All-trans-retinoic-acid (ATRA) is a promising agent in the prevention/treatment of breast-cancer. There is growing evidence that reprogramming of cellular lipid metabolism contributes to malignant transformation and progression. Lipid metabolism is implicated in cell differentiation and metastatic colonization and it is involved in the mechanisms of sensitivity/resistance to different anti-tumor agents. The role played by lipids in the anti-tumor activity of ATRA has never been studied.We used 16 breast cancer cell-lines whose degree of sensitivity to the anti-proliferative action of ATRA is known. We implemented a non-oriented mass-spectrometry based approach to define the lipidomic profiles of each cell-line grown under basal conditions and following treatment with ATRA. To complement the lipidomic data, untreated and retinoid treated cell-lines were also subjected to RNA-sequencing to define the perturbations afforded by ATRA on the whole-genome gene-expression profiles. The number and functional activity of mitochondria were determined in selected ATRA-sensitive and –resistant cell-lines. Bio-computing approaches were used to analyse the high-throughput lipidomic and transcriptomic data.ATRA perturbs the homeostasis of numerous lipids and the most relevant effects are observed on cardiolipins, which are located in the mitochondrial inner membranes and play a role in oxidative-phosphorylation. ATRA reduces the amounts of cardiolipins and the effect is associated with the growth-inhibitory activity of the retinoid. Down-regulation of cardiolipins is due to a reduction of mitochondria, which is caused by an ATRA-dependent decrease in the expression of nuclear genes encoding mitochondrial proteins. This demonstrates that ATRA anti-tumor activity is due to a decrease in the amounts of mitochondria causing deficits in the respiration/energy-balance of breast-cancer cells.The observation that ATRA anti-proliferative activity is caused by a reduction in the respiration and energy balance of the tumor cells has important ramifications for the therapeutic action of ATRA in breast cancer. The study may open the way to the development of rational therapeutic combinations based on the use of ATRA and anti-tumor agents targeting the mitochondria. All-trans-retinoic acid (ATRA) is a non-conventional and promising therapeutic agent acting on different types of solid/hematologic malignancies –5 and it+ mammary tumors are characterized by sensitivity to the anti-proliferative action of ATRA, while the triple-negative counterparts tend to be resistant [Breast-cancer is a heterogeneous disease , althougesistant , 12. Theesistant , 13. ATResistant .There is growing evidence that reprogramming of cellular lipid metabolism contributes to malignant transformation and progression –18. In aCLs) as the main lipid class modulated by ATRA in retinoid-sensitive breast-cancer cell-lines. Mechanistic and RNA-sequencing studies show that the ATRA-dependent down-regulation of CLs in sensitive cell-lines is accompanied by a reduction in the amounts and activity of mitochondria.Here, we evaluate the constitutive lipidomic profiles of breast-cancer cell-lines recapitulating the heterogeneity of the disease , 12 and RARA-C5) and relative control (Vect-C1) clones from MDA-MB-453 cells as well as the RARα silenced (RARsA-sh18) and relative control (Vect-C6) clones from SK-BR-3 cells have been described [The source and the characteristics of the 16 breast-cancer cell-lines used are available in Additional file escribed . The groescribed .Single-cell motility assays were performed on BSA-coated culture wells by time-lapse microscopy, using the Imaging Station CellRˆ  and the software Image J .Untargeted lipidomics studies were performed with Lipostar, a high-throughput software supporting targeted and untargeted liquid-chromatography/mass-spectrometry (LC-MS) lipidomics , 28. FurSK-BR-3, HCC-1419, MDA-MB-361 and HCC-202 lines, mitotracker stained cells were also subjected to quantitative microscopic analysis using the ImageJ software. Cells were counterstained with Hoechst 3342 (Thermofisher) for the determination of cell nuclei. For each experimental point, a minimum of 200 cells/field in at least 4 fields/experimental triplicate were quantitated. Mitochondria were isolated using a described protocol [Mitochondria were stained with MitoTarcker Deep Red FM (Invitrogen) according to the manufacturer instructions. Following staining, cells were fixed with 2% formalin and subjected to quantitative FACS analysis, using a fluorescence activated cell sorter . In the case of the experiments performed on the protocol . The enzprotocol , 31. Theprotocol .− 6 M) for another 24 h. RNA was extracted with the mRNeasy Mini Kit (QIAGEN). RNA sequencing was performed using the Illumina TruSeq RNA library-preparation kit and sequenced on the Illumina NextSeq500 with paired-end, 150 base pair long reads. The overall quality of sequencing reads was evaluated using FastQC [Gencode [v27 release of the Gene Transfer File (GTF). Raw-counts were further processed in the R Statistical environment and downstream differential expression analysis was performed using the DESeq2 pipeline. All the RNA-sequencing data relevant for this study were deposited in the EMBL-EBI Arrayexpress database (Accession No: E-MTAB-8408). Genes characterized by low mean normalized counts were filtered out by the Independent Filtering feature embedded in DESeq2 . DESeq2-computed statistics were used as input for gene-set enrichment testing performed with the pre-ranked version of Camera. Statistical enrichments were determined for gene-sets obtained from the Hallmark (H), which are curated by the Molecular Signature DataBase (MSigDB).Three paired biological replicates of each breast-cancer cell-line were grown in DMEMF12 medium containing 5% charcolated FBS  for 24 h. Cells were treated with vehicle (DMSO) or ATRA  for 10 min. Cells were scraped from the dishes, centrifuged at 13,000 rpm and kept in 0.12 M phosphate buffer containing 4% paraformaldehyde and 2% glutaraldehyde for 2 h. After post-fixation in 0.12 M cacodylate buffer containing 1% OsO4 for 2 h and subsequent dehydration in graded series of ethanol, samples were cleared in propylene oxide, embedded in Epoxy medium (Epon 812 Fluka) and polymerized at 60 °C for 72 h. Ultrathin sections (70 nm thickness) were obtained with a Leica EM UC6 ultramicrotome, counterstained with uranyl acetate and lead citrate and examined with an Energy Filter Transmission Electron Microscope  equipped with a yttrium aluminium garnet (YAG) scintillator slow-scan charge-coupled device (CCD) camera . The numerical density of mitochondria  was estimated by morphometrical analysis using 30 digitized electron microscope fields of cells for each group acquired by the iTem software  and digitally superimposing an orthogonal grid with ImageJ (1.52a version). Briefly, the mitochondrial profile area density (NA) was estimated by the ratio between the number of mitochondria and the cytoplasmic area. Mitochondrial volume density (VV) was determined by the ratio of grid points falling over mitochondria divided by the total number of points of the grid contained in the cytoplasm. The numerical density of mitochondria (NV) was then estimated for each cell using the formula: NV = (1/β) (NA3/2 / VV1/2), where β is the shape coefficient for ellipsoidal mitochondria, calculated from the ratio of the armonic mean of major and minor axis of mitochondria sections measured on digital images. The mean mitochondrial volume was calculated for each cell as the ratio of mitochondrial volume density VV and numerical density NV.ATRA-sensitive − 6 M) for 30 min at 37° and the fluorescence polarization values were determined using a fluorescence detector . The fluorescence polarization (FP) value is a function of the emission value (emission = 420 nm), which was detected through analyzers oriented in parallel (FP1) and perpendicular (FP2) to the direction of polarization of the excitation beam (excitation = 365 nm), according to the eq. FP = (FP2 - FP1 / FP2 + FP1).The microviscosity of mitochondrial membranes was measured as described by M. Salmona et al. following staining with 1,6-diphenyl-1,3,5-hexatriene as a fluorescent probe [TN) breast-cancers, while the other 8 cell-lines derive from luminal tumors characterized for HER2 and ER/PR expression. Consistent with the tumor origin, the constitutive gene-expression profiles determined by RNA-sequencing classify the cell-lines in two distinct basal and luminal groups  and 4 luminal  cell-lines are classified as highly sensitive to ATRA. Three luminal  and 3 basal  cell-lines are endowed with intermediate sensitivity. One luminal (HCC-1419) and 4 basal  cell-lines show low sensitivity/resistance to ATRA.To conduct the study, we used 16 cell-lines whose degree of sensitivity to the anti-proliferative action of ATRA has been determined , 12. EigPCs) consist of 203 features, while 40 cardiolipins (CLs) are identified. PCA of the constitutive lipid profiles separates luminal from basal cell-lines  and PCs, some diacylglycerophosphoinositols or phosphatidylinositols (PIs), neutral glycosphingolipids (N-glyco-SPs), CLs, lysophosphatidylcholines (LPCs), sphingomyelins (SMs) and 1-alkyl-2-acylglycerophosphocholines/1-alkenyl-2-acylglycerophosphocholines (o-PCs/ p-PCs), as well as all the identified steryl-esters (SEs), monoacylglycerophosphoethanolamines  and 1-alkenyl-phosphatidylethanolamine (p-PE) , 2 PCs, 1 p-PC, 1 ceramide (CER) and 1 diacylglycerophosphoethanolamine  are higher in luminal than basal cell-lines. Although luminal and basal cell-lines show different complements of individual features, the average constitutive levels of the 23 classes of lipids do not vary in the two groups  for 48 h. The selection of the retinoid concentration and exposure time is based on pilot studies conducted in SK-BR-3 cells. These studies demonstrate that maximal alterations in the lipidomic profiles are observed with 10− 6 M ATRA, while the 48-h exposure time precedes any significant effect on the number of cells [We compared the mass-spectrometry data obtained in all the cell-lines exposed to DMSO or ATRA  which results in the conversion of all-trans retinol into all-trans retinyl ester, a storage form of Vitamin A [d groups . SimilarTRA Fig. , left. Tore Fig. , right. itamin A .PSs are cell membrane glycerophospholipids differing in their fatty acid composition and they are involved in cell-cycle signaling and apoptosis [SK-BR-3, HCC-1500, CAMA1 and MDA-MB-361 cell-lines, ATRA causes a reproducible reduction in the total amounts of the PSs identified  or vehicle (DMSO) for 16 h and subjected to a random-motility assay for another 24 h . To obtain insights into the mechanism underlying the ATRA-dependent down-regulation of CLs, we evaluated the RNA-sequencing data obtained in all the 16 cell-lines exposed to ATRA (10− 6 M) or vehicle for 24 h . Using these data, we determined the effects of ATRA on the expression of the 77 genes involved in the glycerophospholipid metabolic pathway  between the ATRA/DMSO expression ratio of any of these genes and the ATRA-score values. The results support the idea that the ATRA-dependent CLs decrease observed in retinoid sensitive cell-lines cannot be explained by up-regulation of specific genes controlling the catabolism of CLs or down-regulation of genes involved in their biosynthesis. CLs are predominantly located in the inner membrane of mitochondria, where they are involved in oxidative phosphorylation [Oxidative Phosphorylation” lays in third position among the 4 top Hallmark gene-sets enriched for genes down-regulated by ATRA in sensitive luminal and basal cell-lines  situated in the inner mitochondrial membrane. ATRA down-regulates approximately half of the “Oxidative Phosphorylation” genes in the luminal and basal cell-lines characterized by high/intermediate sensitivity to the retinoid  for 8 h . Indeed, PCA demonstrates that the constitutive lipidomic profiles of the ATRA-sensitive SK-BR-3 and the ATRA-insensitive HCC-1419 cell-lines are very similar  for 24, 48 and 72 h, stained with Mitotracker and subjected to quantitative fluorescence microscopy  for 48 h  for 48 h. We measured the enzymatic activity of respiratory complexes I-V following normalization for the levels of citrate synthase activity  or the corresponding void vector (Vect-C1) [RARA-C5 cells are characterized by higher RARα expression levels than the Vect-C1 counterparts and the nuclear retinoid receptor is functionally active as indicated by its ATRA-dependent degradation  for 48 h does not alter the overall levels of CLs in Vect-C1 cells, while it causes a reproducible decrease in RARA-C5 cells  . As expeion Fig. a, right,53 cells , which aoid Fig. a, left. y action . The liplls Fig. d. In addSK-BR-3 cells stably infected with a retroviral construct containing a RARα-targeting shRNA (RARA-sh18) and the corresponding void vector (Vect-C6) [RARA-sh18 are characterized by lower levels of functionally active RARα than the corresponding Vect-C6 control cell-line  for 48 h decreases CL levels  . As expeine Fig. b, right.A RARA-sh8 and theion Fig. b, left. lls Fig. a, exposu to ATRA − 6 M for to ATRA − 6 M for to ATRA − 6 M for to ATRA − 6 M forCLs and the associated reduction in mitochondrial membrane microviscosity are dependent on RARα, which is a crucial determinant of ATRA anti-tumor activity in breast cancer [All these results indicate that the ATRA-induced decrease in t cancer .+ breast cancer patients > 65 years of age undergoing neo-adjuvant treatment with aromatase inhibitors . The present report provides new insights into the mechanisms underlying the anti-tumor action of ATRA, a non-conventional therapeutic agent [ATRA is a promising anti-tumor agent and it is the object of an approved window-of-opportunity clinical trial we are conducting in the context of the personalized treatment of ERic agent , 12, 26.ic agent . In partic agent .CLs, a class of lipids predominantly located in the inner mitochondrial membrane and playing an important role in oxidative phosphorylation [CLs and this effect is correlated to the growth inhibitory activity of the retinoid. Down-regulation of CLs is accompanied by a significant reduction in the number and functional activity of mitochondria, two phenomena which are likely to contribute to the growth inhibitory action of ATRA in sensitive cell-lines. Our transcriptomic data indicate that ATRA-dependent down-regulation of CLs is consequent to a decrease in the assembly of mitochondria. In fact, ATRA does not exert direct effects on the expression of genes involved in the biosynthesis/catabolism of CLs, while the retinoid down-regulates numerous genes coding for mitochondrial proteins via direct or indirect transcriptional events mediated by RARα. In addition, the ATRA-dependent down-regulation of nuclear genes encoding mitochondrial proteins is a relatively early event, preceding the decrease in CL levels. Although CLs down-regulation is unlikely to be a determinant of the reduction in the amounts of mitochondria, the process may contribute to the functional deficiency of these organelles induced by ATRA in sensitive cells. In fact, the decrease in the enzymatic activity of mitochondrial complexes I, III IV and V observed in SK-BR-3 cells may be explained by the fact that ATRA induces a relative deficit of CLs in the mitochondrion. This, in turn, reduces the microviscosity of mitochondrial membranes, a phenomenon which is potentially at the basis of the ATRA induced decrease in mitochondrial complexes activity [The most interesting effects of ATRA are observed on rylation . ATRA reactivity –52.CLs caused by ATRA must be further investigated. Nevertheless, we recently observed that exposure of retinoid-sensitive luminal and basal breast cancer cell lines to ATRA induces the expression of various endogenous retroviral RNAs, resulting in an unusual type of “viral-mimicry” [viral-mimicry” causes the induction of the transcriptional factor, IRF1 (Interferon Responsive Factor 1), via RARα activation and it is associated with the up-regulation of numerous other interferon-responsive genes. Interestingly, viruses [viral-mimicry” and the consequent cascade of transcriptional effects involving interferon-dependent genes are at the basis of the reduction in the number and functional activity of mitochondria observed in sensitive breast cancer cell lines exposed to ATRA.The molecular mechanisms underlying the reduction in the number of mitochondria and the consequent decrease in the levels of mimicry”  response viruses , IRF1 [5 viruses  and otheCLs depletion, which is at the basis of the induced michondrion-dependent apoptotic response [CLs.The involvement of mitochondria in the anti-proliferative action exerted by ATRA on the neoplastic cell is of relevance for the development of innovative approaches in the treatment of breast-cancer. In fact, cancer cell mitochondria are novel therapeutic targets, given their structural and functional differences from the normal counterparts , 57. Theresponse , 60. TheOverall, the study provides new insights into the mechanisms underlying the anti-tumor activity of ATRA in breast cancer. To the best of our knowledge, this is the first study which demonstrates that the anti-proliferative effects of the retinoid are, at least partially, the consequence of a decrease in the amounts of mitochondria which leads to deficits in the respiration and energy balance of the neoplastic cells. From a basic point of view, our integrated approach defines the constitutive lipidomic profiles of luminal and basal breast-cancer cells. In addition, it demonstrates that the anti-proliferative effects of ATRA are, at least partially, the consequence of a decrease in the amounts of mitochondria which leads to deficits in the energy balance of the breast-cancer cell. As for the applicative aspect, the study opens the way to therapeutic strategies based on rational combinations involving ATRA and organic molecules targeting the mitochondria, an emerging group of anti-tumor agents.Additional file 1: Figures S1-S12. Supplementary Information, Supplementary Methods, Supplementary References.Additional file 2: Table S1. Lipidomics raw data, Highthroughput lipid ID.Additional file 3: Table S2. Effects of ATRA on cardiolipin amounts in SK-BR-3 cells.Additional file 4: Table S3. Effects of ATRA on cardiolipin amounts in genetically engineered breast cancer MDA-MB-453 and SK-BR-3 cells.


Introduction: Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, and new therapeutic strategies are still required. Here we screened a synthetic cannabinoid library to identify compounds that uniformly reduce the viability of seven CRC cell lines.Material and Methods: Seven distinct CRC cell lines were treated with 10 μM cannabinoid compounds (from a library of 370 molecules) for 48 h, and cell viability was subsequently measured with MTS assay. Dose–response curves were conducted for compounds that were found to reproducibly reduce cell viability of one or more cell lines.Results: We identified 10 compounds from the library that were able to reduce cell viability of CRC cell lines (with an IC50 ≤ 30 μM). Of these compounds, seven were specific for CRC cells, and six were effective in all CRC cell lines tested. Treatment with traditional phytocannabinoids (THC or CBD) was either ineffective or much less potent and only partially efficacious. Treatment with antagonists for the known cannabinoid receptors  failed to block the activity of the most potent of identified compounds.Conclusion: We identified three families of cannabinoid compounds that reduce CRC cell viability through a noncanonical receptor mechanism. Future modification of these compounds may lead to the development of novel therapies to treat this disease. Despite this knowledge, attempts to target the Wnt/β-catenin pathway to inhibit cancer growth have been largely unsuccessful. In light of these facts, new therapeutic approaches need to be undertaken.With an estimated 97,220 new cases and over 50,000 deaths each year, colorectal cancer (CRC) is the third most common cancer and the third most common cause of cancer death.5–7; however, a number of studies have proposed the use of cannabinoid compounds as anti-tumor agents.8–11 These plant-derived cannabinoids interact with the endocannabinoid system and have a much higher affinity for the receptors than do endogenous ligands. The expression of cannabinoid receptors 1 and 2 (CB1 and CB2) and GPR55 has been reported to have increased in CRC, and this is associated with a poorer prognosis and more advanced disease.12–17Over the past decade, the number of countries and states that have legalized medical cannabis has grown rapidly, and cannabinoid compounds may serve as a novel therapeutic agent to combat a number of diseases. With regard to cancer, medical cannabis has largely been utilized for palliative purposesAPC, the most common activating mutation of the Wnt/β-catenin pathway in CRCs; in contrast, HCT116 and LS174 have direct activating mutations in CTNNB1, the gene that encodes β-catenin, while RKO cells have intact Wnt/β-catenin signaling. SW480 and SW620 cells were isolated from the same individual, with SW480 cells representing a primary tumor and SW620 a lymph node metastatic tumor. Our approach of analyzing CRC cell lines with distinct Wnt signaling mutations allowed us to determine whether the genetic lesion or tumor subtype could influence sensitivity to cannabinoid treatment. Here we identified several cannabinoid compounds that show potential as novel therapies to combat CRC. Intriguingly, these agents do not appear to act through traditional cannabinoid receptor signaling.The objective of our study was to identify compounds from a synthetic cannabinoid library that universally reduces cell viability of seven distinct CRC cell lines . Importantly, these cell lines represent a diversity of tumor types: the first four cell lines contain mutations in 2. CCD 841 CoTr cells were cultured as described above, but grown at the permissive temperature of 33°C.The human CRC cell lines SW480, SW620, HT29, DLD-1, HCT115, LS174, RKO; the temperature-sensitive t-antigen-transformed normal human colonic epithelial cell line CCD 841 CoTr; and the human embryonic kidney cell line HEK 293 were obtained from the American Type Culture Collection (ATCC). Each line, with the exception of the RKO line that was cultured in RPMI, was cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 2 mM Glutamax, 10 U/mL penicillin, 10 μg/mL streptomycin, and 0.25 μg/mL Amphotericin B at 37°C in 5% CO19 Target gene expression was measured by quantitative real-time polymerase chain reaction using TaqMan™ probes for CNR1 (Hs01038522_s1), CNR2 (Hs00275635_m1), GPR55 (Hs00271662_s1), TRPV1 (Hs00218912_m1), and GAPDH (Hs99999905_m1) . Relative levels were determined using the 2−ΔΔCT method.20RNA isolation and cDNA synthesis were performed as previously described.Cells were seeded in 96-well plates at a density of 20,000 cells per well, incubated for 8 h, and then treated with members of a synthetic cannabinoid library  at 10 μM. This library contains 370 distinct molecules consisting of parent compounds along with positional isomers, analogs, and homologs. Cells were treated with the compounds for 48 h, and then cell viability was measured using the MTS assay following the manufacturer's protocol . Cells were incubated with MTS for 1.25 h , after which absorbance at 590 nm was measured using a FlexStation 3 spectrophotometer . Cell viability for DMSO-treated controls was set to 100%. A z-score was calculated for individual plates, and compounds that displayed a z-score of −1.5 or greater were selected for repeat screening . Importantly, any compounds identified that reduced viability of one of the cell lines tested were rescreened against all cell lines, to reduce the possibility that potential compounds would be overlooked. These experiments were supplemented with the traditional phytocannabinoids  at the same concentration. For select compounds, MTS results were confirmed with trypan blue staining. Cells were plated and treated as described, and after 48 h, adherent and nonadherent cells were collected, stained with 0.2% trypan blue, and counted on a hemocytometer.Any compounds that reduced viability upon rescreening in either the original cell line or in a second cell line were pursued, and dose–response curves were performed on these compounds for all cell lines. Cells were seeded as described above, incubated for 8 h, and treated with select cannabinoid compounds at concentrations of 100 nM, 333 nM, 1 μM, 3.3 μM, 5.6 μM, 10 μM, 33 μM, and 100 μM. Viability was measured as described in the To explore the receptors mediating cell death, experiments were conducted using selective antagonists to inhibit each of the four primary cannabinoid receptors. SW480 cells were seeded as described in the n=3) dose–effect curves in all of the cell lines. Statistical significance was determined using the Student's t-test .Each compound identified in the primary screen that reduced viability was rescreened two additional times; therefore, each potential compound identified from the original screen was tested three times at 10 μM in all seven cell lines. Moreover, 30 compounds were subjected to replicate . Levels of cannabinoid receptor 1 (CNR1) were similar between most of the cell lines tested; however, mRNA expression was markedly higher in HT29 and LS174 cells compared with other cell lines (CNR2) was undetected in each of the cell lines tested (using multiple dilutions of the prepared cDNA). The expression of G protein-coupled receptor 55 (GPR55), sometimes referred to as CB3,22 was similar to that observed for CNR1, with the highest expression in HT29 and LS174 cells (TRPV1) was similar between all colon cell lines (To determine if cannabinoid receptor messenger RNA (mRNA) expression levels vary in our cell line panel , we conducted reverse transcribed-quantitative polymerase chain reaction (RT-qPCR) analysis of the four most common cannabinoid receptors , PTI-2, and PTI-1 were not selective and inhibited normal epithelial cells and/or HEK 293 cells (data not shown).Dose–response curves were performed for each of the 30 compounds that reduced viability of the rescreening process . Of these compounds, only 10 were identified that had an ICTHC, while not effective at reducing cell viability at 10 μM, did reduce cell viability at higher concentrations. THC was effective in SW480  and HCT1We next sought to identify which cannabinoid receptor was mediating the action of our identified cannabinoids. We repeated the cannabinoid treatment with our most potent cannabinoid  and antagonists to each of the four major receptors using SW480 cells. Treatment with these antagonists  failed to prevent the reduction in cell viability observed with (±)-5-epi CP 55,940 .24–28 However, a large-scale screening of these compounds to assess the potency and efficacies of different synthetic cannabinoids has not previously been performed. Here, we demonstrated that 10 synthetic compounds are highly efficacious and moderately potent for reducing the viability of seven CRC cell lines. The structures of these compounds are illustrated in Previous work has demonstrated the ability of cannabinoid compounds to reduce the viability of CRC cell lines.50 of 16.3–23.1 μM and efficacy in only three cell lines. Additionally, for CP 55,940 and CP 47,497, we found that certain isomers were more potent than others. In particular, of the four CP 55,940 compounds in the library, only one  failed to reduce CRC cell viability, suggesting that the (+)-stereoisomer is an active form as this was present in the other three derivatives. With regard to CP 47,497, the library contains nine derivatives of which only three reduced viability by >50% . Of the three compounds that were identified, two were stereoisomers of the more potent C-8 homolog and one was a stereoisomer of the parental C-7 isomer, (−)-CP 47,497.29 Mixtures containing the (+)-stereoisomer of the parental compound or the (+)-stereoisomer alone were unable to reduce CRC cell viability. Additionally, neither the para-quinone analog nor the C-6 or C-9 homologs were able to reduce CRC cell viability.Interestingly, these compounds can be placed into three families: CP 55,940, CP 47,497, and PTI. Additionally, both CBD and the synthetic derivative of CBD, HU-331, exhibited the ability to reduce CRC cell viability, although CBD displayed a weak activity with an IC30–32 The ability of CP 55,940 to antagonize GPR55 is consistent with our finding that CBD, but not THC, was able to reduce the viability of CRC cells. THC is an agonist of GPR55, whereas CBD has been shown to be an antagonist of GPR55.33 There are no published data on the pharmacological activity of the identified synthetic cannabinoids against GPR55. While this is an attractive idea, we did not detect any effect on cell viability by GPR55 antagonist ML-193. This suggests that the cannabinoids may be signaling through GPR55 in a manner different from ML-193, or that antagonism of GPR55 is not the mechanism of action of the identified cannabinoids.Despite having a similar binding affinity for CB1 and CB2 as CP 55,940 and CP 47,497, WIN 55,212-2 was much less potent and less efficacious than these two compounds -CP 47,497 C-8 homolog while of low potency was efficacious in five of the cell lines tested, but was ineffective in both SW480 and SW620 cells that were derived from the same patient. We also failed to observe a complete reduction of viability of any cell line. Our most potent and efficacious compounds only reduced viability to between 10% and 15% even at the highest dose (100 μM) tested. This could be due to new cell divisions occurring during the 48 h treatment period, and the daughter cells not being exposed to the synthetic cannabinoid for a long enough time period. This finding could also be due to the degradation of cannabinoid or reduced concentration of the cannabinoid over time. It is also possible that, given the limited solubility of these agents, we were simply unable to deliver high enough concentrations.In the majority of CRC tumors, there is aberrant activation of the Wnt/β-catenin pathway. Of the cell lines tested, six of the seven had mutations that activated this pathway; however, we did not observe any difference in susceptibility to cannabinoids based on the activation of this pathway. Interestingly, we did find that only tumor cell lines isolated from primary tumors, with mutations in the APC gene , were more susceptible to CBD, and cells that contained mutations in the β-catenin gene (HCT116 and LS174) were only moderately impacted by CBD where viability did not typically drop below 50% even at the highest concentrations tested. The treatment-resistant metastatic cell line SW620 was also not susceptible to CBD. This might suggest that CBD is able to reduce the activity of Wnt/β-catenin pathway before the translocation of β-catenin into the nucleus.In summary, we identified 10 synthetic cannabinoids demonstrating reproducible activity against human cells, with seven compounds reducing viability only in CRC cells. These compounds fall into three families of molecules, CP 55,940, CP 47,497, and PTI. Interestingly, CBD had only incomplete activity against a subset of CRC lines, and THC was without activity at 10 μM. We also identified potential isomers and modifications that may contribute to increasing the potency and efficacy of CP 55,940 and CP 47,497 that may lead to novel drugs to treat this deadly disease.


EGFR mutations are routinely explored in lung adenocarcinoma by sequencing tumoral DNA. The aim of this study was to evaluate a fluorescent-labelled erlotinib based theranostic agent for the molecular imaging of mutated EGFR tumours in vitro and ex vivo using a mice xenograft model and fibred confocal fluorescence microscopy (FCFM).EGFR  and one with wild-type EGFR (A549) were xenografted on 35 Nude mice. MTT viability assay was performed after exposure to our tracer. In vitro imaging was performed at 1 μM tracer solution, and ex vivo imaging was performed on fresh tumours excised from mice and exposed to a 1 μM tracer solution in PBS for 1 h. Real-time molecular imaging was performed using FCFM and median fluorescence intensity (MFI) was recorded for each experiment.The fluorescent tracer was synthesized in our laboratory by addition of fluorescein to an erlotinib molecule. Three human adenocarcinoma cell lines with mutated EGFR from those lines with wild-type EGFR (p < 0.001). Ex vivo FCFM imaging of xenografts with mutated EGFR had a significantly higher MFI than wild-type (p < 0.001). At a cut-off value of 354 Arbitrary Units, MFI of our tracer had a sensitivity of 100% and a specificity of 96.3% for identifying mutated EGFR tumours.MTT viability assay confirmed that addition of fluorescein to erlotinib did not suppress the cytotoxic of erlotinib on tumoral cells. In vitro FCFM imaging showed that our tracer was able to distinguish cell lines with mutated EGFR mutations.Real time molecular imaging using fluorescent erlotinib is able to identify ex vivo tumours with The online version of this article (10.1186/s12890-018-0760-z) contains supplementary material, which is available to authorized users. EGFR can drive tumour growth -2,5-diphényltétrazolium bromide test (MTT) . Cell viability was assessed at increased concentration of tracer and genuine erlotinib. As our tracer had a 3.5-fold higher molar mass than erlotinib, cell viability assay was also performed at equimolar erlotinib concentrations. For each cell line, we calculated the concentration that resulted in 50% cell death (IC50). MTT assay was chosen because of its lack of interference with the emission wavelength of our tracer.6 cells were re-suspended in a non-fluorescent culture container using 0.5 ml of PBS containing 1 μmol/l of tracer, 1 μmol/l of erlotinib or PBS alone during an hour at 37 °C, in the dark, with 5% CO2 atmosphere. Cells were then washed twice using PBS. Each washing was followed by 3 min centrifugation at 2000 rpm. FCFM imaging was then immediately performed by application of the probe on the cell pellet. Experiments and imaging were performed six times for each cell line. Results of imaging are expressed as median fluorescence intensity (MFI), in arbitrary units (A.U).In vitro imaging was performed on centrifuged cell pellet. Cells were removed from the plates at confluence by trypsination (1 ml of Trypsin in 0.05% EDTA), suspended in 8 ml of culture medium, centrifuged twice at 2000 rpm for 3 min and finally re-suspended in PBS at room temperature. 1 × 10For ex vivo imaging, xenografts were excised under general anaesthesia after intra-peritoneal injection of 100 mg/kg of ketamine and 10 mg/kg of xylazine. After xenograft excision, and under general anaesthesia, mice were sacrificed by cervical dislocation. The xenograft was then immerged in a 2 ml solution containing 1 μmol/l of tracer at room temperature and kept out of light. After an hour, each tumour was washed by consecutive immersion in 3 separate wells containing 2 ml of 0.9% saline serum. FCFM imaging was then immediately performed by application of the optical probe on the surface of each xenograft. Experiments and imaging were performed at least eight times for each cell line. Results of imaging are expressed as MFI.Results are expressed as headcounts and percentages, medians, and first and third quartiles (IQR) or means and standard deviation (SD). Comparisons were performed using Student T-test and ANOVA test. Correlations were assessed using the Spearman correlation coefficient. ROC-curve was used to establish sensitivity and specificity and to determine optimal cut-off value. All tests were two-sided, the type I error rate was set at 0.05. Analyses were performed using GraphPad Prism 6® for Mac OS X® .p = 0.1) . The tracer was able to distinguish cell lines with a mutated EFGR from wild-type EGFR cell lines, and to distinguish cell lines with a sensitive phenotype (HCC827) from those with a resistant phenotype (H1975 and H1650) (p = 0.002) . Using a cut-off value of 354 A.U, MFI had a sensitivity of 100% and a specificity of 96.3% for identifying mutated EGFR tumours. MFI did not differ significantly between xenografts derived from the three cell lines with a mutation of EGFR . Therefore, ex vivo imaging could not distinguish tumours with a sensitive phenotype from those with a resistant phenotype to erlotinib


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