elucidated.In our study, DAW22, a compound isolated from the plant  Figures . The apoAccumulating evidence indicated several pathways are highly related with MPNST transformation ability. NF1‐related MPNST cancer patients have activated RAS signaling, which subsequently cause activation of PI3K/AKT/mTOR and MAPK pathways. Sporadic MPNST patients also showed mutations in these pathways at the advanced disease stages. Moreover, significant activation of WNT/CTNNB1 pathway has been shown to drive human Schwann cell transformation and tumor maintenance in development of MPNST. The important roles of these pathways were further validated using inhibitors targeting AKT, mTOR, MEK, and WNT pathways either singly or in combinations.50s of AKT inhibitor AZD5363 in MPNST cancer cell lines (Figure Here, we demonstrated that DAW22 inhibited phosphorylation of AKT, ERK, and active form of CTNNB1. The data indicated that DAW22 could target multiple signaling pathways involved in MPNST disease progression Figure . In addis Figure . As AKT,In summary, our current study showed that DAW22 inhibited both sporadic and NF1‐related MPNST cancer cell proliferation and induced apoptosis by targeting AKT, ERK, and CTNNB1 pathways. In addition, DAW22 delayed tumor growth of STS‐26T cell transplanted in xenograft mice, providing strong evidence for DAW22 as a potential novel alternative therapeutic treatment for patients with MPNST.The authors have no conflict of interest. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Candidatus Profftella armatura (Betaproteobacteria), an organelle-like defensive symbiont harbored by a plant sap-sucking insect, Asian citrus psyllid Diaphorina citri (Hemiptera: Liviidae). Diaphorin belongs to the pederin family, a group of compounds that share much of their core structure with that of pederin, which is characterized by two dihydropyran rings bridged by an N-acyl aminal. Most members of this family have potent antitumor activity, making them promising anticancer drug candidates. The present study assessed the therapeutic potential of diaphorin for its antitumor activity against 39 human cancer cell lines including those from breast, brain, colon, lung, skin, ovary, kidney, stomach, and prostate. The results showed that diaphorin had inhibitory activity against all 39 cancer cell lines tested. The GI50, TGI, and LC50 values ranged from 0.28 μM– 2.4 μM, 1.6 μM –11 μM, and 7.5 μM–> 100 μM, respectively. These values are among the highest in the pederin family, indicating that the anticancer activity of diaphorin is milder than those of other pederin congeners. The inhibitory effects of diaphorin significantly differed among the distinct cancer types. The maximum difference was about 10-fold, which was similar to those of most other pederin congeners.Diaphorin is a polyketide produced by  N-acyl aminal  heated to 35°C. The mobile phase was isocratic 20% acetonitrile in water, with a flow rate of 1.5 mL/min. Diaphorin was detected at a wavelength of 200 nm. The purified samples were combined and dried in vacuo. Diaphorin was re-dissolved in methanol and quantified in an HPLC system as described above, except the mobile phase was 15% acetonitrile in water, with a flow rate of 1.0 mL/min, and an Inertsil ODS-3 analytical column  was used.Diaphorin was extracted from escribed . Briefly2.The human cancer cell line panel prepared by the Japanese Foundation for Cancer Research  consisted of 39 cell lines as follows : breast −8, 10−7, 10−6, 10−5, or 10−4 M final concentration) of diaphorin were then added to each cell line. After 48 h of additional incubation at 37°C, the plates were fixed with TCA, stained with SRB, and read with an automated microplate reader at a wavelength of 515 nm. Percentage growth inhibition was calculated as:Inhibition of the growth of human cancer cell lines was assessed using a standard sulforhodamine B (SRB) assay as described previously . Briefly[(Ti-Tz)/(C-Tz)] × 100, when Ti≥Tz[(Ti-Tz)/Tz] × 100, when Ti<Tz50: 50% growth inhibition) was calculated from [(Ti-Tz)/(C-Tz)] × 100 = 50. The concentration of diaphorin resulting in total growth inhibition (TGI) was calculated from Ti = Tz. The concentration of diaphorin resulting in a 50% reduction of the number of cells at the end of the treatment as compared with that at the beginning  was calculated from [(Ti-Tz)/Tz] × 100 = -50.. The concentration of diaphorin resulting in a 50% reduction in the cell number increase as compared with the control  .−8, 10−7, 10−6, 10−5, and 10−4 M (p < 0.05). The concentrations of diaphorin required to exert the growth inhibitory activity were at the micromolar level, which is much higher than those reported for other members of the pederin family including pederin, mycalamides, onnamides, theopederins, psymberin, and their synthetic analogs that exhibit antitumor activity at nanomolar or even subnanomolar concentrations [O-desmethyl analog of pederin . The maximum difference was about 10-fold, which is similar to those observed for most other pederin congeners. Psymberin is the only exception to this within the pederin family thus far, showing more than a 10,000-fold difference in its inhibitory activity toward distinct cancer types. Indeed, several melanoma, breast, and colon cancer cell lines were highly sensitive (LC50 < 2.5 nM) to psymberin, whereas six leukemia cell lines were invariably insensitive (LC50 > 25 μM), indicating a >104-fold difference in inhibitory activities [The sensitivity toward diaphorin significantly differed among the distinct cancer types Click here for additional data file.


Cell mechanical properties have been identified to characterize cells pathologic states. Here, we report our work on high-throughput mechanical phenotyping of androgen-sensitive and non-sensitive human prostate cancer cell lines based on a morphological rheological microfluidic method. The theory for extracting cells’ elastic modulus from their deformation and area, and the used experimental parameters were analyzed. The mechanical properties of three types of prostate cancer cells lines with different sensitivity to androgen including LNCaP, DU145, and PC3 were quantified. The result shows that LNCaP cell was the softest, DU145 was the second softest, and PC3 was the stiffest. Furthermore, atomic force microscopy (AFM) was used to verify the effectiveness of this high-throughput morphological rheological method. Prostate cancer is a commonly diagnosed cancer and the second leading cause of cancer-related death in men ,2,3. TheThere has been increasing evidence that mechanical properties of cells can be used as label-free markers to determine cell physiology or pathology, such as cell metastatic potential, and differentiation degree ,11,12,13In order to reveal the underlying difference in mechanical properties between androgen-sensitive and androgen-non-sensitive prostate cancer cells lines, this morphological rheological method was utilized to quantify the mechanical properties of three types of prostate cancer cells lines  at a high-throughput of over 100 cells/s. The results show that androgen-non-sensitive prostate cancer cells line PC3 and DU145 are stiffer than androgen-sensitive prostate cancer cells line LNCaP, which suggests that the mechanical properties of prostate cancer cells can be used as potential biomarkers for early identification of androgen-independent prostate cancer.A silicon-based mask manufactured using a traditional lithography process was utilized to fabricate the polydimethylsiloxane (PDMS)-based chip. The PDMS layer was manufactured as follows: the prepolymer mixed with the curing agents  in a ratio of 10:1 was poured into the prefabricated mask and cured for 30 min at 80 °C. Then the cured PDMS film was carefully peeled off and bonded with a glass substrate through an oxygen plasma process . In this study, the constriction channel on the microfluidic chip possessed a length of 300 μm and a cross-section of 25 μm × 25 μm. The size of the channel was larger than the measured cells which have an averaged diameter of ~20 μm, which enabled the cell to be deformed by flow shear stress, not by the channel walls.F is the applied loading force of the AFM probe, E is the Young’s modulus of the cell, δ is the indentation depth, μ is the Poisson’s ratio, and R is the contact radius. Cells are generally considered to be incompressible materials, so we used a Poisson’s ratio value of 0.5 [In this work, AFM was used to verify the accuracy of this morphological rheological method. The AFM experiment was performed with the Bioscope Resolve AFM  which was integrated with an inverted microscope . The PFQNM-LC-A-CAL probes  with spring constants of 0.1 N/m, a tip radius of 65 nm, and a tip half-angle of 18° were used for the experiment. In this experimental case, a linearized Hertz model equation can be used to fit the experimental data into the spherical indenters to calculate Young’s modulus of the cell :(1)F2/3=e of 0.5 .2 humidified atmosphere at 37 °C. PC3 cells were cultured in F12 medium supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin  in a 5% CO2 humidified atmosphere at 37 °C. To prepare the cell suspension, cells in a petri dish were firstly washed twice using phosphate buffer saline (PBS)  to remove dead cells and other impurities. Then the cells were trypsinized from the petri dish using 0.25% Trypsin/EDTA , followed by centrifugation at 1000 rpm. Then, the cells were suspended again using PBS buffer which contained methylcellulose  at a concentration of 0.5% (w/v). Here, the methylcellulose was used to reduce cell sedimentation and increase the shear viscosity [5–106 cells/mL. The prepared cells were transferred to a syringe for further experiments.All prostate cancer cells lines used in this report were purchased from ScienCell . DU145 and LNCaP cells were cultured in RPMI 1640 medium in a 5% COiscosity . The celIn the AFM experiments, all measurements were completed within two hours to ensure that the cells were in physiological state and adherent during the imaging process. A and L are the area and perimeter of the extracted cell, respectively. D is defined as the deformation of the cell.A self-developed cell contour extraction method was modified on the real-time deformability cytometry (RT-DC) method . The imaThe extracted contour data of cells was then analyzed by MATLAB R2014b and GraphPad Prism7.Q represents the flow rate, η is the viscosity, L is channel size, and E0 is the elastic moduli by simulation [Q’, η’, L’ represent the corresponding experimental parameters, respectively. In this work, the injection rate Q’ was 0.20 μL/s. According to this equation, the iso-elasticity lines were simulated as shown in In the microfluidic channel, cell deformation depends not only on its elastic modulus, but also on its size, the channel size, and flow rate . Reportsmulation . Q’, η’,In this section, the velocity distribution of PC3 cell in the 300 μm microfluidic constriction channel with an injection rate of 0.20 μL/s was analyzed. As shown in In this report, the deformation degree and elastic modulus of three types of prostate cancer cell lines PC3, DU145, and LNCaP were quantified with an injection rate of 0.2 μL/s. PC3 and DU145 are androgen-non-sensitive prostate cancer cells lines, while LNCaP is an androgen-sensitive prostate cancer cells line. Here, LNCaP, DU145, and PC3 are prostate cancer cell lines with low, moderate, and high metastatic potential, respectively . AccordiIn this work, we have applied a morphological rheological microfluidic method to quantify the mechanical properties of three types of prostate cancer cell lines  at a throughput of over 100 cells/s. The velocity distribution of cells during flowing through the narrow channel, and the deformation degree of cells induced by different flow rate was analyzed. The result showed that the elastic modulus of androgen-non-sensitive prostate cancer cell lines  were larger than that of an androgen-sensitive prostate cancer cell line , which was also supported by the results measured using AFM. The results suggest that the mechanical properties of prostate cancer cells can be used as potential biomarkers to identify and predicate the difference of androgen-sensitive and androgen-non-sensitive prostate cancer cell lines. This morphological rheological microfluidic method may be an alternative technique for illustrating how the androgen-sensitive prostate cancer progress into androgen-non-sensitive prostate cancer during executing ADT from a cellular mechanical view.