Juncus tenuis, focusing on the isolation of phenanthrenes, and the preparation of semisynthetic derivatives of the isolated compounds. From the methanolic extract of J. tenuis, three phenanthrenes  were isolated. Juncusol and effusol were transformed by hypervalent iodine(III) reagent, using a diversity-oriented approach. Four racemic semisynthetic compounds possessing an alkyl-substituted p-quinol ring (1–4) were produced. Isolation and purification of the compounds were carried out by different chromatographic techniques, and their structures were elucidated by means of 1D and 2D NMR, and HRMS spectroscopic methods. The isolated secondary metabolites and their semisynthetic analogues were tested on seven human tumor cell lines  and on one normal cell line (MRC-5), using the MTT assay. The effusol derivative 3, substituted with two methoxy groups, showed promising antiproliferative activity on MCF-7, T47D, and A2780 cell lines with IC50 values of 5.8, 7.0, and 8.6 µM, respectively.The occurrence of phenanthrenes is limited in nature, with such compounds identified only in some plant families. Phenanthrenes were described to have a wide range of pharmacological activities, and numerous research programs have targeted semisynthetic derivatives of the phenanthrene skeleton. The aims of this study were the phytochemical investigation of  Distichia, Juncus, Luzula, Marsippospermum, Oxychloe, Patosia, and Rostkovia). Among them, Juncus is the largest one, which comprises approximately 340 species benzene (PIFA) was used as oxidizing agent . The progress of reactions and the efficacy of separation were monitored by TLC on Kieselgel 60FJuncus tenuis Willd. were collected during the flowering period in the Botanical Garden of the University of Szeged, in June 2019. The plant material was identified by László Bakacsy  and Dragica Purger . A voucher specimen (No. 889) was deposited at the Herbarium of the Department of Pharmacognosy, University of Szeged, Szeged, Hungary.Aerial parts of Juncus tenuis (1.68 kg) were ground and percolated with MeOH (50 L), at room temperature. The crude MeOH extract was concentrated under reduced pressure (130.0 g); the residue was dissolved in 50% MeOH and subjected to solvent–solvent partitioning with n-hexane (1.5 L) and CH2Cl2 (3 L), respectively. After evaporation, the CH2Cl2 phase (22.5 g) was chromatographed by VLC, on silica gel, with a gradient system of cyclohexane–EtOAc–MeOH (from 98:2:0 to 5:5:1 (1000 mL/eluent); volume of each fractions was 100 mL), to yield 3 major fractions (I–III).The air-dried aerial parts of n-hexane–EtOAc gradient (linear gradient from 96:4 to 5:95) solvent system, to obtain 2 subfractions (the volume of the collected fractions was 25 mL). Subfraction 1/2 was re-chromatographed on reversed-phase flash chromatography, with H2O-MeOH gradient (linear gradient from 8:2 to 0:1) elution, and juncusol (176 mg) and effusol (207 mg) were obtained. Fraction III (270 mg) was further chromatographed by RPC, on silica gel, using n-hexane–acetone gradient system , to obtain 2 subfractions. Subfraction III/2 was purified by RP-HPLC, applying a Zorbax ODS  column and MeOH–H2O gradient system (linear gradient from 25:75 to 94:6) as eluent (flow rate 3.0 mL/min), and 2,7-dihydroxy-1,8-dimethyl-5-vinyl-9,10-dihydrophenanthrene  was isolated.Fraction I (650 mg) was separated by flash chromatography, on normal-phase silica gel with In each reaction process, 50 mg of starting material  was dissolved at a concentration of 1 mg/mL, in the corresponding solvent , and stirred with 2 equivalents of [bis-(trifluoroacetoxy)]iodobenzene (PIFA), for 30 min, at room temperature. After evaporating the solvent under reduced pressure, SPE on silica gel was applied to absorb the remaining oxidizing agent and the possibly decomposed compounds.n-hexane–EtOAc gradient solvent system, to obtain 2 fractions (I/1-2). From fraction I/2, compounds 1a and 1b (5.3 mg both) were isolated by using an Agilent Eclipse XDB C-8  column and MeCN–H2O (1:1) as mobile phase (flow rate 3 mL/min). Reaction mixture II was fractionated by MPLC with n-hexane–EtOAc gradient solvent system, to obtain 2 fractions (II/1-2). Fraction II/1 was further purified by RP-HPLC, to gain compounds 2a and 2b , using an Agilent Eclipse XDB C-8  column and MeCN–H2O (1:1) as mobile phase (flow rate 3 mL/min). Reaction mixture III was fractionated by MPLC with a gradient solvent system of n-hexane–EtOAc, to obtain two fractions (III/1-2). From fraction III/2, compound 3 (4.3 mg) was isolated. Finally, reaction mixture IV was fractionated by MPLC with an n-hexane–EtOAc gradient system, to afford 4 fractions (IV/1-4). From fraction IV/1 compounds 4a and 4b (1.4 mg both) were isolated by RP-HPLC, using an Agilent Eclipse XDB C-8  column and MeCN–H2O (6:4) as eluent .Reaction mixture I was fractionated by MPLC with 1H and 13C-NMRdata, see m/z 349.1412 [M + Na]+ .Light brown solid; 1H and 13C-NMRdata, see m/z 349.1412 [M + Na]+ .Light brown solid; 1H and 13C-NMR data, see m/z 355.1900 [M + H]+ .Pale yellow solid; 1H and 13C-NMR data, see m/z 355.1900 [M + H]+ .Pale yellow solid; 1H and 13C-NMR data, see m/z 313.1434 [M + H]+ .Pale yellow solid; 1H and 13C-NMR data, see m/z 341.1746 [M + H]+ .Pale yellow solid, 1H and 13C-NMR data, see m/z 363.1568 [M + Na]+ .Pale yellow solid, ® HTB-22) and the drug-resistant subline of the human breast cancer MCF-7  were purchased from LGC Promochem . Both cell lines were cultured in Eagle’s Minimal Essential Medium  supplemented with a non-essential amino acid mixture, a selection of vitamins, and 10% heat-inactivated fetal bovine serum. In every third passage, 0.56 µg/mL doxorubicin was added to the medium, in order to maintain the ABCB1 (P-glycoprotein) expression in KCR cells. A2780 human ovarian cancer cell line  was purchased from Merck KGaA . The cisplatin-resistant human ovarian cancer cell line A2780cis  was purchased from Merck KGaA . The human ovarian cancer cell lines were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum. The RPMI 1640 medium of the cisplatin resistant cell line A2780 was supplemented with 1 µM cisplatin. HeLa (ATCC® CCL-2™) human cervix carcinoma cell line was purchased from LGC Promochem . The cells were cultured in Eagle’s Minimal Essential Medium  supplemented with a non-essential amino acid mixture, a selection of vitamins, and 10% heat-inactivated fetal bovine serum. MRC-5 human embryonal lung fibroblast cell line (ATCC® CCL-171) was purchased from LGC Promochem, Teddington, UK. The cell line was cultured in Eagle’s Minimal Essential Medium  supplemented with a non-essential amino acid mixture, a selection of vitamins, and 10% heat-inactivated fetal bovine serum. HTB-26 breast adenocarcinoma cell line was purchased from LGC Promochem, Teddington, UK. The cell line was cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum. T-47D (ATCC® HTB-133™) ductal carcinoma cell line was purchased from LGC Promochem, Teddington, UK. The cells were cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum, 2 mM l-glutamine, 1 mM Na-pyruvate, and 100 mM Hepes. All of the cells were incubated at 37 °C, in a 5% CO2 and 95% air atmosphere.Breast cancer cell line MCF-7  was added to the cells. The effects of increasing concentrations of compounds on cell proliferation were tested in 96-well flat-bottomed microtiter plates. The compounds were diluted in the appropriate medium; the dilutions of compounds were performed in separate plates and then added to the cells. The starting concentration of the compounds was 100 μM, and two-fold serial dilution was performed (concentration range: 100–0.19 μM). The culture plates were incubated at 37 °C for 72 h; at the end of the incubation period, 20 μL of MTT  solution (from a stock solution of 5 mg/mL) was added to each well. After incubation at 37 °C for 4 h, 100 μL of sodium dodecyl sulfate (SDS) (Sigma) solution (10% in 0.01 M HCI) was added to each well, and the plates were further incubated, at 37 °C, overnight. Cell growth was determined by measuring the optical density (OD) at 540/630 nm, with a Multiscan EX ELISA reader . Mean IC50 values were obtained by best-fitting the dose-dependent inhibition curves in GraphPadPrism5 program  from four parallel experiments for each cell line.The antiproliferative effect of the compounds was determined on human breast , cervical (HeLa), and ovarian (A2780 and A2780cis) cancer cells, and on MRC-5 (human embryonic lung fibroblast) cell lines. The adherent cells were cultured in 96-well flat-bottomed microtiter plates, using EMEM supplemented with 10% heat-inactivated fetal bovine serum or RPMI 1640 supplemented with 10% heat-inactivated fetal bovine serum, respectively. The density of the cells was adjusted to 6 × 10Inhibition of the cell growth was determined according to the formula below:50, defined as the inhibitory dose that reduces the proliferation of the cells exposed to the tested compounds by 50% [Results are expressed in terms of ICs by 50% .J. tenuis, three phenanthrenes, namely juncusol, effusol, and 2,7-dihydroxy-1,8-dimethyl-5-vinyl-9,10-dihydrophenanthrene, were isolated. All of them were isolated for the first time from the plant. Oxidation of juncusol and effusol with hypervalent iodine(III) reagent in CH3CN-alcohol media resulted in the identification of phenanthrene derivatives substituted with methoxy and ethoxy groups at C-1 and C-5a. The semisynthetic derivatives contain substituted o- and p-quinol rings. The semisynthetic derivatives are reported here for the first time. The substitution and position of the substituents on ring C significantly affect the antiproliferative activity of the compounds.From the methanolic extract of


ABCB1 was an important determinant of YM155 resistance. The activity of the ABCB1 inhibitor zosuquidar ranged from being similar to that of the structurally different ABCB1 inhibitor verapamil to being 65-fold higher. ABCB1 sequence variations may be responsible for this, suggesting that the design of variant-specific ABCB1 inhibitors may be possible. Further, we showed that ABCC1 confers YM155 resistance. Previously, p53 depletion had resulted in decreased YM155 sensitivity. However, TP53-mutant cells were not generally less sensitive to YM155 than TP53 wild-type cells in this study. Finally, YM155 cross-resistance profiles differed between cells adapted to drugs as similar as cisplatin and carboplatin. In conclusion, the large cell line panel was necessary to reveal an unanticipated complexity of the YM155 response in neuroblastoma cell lines with acquired drug resistance. Novel findings include that ABCC1 mediates YM155 resistance and that YM155 cross-resistance profiles differ between cell lines adapted to drugs as similar as cisplatin and carboplatin.The survivin suppressant YM155 is a drug candidate for neuroblastoma. Here, we tested YM155 in 101 neuroblastoma cell lines . Seventy seven (77) cell lines displayed YM155 IC The inhibitor of apoptosis protein (IAP) survivin has multifaceted roles in cellular signalling. It is absent from most somatic cells but highly abundant in cancer cells and mediates cancer cell survival and proliferation. Elevated survivin levels have been associated with more aggressive and advanced cancer disease and lower survival rates. Hence, survivin is a potential drug target in cancer entities including neuroblastoma ,7,8,9,10YM155 (sepantronium bromide) was introduced as a suppressor of survivin expression that displayed anti-cancer activity in pre-clinical models of different cancer entities including neuroblastoma ,8,10,13.Here, we investigated the effects of YM155 in a larger panel of 101 neuroblastoma cell lines that focused on acquired drug resistance containing 82 drug-adapted neuroblastoma cell lines, which reflected resistance to 15 anti-cancer drugs.50 values in the parental cell lines ranged from 0.49 nM (UKF-NB-3) to 248 nM (LAN-6)  . All partrations  that werIn concert with previous studies ,10, high50 ranged from 0.49 nM (UKF-NB-3) to 77 nM (NB-S-124) in MYCN-amplified cells and from 3.55 nM (SK-N-AS) to 248 nM (LAN-6) in not MYCN-amplified cells  cells express a tetracycline-controllable MYCN transgene. They display low MYCN levels in the presence of tetracycline antibiotics and high MYCN levels in the absence of tetracycline antibiotics [MYCN (TET21N) cells displayed similar YM155 IC50 values in the absence or presence of doxycycline  that harbours a G245C loss-of-function TP53 mutation [TP53 mutant sub-lines of two clonal p53 wild-type UKF-NB-3 sub-lines  and the TP53 wild-type neuroblastoma cell line UKF-NB-6  being the highest fold change   .50 resistant sub-line/ YM155 IC50 respective parental cell line > 2). Twelve of these cell lines showed a fold change YM155 IC50 resistant sub-line/ YM155 IC50 respective parental cell line of > 2 and < 10, 18 (26%) cell lines a fold change > 10 and < 100, and 11 (16%) cell lines a fold change >100. 20 (29%) resistant cell lines were similarly sensitive to YM155 like the respective parental cell lines (fold change < 2 and > 0.5). Seven (10%) resistant cell lines were more sensitive to YM155 than the respective parental cell lines (fold change < 0.5) (Forty one (41) of the resistant cell lines (60%) displayed cross-resistance to YM155 (YM155 ICe < 0.5) . There we < 0.5) .50 values in the drug-resistant cell lines ranged from 0.40 nM (UKF-NB-3rGEMCI10) to 21,549 nM (IMR-5rDOCE20) (The YM155 ICrDOCE20) . Drug clrDOCE20) .50 values <56 nM. All of the eight parental, four alkylating agent-resistant, five topoisomerase I inhibitor-resistant, and six nucleoside analogue (gemcitabine)-resistant cell lines displayed YM155 IC50 values < 56 nM. Only 15 out of 20 (75%) platinum drug-adapted, nine out of 14 (64%) topoisomerase II inhibitor-adapted, five out of 12 (42%) vinca alkaloid-adapted, and two out of six (33%) taxane (docetaxel)-adapted cell lines exhibited YM155 IC50 values < 56 nM - and platinum drug -adapted cell lines, we had sufficient data to perform drug-specific analyses. For six cell lines, we had doxorubicin- and etoposide-resistant sub-lines . To detesistance .In addition, the project cell line panel included carboplatin-, cisplatin-, and oxaliplatin-resistant sub-lines of five neuroblastoma cell lines . CisplatABCB1) cells after 24h of incubation, reduced cellular survivin levels in UKF-NB-3ABCB1 cells in the presence of the ABCB1 inhibitors verapamil and zosuquidar  were reduced to levels below 56 nM by verapamil and zosuquidar. Notably, the fold sensitisation by ABCB1 inhibitors was low  in NGPrETO400, although the YM155 IC50s were reduced below 56 nM. This suggests that ABCB1 expression is not a dominant YM155 resistance mechanism in NGPrETO400 cells  increased the YM155 IC50 values by mechanisms that appear to be unrelated to ABCB1  for additional confirmatory experiments. The YM155 IC50s in these four cell lines in the absence and presence of zosuquidar determined MTT  were very similar to those determined by CellTiterGlo   . Both drduction) . Moreovesuquidar .rVCR10 cells were sensitised by verapamil but not by zosuquidar to YM155  expression but do not express ABCB1 [rVCR10 cells but not in the parental NLF cell line that does not express ABCC1 . In 24 of these drug-adapted cell lines, the YM155 IC50 remained > 2-fold higher in the presence of verapamil compared to the YM155 IC50 of the respective parental cell line in the presence of verapamil  but not by zosuquidar (fold change 1.4). UKF-NB-6rCARBO2000 cells were sensitised by zosuquidar to YM155 by > 2-fold (fold change 2.3) but not by verapamil (fold change 1.3)  . The rea50 values in a number of cell lines, we compared both drugs in a wider panel of 74 cell lines . This included 33 cell lines that were neither sensitised to YM155 by verapamil nor by zosuquidar and 12 cell lines that were sensitised by verapamil and zosuquidar by more than 2-fold and in a similar fashion (Since we had detected differences between the effects of the ABCB1 inhibitors verapamil and zosuquidar on the YM155 ICll lines . In 45 c fashion . 50 in the presence of verapamil/YM155 IC50 in the presence of zosuquidar < 0.5). Only one of these cell lines (UKF-NB-6rETO200) was sensitised by both verapamil and zosuquidar to YM155 by more than 2-fold. The remaining cell lines were either only sensitised to YM155 by more than 2-fold by verapamil and not by zosuquidar, or zosuquidar increased the YM155 IC50 by mechanisms that do not appear to be associated with effects on ABCB1 (Thirteen cell lines were more strongly sensitised to YM155 by verapamil than by zosuquidar (YM155 ICon ABCB1 . 50 in the presence of verapamil/ YM155 IC50 in the presence of zosuquidar > 2.0). NGPrGEMCI20 cells were only sensitised by zosuquidar to YM155 but not by verapamil. The other 15 of these cell lines were sensitised by more than 2-fold to YM155 by both compounds with zosuquidar exerting more pronounced effects. The relative differences between these two drugs on the YM155 sensitivity of these cell lines ranged from 2.2  to 65.2 (UKF-NB-3rDOCE10) (Sixteen cell lines were more strongly sensitised to YM155 by zosuquidar than by verapamil (YM155 ICrDOCE10) .50 values in the range of clinically achievable plasma concentrations up to 56 nM [YM155 has been suggested as therapeutic option for the treatment of neuroblastoma including therapy-refractory disease ,7,10. Into 56 nM ,22,23. Although MYCN amplification is a major indicator of poor prognosis in neuroblastoma ,12, the rVCR10 cells  is generally accepted as prognostic factor in neuroblastoma . Interesot ABCB1 ) were seot ABCB1 ) but notot ABCB1 ). MoreovrDOCE10) more pronounced effects than verapamil. Hence, the relative effects of verapamil and zosuquidar on ABCB1-mediated YM155 transport differ in individual cell lines from similar efficacy (in 12 cell lines) to 65-fold increased zosuquidar efficacy over verapamil. Since zosuquidar is regarded as highly specific ABCB1 inhibitor [Verapamil and zosuquidar further differed in their effects on neuroblastoma cell sensitivity to YM155. Zosuquidar protected some ABCB1-negative cell lines from YM155-induced toxicity by unknown mechanisms. More strikingly, we identified cell line-specific differences in the relative potencies of verapamil and zosuquidar on YM155 activity. Twelve ABCB1-expressing cell lines were similarly sensitised to YM155 by verapamil and zosuquidar. Few cell lines were stronger sensitised by verapamil than by zosuquidar. These data are difficult to interpret with regard to ABCB1 because verapamil interacts with a broader range of transporters than the specific ABCB1 inhibitor zosuquidar ,31,47. Hnhibitor ,49, whicnhibitor .TP53-mutant neuroblastoma cell lines displayed a >2-fold increased YM155 IC50 relative to the respective parental cell line. All nutlin-3-resistant cell lines remained sensitive to low nanomolar YM155 concentrations with IC50 values ranging from 0.40 to 1.50 nM. This shows that the role of p53 depends on the individual cellular context and that the TP53 status on its own does not indicate neuroblastoma cell sensitivity to YM155. These data are in accordance with initial findings that reported the activity of YM155 to be unrelated to the TP53 status [Although RNAi-mediated p53 depletion decreases neuroblastoma cell sensitivity to YM155 as previously demonstrated , only 4 3 status ,51.The adaptation of neuroblastoma cell lines to drugs from different classes was associated with varying YM155 sensitivity profiles. Neuroblastoma cell adaptation to topoisomerase I inhibitors, the nucleoside analogue gemcitabine, or alkylating agents was not associated with a pronounced YM155 resistance phenotype. In contrast, neuroblastoma cell lines adapted to the taxane docetaxel or vinca alkaloids exhibited distinct cross-resistance to YM155. Topoisomerase II inhibitors and platinum drugs displayed intermediate potential to induce YM155 resistance.For the topoisomerase II inhibitor- and platinum drug-adapted neuroblastoma cell lines, we could perform drug-specific sub-analyses. Among the topoisomerase II inhibitors, doxorubicin-resistant cells were more likely to be YM155-resistant than etoposide-resistant cells. This result may not be too surprising. Although doxorubicin and etoposide both inhibit the religation of the topoisomerase II cleavage complexes, they are structurally different compounds that differ in their exact interaction with this target and may exert additional varying effects ,53,54. PYM155 (sepantronium bromide) and zosuquidar were purchased from Selleck Chemicals via BIOZOL Diagnostica GmbH , and verapamil was from Sigma-Aldrich .The MYCN-amplified neuroblastoma cell lines UKF-NB-2, UKF-NB-3, UKF- and UKF-NB-6 were established from stage 4 neuroblastoma patients ,57,58. Uwww.kent.ac.uk/stms/cmp/RCCL/RCCLabout.html).Resistant neuroblastoma cell lines were established by continuous exposure to increasing drug concentrations as previously described  and deriAll cells were propagated in Iscove’s modified Dulbecco’s medium (IMDM) supplemented with 10% foetal calf serum (FCS), 100IU/ml penicillin and 100µg/ml streptomycin at 37 °C. Drug-adapted cell lines were continuously cultivated in the presence of the respective adaptation drugs but were released from the respective adaptation drugs before they were used for experiments. Cells were routinely tested for mycoplasma contamination and authenticated by short tandem repeat profiling.www.lentigo-vectors.de). SH-EP-MYCN (TET21N) were cultured and induced as previously described [p53-depleted and ABCB1-transduced neuroblastoma cells were established as described previously  using thescribed .Cell viability was determined by the 3--2,5-diphenyltetrazolium bromide (MTT) dye reduction assay as described previously  or by CeCells were lysed using Triton-X-100 sample buffer, and proteins were separated by SDS-PAGE. Detection occurred by using specific antibodies against β-actin , ABCB1, poly (ADP-ribose) polymerase (PARP) , MYCN , and survivin . Protein bands were visualised by laser-induced fluorescence using infrared scanner for protein quantification .6 cells were re-suspended in 200 µl resuspension buffer R including 2.5 µM siRNA. The electroporation was performed using two 20 millisecond pulses of 1400 V. Subsequently, the cells were transferred into cell culture plates or flasks, containing pre-warmed cell culture medium.Transient depletion of ABCB1 was achieved using synthetic siRNA oligonucleotides (ON-TARGETplus SMARTpool) from Dharmacon . Non-targeting siRNA (ON-TARGETplus SMARTpool) was used as negative control. Cells were transfected by electroporation using the NEON Transfection System  according to the manufacturer protocol. Cells were grown to 60–80 % confluence, trypsinised, and 1.2 x 10TP53 gene sequencing on cDNAs was performed using the following four pairs of primers: TP53 Ex2-3-f GTGACACGCTTCCCTGGAT and TP53 Ex2-3-r TCATCTGGACCTGGGTCTTC; TP53 Ex4-5-f CCCTTCCCAGAAAACCTACC and TP53 Ex4-5-r CTCCGTCATGTGCTGTGACT; TP53 EX6-7f GTGCAGCTGTGGGTTGATT and TP53 Ex6-7r GGTGGTACAGTCAGAGCCAAC; Tp53 Ex8-9-f CCTCACCATCATCACACTGG and TP53 Ex8-9-r GTCTGGTCCTGAAGGGTGAA. In addition, all cell lines were examined for TP53 mutations by sequence analysis of genomic DNA as described previously [eviously . PCR waseviously . Each amResults are expressed as mean ± S.D. of at least three experiments. Comparisons between two groups were performed using Student’s t-test. Three or more groups were compared by ANOVA followed by the Student-Newman-Keuls test. P values lower than 0.05 were considered to be significant. The investigation of YM155 in 101 neuroblastoma cell lines revealed complex sensitivity profiles and that larger panels of model systems are needed to uncover this complexity. Our findings confirm 1) that YM155 is a drug candidate for neuroblastoma including therapy-refractory disease with the majority of cell lines being sensitive to clinically achievable YM155 concentrations and 2) that ABCB1 is an important determinant of YM155 sensitivity. Notably, there were substantial differences in the relative efficacy of the ABCB1 inhibitors verapamil and zosuquidar in sensitising ABCB1-expressing cells to YM155. These differences may be caused by sequence variations in the transporters in the different cell lines, which suggests that it may be possible to design variant-specific ABCB1 inhibitors. Moreover, we present novel findings indicating 1) that YM155 resistance is also mediated by ABCC1 (an ABC transporter of prognostic relevance in neuroblastoma ), 2) tha


CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets. The integration of independent pan-cancer CRISPR-Cas9 datasets allows better representation of genomic heterogeneity across different cancer types. Here, the authors propose a strategy for the integration of two large CRISPR-Cas9 screens and report increased coverage of molecular diversity and genetic dependencies. In order to identify and prioritize new potential therapeutic targets for precision cancer therapy, analyses of cancer vulnerabilities are increasingly performed at a genome-wide scale and across large panels of in vitro cancer models11. This has been facilitated by recent advances in genome editing technologies allowing unprecedented precision and scale via CRISPR-Cas9 screens. Of particular note are two large pan-cancer CRISPR-Cas9 screens that have been independently performed by the Broad and Sanger institutes12. The two institutes have also joined forces with the aim of assembling a joint comprehensive map of all the intracellular genetic dependencies and vulnerabilities of cancer: the Cancer Dependency Map (DepMap)14.Cancer is a complex disease that can arise from multiple different genetic alterations. The alternative mechanisms by which cancer can evolve result in considerable heterogeneity between patients, with the vast majority of them not benefiting from approved targeted therapies3. Consequently, the integration of these two datasets will be key for the DepMap and other projects aiming at systematically probing cancer dependencies. These integrated datasets will provide a more comprehensive representation of heterogeneous cancer types and form the basis for the development of effective new therapies with associated biomarkers for patient stratification15. Further, designing robust standards and computational protocols for the integration of these types of datasets will mean that future releases of data from CRISPR-Cas9 screens can be integrated and analyzed together, paving the way to even larger cancer dependency resources.The two generated datasets collectively contain data from over 1000 screens of more than 900 cell lines. However, it has been estimated that the analysis of thousands of cancer models will be required to detect cancer dependencies across all cancer types16. The reproducibility of these CRISPR screens holds despite extensive differences in the experimental pipelines underlying the two datasets, including distinct CRISPR-Cas9 sgRNA libraries. Here, we investigate the integrability of the full Broad/Sanger gene-dependency datasets, yielding the most comprehensive cancer dependency resource to date, encompassing dependency profiles of 17,486 genes across 908 different cell lines that span 26 tissues and 42 different cancer types. We compare different state-of-the-art data-processing methods to account for heterogeneous single-guide RNA (sgRNA) on-target efficiency, and to correct for gene independent responses to CRISPR-Cas9 targeting18, evaluating their performance on common use cases for CRISPR-Cas9 screens nes Fig.  and for nes Fig. . These p16, without biasing the correction toward specific cell line lineages.Between the two datasets, there was an overlap of 168 cell lines screened by both institutes, encompassing 16 different tissue types  through joint analysis of multiple screens; and CERES, a method that simultaneously corrects for CNA effects and accounts for differences in guide efficacy12, also analyzing screens jointly.Known biases in CRISPR screens arise due to nonspecific cutting toxicity that increases with copy number amplifications (CNAs)16.Technical differences in screening protocols, reagents and experimental settings can cause batch effects between datasets. These batch effects can arise from factors that vary within institute screens  as well as between institutes (such as differences in assay lengths and employed sgRNA libraries). When focusing on the set of cell lines screened at both institutes, a principal component analysis (PCA) of the cell line dependency profiles across genes (DPGs) highlighted a clear batch effect determined by the origin of the screen, irrespective of the pre-processing method, consistent with previous results Fig. 16.Fig. 325 (Methods). Following ComBat correction, the combined datasets on the overlapping cell lines showed reduced yet persistent residual batch effects clearly visible along the two first principal components , showed enrichment for metabolic processes  in the first principal component. The enrichment of metabolic processes is consistent with differences identified across these datasets due to different media conditions employed in the underlying experimental pipelines27. The second principal component contained significant enrichments for protein complex organization and assembly   from the other study following batch correction. To quantify the agreement, we calculated for each DPG its similarity to all other screen DPGs using a weighted Pearson’s (wPearson) correlation (Methods). We then calculated the proximity of a cell line to its counterpart compared to all other cell lines using the wPearson as a metric  Fig. .k = 1) followed by CRISPRcleanR with 272 cell lines (81%) and CCR-JACKS with 215 (64%). The recall of cell line identity was high for each integration pipeline with normalized Area under the curve (nAUC) values of 0.98 for CCR-JACKS and 0.99 for CRISPRcleanR and CERES when considering the best performing ComBat+QN + PC1-2 batch correction method.The best performances were obtained when removing either the first or the first two principal components following ComBat and quantile normalization, i.e., ComBat+QN + PC1 or ComBat + QN + PC1-2. Across pre-processing methods, CERES performed best with 302 (90%) of the cell lines being closest to their counterpart from the other study  essential and non-essential genes (ii) lineage subtypes (iii) biomarkers of selective dependencies, and (iv) functional relationships.11 across all integrated datasets. As a measure of separation we used the null-normalized mean difference (NNMD)28, defined as the difference between the mean DS of the common essential genes and non-essential genes divided by the standard deviation of the DSs of the non-essential genes.A cell line DPG with a large separation of dependency scores (DS) of common essential and non-essential genes should yield lower misclassification rates when identifying dependencies specific to that cell line. For each cell line we measured the separation of dependency scores (DS) between known common essential and non-essential genes23. Consistently, CERES  showed better NNMD values than CRISPRcleanR  N = 908 largest p-value 1.86e-114) and CCR-JACKS ), and similarly CCR-JACKS had better NNMD values than CRISPRcleanR  , especially for DPGs derived from lower quality experiments, or reporting weaker depletion phenotypes08) Fig. . Compari2 contingency table p-value 1.87 × 10−11, N = 1.43 × 107) and CCR-JACKS outperformed CRISPRCleanR . Comparing the correction methods, the differences between ComBat and ComBat+QN and between ComBat+QN + PC1 and ComBat+QN + PC1-2 were generally not significant across pre-processing methods, while the difference between either ComBat or Combat+QN and either ComBat+QN + PC1 or ComBat+QN + PC1-2 were generally significant . As a final test of control separation, we used the unexpressed genes as an empirical null distribution for each DPG to estimate p-values for all DS and thus false-discovery rates (FDRs) within each DPG. We calculated the recall of a reference set of common essential genes11 at 10% FDR  p-value 1.96 × 10−3 (N = 830).Next, we evaluated the gene-dependency false-positive rates across all integrated datasets. For each cell line DPG, we defined a set of putative negative controls composed of genes not expressed at the basal level in that cell line (Methods). False positives were calculated as the sum of negative controls identified as significant dependencies (in the top 15% most depleted genes) normalized by their total number across the DPG. There was little difference in false-positive rates across the four different batch correction pipelines, with a slight improvement when two principal components were removed Fig. . CERES oFDR Fig. . Again, k-means clustering of each of the 12 datasets, with k equal to the number of tissue lineages screened in at least one study. We then calculated the adjusted mutual information  between each DPG clustering and the partition of the cell lines induced by their lineage labels. We observed higher than chance AMI between the obtained k clusters and the tissue lineages of the cell line DPGs, regardless of the starting batch-corrected dataset . For the lung cancer cell lines, the percentage of cell lines whose closest neighbor was from the same subtype (k = 1) was greatest for CERES (64–65% across batch correction methods) followed by CRISPRcleanR (61–64%) and CCR-JACKS (50–57%), with slight improvement with the removal of one or two principal components , across 17 different tissue types. The CFEs encompass mutations in cancer driver genes, amplifications/deletions of chromosomal segments recurrently altered in cancer, hypermethylated gene promoters and microsatellite instability status. For each CFE and tissue type, we performed a Student’s t-test for each selective gene dependency  contrasting two groups of cell lines based on the status of CFE under consideration (present/absent), for a total number of 2,142,162 biomarker/dependency pairs tested.Interesting potential novel therapeutic targets are genes that show a pattern of selective dependency, i.e., exerting a strong reduction of viability upon CRISPR-Cas9 targeting in a subset of cell lines. Furthermore, these selective dependencies are often associated with molecular features that may explain their dependency profiles (biomarkers). We investigated each of the integrated datasets’ ability to reveal tissue-specific biomarkers of dependencies. As potential biomarkers we used a set of 676 clinically relevant cancer functional events  or CERES  while little significant difference was found between CCR-JACKS and CERES  , we considered the oncogenes as positive controls in cell lines where they had indicated oncogenic or likely oncogenic gain of function alterations, and negative controls in all others. For each oncogene, we measured the NNMD between positive and negative cell lines . For each pair of genes known to have a functional relationship, we selected a random pair of genes with similar mean dependency scores across cell lines to serve as null examples. We calculated the false-discovery rate for the known pairs using the absolute Pearson correlation of their dependency profiles versus those of the null examples. Recovery of known relationships was unsurprisingly low, since many genes with known functional relationships do not exhibit selective viability phenotypes. ComBat + QN + PC1 or PC1-2 recovered the greatest number of expected gene-dependency relations at 10% FDR , as well as increased diversity of genetic dependencies and biomarker associations (using CRISPRcleanR).n cell lines  for a fixed number of cell lines n  of the integrated dataset, as well as in the individual Broad and Sanger datasets. The integrated dataset identified more dependencies, indicating greater coverage of molecular features and dependencies than in the individual datasets 2. The majority of genes called common essentials according to one of ADaM or 90th percentile methods was also identified by the other . The integrated set of common essentials (Tier 1 and 2) showed greater recall of known essential genes compared to Behan and Hart, and increased recall over the individual datasets for 5 out of the 6 gene sets  and mitochondrial gene expression , indicative of essential cellular processes. Similar results were observed for the 1,159 genes in the integrated set of common essentials but neither of the existing datasets (Behan and Hart) . We then calculated the proportion of negative controls in each set of common essentials genes. The best performance was found with the Hart gene set (0%) followed by the integrated dataset (0.33%) Fig. . As the MDM2 in TP53 mutant lung cell lines for the Sanger dataset, and increased dependency on STAG1 in STAG2 mutated central nervous system cancer cell lines for the Broad dataset  datasets. Performing systematic biomarker analysis using CFEs on cell lines from individual tissue lineages unveiled 52 additional significant associations in the integrated dataset  with respect to those using the Sanger dataset alone, and 68 with respect to the Broad dataset   with that obtained when estimating and correcting batch effects with using >90 cell lines.The 168 overlapping cell lines were from 16 different lineages. To investigate the impact of lineage composition of the cell lines on the batch correction we also used a single lineage to estimate the batch effects. In the overlapping cell lines the lung lineage had the most cell lines . We subsampled the lung cell lines to include 8, 17, or 25 cell lines  yields a final dataset better able to identify prior known essential and non-essential genes and clustering of cell lines by lineage. In contrast, CRISPRcleanR (a per sample method) was better able to detect associations between selective dependencies and potential biomarkers, and had better recall of known oncogenic addictions. Therefore, results from both processing methods provide the best overall data-driven functional Cancer Dependency Map.41 we would expect the coverage and accuracy of the integrated datasets to also improve.The data integration strategies and sample size guidelines outlined here can be used with future and additional CRISPR-Cas9 datasets to increase coverage of cancer dependencies. This will be important for oncological functional genomics, for the identification of novel cancer therapeutic targets, and for the definition of a global cancer dependency map. Further, as library design improveshttps://score.depmap.sanger.ac.uk/). The CRISPRcleanR corrected counts were used as input into JACKS, for the CCR-JACKS processing method. Raw counts and the copy number profiles for the Sanger dataset downloaded were processed with CERES20. The Broad data processed with CERES  version 20Q2 scores were downloaded from the Broad DepMap portal20. The raw counts for Broad data 20Q2 were processed with CRISPRcleanR (v0.5) and the CRISPRcleanR corrected counts processed with JACKS. Gene names were matched across the Broad and Sanger datasets by updating both to the current version of HUGO gene symbols from the HGNC website. Missing entries were mean imputed for the principal component removal and then re-assigned as NA in the final matrix. Cell lines processed by both CERES and CRISPRcleanR were used for analysis. Tissue annotations for each cell line were obtained from the Cell Model Passports (https://cellmodelpassports.sanger.ac.uk/)42.Sanger data processed with CRISPRcleanR were obtained from the Score website  to the average gene fold-change across cell line DPGs. Each DPG was then adjusted to remove the difference between the fitted spline and the diagonal. The overlapping cell lines were then batch-corrected using three different methods. A standard least squares model was fitted in R. The ComBat correction was performed using the sva package (v3.34.0) in R44.The dependency profiles across genes (DPGs) for overlapping cell lines from each institute were first quantile normalized using the preprocessCore package (v1.48.0) in Rk in the resulting rank we then defined a k-neighborhood of the query DPG composed of all the other DPGs whose rank position was ≤ k. Finally, we determined the number of cell line DPGs that had the DPG derived from screening the same cell line in the other dataset (a matching DPG) in its k-neighborhood. The final rank for each cell line was defined based on the minimum rank obtained for each cell line when considering the DPG for that cell line from the Broad data compared to all DPGs, and similarly the DPG for the cell line in the Sanger dataset compared to all DPGs.Cell lines’ rank neighborhoods were based on a weighted Pearson correlation metric. The weights were defined as the absolute mean (over the Broad and Sanger datasets) of a gene-dependency signal skewness across the 168 overlapping cell lines for the Broad and Sanger datasets. Using skewness upweights genes with a variable and sufficiently selective fitness profile while down weighting those that show weak/no-signal or unselective dependencies. Then for each query DPG we ranked all the others based on how similar they were to the fixed one in decreasing order, according to the wPearson scores. For each position https://www.gsea-msigdb.org/) and were tested against the Gene ontology Biological Processes, Hallmark and Canonical Pathway databases. The top 10 significantly enriched  gene sets were downloaded from the website.The first two principal components (PCs) were extracted from ComBat corrected CRISPRcleanR data using the prcomp function in R from the stats package (v3.6.0). The top 500 genes  were selected for enrichment analysis. The gene lists were used as input into the GSEA website  for each batch based on the analysis of 168 cell lines common to both initial datasets were computed. The ComBat correction using these estimates was then applied to all screens, i.e., the union of the two initial datasets. Particularly, each individual cell line DPG was shifted and scaled gene-wise using the batch correction vectors outputted by ComBat.Further adjustments were then applied to all screens including quantile normalization, and the removal of either the 1st principal component of the joint datasets or the first two. Finally, DPGs for overlapping cell lines passing a similarity threshold (detailed below) were averaged. Across the three pre-processing methods the number of cell lines that matched their counterparts exactly after ComBat correction ranged from 51% to 86% Fig. , suggestk-means clusterings cell lines using the high variance genes for each pre-processing and batch-correction method. For each clustering, we calculated the adjusted mutual information between the obtained clusters and the cell line tissue labels as specified in the annotation provided by the sample_info file of the DepMap_public_20Q2 dataset20 using sklearn’s (v0.23.1) python function adjusted_mutual_info_score (https://scikit-learn.org/stable/).We selected 500 genes with the highest variance in the CERES ComBat integrated dataset and performed repeated 100 32, protein-protein interactions identified by Li et al.33, and CORUM complex comemberships34. For a given dataset, for each pair of related genes, we calculated a Pearson correlation coefficient between those genes’ dependency scores across cell lines. We then binned each gene that appeared in the list of known gene relationships according to its mean gene score using 20 equally spaced bins. For pairs of genes in the related genes pairs, we chose one as the query gene and replaced its related partner with another randomly selected gene of similar gene mean, i.e., belonging to the same bin, excluding genes known to be related to the query gene. We calculated Pearson’s correlation coefficients between these randomly selected gene pairs to generate a null distribution, from which we calculated empirical p-values and Benjamini–Hochberg FDRs for known related gene pairs. Ensuring that the pairs of genes used in the null distribution have similar distributions of mean gene effect as the pairs of known related genes is necessary because variable screen quality can produce a high but artefactual correlation between any pair of common essential genes, and CORUM is highly biased towards common essentials. This is discussed further in the comparisons of batch corrections in Dempster et al.28.We assembled a set of functionally related gene pairs using paralogs identified by EnsemblCompara2(Transcripts per million +1) of its DepMap expression was <0.0145. Any score of an unexpressed gene in a cell line was called a false positive if it fell in the bottom 15% of gene scores for that cell line.We defined a gene as unexpressed in a cell line if the log46 (v7.3-51.4). For the skew t-distribution the st.mple function from the sn package (v1.6-3) was used to calculate the likelihood. If the fitting procedure failed different degrees of freedom were used iteratively until a solution was found. The degrees of freedom used in order were 2, 5, 10, 25, 50, and 100.NormLRT and likelihood of normal distribution was calculated in R using the MASS packaget-test  to assess the differential essentiality of each gene across a dichotomy of cell lines defined by the status (present/absent) of each CFE in turn. We tested genes whose NormLRT values were greater than 200 in any integrated dataset. From these tests, we obtained p-values against the null hypothesis that the two compared populations had an equal mean, with the alternative hypothesis indicating an association between the tested CFE/gene-dependency pair. p-values were corrected for multiple hypothesis testing using Benjamini–Hochberg (method “fdr” using the p.adjust function in R (v3.6.0)). We also estimated the effect size of each tested association using Cohen’s Delta (ΔFC), i.e., the difference in population means divided by their pooled standard deviations.We performed a systematic two-sided unpaired Student’s 31 . The table was filtered first for genes that were (likely) oncogenic and alterations that were (likely) gain-of-function or switch-of-function. For each alteration, the DepMap public 20Q220 mutation and fusion calls were used to identify which cell lines had the alteration. These cell lines were treated as positive controls for the gene in question, with all other cell lines treated as negative controls. Only oncogenes with at least one positive cell line were retained. For each integrated dataset, we calculated the ROC AUC between all positive oncogene-cell line pairs and negative pairs. Then, for each oncogene with at least two positive cell lines, we calculated the NNMD between its positive and negative cell lines.A table of all annotated oncogene variants was downloaded from OncoKB16 finds for each gene the cell line on the boundary of its 90th percentile least dependent cell lines. It then calculates the rank of that gene in that cell line, by sorting all the genes based on their dependency score in increasing order. A mixture of two normal distributions is then fitted to the rank positions of all genes. Those genes with ranks below the crossover point of these two distributions are labeled as common essentials.The 90th percentile method2. The ADaM method determines the number of cell lines dependent on a gene required to call that gene a common essential. The number of cell lines is calculated by maximizing the tradeoff between true-positive rate  and the deviance from the null expected rate . Common essential genes were identified for each tissue separately (according to the cell line annotation from the Cell Model Passports42) and were then used as input into ADaM to determine pan-cancer common essential genes.Binary depletion matrices for the integrated datasets were calculated as outlined in the next section and used with the ADaM method (ADaM2 R package v0.1.0) as described in Behan et al.11 .Binary depletion calls were computed by considering each cell line DPG as a rank-based classifier of essential/non-essential genesr guaranteeing a false-discovery rate (FDR) < 5%, when the predicted essential genes are those with a rank position ≤ r. This allowed us to assign to each gene in each cell line, in each of the two datasets, a binary dependency score.The fitness effect threshold was then fixed as that corresponding to the largest rank position E) and non-essential (N) genes, respectively, derived from Hart et al.11.To identify significantly depleted genes for a given cell line at a 5% FDR, we ranked all the genes in the cell line DPG in increasing order based on their depletion log fold-changes. We used the ranked list to calculate the precision curve using a set of prior known essential  = {s ∈ E ∪ N: r(s) ≤ k}, with r(s) indicating the rank position of s, and the corresponding positive predictive value (or precision) PPV(k) as:To estimate the rank position corresponding to the 5% FDR threshold we calculated for each rank position k* with PPV(k*) ≥ 0.95 . The 5% FDR logFCs threshold F* was defined as the logFCs of the genes such that r(s) = k*. We called all genes with a logFC < F* as significantly depleted at 5% FDR.We then determined the largest rank position Binary dependency matrices were defined as gene by cell lines matrices with non-null entries corresponding to significant dependency genes at 5% FDR, for each cell line, i.e., column.2.To generate sets of prior known common essential genes we downloaded gene sets from MsigDB (v7.2) using the R package qusage (v2.20.0). The gene sets used were from KEGG were KEGG_SPLICEOSOME, KEGG_RIBOSOME, KEGG_PROTEASOME, KEGG_RNA_POLYMERASE and KEGG_DNA_REPLICATION. For the histone gene set we combined two Reactome gene sets REACTOME_HATS_ACETYLATE_HISTONES and REACTOME_HDACS_DEACETYLATE_HISTONES as well as the curated histones gene set from2(Transcripts per million +1) of its CCLE expression20 and the 90th percentile method  performing the 90th percentile method was −1*log2(Transcripts per million+1) to ensure correct ranking). A gene defined as constitutively unexpressed was, therefore, one that was still lowly expressed in its highly ranked (90th percentile) most expressed cell line.We compiled a set of negative controls for the common essential genes as those genes that were not expressed across all cell lines. We defined a gene as unexpressed across the panel of cell lines using the logWe down sampled 50 times the overlapping cell lines at different levels between 5 and 90%. Random samples were generated using probabilities of selecting a cell line based on the relative proportions of each cell line lineage in the overlapping dataset. Using the down sampled set of overlapping cell lines ComBat was used to calculate the batch adjustment vectors. The batch adjustment vectors were then applied to all 1074 cell lines. The correlation of a cell lines fold changes batch-corrected using the down sampled datasets and the full 168 overlapping cell lines was calculated and compared to the correlation with no batch correction.To evaluate the batch correction, we also used the average silhouette width as a measure of clustering. We calculated the average silhouette width for each batch-corrected dataset (using samples of the overlapping cell lines) using the institute of origin as the cluster label. The average silhouette width is 1 for perfect clustering (or complete separation of cell lines by the institute of origin) with 0 indicating random performance of the clusters.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Two reference compounds, phenol and p-cresol, were used for assessing the cytotoxicity assays using two colon epithelial cell lines (HT-29 and HCT 116) and different assay conditions (FW dilution and incubation time). For the two cell lines and all assay conditions, the means of percentage cell viability were higher (lower cytotoxicity) for samples collected after the red wine intervention than for those collected before, although significant (p < 0.05) differences were only found in certain assay conditions for both cell lines. Significant positive correlations between the percentage cell viability and the contents of some faecal metabolites (short-chain fatty acids (SCFA) and phenolic acids (PA)) were found for the more resistant cell line (HCT 116), suggesting that the reduction in FW cytotoxicity observed after moderate red wine consumption was related to the production of microbial-derived metabolites such as SCFA and PA, whose faecal contents have been shown to increase after wine consumption. FW cytotoxicity can be deemed as a holistic biomarker that involves diet, gut microbiota and host.There are some studies that suggest that moderate consumption of wine, as part of a healthy and balanced diet, has a favourable effect on intestinal health. This study evaluates the effect of moderate wine consumption on faecal water (FW) cytotoxicity as a parameter of gut health. To that end, faecal samples before and after a red wine intervention study  in healthy volunteers ( Wine is considered one of the foods with the highest content of polyphenols, to which the health effects associated with moderate consumption of wine are mainly attributed. Although the greatest accumulation of polyphenols and phenolic metabolites  takes place at the intestinal level, studies on the effects of wine on human digestive function and health are still scarce .Ruminococcaceae and “high cytotoxic” with a major abundance of Proteobacteria, Bacteroides and Lachnospira in faecal samples from an intervention study with casein and soy protein. In mice, the increase in FW cytotoxicity observed after dietary supplementation with haem (the iron-porphyrin pigment of red meat) was associated with an increase in Bacteroidetes and a decrease in Firmicutes in colonic contents [The colon lumen harbours metabolites resulting from food fermentation, the inhabitant microbiota and colonic cells. Gut microbiota enable the metabolism of both dietary and endogenous substrates to be used by the host, but microbial-derived metabolites can also modulate the immune system through impacting the physiology and gene expression of host cells . Among dcontents . On the contents ,12. Intrcontents . Howevercontents .n = 3) were collected and assayed for FW cytotoxicity towards to two colon epithelial cell lines (HT-29 and HCT 116). Then, and in order to establish a possible relationship between FW cytotoxicity and faecal metabolites, Pearson’s correlations between the percentage cell viability and the concentration of two target classes of microbial-derived metabolites (short-chain fatty acids (SCFA) and phenolic acids (PA)) were carried out.Therefore, the aim of this work was to assess the changes in the biomarker “FW cytotoxicity” after a wine nutritional intervention in healthy volunteers. To that end, and after an assay assessment using phenol and p-cresol as reference compounds, faecal samples before and after a red wine intervention study  in healthy volunteers (n = 8) and in a parallel control group  and Alfa Aesar , respectively. Solutions of phenol  and p-cresol  compounds were prepared in cell culture medium.Phenol and A young red wine  was selected for the intervention study. The selection was based on its high phenolic compounds and good organoleptic properties. The ethanol content in the wine was 12%. The total phenolic content reached 1758 mg of gallic acid equivalent/L, comprising total anthocyanins  and total catechins (1612 mg of (+)-catechin/L). The concentration of individual phenolic compounds in this wine is reported elsewhere .initial) and at the end of the intervention period   . Faeces w/v) in Dulbecco’s phosphate-buffered solution (DPBS) . Then, they were ultracentrifuged at 20,000 g for 60 min  and the supernatants were filtered through 0.22 µm PVDF filters . Three dilutions—1:2, 1:5 and 1:10—of the faecal water were made with serum-free cell culture medium.Faecal water (FW) was prepared as indicated in ® HTB-38™) and HCT 116 (ATCC® CCL-247™), were used to evaluate cell cytotoxicity. HT-29 cells were cultured in McCoy’s 5A with L-glutamine  supplemented with 1% (v/v) penicillin/streptomycin solution  and 10% (v/v) foetal bovine serum . HCT 116 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEN), high glucose (4.5 g/L)  supplemented with 1% (v/v) penicillin/streptomycin solution and 10% (v/v) FBS. Cells were grown in 75 cm2 flasks  at 37 °C and in a 5% CO2 atmosphere and the media were renewed every 3 days.Two human colon adenocarcinoma cell lines, HT-29 -2,5-diphenyltetrazolium bromide (MTT) to formazan, an insoluble intracellular blue product, by cellular dehydrogenases. Cells were seeded at a density of 3.6 × 105 cells/mL on well plates, and washed with warm DPBS. Then, they were incubated (100 µL/well) with diluted FW, phenolic compound solutions or serum-free medium (used as control) for 4 or 24 h. After that, supernatants were removed from the wells, and MTT  solution (0.5 mg/mL) was added (100 µL/well). The plates were immediately incubated for 3 h in darkness. After this time, MTT reagent was aspirated and formazan crystals were diluted in 100 µL of dimethyl sulfoxide . Absorbance at 570 nm was measured on a Multiskan plate reader . Cell viability (%) was calculated as Abssample/Abscontrol × 100. Assays were performed in triplicate and three independent experiments were carried out.50  values for phenol and p-cresol were estimated using a sigmoidal dose-response curve with variable slope, utilising the software Prism 6 .ICPhenolic metabolites were determined by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) following a previously reported method . The liqShort-chain fatty acids (SCFA) were determined by Solid Phase Micro-extraction-Gas Cromatography-mass spectrometry (SPME-GCMS) following the method developed previously as reported by Cueva et al. . The extinitial) and after  red wine intervention were performed. Before–after graphs were made using GraphPad Software v6.0 . Pearson’s correlation coefficients between the percentage of cell viability and faecal content in fatty acids and phenolic metabolites were calculated. A value of p = 0.05 was fixed for the level of significance of the statistical analysis.The statistical analysis was performed using IBM SPSS Statistics v26 for Windows . Paired-sample T-tests for assessing significant differences in cell viability (%) between faecal samples before  of phenol and p-cresol against both HT-29 and HCT 116 cell lines. Greater toxicity  was observed for p-cresol than for phenol, and HT-29 cells were found to be more sensitive to both compounds than HCT 116 cells  from 4 to 24 h incubations for both compounds and cell lines, but IC50 values still remained in the mM range  and two incubation times (4 and 24 h) were carried out in an effort to reproduce the physiological conditions as faithfully as possible. Two compounds, phenol and n faeces , were usn faeces . Table 1cubation . Among ocubation  found thp-cresol on human colonic epithelial cells in vitro. Additionally, these first results with reference compounds confirmed the suitability of the proposed assays for the assessment of FW toxicity.In summary, these results were consistent with previous data reported in the bibliography that showed a direct toxic effect of phenol and n = 3) and case (n = 8) groups is depicted in final) than before  red wine intervention for the case group, although significant differences (p < 0.05) were only found in certain assays conditions. As regards the HT-29 cell line, the assay conditions of 1:2 (v/v) for FW dilution and 4 h for incubation time showed the highest difference in percentage cell viability  when comparing samples before (69.8%) and after (78.4%) the red wine intervention (p > 0.05) between samples collected before and after the red wine intervention were noted (FW cytotoxicity to HT-29 and HCT 116 cells (as means of percentage cell viability) of faecal samples collected before and after red wine intervention for the control  and an incubation time of 24 h that led to the highest difference in percentage cell viability  when comparing samples before (42.7%) and after (56.1%) the red wine intervention (25 (effective concentration at 25% reduction of cell viability) for FW [As for the more resistant cell line (HCT 116), it was the combination of an FW dilution of 1:5  for FW  could beOur results from both HT-29 and HCT 116 cell models showed a reduction in FW cytotoxicity for the volunteers that consumed red wine (250 mL/day) over a period of 4 weeks, which suggested a protective effect derived from moderate wine consumption in the intestinal lumen. To the best of our knowledge, this is the first study to report data on FW cytotoxicity after the intake of wine or wine-related products in humans. We have only found one related reference, Bastide et al. , who repSeveral studies have investigated how diet could modulate FW cytotoxicity, as a biomarker of bowel health. Among others, prebiotics have gained special attention. Adebola et al.  found a Lactobacillus strains led to significantly lower FW cytotoxicity, measured in Caco-2 cells. In another study, the FW cytotoxicity towards Caco-2 cells decreased after feeding chickens with a probiotic preparation [The use of probiotics for reverting toxic effects of microbial-derived and host metabolites has also been tested by evaluating FW cytotoxicity. Klewicka et al.  found thparation . In contparation . In the paration . In relaparation  demonstrIn terms of the whole diet, Federici et al. , studiedIn summary, our results indicate that moderate red wine consumption reduces FW cytotoxicity in vitro, as has been seen for other foods  or whole diets. In other words, moderate red wine consumption modified the luminal metabolite content in such a way that it resulted in lower FW cytotoxicity, with this effect being extremely individual-variable. On the other hand, the fact of selecting healthy volunteers that had not suffered from any disease or intestinal disorders was relevant in the sense that it confirmed the preventive action of moderate wine consumption in the development of certain intestinal diseases.v/v) for FW dilution in the HT-29 assay and 1:5 (v/v) in the HCT 116 assay; p < 0.05) positive correlations were found for the more resistant cell line (HCT 116)  after moderate red wine consumption ,35, whicHCT 116) .In summary, these results suggested that the reduction in FW cytotoxicity observed after moderate red wine consumption seems to be related to the production of microbial-derived metabolites such as SCFA and PA, whose faecal contents have been shown to be increased after red wine consumption . In any p-cresol) that can be useful in the development and validation of new protocols. From a methodology point of view, the use of the HTC 116 cell line is also relevant, as these results may be complementary to other cell lines such as HT-29, widely used in cytotoxic and genotoxic tests. The results from a first intervention study (n = 11) concerning this matter indicated that moderate red wine consumption seemed to reduce FW cytotoxicity, with this effect being extremely individual-dependent. FW cytotoxicity was found to be inversely correlated with faecal concentrations of some microbial-derived metabolites such as SCFA and PA, compounds that have been found to be extensively generated after red wine consumption. Therefore, FW cytotoxicity could be seen as a holistic biomarker that involves diet, gut microbiota and host, and can be useful in evaluating nutritional interventions that promote intestinal health maintenance or improvement. Moderate red wine consumption has long been associated with beneficial effects in cardiovascular disease, but the findings of this paper should encourage further studies concerning the effects of moderate red wine consumption on intestinal health, also involving gut microbiota-related diseases such as cognitive and neurodegenerative pathologies.This paper reports value data of FW cytotoxicity for reference compounds (phenol and


Recently, antimicrobial peptides (AMPs) have been investigated for their use in cancer therapy. They have been reported to selectively target and kill cancer cells whilst leaving normal healthy cells unaffected. Certain Anura AMPs have expressed selective cytotoxicity against tumour cells.  To test the potential of Anura AMPs bombinin H2, bombinin H4, temporin A, and temporin L for use as therapeutic agents for non-small cell lung carcinoma (NSCLC).  Cytotoxic effects on NSCLC cell lines A549 and Calu-3 and normal epithelial cell line Beas-2B were tested using the CellTox Green Cytotoxicity Assay. Their haemolytic effects on human erythrocytes were also tested for their clinical relevance. Cell membrane profiling, using MALDI-TOF, was performed to ascertain if membrane characteristics of the NSCLC and Beas-2B cell lines may contribute to the AMPs mode of action. μM, p < 0.05) and temporin A  showed selective cytotoxicity towards the NSCLC cell lines. Furthermore, they exhibited low levels of haemolytic activity  comparable to untreated cells. Cell membrane profiling showed the phospholipid composition of normal epithelial cell line Beas-2B to be divergent from the cancerous cell lines. However, there was an overlap in the phospholipid profiles of the NSCLC cell lines supporting the hypothesis that the AMPs may have a selective affinity via the membrane composition of cancerous cell lines.  Bombinin H4 (100–1.5 in vitro and in vivo studies are required to develop a greater understanding of their use as anticancer agents. These results suggest that bombinin H4 and temporin A show potential for application in lung cancer therapies. Further  In 2018, it was reported that there were an estimated 17 million new cancer cases and 9.6 million cancer related deaths worldwide, with lung cancer being the most common cause of death . FollowiAntimicrobial peptides (AMPs) are a group of compounds that are a conserved element of the innate immune response , found iAMPs found in Anura frog skin secretions have been used medicinally for many years. The dorsal skin secretions collected from Anura are amongst the richest sources of naturally formed AMPs, acting as part of the defence mechanisms to protect the frog skin from pathogen invasion and predator ingestion . The AnuRana temporaria (temporin A and temporin L) [Bombina variegata (bombinin H2 and bombinin H4) [The purpose of this study is to investigate antimicrobial peptides from the Anura species porin L)  and Bombinin H4)  to deterTemporin A [FLPLIGRVLSGIL], temporin L [FVQWFSKFLGRIL], bombinin H2 [IIGPVLGLVGSALGGLLKKI], and bombinin H4 [I– GPVLGLVGSALGGLLKKI] were synthesized by Selleck Chemicals .μl of 0.1% formic acid to solubilise the peptide. The matrix solution was made up of α-cyano-4-hydroxycinnamic acid (10 mg/ml) combined with a 50 : 50 solution acetonitrile and 0.1% trifluoroacetic acid. When all the samples were dry, the MALDI plate was loaded into the Voyager-DE STR Biospectrometry Workstation . Data acquisition parameters were set: ion mode, positive; instrument mode, reflector; instrument range, 1000 to 4000 Da; low mass gate, 1000 Da; total scans, 100 shots per spectrum; accelerating voltage, 20,000 V. The results were analysed using Voltage Data ExplorerTM version 4.0 . The spectrum was calibrated against the reference proteins .Matrix assisted laser desorption/ionisation (MALDI) mass spectrometry analysis was carried out on the samples following the addition of 100 μl of methanol before being pipetted into the LTQ Orbitrap XL . The peptides were fragmented in higher-energy collisional dissociation (HCD) mode with an isotope band width of 1.5 m/z.Electrospray ionisation (ESI) mass spectroscopy was used in combination with MALDI mass spectroscopy to further validate the peptide identities. Each peptide was mixed with 100 Staphylococcus epidermidis 1457 and S. epidermidis 5179-R1 were cultured on horse blood agar plates for 24 hours at 37°C and subsequently used to inoculate 3 ml of prewarmed Tryptic Soy Broth . Precultures were grown in a shaking incubator for 2–3 hours at 37°C and used to inoculate 200 μl fresh TSB in 96-well tissue culture plates  to a starting OD600 of 0.05. To each test well, 100 μM of peptides was added. This concentration was chosen as it was the upper limit of the concentrations being investigated for anticancer potential. Untreated bacteria were used as controls.The 96-well plate was inserted into a BMG FLUOstar Omega microplate reader  set to a wavelength of 600 nm. The OD of each well was read every hour over 24-hour period, to determine the effect of the AMPs on the growth rate/population density of the bacteria. All experiments were performed in triplicate, and each strain and condition tested in triplicate.oC and 5% CO2.NSCLC cell line A549 (ATCC–CCL185) and normal epithelial cell line Beas-2B (ATCC CRL-9609) were routinely grown in DMEM  supplemented with 10% foetal bovine serum (FBS) , 2 mM L-glutamine , and 1% penicillin/streptomycin . The NSCLC cell line Calu-3 (ATCC HTB55) was routinely grown in RPMI-1640  supplemented with 10% FBS, 2 mM L-glutamine, and 1% penicillin/streptomycin. All cells were incubated at 37μl dye: 1000 μl cell stock. Phenol-free medium decreases the background noise of the fluorescence reading so that a more accurate reading can be taken. This solution was then used to dilute the peptide to specific concentrations . Once the cells were dosed, the plates were maintained at a temperature of 37°C and 5% CO2. Untreated cells, comprising media and CellTox Green Dye, were used as a control; cell lysis buffer (provided in the CellTox Cytotoxicity Assay kit) was used as a positive control, indicating cell death.The CellTox Green Cytotoxicity Assay was used to determine reduction in membrane integrity due to AMP treatment. CellTox Green Dye was mixed with cell stock made in phenol-free, serum-free medium to a ratio of 1 Fluorescence readings were taken using a POLARstar Omega plate reader  at 24 hours in accordance with published literature , 23. TheThe HaemoScan Biomaterial Haemolytic Assay  was used to investigate the cytotoxicity of the AMPs on human erythrocytes. Only the AMPs which displayed selective cytotoxicity towards the cell lines A549 and Calu-3 were tested for haemolytic properties.The protocol of the assay was followed according to the manufacturers' instructions. In summary, the erythrocyte suspension was prepared by adding 5 ml of the supplied wash buffer to the erythrocyte suspension and centrifuging at 400 × g for 10 minutes. The supernatant was then removed. This step was repeated twice with the wash buffer, and the steps were then repeated replacing wash buffer with dilution buffer twice more. The final pellet was then suspended in 5 ml of dilution buffer to form a colourless supernatant.μM; temporin A, 50 μM). A haemoglobin calibration curve was also produced to establish the haemoglobin concentrations present in the treatment groups of the erythrocyte suspension after exposure to the AMPs.The minimum concentration of each peptide that had a significant effect on A549 and/or Calu-3 without significantly effecting noncancer cells (Beas-2B) was added to 0.5 ml of erythrocyte suspension . The absorbance was read at three wavelengths, 380, 415, and 450 nm (Harboe method), and the calculation (2 × 415)–(450 + 380) was applied to give a final OD.Both the calibrator and treatment groups were then incubated at 37°C for 24 hours. After 24 hours of incubation the samples were centrifuged at 4000 × g for 1 minute and 20 Haemoglobin concentrations of the test samples were then established by comparing the treatment readings against the haemoglobin calibration curve. The haemoglobin concentration of the treatment groups was presented as a percentage of the total haemoglobin present in the sample, expressed as percentage haemolysis. All experiments were performed in triplicate.Prior to phospholipid analysis, cultured cell lines were harvested as follows: culture medium was discarded from the flasks; the cells were washed with 10 ml PBS and were subsequently incubated with 3 ml 0.025% trypsin/EDTA for 3–5 min. The reaction was terminated by the addition of 10 ml cell culture medium, and the samples were centrifuged at 10,000 × g for 5 min.TM version 4.0 . Any background noise was removed, m/z values between 1 and 1600 were collected, and new spectra were established.Cell pellets were then solubilised in 200 ul of chloroform: methanol (2 : 1) and then diluted (1 : 5) in either dihydroxybenzoic acid (DHB) at a concentration of 10 mg/ml in methanol containing 0.1% trifluoroacetic acid or para-nitroaniline (PNA) at a concentration of 10 mg/ml in chloroform: methanol (2 : 1). One microliter of each mixture was then spotted on a clean MALDI plate (Applied Biosystems). Samples were analysed using a Voyager-DE STR, MALDI-TOF mass spectrometer . A 337 nm specific laser wavelength was used to ensure that only the outer membrane was detected. Data acquisition parameters were set: time delay of 200 nanoseconds, total scans of 100 shots per spectrum, and accelerating voltage of 20,000 V. The two matrices (DHB and PNA) were utilised and run in both the positive and negative modes, respectively. The m/z signal was aligned across each sample within 0.5 of each other using Excel. The results were analysed and normalised using Voltage Data ExplorerPossible identities of each membrane component at specific m/z values were assigned according to the work of Estrada and Yappert , e.g., Pp value greater than 0.05 when compared with the expected distribution of results. Any outliers identified were excluded from further statistical analysis. A one-sample Kolmogorov–Smirnov (K–S) test was carried out on each data group to test the normality of the data to determine appropriate further data analysis tests. All data was shown to be of a non-normal distribution and therefore a Kruskal–Wallis nonparametric analysis was carried out. To identify any significant differences between groups, a Mann–Whitney test was used to test individual treatment groups against the untreated control group. A p value of <0.05 was considered significant.Intragroup data readings were checked for outliers using the Grubbs test, identifying outliers with a MALDI and ESI mass spectrometry confirmed that the synthetically made identities of each peptide accurately represented the peptide found from the natural source, Anura bombinin H2 and its diastereomer bombinin H4, containing a D-alloisoleucine at the second N-terminal residue as a result of a posttranslational modification, temporin A, and temporin L .S. epidermidis 1457 and 5179-R1 underwent rapid proliferation throughout the initial 8 hours, reaching a maximum OD600 of 0.95 and 0.7, respectively, after 10 hours before entering a stationary phase. For S. epidermidis 1457, bombinin H2, bombinin H4, and temporin L (100 μM) significantly inhibited proliferation over the 24 hours  significantly inhibited growth of S. epidermidis 5179-R1 . For Calu-3 cells, bombinin H2 resulted in significant cell death, when compared to the control, at concentrations of 50 μM and 100 μM (p ≤ 0.001)  . HoweverμM to 100 μM (p ≤ 0.05). However, in the Calu-3 cell line, significant cell death was only observed in the higher concentrations, 50 μM and 100 μM (p ≤ 0.001), when compared to the untreated cells (μM–100 μM (p ≤ 0.05). As bombinin H4 showed selective cytotoxicity to A549 cells at the lowest concentration tested but not in the Beas-2B cells, it is suggested that bombinin H4 exhibits some level of selective cytotoxicity.Bombinin H4 proved to be highly toxic to the A549 cell line after 24-hour exposure, with significant cell death being observed from 1.5 ed cells . In the μM and 100 μM (p ≤ 0.001), significant cell death was observed. However, at concentrations of 3 μM–25 μM, a significant increase in cell viability was observed (p ≤ 0.050). For Calu-3 cells, 50 μM and 100 μM temporin A induced significant cell death (p ≤ 0.01) when compared to the untreated cells but, unlike A549, did not increase cell viability at the lower concentrations (μM (p ≤ 0.05). From these results, it could be suggested that temporin A is selectively cytotoxic at 50 μM.Interestingly, temporin A showed a dual effect on the A549 cell line. At concentrations of 50 trations . When teμM–25 μM (p ≤ 0.05) resulted in significant cell death when compared to the untreated cells. In the Calu-3 cell line, temporin L induced significant cell death across the whole range of concentrations, 1.5 μM–100 μM (p ≤ 0.05), when compared to the untreated cells (μM–100 μM (p ≤ 0.05), when compared to the untreated cells. Temporin L, therefore, was shown not to be selectively cytotoxic.The A549 cell line treated with temporin L at concentrations of 1.5 ed cells . As withμM and 50 μM, respectively. Interestingly, whilst bombinin H4 did not produce a cytotoxic effect on erythrocytes, levels of haemolysis were significantly less than untreated cells (p ≤ 0.001), suggesting that bombinin H4 may have a protective effect on erythrocytes viability. Temporin A, though nontoxic to erythrocytes with a haemolysis value comparable to untreated cells (p ≤ 1.000), did not display the same protective attributes as bombinin H4  using positive ionisation MALDI-TOF  showed tp < 0.05); thirteen of the twenty-four had a p value of <0.005 (p < 0.05) with twenty of those having a p value of 0.005.Membrane phospholipid expression profiles were also compared to try and identify any differences in cell membrane composition, which may account for the different effect the AMPs had on cell toxicity. When comparing the normal epithelial cell line Beas-2B with the NSCLC cell line A549, twenty-four significant peaks were acquired where the phospholipid was more abundant in either Beas-2B or A549 (f <0.005 . When cop < 0.05). When Calu-3 and A549 cells were compared, Calu-3, the stage III cancer cell line, was found to be more negatively charged (1.7%) than the A549 cell line.The variation in abundance of the phospholipids between cell lines  represenBombina variegate) and temporin A and L (from the Anura species Rana temporaria) displayed selective anticancer activity, thus offering potential as new chemotherapeutic agents to treat lung cancer.Cancer treatments can vary: surgery, chemotherapy, radiotherapy, or a combination of these treatments. With specific regard to lung cancer, 90% of individuals with small cell lung cancers will initially respond to chemotherapeutic treatment, but almost all will relapse with multidrug resistant disease, whilst patients with non-small cell lung cancer have a low response rate to chemotherapy due to inherent drug resistance . In receIn this study we have been able to demonstrate that temporin A and bombinin H4 interacted more effectively towards the NSCLC cell lines than in the normal lung cell line. Temporin A showed the greatest potential as an anticancer agent due to its ability to initiate significant cell death in both NSCLC cell lines, at the same concentration, without significant cell death in the Beas-2B cell line. Interestingly, bombinin H4 selectively killed A549 cells but not Calu-3 cells. The difference in the ability of bombinin H4 to induce cytotoxic activity in the cancer cell lines suggests a difference in the cell membrane protein composition of the two cancer cell lines as a possible explanation for this observed effect. The membrane phospholipid profile demonstrated that the cell lines clustered into distinct groups, suggesting that the cells could have a distinct phospholipid compositional signature on their outer leaflets. However, upon investigation of the outer membranes in negative-mode MALDI-TOF, PCA showed that whilst Beas-2B cells have a distinct phospholipid signature from that of the cancerous cells, the cancerous cells overlapped, implying that the cancer cell lines have similar phospholipid profiles. Thus, cell membrane phospholipid composition, alone, cannot explain why bombinin H4 was cytotoxic to A549 cells but not Calu-3.The charge of the cell membrane can also affect the affinity of the AMP for selective targeting of cancerous cells. As expected, we found that normal epithelial Beas-2B cells had a greater percentage of positive phospholipids and fewer negative phospholipids in comparison to the A549 and Calu-3 cell lines. Although PCA could not identify specific differences between the cell lines, normal mammalian cells predominantly contain zwitterionic phospholipids within their outer leaflet such as PC and SM, whilst negatively charged phospholipids such as PS and PG reside on the inner leaflet. In contrast, in cancer the negatively charged phospholipids become externalised to the outer leaflet –30, enhaWhen A549 and Calu-3 phospholipid chargers were compared, we found Calu-3, the stage III cancer cell line, to be more negatively charged (1.7%) than the A549 cell line. Given that AMPs exert their cytotoxic effects through electrostatic interaction, it is surprising that the more negatively charged Calu-3 cells were not as susceptible to bombinin H4 as the A549 cells.Thus, another factor that needs to be taken into consideration is the properties of the AMPs themselves. Temporin A was cytotoxic to both cancer cell lines at the same concentration, yet bombinin H4 induced cell death at much lower concentrations in A549 than Calu-3. Thus, it may be argued that the peptide sequence of the AMP, itself, plays a major role in its cytotoxic potential. Indeed, the sequence of an AMP can affect its ability to interact with cell membranes. Several early studies have demonstrated that peptide helicity is important for toxicity , 32. IncMany studies investigating the anticancer properties of AMPs use cell lines from different cancer types , 36, 37;μM, whereas the erythrocytes have a diameter of 6-8 μM [For AMPs to be viable anticancer agents they must not induce haemolytic responses in the patient. Temporin A and bombinin H4 were tested on human erythrocytes to investigate their haemolytic properties at the minimum concentrations required to cause significant cell death in NSCLC cell lines, A549 and Calu-3. Encouragingly, for temporin A, we observed a haemolysis value comparable to untreated cells. With bombinin H, haemolysis was significantly less than the untreated control cells, suggesting that it actually had a protective effective on erythrocyte viability. The membrane of erythrocytes is asymmetrically distributed with glycolipids, phosphatidylcholine, and sphingomyelin expressed on the outer membrane to form a hydrophilic environment. The surface charge of erythrocytes has been shown to have a zeta potential of 10 mV, which is considered to be a neutral charge . Thus, tf 6-8 μM , 40. TheS. epidermidis 1457 and 5179-R1 at 100 μM, whilst temporins A and L exhibited some antimicrobial effects on the bacteria but not to the same extent as the bombinins. Given that bombinin H2 and temporin L were not selectively cytotoxic and temporin A demonstrated the most efficacious anticancer activity, the antimicrobial effect of an AMP cannot be used as an indicator for its anticancer potential.It is generally accepted that the selective cytotoxicity of AMPs towards bacteria and cancer cells is due to both cell types possessing a negative charge on their cell membranes. Therefore, we sought to investigate if antimicrobial activity could be used as an indicator of an AMPs anticancer potential. Bombinins H2 and H4 showed significant antimicrobial activity towards in vitro and in vivo work is now required to fully ascertain their anticancer potential and mode of action.Research on the anticancer properties of AMPs has increased in recent years. Many of these studies use different cancer cell lines but usually only one from a particular cancer type. Here we show that the effectiveness of AMPs as anticancer agents varies not just between AMPs but also between cancer types, even within a particular cancer type. Therefore, it is imperative that studies are conducted using more than one cell line from the same cancer type to ascertain if an AMP cytotoxicity is cell line specific. Furthermore, our preliminary investigation of four Anura AMPs demonstrated that temporin A and bombinin H4 may hold potential as anticancer agents, and further