Nanomolar concentrations of geldanamycin significantly reduced cell growth, invasion, and migration in vitro. However, administration of geldanamycin in vivo did not result in significant anti-cancer activity. Our findings show that myogenesis and muscle differentiation pathways are upregulated in pleural mesothelioma which may be related to the invasive behaviour. However, geldanamycin as a single agent does not appear to be a viable treatment for mesothelioma.Mesothelioma is characterised by its aggressive invasive behaviour, affecting the surrounding tissues of the pleura or peritoneum. We compared an invasive pleural model with a non-invasive subcutaneous model of mesothelioma and performed transcriptomic analyses on the tumour samples. Invasive pleural tumours were characterised by a transcriptomic signature enriched for genes associated with MEF2C and MYOCD signaling, muscle differentiation and myogenesis. Further analysis using the CMap and LINCS databases identified geldanamycin as a potential antagonist of this signature, so we evaluated its potential  Mesothelioma is a cancer that usually arises in the pleura, occasionally developing from other serous membranes . Its devFor many mesothelioma patients, palliative chemotherapy with cisplatin/pemetrexed is the first-line therapy . RecentlRecent developments in systems biology have allowed gene expression profiling to be used not only for comparing specific pathology but also to unravel drug-disease associations . The conHere, using transcriptomic data from invasive and non-invasive models of murine mesothelioma, we aimed to map an invasive signature of mesothelioma and identify drug repurposing candidates with potential anti-mesothelioma activity.−8, z-score = 2.78) and myocardin (MYOCD), a co-transcriptional activator of serum response factor that inhibits the cell cycle and induces smooth muscle differentiation Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Morales et al investigate geldanamycin as a treatment for mesothelioma. They implant two murine mesothelioma cells subcutaneously and orthotopically into mice and identify differentially expressed genes between the invasive growth in the orthotopic location and the non-invasive growth of the sucutaneously implanted tumors. They perform pathway and database analyses with the differentially expressed genes to identify a gene expression signature associated with invasive growth and geldanamycin as a potential antagonist of this signature. They subsequently test geldanamycin in vitro and in vivo and find reduction of cell growth, migration and invasion in vitro but no inhibition of tumor growth or invasion in vivo.The experiments appear technically well performed and the manuscript is well written.I have two main concerns:1) The difference in growth characteristics between the models is dictated by the location and may thus not reflect the aggressiveness of the tumor cells. I would be more interested in a difference between cell models that do and do not grow invasively in the pleura.2) The authors do not explore the difference between their in vitro and in vivo findings. According to the literature HSP90 is the target of geldanamycin. Is this target present in their models? How is it affected by geldanamycin treatment in vitro and in vivo? Have others found efficacy of geldanamycin in vivo in other cancers?Reviewer #2: The Authors compared an invasive pleural model with a non-invasivesubcutaneous model of mesothelioma. They performed transcriptomic analyses on the tumour samples and used differentially expressed genes between pleural and subcutaneous mesothelioma to interrogate platforms allowing to identify drugs able to produce a similar transcriptional difference. Within the list of drugs, the Authors select geldanamycin, an hsp90 inhibitor. This drug has already been shown to inhibit mesothelioma cell growth in vitro and the Authors confirm that nanomolar concentrations of geldanamycin inhibit cancer cell growth, invasion, and migration in vitro in the four mesothelioma cell lines tested. However, they observed no effect in vivo on growth or invasion in an orthopic model using cells labeled with a luciferase reporter.This study includes some interesting observations, however, several issues have to be addressed before this manuscript can be considered for publication.Major:1. If the Authors select log fold change >1, and exclude Ig genes, where differential expression might result from the tumor microenvironment, the remaining 99 genes allow to identify “mesothelial cell signature” in the enrichr plateform that they used. This means that the pleural environment is more favorable to mesothelial differentiation program commitment. This is an important information that the Authors should add for the benefit of the community. It also provides a slightly different perspective compared to what the Authors have proposed as data “suggesting that muscle development signatures were related to the invasive phenotype of pleural mesothelioma”. Indeed, it adds more emphasis on the fact that orthotopic environment favors the maintenance of mesothelial differentiation. This does not preclude the selection of geldanamycin for further investigations.2. The Authors should provide evidence whether the AB1-luciferase cell line used in the orthotopic model has the same profile as parental cells. For example they could test whether they maintain the high expression of UPK3B and LRRN4 as parental cells.3. The Authors could compare whether AB1 and AB1-luc show the same sensitivity to geldanamycin growth inhibition effects.4. The Authors could also test whether there are different levels of HSP90 and a different ratio of HSP90 alpha vs beta isoforms between AB1 cells used for the transcriptomic analysis and the AB1-luciferase cells, which may help explain difference in sensitivity.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoYes: Emanuela Felley-BoscoReviewer #2: **********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  21 Mar 2023PONE-D-22-23207R1Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesotheliomaOrozco Morales et al. Point-by-point reply to reviewers’ comments. We thank the reviewers for their constructive comments, which have helped us to improve our manuscript. Here, we present a point-by-point reply addressing all comments.Reviewer #1: Morales et al investigate geldanamycin as a treatment for mesothelioma. They implant two murine mesothelioma cells subcutaneously and orthotopically into mice and identify differentially expressed genes between the invasive growth in the orthotopic location and the non-invasive growth of the sucutaneously implanted tumors. They perform pathway and database analyses with the differentially expressed genes to identify a gene expression signature associated with invasive growth and geldanamycin as a potential antagonist of this signature. They subsequently test geldanamycin in vitro and in vivo and find reduction of cell growth, migration and invasion in vitro but no inhibition of tumor growth or invasion in vivo.The experiments appear technically well performed and the manuscript is well written.I have two main concerns:1) The difference in growth characteristics between the models is dictated by the location and may thus not reflect the aggressiveness of the tumor cells. I would be more interested in a difference between cell models that do and do not grow invasively in the pleura.We agree with the reviewer that having a cell line that does not grow invasively in the pleural cavity would be of great interest, but the two different cell lines that we tested (AB1 and AE17), in two different mouse strains (C57BL/6 and BALB/c), showed invasive and fast-proliferative behaviour in the pleural space, but not in the subcutaneous space, which is why we compared the transcriptomes of the tumours derived from the same cell line, yet growing in different anatomical compartments.2) The authors do not explore the difference between their in vitro and in vivo findings. According to the literature HSP90 is the target of geldanamycin. Is this target present in their models? How is it affected by geldanamycin treatment in vitro and in vivo? Have others found efficacy of geldanamycin in vivo in other cancers?We chose geldanamycin using the LINCS drug repurposing database as it was predicted to antagonise the invasion-associated gene expression profile. The source data in the LINCS database are derived from cell lines treated with thousands of compounds. The gene expression profile induced by geldanamycin can be HSP90-independent. Indeed, geldanamycin was initially discovered as an antibiotic, and only decades later was to be shown to bind to HSP90. However, to address the presence of HSP90 present in our models and to observe if geldanamycin downregulates HSP90 in vitro, we now added western blots in Supplementary figure 2  from both cell lines that were used for in vitro and in vivo experiments, AB1 and AB1-Luc respectively. These data show the same basal expression of HSP90 in both cell lines, as well as an unchanged expression after addition of geldanamycin at 500 nM, a concentration which in our migration experiments reduced cell migration. These data agree with others that have shown that geldanamycin treatment does not downregulate HSP90 protein expression levels in urinary bladder cancer cell lines RT4 and T24 , and breast cancer cell line MCF-7 , but rather inhibits HSP90’s downstream targets  , suggesting that geldanamycin’s mechanism of action is either HSP90-independent or works through inhibition of its downstream targets as per the literature. We have added the following sentences to the Discussion section : “In our study, we observed that addition of geldanamycin did not affect the expression of HSP90 itself. These data agree with others that have shown that geldanamycin treatment does not downregulate HSP90 protein expression levels as such (49) (50), but rather inhibits HSP90’s downstream targets (49) (50). Another explanation for geldanamycin’s mechanism of action as observed in our in vitro experiments is that it is HSP90-independent.”Supplementary Fig S2. Luciferase transduction does not affect the AB1 cell line phenotype (H) Western blot for cell lines AB1 and AB1-Luc shows housekeeping protein GAPDH at 37 kDa, and HSP90 protein at 90 kDa.Reviewer #2: The Authors compared an invasive pleural model with a non-invasive subcutaneous model of mesothelioma. They performed transcriptomic analyses on the tumour samples and used differentially expressed genes between pleural and subcutaneous mesothelioma to interrogate platforms allowing to identify drugs able to produce a similar transcriptional difference. Within the list of drugs, the Authors select geldanamycin, an hsp90 inhibitor. This drug has already been shown to inhibit mesothelioma cell growth in vitro and the Authors confirm that nanomolar concentrations of geldanamycin inhibit cancer cell growth, invasion, and migration in vitro in the four mesothelioma cell lines tested. However, they observed no effect in vivo on growth or invasion in an orthopic model using cells labeled with a luciferase reporter.This study includes some interesting observations, however, several issues have to be addressed before this manuscript can be considered for publication.Major:1. If the Authors select log fold change >1, and exclude Ig genes, where differential expression might result from the tumor microenvironment, the remaining 99 genes allow to identify “mesothelial cell signature” in the enrichr plateform that they used. This means that the pleural environment is more favorable to mesothelial differentiation program commitment. This is an important information that the Authors should add for the benefit of the community. It also provides a slightly different perspective compared to what the Authors have proposed as data “suggesting that muscle development signatures were related to the invasive phenotype of pleural mesothelioma”. Indeed, it adds more emphasis on the fact that orthotopic environment favors the maintenance of mesothelial differentiation. This does not preclude the selection of geldanamycin for further investigations.We thank the reviewer for this observation. We have performed this analysis ourselves and have indeed observed a mesothelial cell signature for different cell types. We added the data as Supplementary figure 1  and discussed in the Results section : “Further analysis with the Descartes dataset in Enrichr (24) (25) (26) of the IPL differentially expressed genes (log fold change >1) while excluding immunoglobulin-related genes resulted in a mesothelial cell signature , suggesting that environmental cues from the pleural space drive this mesothelial program more than the subcutaneous space”.Figure S1A. Enrichment analysis of IPL (log fold change >1) differentially expressed genes using the Descartes dataset in Enrichr, p values 0.05 and 0.01 are represented with dotted line on –log10 = 1.3 and 2, respectively.2. The Authors should provide evidence whether the AB1-luciferase cell line used in the orthotopic model has the same profile as parental cells. For example they could test whether they maintain the high expression of UPK3B and LRRN4 as parental cells.3. The Authors could compare whether AB1 and AB1-luc show the same sensitivity to geldanamycin growth inhibition effects.To address questions 2 and 3 from Reviewer #2, we looked at the expression of the surface markers PD-L1, MHC-I and podoplanin with flow cytometry, and performed in vitro scratch migration and cytotoxicity assays with geldanamycin in both cell lines. We did not observe any difference between AB1 and AB1-Luc. These results in Supplementary figure 1  under the section: Geldanamycin treatment does not have anti-mesothelioma activity in vivo , in page 7, lines 120 – 125: “To assess any therapeutic effect of geldanamycin on mesothelioma invasion and growth in vivo, we used our optimised model of orthotopic invasive mesothelioma (14) with AB1 cells expressing luciferase (AB1-Luc). The luciferase transduction had not changed the phenotype and geldanamycin sensitivity of the AB1 cell line, in terms of its expression of PD-L1, MHC-I and podoplanin, nor its in vitro invasive potential . In addition, the basal levels of HSP90 did not change between cell lines, with or without geldanamycin .”Supplementary Fig S2. Luciferase transduction does not affect the AB1 cell line phenotype (A - C) Flow cytometry for surface markers (A) PD-L1, (B) MHC-I, and (C) Podoplanin in AB1 and AB1-Luc cell lines. Data is represented as MFI (Median). N = 5. (D) MTT assay in AB1 and AB1-Luc cell lines. The curve is presented as a non-linear regression; log(geldanamycin) versus response. IC50 values are in nM. Data are presented as means ± SD of 3 replicates. (E - G) Scratch assay for migration in (E) AB1 and (F) AB1-Luc cell lines. Geldanamycin was serial diluted from 400 nM to 1.25 nM, with 0 nM as control. (G) AB1 and AB1-Luc side by side comparison. (H) Western blot for cell lines AB1 and AB1-Luc shows housekeeping protein GAPDH at 37 kDa, and HSP90 protein at 90 kDa.4. The Authors could also test whether there are different levels of HSP90 and a different ratio of HSP90 alpha vs beta isoforms between AB1 cells used for the transcriptomic analysis and the AB1-luciferase cells, which may help explain difference in sensitivity.To address question 4, as previously mentioned in question 2 from Reviewer #1, we performed western blot on both cell lines to determine the basal levels of HSP90 and after geldanamycin treatment at 500 nM. These results are similar to what others have found when using geldanamycin at similar concentrations in vitro. This could indicate that geldanamycin does not directly downregulate HSP90 and instead reduces is action, affecting its downstream targets. These results are now discussed in the Discussion section  .AttachmentResponse to reviewers - PONE-D-22-23207R1.docxSubmitted filename: Click here for additional data file. 27 Mar 2023Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesotheliomaPONE-D-22-23207R1Dear Dr. Orozco Morales,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Luka BrcicAcademic EditorPLOS ONEAdditional Editor Comments :Thank you fro additional analyses and changes according to reviewers´ comments, which improved the quality and clarity of your manuscript.Reviewers' comments: 25 Apr 2023PONE-D-22-23207R1 Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesothelioma Dear Dr. Lesterhuis:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Luka Brcic Academic EditorPLOS ONE


Drug resistance continues to be a significant problem in cancer therapy, leading to relapse and associated mortality. Although substantial progress has been made in understanding drug resistance, significant knowledge gaps remain concerning the molecular underpinnings that drive drug resistance and which processes are unique to certain drug classes. The NCI-60 cell line panel program has evaluated the activity of numerous anticancer agents against many common cancer cell line models and represents a highly valuable resource to study intrinsic drug resistance. Furthermore, great efforts have been undertaken to collect high-quality omics datasets to characterize these cell lines. The current study takes these two sources of data—drug response and omics profiles—and uses a multi-omics investigation to uncover molecular networks that differentiate cancer cells that are sensitive or resistant to antifolates, which is a commonly used class of anticancer drugs. Results from a combination of univariate and multivariate analyses showed numerous metabolic processes that differentiate sensitive and resistant cells, including differences in glycolysis and gluconeogenesis, arginine and proline metabolism, beta-alanine metabolism, purine metabolism, and pyrimidine metabolism. Further analysis using multivariate and integrated pathway analysis indicated purine metabolism as the major metabolic process separating cancer cells sensitive or resistant to antifolates. Additional pathways differentiating sensitive and resistant cells included autophagy-related processes  and adhesion/cytoskeleton-related pathways . Volcano plot analysis and the receiver operating characteristic (ROC) curves of top selected variables differentiating Q1 and Q4 revealed the importance of genes involved in the regulation of the cytoskeleton and extracellular matrix (ECM). These results provide novel insights toward mechanisms of intrinsic antifolate resistance as it relates to interactions between nucleotide metabolism, autophagy, and the cytoskeleton. These processes should be evaluated in future studies to potentially derive novel therapeutic strategies and personalized treatment approaches to improve antifolate response. Cancer drug resistance represents a formidable obstacle that severely impacts patient outcomes. The emergence of intrinsic resistance, preexisting prior to treatment initiation, as well as acquired resistance, acquired during or after therapy, significantly diminishes the efficacy of not only conventional chemotherapeutic agents but also novel tumor-targeted and immunotargeted therapies . The curOne of the most commonly used chemotherapeutic drug classes is antifolates. Antifolate drugs play a crucial role in cancer therapy by inhibiting folate metabolism, which is essential for DNA synthesis and cell proliferation. These drugs target enzymes involved in the folate pathway, such as dihydrofolate reductase (DHFR) and thymidylate synthase (TS), thereby disrupting nucleotide production and impairing cancer cell growth . HoweverThe advent of high-throughput omics technologies, encompassing epigenomics, genomics, proteomics, and metabolomics, and others, has provided unprecedented opportunities to unravel the complex molecular landscape associated with drug resistance in cancer. Utilizing these multi-omics approaches allows for the identification of key molecular signatures and networks implicated in drug resistance, thereby enabling the development of personalized treatment modalities tailored to individual patients . Additiohttps://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4296, and replicate measurements were averaged [https://www.ebi.ac.uk/pride/archive/projects/PXD013615) (accessed on 25 May 2023) [https://dtp.cancer.gov) (accessed on 24 May 2023). Lastly, copy number variant (CNV) data and DNA methylation data (averaged by gene) were downloaded from CellMiner (accessed on 21 July 2023) [All baseline omics datasets of NCI-60 cell lines were acquired from publicly available datasets. Transcriptomics data were obtained from averaged ,14 (accely 2023) ,17. Two 50 values for each compound for each cell line [Drug response data for all compounds screened in the NCI-60 cell line program were downloaded from CellMiner in the form of z-scores which are autoscaled GIell line . Mechaniell line . For eacp-values were calculated for metabolites, proteins, and transcripts between cell lines in the top quartile  and the bottom quartile . Fold changes were calculated using average values in Q4 and average values in Q1. p-values were calculated using Students’ t-test (two-tailed). Metabolites (as KEGG IDs), proteins (as Uniprot IDs), and transcripts  with a p < 0.1 were input into OmicsNet for integrated network analysis [Fold changes and analysis . KEGG waanalysis .Relative abundance values for metabolomics, transcriptomics, proteomics, CNVs, and DNA methylation datasets were imported into Omicsanalyst for integrated multivariate analysis . Quartilhttps://pathview.uncc.edu/overview was utilized, incorporating data from metabolomics, proteomics, and transcriptomics datasets [p-value less than 0.1 were included in the analysis. For the species database, hsa-Homo sapiens was selected. Positive fold changes were indicative of an increase in Q4 of the total rank order of z-scores, representing variables that increased in cells more resistant to antifolate treatment.In order to visualize pathway perturbations using multi-omic data, the Pathview software datasets ,24. The 50 response data were sixteen entries for compounds that were defined by CellMiner as having an antifolate mechanism of action. These were broadly defined as antifols as well as inhibitors of specific enzymes including dihydrofolate reductase (DHFR), thymidylate synthetase (TYMS) inhibitors, and phosphoribosylglycinamide formyltransferase (GART)  . The dislyst 5.0 . Unsuperp-values for all metabolites, transcripts, proteins, CNVs, and DNA methylation values were calculated between Q1 and Q4  represented the cell lines with the highest z-score rank, indicating greater resistance to antifolate agents, while the first quartile (Q1) represented the cell lines with the lowest z-score rank, indicating higher sensitivity to antifolate agents . Using t1 and Q4 .p < 0.1 between Q1 and Q4 to determine the overall differentiating cellular processes between the two groups. Joint pathway analysis with metabolites and transcripts revealed ribosome (p = 2.51 × 10−16), splicosome (p = 5.60 × 10−13), endocytosis (p = 2.06 × 10−10), RNA transport (p = 6.17 × 10−10), and RNA degradation (p = 6.17 × 10−10) as among the most significant pathways differentiating Q1 and Q4 (p = 1.21 × 10−21), ribosome biogenesis (p = 4.60 × 10−19), splicosome (p = 1.42 × 10−15), DNA replication (p = 1.65 × 10−14), and RNA polymerase (p = 2.13 × 10−12) as among the most significant pathways differentiating Q1 and Q4  was analyzed separately using PCA. This analysis revealed the separation of Q1 and Q4 in each dataset in an unsupervised manner A–E. ResuAdditional analyses were performed on DIABLO-selected variables. Firstly, volcano plot analysis was performed to identify molecules most significantly increased or decreased in Q4 cells. Results showed that transcripts overall had the largest fold changes between Q1 and Q4, with many genes related to adhesion/cytoskeletal structure  and other notable differences in nuclear functions  in calcium regulation  . Receivep < 0.1 between Q1 and Q4 to understand the overall increases or decreases in the activities of these pathways in Q4 samples  and network analysis approaches in the multi-omics analysis. These orthogonal approaches allowed for the identification of common key pathways and molecules, highlighting the importance of autophagy, cytoskeleton, and nucleotide metabolism-related pathways as key for differentiating cells sensitive or resistant to antifolates. Interestingly, the current study did not identify DHFR or TS as major differentiators of sensitive and resistant cells, but these have been primarily studied in acquired resistance settings. As our previous work has shown with alkylating agents, intrinsic and acquired resistance seem to be defined by distinct metabolic mechanisms, which may explain this difference ,27,28.The cytoskeleton and autophagy are two essential cellular processes that play critical roles in maintaining cell structure, function, and overall homeostasis. The cytoskeleton is a dynamic network of protein filaments found in the cytoplasm of eukaryotic cells, comprising microtubules, actin filaments (microfilaments), and intermediate filaments . These fThe connection between cytoskeleton activity and autophagy becomes evident when considering the process of autophagosome formation and intracellular transport. Autophagosome formation relies on certain components of the cytoskeleton, particularly microtubules, which provide the necessary tracks for the movement of motor proteins to transport vesicles and organelles throughout the cell . The proThe connection between autophagy and cancer drug resistance is a multifaceted and dynamic field of study within cancer biology. The role of autophagy in cancer is complex and context-dependent, as it can have both pro-survival and pro-death functions, depending on various factors including the stage of cancer, the specific tumor microenvironment, and the type of cancer treatment being administered . AutophaAside from metabolic changes related to autophagy, the current study also identified purine metabolism as a major differentiator between sensitive and resistant cells. Cancer cells exhibit various mechanisms to rewire nucleotide metabolism and develop resistance to drug treatments . By blocIn conclusion, this study employed a comprehensive approach to categorize cancer cell lines in the NCI 60-cell line panel as sensitive or resistant to antifolates based on drug response data and identified molecular networks that differentiate between these two groups. The analysis revealed significant associations between antifolate response and core molecular processes, particularly nucleotide metabolism and autophagy-related pathways, as well as changes in cell structure-related pathways, including cytoskeletal pathways. Understanding these resistance mechanisms is critical for developing effective strategies to combat drug resistance and enhance the efficacy of antifolate-based therapies. The findings of this study contribute valuable insights into the molecular underpinnings of antifolate response and open new avenues for exploring potential targets to overcome drug resistance in cancer treatment.


Monepantel is an anti‐helminthic drug that also has anti‐cancer properties. Despite several studies over the years, the molecular target of monepantel in mammalian cells is still unknown, and its mechanism‐of‐action is not fully understood, though effects on cell cycle, mTOR signalling and autophagy have been implicated.Viability assays were performed on >20 solid cancer cell cells, and apoptosis assays were performed on a subset of these, including 3D cultures. Genetic deletion of BAX/BAK and ATG were used to establish roles of apoptosis and autophagy in killing activity. RNA‐sequencing was performed on four cell lines after monepantel treatment, and differentially regulated genes were confirmed by Western blotting.We showed that monepantel has anti‐proliferative activity on a broad range of cancer cell lines. In some, this was associated with induction of apoptosis which was confirmed using a BAX/BAK‐deficient cell line. However, proliferation is still inhibited in these cells following monepantel treatment, indicating cell‐cycle disruption as the major anti‐cancer effect. Previous studies have also indicated autophagic cell death occurs following monepantel treatment. We showed autophagy induction in multiple cell lines; however, deletion of a key autophagy regulator ATG7 had minimal impact on monepantel’s anti‐proliferative activity, suggesting autophagy is associated with, but not required for its anti‐tumour effects. Transcriptomic analysis of four cell lines treated with monepantel revealed downregulation of many genes involved in the cell cycle, and upregulation of genes linked to ATF4‐mediated ER stress responses, especially those involved in amino‐acid metabolism and protein synthesis.As these outcomes are all associated with mTOR signalling, cell cycle and autophagy, we now provide a likely triggering mechanism for the anti‐cancer activity of monepantel. We recently reported similar (micromolar) activity to that seen in ovarian cancer cell lines on a small number of cell lines reflecting the cancer types studied in the Phase I trial, though this was not wide‐ranging.22.12 except U‐87 MG and SW620 which were grown in 5% CO2. Cell lines were cultured in the following media with 10% (v/v) FBS  and 1% (v/v) penicillin/streptomycin : HOSE 6‐3, HOSE 17‐1, HCT 116: DMEM/F12 (Gibco); SW620: DMEM/F12, 1% (v/v) HEPES (Gibco); all melanoma lines, MDA‐MB‐435, SW480: RPMI 1640(−Glutamine) (Gibco), 2 mM GlutaMAX (Gibco); MDA‐MB‐231: RPMI 1640(+Glutamine) (Gibco); OVCAR‐3, OVCAR‐5: DMEM high glucose (Gibco); A549, U‐87 MG, COLO 320: DMEM/F12 (Gibco), 2 mM GlutaMAX; DMS53: Waymouth (Gibco); PC‐3: RPMI 1640(−Glutamine) (Gibco); Fibroblasts: DMEM high glucose (Gibco), 50 μM 2‐mercaptoethanol, 250 μM L‐Asparagine (Gibco), 1 mM HEPES (Gibco).Monepantel was provided by PharmAust and a stock solution prepared at 10 mM in 100% ethanol and stored at −20°C. Vorinostat was purchased from Selleck (Cat#A1047) and 5FU from Sigma (Cat#F6627‐1G). Both were prepared as 10 mM stock solutions in phosphate‐buffered saline (5FU) or DMSO (Vorinostat). Cell lines were originally obtained from ATCC except HCT 116 which was gift from Associate Professor Grant Dewson (WEHI) with identities confirmed by STR profiling and shown to be mycoplasma negative based on in‐house MycoAlert assays (Lonza). All were maintained at 37°C with 10% CO2.2The 5FU‐resistant SW620 colon cancer cells were generated by continuous culture in the presence of increasing concentrations of 5FU. Exponentially growing SW620 cells were initially treated with 1 μM 5FU and when they reached confluence passaged 1:10 into a fresh flask with increasing concentrations of 5FU on each passage as follows: 4, 6, 8, 10, 12, 15, 30, 50, 100 μM. The same process was followed for the Vorinostat‐resistant cells with cells initially treated with 1 μM Vorinostat which was increased on each passage with the following doses: 4, 6, 8, 10, 12, 15 μM.2.3CellTiter‐Glo‐based viability assays were performed as previously described.2.4Cells  were seeded into 24‐well plates, then 16–24 h later treated with monepantel or vehicle prepared in media used for each cell line. Live and dead cells were harvested and pelleted by centrifugation then incubated with Annexin V‐APC (BD Biosciences) and propidium iodide (PI) (Sigma Aldrich) in Annexin V binding buffer (BD Biosciences). FACS analysis was performed on a BD FACSCanto II flow cytometer (BD Biosciences). Data were analyzed using FlowJo Software Version 10 (FlowJo‐LLC) with the number of viable cells (Annexin V negative/PI negative) normalized relative to the number of viable cells cultured in the vehicle control.2.55; LM‐MEL‐28, A549) from cultured cell lines were harvested, pelleted, and resuspended in 100 μL of Cultrex RGF BME, Type 2 (R&D Systems). Drops (4–8) of the suspension were then added to each well of a pre‐warmed 12‐well plate, then incubated inverted at 37°C for 30 min before media was added. Cells were then incubated at 37°C in 10% CO2 for 2–4 weeks. Media was then removed before cold TryPLE was added and cells resuspended by pipetting, then incubated for 10 min at 37°C before further shearing by pipetting. This resuspension process was then repeated before the wells are examined under a microscope to ensure a single‐cell suspension is obtained. Cells were then centrifuged at 320 g for 5 min, resuspended in Cultrex and split between two wells of a 12‐well plate with 3–4 domes per well. Spheroids were allowed to grow for ~1 week before media was changed into media containing the vehicle or 25 μM monepantel. Spheroids were imaged at Day 0 and 168 h posttreatment before FACS analysis.Cells  and propidium iodide (PI) (Sigma Aldrich) in Annexin V binding buffer (BD Biosciences). FACS analysis was performed on a BD FACSCanto II flow cytometer (BD Biosciences) and data were analyzed as described above.2.7Cell cycle analysis was performed similarly to the apoptosis assays. After drug treatment, cells were harvested, pelleted, and resuspended in PI buffer  Triton x‐100) prior to FACS analysis on a BD FACSCanto II flow cytometer using appropriate gating parameters for cell cycle. Data were analyzed using FlowJo Software Version 10 cell cycle platform (FlowJo‐LLC).2.86) for 1 h at 4°C in Onyx lysis buffer  glycerol and 1% (v/v) Triton‐x‐100; Sigma‐Aldrich) for all blots except those probed with antibodies to CHOP and BiP where enhanced RIPA  Igepal, 0.3% (w/v) sodium deoxycholate, 1% (w/v) SDS) or for 4EBP1 where Igepal buffer  Igepal) was used instead. Lysis buffers were supplemented with protease inhibitors ). Lysate supernatants  were separated by SDS‐PAGE  before transferring to nitrocellulose and probing with antibodies to: BAK , BAX , ATG7 , BiP , CHOP , actin , p62 , LC3 , 4EBP1 , NOXA .Total protein extracts were prepared by lysing cells  and allowed to adhere overnight. The media was then replaced with media containing 25 μM monepantel or an equivalent volume of vehicle (ethanol) and incubated at 37°C for 48 h. Cells were collected and counted and pellets containing 1 × 105 cells snap‐frozen prior to total RNA extraction performed using the miRNeasy micro kit (Qiagen) following the manufacturer's instructions. The RNA concentration was determined using RNA ScreenTape on the TapeStation instrument (Agilent).Cell lines  were seeded into 6‐well plates . RNAseq libraries were pooled prior to Next Gen Sequencing on an Illumina NextSeq500 platform performed at the Walter and Eliza Hall Institute .2.10Subread aligner in the Rsubread package.featureCounts program.voom function in limma package.t‐statistic was used to assess differential expression of genes.goana and kegga functionslimma were used to test the enrichment of GO (Gene Ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, respectively, in cell lines treated with monepantel compared to vehicle controls. The barcodeplot function in limma was used to generate barcode plots displaying enrichment of KEGG pathways of particular interest. Statistical significance of enrichment of these pathways was tested using the limma function roast.Sequencing reads were mapped to the GRCh38/hg38 reference genome using the 2.11portals.broadinstitute.org/goox/crispick/public) providing the sequences: (1) 5′‐GAAGCTGAACGAGTATCGGC‐3′ and (2) 5′‐GCTGCCAGCTCGCTTAACAT‐3′. Expression of sgRNA was induced by treatment with doxycycline  for at least 3 days prior to Western blotting to determine gene deletion efficiency.Deletion of ATG7 was performed using a lentiviral vector system2.12Bak−/−Bak−/− or Atg7−/− cells) with Prism 8 (GraphPad) software using an unpaired t‐test with Welch's correction. In the case of titrations, the same test was applied to data corresponding to any individual doses where there was an obvious difference in response between cell lines, or using Wilcoxon matched‐paired signed rank test to compare entire dose–response curves.For all apoptosis assays where treatments resulted in >10% change in cell viability, statistical analysis was performed to compare vehicle versus treatments or corresponding treatments between cell genotypes , DMS 53 (lung), OVCAR‐3 (ovarian), HCT 116 (colon)) but not other lines (OVCAR‐5 (ovarian), A549 (lung), U‐87 MG (brain), MEF (mouse embryonic fibroblast)), including the HOSE 6‐3 line that showed only a weak response in the CTG assay Figure . In all We also extended our studies to more physiologically relevant 3D cultures. Here, we generated spheroids of LM‐MEL‐28 cells that undergo significant apoptosis in 2D culture, as well as A549 cells that only showed decreased proliferation with minimal apoptosis. Both 3D cultures responded to monepantel very similarly to how they responded in the 2D cultures; the LM‐MEL‐28 cells underwent apoptosis, while the A549 monepantel‐treated spheroids remained as viable as those treated with vehicle Figures  but grewBAX−/−BAK−/− cells)  for BAX and BAK  Figure . BAX and) Figure . Consist) Figure . This su3.3required for monepantel activity, we used CRISPR‐Cas9 gene editing to generate OVCAR‐3 and MEF cells that were deficient in ATG7 (ATG7−/−), an enzyme required for lipidation of LC3B‐I, converting it to LC3B‐II, an essential step in autophagosome maturation. Both ATG7−/− lines showed significantly decreased ATG7 protein levels , prior to RNA isolation and sequencing.Multi‐dimensional scaling of the data revealed clustering of treated and untreated samples from the same cell lines Figure . DiffereATF4, a master transcription factor associated with ER stress responses, EIF1, EIF4EPB1 and six different amino acid tRNA synthetase 1 genes which are associated with regulation of protein synthesis, and DDIT4 and SESN2 which are associated with mTOR signaling  analysis Table . Of the g Figure . Combineg Figure .The eight commonly downregulated genes were predominantly associated with cell cycle regulation consistent with our data that monepantel impacts cell proliferation. Moreover, barcode plots generated for individual cell lines following enrichment analysis showed strong negative enrichment for genes associated with cell cycle following monepantel treatment Figure .To gain potential insights into the molecular basis for the differential sensitivity of cell lines to monepantel, we next identified genes differentially regulated in the most responsive cell lines  but not in the HOSE 6‐3 cell line Figure . This siHence, the RNAseq data strongly points to two major cellular processes with monepantel treatment—the ER stress pathway and cell cycle regulation.3.5To functionally validate the downregulation of cell cycle genes, particularly in the most responsive cell lines, we next examined the effect of monepantel on cell cycle kinetics. Consistent with the gene expression changes, monepantel induced an increase in the percentage of cells in G0/G1 and a reduction in the percentage of cells in S phase in sensitive OVCAR‐3 cells. While a similar trend was observed in HOSE 6‐3 cells, effects were less pronounced Figure . As thisPMAIP which encodes the pro‐apoptotic BH3‐only protein NOXA, which was upregulated by monepantel in all cell lines irrespective of apoptotic response (Table We next examined induction of se Table . Consistse Table . It shouAtf4 was deleted (Atf4−/−) with monepantel  and time‐dependent (48–120 h) effects on cell viability across the cell lines tested. As previously reported, non‐malignant cell lines tested were generally less sensitive compared to the majority of cancer lines examined. Interestingly, we observed evidence of apoptosis induction in approximately 50% of a representative subset of cell lines examined which is inconsistent with previous studies that concluded apoptosis is not induced by monepantel. The use of BAX/BAK‐deficient cells has become the gold standard for confirming whether drugs are acting through the BCL‐2‐regulated pathway.We first showed that monepantel has broad anti‐cancer effects across multiple cancer types  with similar dose  metabolism. At a protein level, we observed further evidence of an ER‐stress response including increased levels of the ER chaperone protein BiP and the major transcription factor acting downstream of ATF4, CHOP.Notably, deletion of ATF4 increased the sensitivity of MEFs to monepantel, strongly suggestive of ER stress being induced as a survival mechanism, presumably elicited by the unfolded protein response (UPR) associated with ER stress.EIF4EBP gene was transcriptionally upregulated, as was the encoded 4E‐BP1 protein, in response to monepantel treatment. Other reports on the anti‐cancer effects of monepantel place 4E‐BP1 along the mTOR signaling axis and induction of autophagy as critical processes underlying drug activity. Consistent with this, our data show upregulation of two critical negative regulators of mTORC1, DDIT4,SESN2One consequence of the UPR is the global suppression of protein translation which is mediated by the 4E‐BPs (direct targets of ATF4) that inhibit eIF4F assembly required for translation initiation.As in previous reports, we showed evidence of autophagy induction triggered in response to monepantel treatment. However, those previous studies did not provide any details on why or how it was induced. The compelling evidence reported here on the induction of ER stress responses likely fills that gap. Indeed, autophagy induction and mTOR signaling pathways are major consequences of ER stress.In summary, our data provide compelling evidence for the induction of ER stress and associated UPR as being involved in the mechanism‐of‐action of monepantel, and strongly suggests that cell cycle arrest is the major consequence of treatment, although apoptosis and autophagy can both occur downstream of the stress response Figure . What isTiffany J. Harris: Data curation ; formal analysis ; investigation (lead); writing – review and editing (supporting). Yang Liao: Formal analysis (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Wei Shi: Formal analysis (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Marco Evangelista: Investigation (supporting); writing – review and editing (supporting). Bhupinder Pal: Formal analysis (supporting); writing – original draft (supporting); writing – review and editing (supporting). Hamsa Puthalakath: Resources ; writing – original draft (supporting); writing – review and editing (supporting). Roger Aston: Resources (supporting); writing – review and editing (supporting). Richard Mollard: Funding acquisition (supporting); resources (supporting); writing – original draft (supporting); writing – review and editing (supporting). John M. Mariadason: Resources (supporting); writing – original draft (supporting); writing – review and editing (supporting). Erinna F. Lee: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); methodology (lead); project administration (lead); resources (lead); supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Walter D. Fairlie: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); investigation ; methodology ; project administration (lead); resources (lead); supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead).Figure S1‐S7Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.


The immune system has been shown to play a critical role in controlling tumor development. Therefore, therapeutic strategies based on targeting tumors from this point of view are becoming increasingly important. One of the approaches offered by nanotechnology and small molecules in this context is the induction of immunogenic cell death (ICD). In this work, a characterization of the antitumor response of paramagnetic iron nanoparticles conjugated with a platinum compound is presented. A highlight of this work is the significant improvement in the antitumor properties of the nanoconjugate comparing it with the platinum compound. Moreover, the conjugation has been shown to have crucial properties for the induction of ICD, such as the activation of cellular endoplasmic reticulum stress. Our results emphasize the relevance of nanotechnology as a fundamental strategy for the development of new approaches with onco-immunotherapy activity.From chemistry design to clinical application, several approaches have been developed to overcome platinum drawbacks in antitumoral therapies. An in-depth understanding of intracellular signaling may hold the key to the relationship of both conventional drugs and nanoparticles. Within these strategies, first, nanotechnology has become an essential tool in oncotherapy, improving biopharmaceutical properties and providing new immunomodulatory profiles to conventional drugs mediated by activation of endoplasmic reticulum (ER) stress. Secondly, functional proteomics techniques based on microarrays have proven to be a successful method for high throughput screening of proteins and profiling of biomolecule mechanisms of action. Here, we conducted a systematic characterization of the antitumor profile of a platinum compound conjugated with iron oxide nanoparticles (IONPs). As a result of the nano-conjugation, cytotoxic and proteomics profiles revealed a significant improvement in the antitumor properties of the starting material, providing selectivity in certain tumor cell lines tested. Moreover, cell death patterns associated with immunogenic cell death (ICD) response have also been identified when ER signaling pathways have been triggered. The evaluation in several tumor cell lines and the analysis by functional proteomics techniques have shown novel perspectives on the design of new cisplatin-derived conjugates, the high value of IONPs as drug delivery systems and ICD as a rewarding approach for targeted oncotherapy and onco-immunotherapies. Nanotechnology has emerged as a fundamental tool to improve the biopharmaceutical profiles of currently available drugs and small molecules as potential therapeutic drugs. These functionalization strategies have played a key role not only for novel drugs, but also for those conventional therapies that can be reintroduced or repurposed for different clinical applications . A notabNanotechnology and platinum therapies have demonstrated to converge in their antitumor profiles in immunogenic cell death (ICD) . Apart fWithin the wide variety of available nanocomplexes, iron oxide nanoparticles (IONPs) present most of the essential characteristics that make them suitable for onco-immunotherapy. Classical applications of IONPs are related to diagnosis and therapy, such as magnetic resonance images (as contrast agents) or in hyperthermia ,15,16,17® , and 6-well clear flat bottom plates were purchased from Corning . Further details about the reagents of the chemistry section can be found in previous reports   was slowly added dropwise. It was stirred in the dark at 60 °C for 24 h. Next, extraction with methanol was carried out using C-18 cartridges. The solvent was evaporated and the residue was extracted with 4 fractions of methanol (10 mL/fraction). The solution was then slowly applied to a preparative chromatography plate  using isobutyl acetate-methanol (35/65% v/v) as eluent. The compound was extracted with methanol, obtaining 19 mg (17%) of [PtCl(GUDCA)en]. Physical appearance: slightly yellowish solid. Elemental analysis of PtC28H50N3O5Cl : C, 45.49/44.85; H, 6.82/6.73; N, 5.68/5.60; Pt, 26.38/25.67. FT-IR (cm−1) (1H-NMR (3OD): 0.72 , 0.96 , 1.03 , 2.50 , 4.03 . 13C-NMR (3OD): 12.5 (C18), 19.88 (C19), 22.0 (C21), 24.1 (C19), 27.8 (C15), 49.2 , 180.1 (C24), 189.9 (C2′). 195Pt-NMR (3OD): −2269. 2D-NMR  of glycoursodeoxycholic acid (HGUDCA) was added to water (200 mL) and sodium carbonate to facilitate dissolution of bile acid. The solution was then heated at 60 °C until completely dissolved. To this solution, another aqueous solution (200 mL) of , thus obtaining a suspension of IONP@BCP@[PtCl(GUDCA)in] NPs.The platinum complex with glycoursodeoxycholic acid [PtCl(GUDCA)en] was incubated with IONP@BCP NPs for 24 h, incubating a 0.097 mg/mL suspension in a 4.95 × 103 overnight. Dynamic light scattering (DLS) and zeta potential measurements of ferrofluids were performed using a Malvern Zetasizer NS . The measurements were performed after dilution in ultra-pure water and were repeated at least three times on each sample to ensure consistency. Cryo Scanning Transmission Electron Microscopy (cryoSTEM) observations were carried out on a FEI Tecnai F30 microscope .Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was performed in a plasma 40 ICP Perkin-Elmer spectrometer. Samples for ICP-OES were prepared by freeze drying the SPIONs suspensions and digestion of the solid in concentrated HNOThe incubated mixture of the IONPs with the platinum complex, IONP@BCP@[PtCl(GUDCA)en] (1 mL), was placed in an Eppendorf whose top had been replaced by a cellophane membrane . The system was placed in contact with 5 mL of ultrapure water, keeping the device closed to prevent evaporation. It was observed that only the released platinum complex crosses the membrane. The absorbance at 240 nm was then recorded against time. The data fitted a first-order kinetics and a half-life of t1/2 = ln2/k = 266.6 h.® HTB-37™); HT-29 (ATCC® HTB-38™); H460 (ATCC® HTB-177); A549 (ATCC® CCL-185)] were cultured at 37 °C in a humidified CO2 incubator (5% CO2) in complete RPMI media (Jurkat) or DMEM media  (both supplemented with 10% (v/v) FBS and 1% (v/v) P-S). When the cells reached 80% confluence, they were subcultured. For the adherent cells , 0.25% trypsin-EDTA was used to detach the cells. When necessary, the cells were counted using a Neubauer counting chamber and dyed with Trypan Blue.All the human cell lines  with IONPs was confirmed by UV-visible and fluorescence spectra. The visible spectra showed the band at 562 nm corresponding to rhodamine and a charge transfer that hides the characteristic shoulder of the platinum complex. The fluorescence spectrum of the IONP@BCP@[PtCl(GUDCA)en] incubated with the platinum complex displayed a notable increase in fluorescence intensity in rhodamine emission with respect to the NPs without the platinum complex, assigned to FRET mechanism. The proximity of both fluorophores could be calculated by overlapping the emission band of [PtCl(GUDCA)en], which acts as a donor, and the absorption band of rhodamine, which would be the acceptor. This overlap allowed us to calculate the most probable distance (Föster radius) between both fluorophores, which in this case is Ro = 40.3 Å. Values of the Föster radius in the range 20–90 Å indicate an efficient energy transfer between fluorophores  (named in this section as Pt) and novel IONP@BCP@[Pt(GUDCA)en] (named in this section as NP-Pt) were tested in several tumor cell lines. Viability was assessed in five different tumor cells: lung cancer cell lines (H460 and A549), colon carcinoma cell lines (Caco-2 and HT-29), and a T lymphocyte cell line (Jurkat). The time of incubation of the compounds with cells was also taken into consideration (24 h and 72 h) as well as drug concentration  to define an overall perspective of the activity of the platinum compound on its own and after conjugation to the NP . At firs50 values between NP-Pt and Pt were studied. As displayed in 50 value in the NP-Pt treated condition. It was detected that H460 cells showed a significant increase in cytotoxicity for NP-Pt with respect to the Pt. There is also a considerable increase, although not significant, in cytotoxicity in A549 and Jurkat cells between NP-Pt and Pt. However, the most interesting ratio is reached by NP-Pt in the Caco-2 line, where the IC50 value of NP-Pt is decreased up to nine-fold with respect to the reported IC50 of the platinum compound. These results suggest that the properties of the Pt have been improved in certain tumor cell lines by conjugation with NPs, and provide promising results for further research on signaling networks mediated by NP-Pt and Pt and their comparison.Here, the focus was on the effect analysis of the cytotoxicity of the conjugate with respect to Pt. For this purpose, the differences between the ICEvaluation of the intracellular pathways has been carried out by incubation of biotin labeled cell lysates on antibody microarrays as depicted in On one side, apoptosis triggered by DNA in the transition of G1/S phase of the cell cycle, in the DNA replication phase. On the other side, proteins involved in ER stress, a key feature for the development of ICD, have been analyzed B. IntracSubsequently, a more exhaustive analysis of the variations in intracellular protein levels concerning the Pt compound and the NP was conducted. It should be noted that the cells with significant variations were those in which the NP-Pt conjugate promoted greater cytotoxicity. These viability results can be correlated with the variation of intracellular protein concentrations D. AnalyzIn Caco-2 cells, there was a smaller number of significant proteins. In this case, proteins behaved similarly in the NP-Pt and in the Pt compound (SOD2 and IL2) and others that presented an opposite trend (TRAP1 and SOD2). Finally, in Jurkat cells, differences were only observed in two proteins, ABL1 and SOD2. In this case, the SOD2 protein was common to the Caco-2 line and presented different levels in Pt and NP-Pt.This new bile-acid platinum complex and novel nanoconjugate demonstrated cytotoxic properties and immunomodulatory potential profile when testing in different tumor cell lines. The viability of cells was analyzed in different tumor cell lines, demonstrating changes in the behavior of the platinum complex and the nanoconjugate between them. This fact highlights the need to always explore several pharmacological targets to prevent toxicity and to establish a structure–activity relationship of the new compounds synthesized.High-throughput functional proteomics analysis provided a correlation between intracellular signaling, cytotoxicity, and ICD patterns mediated by ER stress activation A. In theHSF1 and SRC proteins play a crucial role in the activation of ER stress and ICD. On the one hand, HSF1 is a transcription factor for the synthesis of “heat shock proteins” such as HSP90 or HSP70, chaperone-like proteins synthesized by the mitochondria in situations of cellular stress, and which are considered DAMPs when released in the cell death process . Along tA similar insight was observed in the Caco-2 cells by activation of mitochondrial signaling. This organelle plays its role in the management of reactive oxygen species through the activation of proteins such as SOD2. This protein converts ROS into non-reactive substances and detoxifies the cell through biochemical proton/electron exchange reactions. In this case, the SOD2 protein is also described as participating in the activation of the UPR . TRAP1, Nanoconjugates from platinum derivates and IONPs antitumoral properties have proved to be very interesting and selective in different tumor cell lines. The improvement of the properties of the platinum complex after inclusion in the nanosystem enhanced the activity significantly. In addition, promising immunogenic properties of the nanoconjugate have been described by ER stress activation. Thus, we can conclude that cisplatin-derived agents together with the high value of IONPs as drug delivery systems and immunogenic cell death are a promising approach to continue searching for novel strategies in nanotechnology and in antitumoral- and immunotherapy. High-throughput functional proteomics-based techniques also provide a suitable approach to deciphering intracellular pathways and slight differences in biopharmaceutical approaches.


Unlike other yeast prions, this prion is unique in that it is efficiently cured by the overexpression as well as the inactivation of Hsp104. However, it is controversial whether curing by overexpression is due to the dissolution of the prion seeds by the trimming activity of Hsp104 or the asymmetric segregation of the prion seeds between mother and daughter cells which requires cell division. To answer this question, we conducted experiments and found no difference in the extent of curing between mother and daughter cells when half of the cells were cured by Hsp104 overexpression in one generation. Furthermore, curing was not affected by the lack of Sir2 expression, which was reported to be required for asymmetric segregation of the [+PSI] seeds. More importantly, when either hydroxyurea or ethanol were used to inhibit cell division, the extent of curing by Hsp104 overexpression was not significantly reduced. Therefore, the curing of [+PSI] by Hsp104 overexpression is not due to asymmetric segregation of the prion seeds, but rather their dissolution by Hsp104.The yeast Sup35 protein misfolds into the infectious [ Saccharomyces cerevisiae, which has made yeast a model organism to study prion transmission  yeast prion, which forms when the translation termination protein, Sup35, misfolds into amyloid fibers or prion seeds [+PSI] prion [+PSI] prion [We have been studying the [on seeds . Since t+] prion ,12,13. H+] prion . It is k+] prion ,20,21. U+] prion . The yea+] prion .+PSI] prion occurs as different variants. Structural studies have shown that the different [+PSI] variants differ in their conformations and biophysical properties [+PSI] variants, which have fewer seeds and more soluble Sup35 than the strong [+PSI] variants, are cured faster by Hsp104 overexpression than the strong [+PSI] variants [+PSI] variants, but not the strong [+PSI] variants  was discovered more than 25 years ago, there is still no consensus on the mechanism of this curing. An understanding of the mechanism is confounded by the finding that other proteins, such as Hsp90 and the cochaperone STI1, also affect the rate of [+PSI] curing by Hsp104 overexpression [+PSI] by Hsp104 overexpression. One proposed mechanism is that this curing is caused by asymmetric segregation of the prion seeds [+PSI] seeds  seeds . However+] seeds . They thAnother model proposed by the Mogk laboratory is that when Hsp104 is overexpressed, it causes curing by binding to the seeds in a nonproductive conformation that inhibits the severing of the seeds . However+PSI] prion, which suggested that this curing is caused by an increase in the trimming of the prion seeds by Hsp104  by Hsp104 overexpression. One prediction is that, if curing is caused by asymmetric segregation of the seeds, then the daughter cells should cure faster than the mother cells, whereas if curing is caused by dissolution of the seeds, mother and daughter cells should cure at the same rate. Another key difference between these models is that curing by asymmetric segregation has been reported to require Sir2 [There are two key differences between the asymmetric segregation model and the dissolution model for the curing of [ire Sir2  and more+PSI] by Hsp104 overexpression. First, using a weak [+PSI] variant, we showed that mother and daughter cells cure at the same rate, consistent with our previous observations [+PSI] variant. In fact, we were able to almost completely block cell division and still cure [+PSI] by Hsp104 overexpression in 10% ethanol nor did curing by Hsp104 overexpression require Sir2. Interestingly, ethanol, which both arrests cell division and induces higher levels of Hsp104 [+PSI] without having to overexpress Hsp104 from a plasmid. These data show that [+PSI] can be cured without cell division and therefore, strongly support the dissolution model of curing by Hsp104 overexpression.Here, we have reexamined the curing of  curing by Hsp104 overexpression from the dissolution model of curing. One feature is the greater retention of seeds in the mother than the daughter cell, which, in turn, leads to faster curing of the daughter than the mother cell [+PSI] strain instead of the stronger [+PSI] variant that we used in our previous study [+PSI] variant was cured by Hsp104 overexpression, we obtained ~50% curing per generation when the weak L1758 [+PSI] variant was used  prior to separating the fluorescent mother cells from the non-fluorescent daughter cells via flow cytometry. These two populations were then plated on ½ YPD plates and the extent of curing was measured using the red/white colony assay. In this assay, only completely red colonies as opposed to red/white sectored colonies indicate cured cells. As shown in +PSI] by Hsp104 overexpression. The L1758 [SIR2 affected the curing of the L1758 [+PSI] strain. Sir2 is a protein that is required for the formation of the polarisome, which, in turn, functions in the transport of misfolded proteins from daughter to mother cell [+PSI] by Hsp104 overexpression would not be affected by Sir2 expression. As shown in +PSI] variant. As with the L1758 [+PSI] strain, the curing of the 779-6A [+PSI] strain by Hsp104 overexpression was not affected by Sir2 expression  in 10% ethanol, ~50% of the L1758 [+PSI] was cured in synthetic medium with glucose as a carbon source  from the SUP35 chromosomal locus. Next, we imaged GFP-labeled Sup35 in [+PSI] strain was cured without cell division by expression of high levels of Hsp104 in 10% ethanol, we first imaged the L2888 [+PSI] strain transformed with the empty vector in galactose and 10% ethanol. As shown in +PSI] strain. These results are consistent with our previous studies that showed that the L1758 [+PSI] strain cures more readily than the L2888 [+PSI] strain and higher level of Hsp104 expression is needed to cure stronger [+PSI] variants [Since the L1758 [variants .+PSI] strain, we transformed the L2888 [+PSI] strain with a plasmid to overexpress Hsp104 from the GAL1 promoter. When Hsp104 is overexpressed from the GAL1 promoter for extended times, there is greater than 10-fold Hsp104 overexpression [To achieve curing of the L2888 [pression . Galacto+PSI] by Hsp104 overexpression. When the cells were retrieved from the chamber slide after overnight imaging, we found that they grew a little more than one generation and during that time, the plating assay showed that 7% of the cells were cured and 44% of the cells were red/white sectored, indicating that the yeasts were cured shortly after being plated. Importantly, none of the cells that we imaged showed formation of large aggregates or tethering of the foci to a subcellular structure, mechanisms that have been proposed to account for asymmetric segregation of the seeds by causing their retention in the mother cell. Therefore, the imaging data showing a loss of foci in the absence of cell division are consistent with the dissolution model rather than the asymmetric segregation model for the curing of [+PSI] by Hsp104 overexpression can occur in the absence of cell division, we applied other methods that have been reported to inhibit cell division. Addition of nocodazole was not effective in inhibiting cell division, but we were more successful when we added the mating pheromone, alpha-factor [+PSI] strain in the absence of Hsp104 overexpression. However, when Hsp104 was overexpressed from the GAL1 promoter for 4 h in L1758 [+PSI], there was ~65% curing of the L1758 [+PSI] strain by Hsp104 overexpression. Since alpha-factor confers at least partial inhibition of growth, it would be expected to affect the extent of curing according to the asymmetric segregation model. However, it did not affect the curing of L1758 [+PSI] yeast by Hsp104 overexpression indicating that this curing is due to the dissolution of the prion seeds. To further confirm that the curing of [a-factor . Based o+PSI] in the absence of Hsp104 overexpression  of the mother–daughter pairs showed no significant difference in prion seed number . In fact+PSI] strain than predicted by the asymmetric segregation model. In ethanol, we effectively completely arrested cell division and still were able to cure the L1758 [+PSI] strain. As a control, since the curing of the [URE3] yeast prion by overexpression of Btn2 has been reported to be due to asymmetric segregation of the [URE3] seeds  seeds ,47, we e+PSI] strain in ethanol. Ethanol stress has been shown to both arrest cell growth and increase Hsp104 expression from the HSP104 promoter [+PSI] yeast, indicating ethanol stress increases the amount of soluble Sup35 [+PSI] variant, it did not cure stronger [+PSI] variants; e.g., L2888 [+PSI]. Furthermore, our results showing that ethanol alone cured the weak L1758 [+PSI] variant might explain why there is such a low frequency of [+PSI] in wild-type strains [+PSI] variants that spontaneously form in yeast. Unexpectedly, we also observed that it was not necessary to overexpress Hsp104 to cure the L1758  cells overexpressing Hsp104 in 10% ethanol. The images showed a progressive loss of foci and a complete loss of foci in some cells without cell division. These results are not consistent with the asymmetric segregation model for curing since the seeds did not form large aggregates and were freely mobile with no anchoring to a subcellular structure. The loss of foci independent of cell division is consistent with our previous observation that Hsp104 causes a loss of prion seeds due to the trimming activity of Hsp104, which ultimately produces curing when all of the seeds are eliminated by trimming [We also imaged GFP-labeldSup35 foci in [trimming . +PSI] is heat shock [+PSI] by asymmetric segregation of the prion seeds, a suggestion that was supported by their finding that deleting proteins which were essential for polarisome formation markedly inhibited curing. However, the findings of the laboratories differ in that the Chernoff laboratory reported that the daughter cells were preferentially cured relative to the mother cells whereas the Serio laboratory reported the opposite; i.e., that the mother cells were preferentially cured relative to the daughter cells. Furthermore, the Chernoff laboratory proposed that the asymmetric segregation of the prion seeds was due to the sequestering of the aggregated prion seeds in the mother cell, whereas the Serio laboratory proposed that the asymmetric segregation was due to the presence of higher levels of Hsp104 in the mother cells than in the daughter cells. Despite the differences between these laboratories, their results make clear that heat shock causes [+PSI] curing by a very different mechanism than ethanol stress. Therefore, one cannot generalize that because heat shock causes [+PSI] curing by the mechanism of asymmetric segregation of the prion seeds, this is also the case for other conditions where Hsp104 is overexpressed; e.g., ethanol stress. Rather, our results clearly show that ethanol stress causes [+PSI] curing by the dissolution mechanism; i.e., curing is caused by the trimming activity of the highly expressed Hsp104. Another stress that cures  by Hsp104 overexpression was also dependent on the N-terminal domain of Hsp104, indicating that this domain functions in the trimming of the prion seeds by Hsp104  yeast were counted as [+PSI]. A minimum of three colonies were averaged for each experiment. Yeasts were grown at 30 °C on synthetic defined medium  with complete supplement mixture or the appropriate amino acid dropout for selection and maintenance of the plasmid (Sunrise Scientific). Synthetic galactose  medium contains both 2% galactose and 2% raffinose in place of glucose. ½ YPD solid medium used in the plating assays contains 0.5% yeast extract, 2% peptone, and 2% glucose. Cultures were always maintained in active growing conditions (ODCells were kept in the logarithmic phase (OD 600 < 0.6) for 24 h prior to experiment and were grown in a 30 °C incubator. At the start of the experiment, cells were diluted to an OD 600 of 0.20. To inhibit cell division, we used 15 µg/mL of nocodazole , 5 µg/mL alpha-factor  or 0.2 M hydroxyurea (Sigma). To arrest with ethanol, 100% ethanol was added to the cells to make a final concentration of 10% ethanol. Yeast growth was also monitored by measuring the absorbance of the samples at 600 nm using the Synergy H1 microplate reader (Biotek). Yeast in a 48-well tissue culture plates (Sarstedt) were maintained with constant shaking at 30 °C. Measurements were obtained at 10 min intervals for 24 h. +PSI] yeasts were kept in the logarithmic phase (OD 600 < 0.6) for 24 h prior to experiment and were grown at 30 °C. For staining, fluorescent disodium 28 brightener (Sigma) was added to the cells at a final concentration of 0.005% for 5 min [L1758 [or 5 min . After 52 chambered coverslips (Cellvis).Fluorescence images were obtained on the Zeiss LSM 880 Airyscan confocal microscope equipped with a 63× 1.4 NA objective. Z-stack confocal images were taken for all cells. The yeasts were imaged in 8-well 25-mmCell lysis and Western blotting were performed as described previously  The foll


Cancer cell lines are invaluable model systems for biomedical research because they provide an almost unlimited supply of biological materials. However, there is considerable skepticism regarding the reproducibility of data derived from these in vitro models.Chromosomal instability (CIN) is one of the primary issues associated with cell lines, which can cause genetic heterogeneity and unstable cell properties within a cell population. Many of these problems can be avoided with some precautions. Here we review the underlying causes of CIN, including merotelic attachment, telomere dysfunction, DNA damage response defects, mitotic checkpoint defects and cell cycle disturbances.In this review we summarize studies highlighting the consequences of CIN in various cell lines and provide suggestions on monitoring and controlling CIN during cell culture. However, this assumption has been disputed by a growing body of research, in which genetic instability has been explicitly characterized in various cell lines during in vitro culture.TP53, PTEN, and KRAS.Genomic instability, one of the enabling characteristics of cancer,In a research setting, CIN is often a consequence of CRISPR‐Cas9 gene editing. One of the major concerns associated with the use of CRISPR‐Cas9 is the potential for unintended off‐target effects.CIN poses a significant challenge to cancer research because the genetic and phenotypic heterogeneity that arises due to CIN in cell lines can compromise the accuracy and reproducibility of experiments. Additionally, the genetic changes that occur in cell lines over time may not accurately reflect the genetic alterations present in the original tumor. While background controls are often included in published studies, they may not fully capture the indirect effects of CIN. For instance, Chromosome segregation errors during mitosis can produce unstable micronuclei that rupture and release genomic DNA into the cytosol.This review will provide an overview of the fundamental mechanisms of CIN, highlight the implications of CIN in fundamental research, and discuss how CIN can be monitored and controlled during cell culture.2Chromosome stability relies on the correct separation of duplicated chromosomes to daughter cells during mitosis.Over the past two decades, extensive work has been dedicated to understanding the molecular principles underlying CIN. To date, numerous processes that contribute to CIN have been identified, including defects in kinetochore‐microtubule (k‐MT) attachment, telomere maintenance, DNA damage response (DDR), mitotic checkpoints and cell cycle regulation. While these mechanisms have varied prevalence across different cell types and were assumed to be independent, they are intrinsically connected and often interact with each other.3Chromosome segregation is the fundamental process of cell division; fidelity in this process during mitosis is crucial for genomic stability. Correct bipolar attachment is a prerequisite for proper chromosome segregation, which demands that kinetochores on every sister chromatid pair attach to microtubules emanating from opposite centrosomes Figure . There a4Another mechanism that has been proposed to induce CIN is defective telomere maintenance. Telomeres are regions of repetitive DNA sequences found at the ends of chromosomes that protect the genetic material from degradation or fusion with other chromosomes.5Chromosomal DNA is constantly damaged by internal and external factors, such as ionizing radiation, ultraviolet radiation, and reactive oxygen species.A novel connection between DDR and CIN was discovered by Bakhoum et al.6The mitotic checkpoint, also known as the spindle assembly checkpoint (SAC), is a critical surveillance mechanism that maintains the correct chromosome number during mitosis.The key components of the SAC include Mitotic arrest deficiency 1 (Mad1), Mad2, budding uninhibited by benzimidazoles 1 (Bub1), Bub3, BubR1, and Cell division cycle protein 20 homolog (Cdc20).7The mechanism of cell cycle disturbances is a broad topic because it is implicated in many of the mechanisms mentioned above. There are many cell‐cycle regulators that play an important role in CIN, such as cyclin E, p53 and forkhead box protein M1 (FoxM1).8Established cell lines have played an essential part in biomedical research as an inexhaustible source of biological material. However, researchers have generally found considerable CIN in many different cell lines, and questions have been raised about the comparability of cell lines to the original cells from which they were derived. Previous studies have shown that cell cultures have the potential to undergo major karyotypic changes over time due to CIN, which results in the constant emergence of variants and subsequent heterogeneity within an assumed monoclonal cell line.Vargas‐Rondón9The source of the cell lines should be carefully selected. Numerous academic or commercial organizations have established cell culture banks, such as American type culture collection (ATCC), Cellbank Australia, and Coriell cell repository.Frozen stocks of continuous cell lines should be produced at specific time points, so researchers can regularly return to these stocks to minimize the passage number or repeat experiments. Also, when a cell line is not used, it is preferable to return it to a cryopreserved bank to avoid unnecessary long‐term cell culture and passaging. When working with cell lines, it is recommended that primary tests be conducted to specify a passage number range that is appropriate for maintaining consistent cell functions that is, only a limited number of passages are performed before returning to another batch of the same stock. Importantly, since there is no easy way to determine how many times a cell culture has been passaged, every passaging must be properly recorded. When thawed from the freezer the passage number continues and is added to the number at freezing.2 levels. Additionally, good laboratory practices are also critical to prevent contamination. Mycoplasma infection is a common contamination issue that can cause CIN by releasing enzymes that break down DNA, leading to mutations and chromosomal aberrations.The growth environment of cell lines is a crucial factor that can significantly impact the stability of the cells. Even slight variations in the culture conditions can lead to significant changes in the characteristics of the cells. Frequent passages and transfers between laboratories can expose cells to varying environments, potentially leading to the selection of clones better adapted to the new conditions.Observing cell morphology under an inverted microscope is considered the simplest way to assess the quality and stability of cells. In general, cell lines with high CIN levels often undergo morphological changes and display heterogeneous cellular morphology.Cell line authentication and characterization should be performed regularly using suitable methods, and baselines should be established to check for unacceptable changes throughout experiments. If certain cellular products or functions are being investigated, these characteristics should also be monitored to determine whether and how they change over time in culture. Currently, short tandem repeat (STR) profiling is widely used as a standard method for authenticating human cell lines because it is inexpensive and easy to interpret.Cell line instability has been a long‐standing issue highlighting concerns about data validity and reproducibility. While it is impossible to prevent a cell line from evolving in culture, researchers should take actions to improve the reliability and reproducibility of cell‐based research. Here we set out some recommendations for researchers to monitor and control CIN during cell culture.10As discussed in this review, CIN is a complex and multifaceted phenomenon that is caused by many different mechanisms and is regulated by hundreds of genes. Further research will help identify the central drivers and suppressors of CIN, as well as their interactions. Ultimately, a better understanding of CIN will enable us to prevent its associated issues during cell culture.Current research on CIN is challenging due to the lack of a standardized definition and measurement criteria for CIN. Many studies based their estimates of CIN levels on their own data, and there is a lack of consensus about how to measure the level of CIN. Such ambiguity makes it difficult to interpret data and compare the results of different studies. CIN is defined as an increase in the rate of gain or loss of whole or fractions of chromosomes.Not only do we need to clarify the definition of CIN, but we also need the appropriate techniques to measure it. The broad range of chromosomal abnormalities makes it extremely difficult to quantify CIN. Apart from common structural aberrations like translocations, duplications, deletions, and inversions, there are many unclassified aberrations that cannot be easily detected or quantified. One such chromosomal aberration is chromothripsis, a process of rapid and massive genome re‐organization that results in highly rearranged chromosomes.As discussed earlier, CIN is, by definition, the rate of gain or loss of fractional or whole chromosomes in a population of cells. Hence, the measurement of CIN requires monitoring of chromosomal changes over time. Such time‐based measurements are feasible on cell cultures but impossible on fixed tissue. Alternatively, CIN can be more readily assessed by measuring cell‐to‐cell variability at a single time point.Understanding the consequences of chromosomal instability (CIN) in cancer cell lines is important for cancer research in several ways. The presence of CIN can be used as a diagnostic and prognostic marker for cancer and help identify subtypes of cancer that are more likely to have a poor prognosis or be resistant to treatment. Bold et al.CIN is a key driver of cancer development and progression. Understanding the mechanisms and consequences of CIN can help identify potential preventive strategies, such as lifestyle changes or targeted interventions to reduce the risk of cancer. Understanding the consequences of CIN in different cancer cell lines can help guide the development of personalized treatment strategies that consider the specific molecular characteristics of an individual's cancer. For example, Martín et al.To conclude, cell lines remain a powerful tool for biomedical research, but the high level of CIN during culture and the variation across cell line strains must be well considered in experimental design and data interpretation. The scientific community need to work together to increase engagement and awareness. Organizations can formulate and refine the guidelines for the handling of cell lines. We, as responsible researchers, should take practical steps to address the issues arising from unstable cell lines and ensure the validity of our research.Zichen He: Investigation (lead); writing – original draft (lead). Andrew David Wilson: Formal analysis (supporting); project administration (supporting); supervision (supporting); writing – original draft (supporting). Fenella Jean Rich: Investigation (supporting); resources (supporting). Diane Kenwright: Investigation (supporting); resources . Aaron John Stevens: Supervision (supporting); writing – review and editing (supporting). Yee Syuen Low: Investigation (supporting); project administration (supporting); resources (supporting). Michelle Thunders: Conceptualization ; funding acquisition ; project administration ; resources ; supervision (lead); writing – review and editing .The authors have stated explicitly that there are no conflicts of interest in connection with this article.This is a review article and did not require consent or ethical approval.


Salvia officinalis L.) collected along the Adriatic coast of Croatia. Scanning electron microscopy (SEM) was used to examine the morphology of the stem and leaf surfaces. Essential oil excretory glands were detected on both the leaves and stem surfaces. The essential oils were isolated by hydrodistillation, and their chemical composition was determined by gas chromatography and mass spectrometry (GC-MS). Sage essential oils were mixtures of terpene compounds, among which the most common were: α- and β-thujone, camphor, and 1,8-cineol. Cytotoxic activity was tested using MTS assay on multiple cell lines: normal and immortalized fibroblasts (HF77FA and HDF-Tert), immortalized lung line (BEAS-2B), and breast adenocarcinoma (MDA-MB-231). The growth of treated cells was determined relative to control conditions without treatment. The immortalized lung line was the least resistant to the activity of the essential oils, whereas immortalized fibroblasts were the most resistant. Statistical analysis has connected the cytotoxic effect and chemical composition of the studied essential oils. To the best of our knowledge, this work is the first testing of the cytotoxic activity of S. officinalis EO’s on the BEAS-2B, HF77FA, and HDF-Tert cell lines. The presented data on essential oil chemical composition and cytotoxic effect on 4 types of human cells supports pharmacotherapeutic potential this plant is known to have.In our study, we investigated the chemical composition and cytotoxic activity of essential oils isolated from Dalmatian sage ( Lamiaceae family  vs. normalized response—Variable slope in-built equation provided in the software. Calculated IC50 values are expressed as mean ± standard deviation. Data distribution was determined by performing Shapiro-Wilk normality test. Data were normally distributed (s tested . To inteS. officinalis using SEM, following two protocols that preserve the natural structure of the tissue surface. We also analyzed the chemical composition of seven S. officinalis EOs using GC/MS and assessed cell viability using MTS assay at various doses of EOs on healthy and cancerous cells.In this study, we investigated the stem and leaf tissue structures of S. officinalis EOs showed that they contain varying percentages of common chemical constituents: α- and β-thujone, camphor, and 1,8-cineole. The biological effects of sage EOs on cell viability were tested on different healthy and cancerous cells. The results showed that skin and immortalized cells were more resistant to sage EO, while the BEAS-2B cell line was the least resistant compared to the other cell lines. Using one cancerous and three healthy cell lines, we investigated the cytotoxic effect of our EOs to get results that simulate physiological conditions. Our results showed that two fibroblast and cancer-derived cell lines were less susceptible to S. officinalis EO than non-cancer cell lines derived from lung tissue. Healthy cell lines were more resistant to cytotoxic agents than cancer cell lines.The chemical analysis of seven S. officinalis EOs are related to the differences in chemical composition.The statistical analysis and correlation of the chemical composition of EO and the tested cell viability have shown that the different cytotoxic activities of S. officinalis EOs on BEAS-2B, HF77FA, and HDF-Tert cell lines. The data presented on the chemical composition of the EOs and the cytotoxic effect on 4 types of human cells support the pharmacotherapeutic potential that this plant is known to possess. High IC50 values of EOs tested on healthy human dermal fibroblasts give more arguments to the traditional use of S. officinalis EOs for skin treatments and cosmetic purposes. Further investigation of biological activities of mixtures of isolated chemical constituents and/or combinations of chemical isolated constituents, present in S. officinalis essential oils, could provide a more in depth understanding of the pharmacological properties of this plant.To the best of our knowledge, this is the first study on the cytotoxic activity of


The Human Pluripotent Stem Cell Registry established a database of clinical studies using human pluripotent stem cells (PSCs) as starting material for cell therapies. Since 2018, we have observed a switch toward human induced pluripotent stem cells (iPSCs) from human embryonic stem cells. However, rather than using iPSCs for personalized medicines, allogeneic approaches dominate. Most treatments target ophthalmopathies, and genetically modified iPSCs are used to generate tailored cells. We observe a lack of standardization and transparency about the PSCs lines used, characterization of the PSC-derived cells, and the preclinical models and assays applied to show efficacy and safety. https://hpscreg.eu/clinical_studies). Persistent lack of standardization and transparency about the source cell lines, characterization of the therapeutic product, applied preclinical models, and efficacy and safety assays are mounting obstacles to clinical study development and reproducibility.In this perspective, Kobold et al. identify 109 clinical studies using pluripotent stem cells performed in 14 countries targeting 44 diseases by analyzing data from hPSCreg ( For efficient progress in the field at this early stage, it is therefore important to have access to information on ongoing clinical studies, transparency about cell sources, derived cells as a final product, relevant efficacy and safety assays, and outcomes. Because this information is difficult to obtain, a clinical study database dedicated to the use of human PSCs as starting material for cell therapies has been established . It is pOur data confirm a strong trend toward studies using human induced PSCs (iPSCs), while human embryonic stem cells (ESCs) continue to be used in a significant portion of roughly 50% of all studies. While the majority of studies up to 2017 utilized ESCs, since 2018, most studies were based on hiPSC-derived products. There is a remarkable drop of clinical studies initiated in 2022 compared with the previous two periods, which may reflect the impact of coronavirus disease 2019 (COVID-19).https://hpscreg.eu) provides a resource for available iPSC and ESC lines and certifies their identity, ethical provenance, and biological properties, among other related information. Together with its clinical study database, a resource linking information on PSC lines and clinical trials is available at hPSCreg. However, most clinical trials using iPSCs do not publicly disclose the PSC lines used or register these lines in hPSCreg. In fact, we are only able to trace the source PSC cell lines to 11 ESC lines and 1 iPSC line, of which 10 and 0 lines, respectively, are registered in hPSCreg . The name of each clinical trial sponsor is a required data element in the ICTRP standards for clinical trial registries who wish to be included in the ICTRP registry network ). In most cases (102 of 109 studies), the sponsor can be identified from the clinical study registry record. Within the last 5 years, industry has sponsored more clinical studies since the first PSC-derived cell therapy study was started in 2010 ;  in 2010 . IndustrThere are clear differences between countries. While in the United States, the number of industry-sponsored trials outweighs academically/government-sponsored trials  and are strongly represented even before 2018, Japan and China have only within the last 5 years started trials with industry sponsors, and the bulk of their trials remains predominantly academically/government sponsored as a result of strong national funding programs . Only a Within the last decade, the number of PSC-derived cell types used for therapies has grown from 3 to 22 cell types, encompassing 44 target indications/diseases . The mosclinicaltrials.gov: NCT02302157).The multiplicity of studies employing a single cell type also reflects different clinical study phases of the same cell product  or clinical application of the cells to diseases where cell degeneration is a common denominator. Another important additional consideration is clinical testing of any medical devices that have been developed to deliver the cell product to the appropriate site in the patient . For exawww.hpscreg.eu/browse/trials). For these 31 publications, 5 include preclinical work, 1 reports only preclinical data, 5 include manufacturing protocols and cell quality assessment parameters, and 30 report clinical study design and outcomes. Reports on termination, withdrawal, or failure of clinical studies are not published and, unfortunately, only in a few cases recorded in public registries. One could assume that those 17 studies that published outcome results are completed, and, thus, that, of the 109 recorded clinical studies, at most 93 are currently active.Disease targets for PSC-based therapies have broadened moderately . While 1Study duration, as defined by time between study start and study, is available for 90 of 109 studies and varies between 1 and 19 years . The time frame from start to completion may serve as a surrogate indicator for follow-up duration. No correlation was found between the duration of study, national regulatory framework, year when the study started, or disease category.https://hpscreg.eu; The sourced database hosted by hPSCreg uses a combination of search tools to gather information on worldwide clinical studies using PSCs as source material. The data are manually curated before being entered into the clinical study database and made publicly accessible at the hPSCreg portal (https://icmje.org/recommendations/). Moreover, information on cells used  is only occasionally available. Furthermore, although employed in hPSCreg, standardized ontologies, taxonomies, or terms to describe cell- or disease-specific data are rarely used in other clinical trial registries. Access to the data, comparability, and searchability require further use of standard terms and taxonomies; for example, for therapeutic cell type, target clinical feature or disease, or experimental procedures.Public clinical study registries, resources, and trial publications often do not use the same standards for reporting, which makes comparisons of data across the field and understanding potential causes of trial failure difficult. The hPSCreg database orients its reporting requirements on the WHO data fields to ease cross-linking of information, adhering to the International Committee of Medical Journal Editors (ICMJE) recommendations  encourages sharing of output data and study-related publications, albeit on a voluntary basis. For example, the ICMJE introduced a requirement for authors to report how individual patient data (IPD) will be shared when publishing clinical trial results . Relevant resources are now emerging, such as the European Union (EU)-funded COST (Cooperation in Science & Technology) innovation network “Generation of Human Induced Pluripotent Stem Cells From Haplo-Selected Cord Blood Samples” ; however, haplotyped iPSC lines have not yet been broadly exploited for clinical studies , the United States (Regenerative Medicine Advanced Therapy [RMAT] designation), and now in the EU (Priority Medicines [PRIME]) to streamline the process for approval of cell therapies . These trends confirm an enormous imbalance of global resources and likely will perpetuate unequal access to therapeutic benefits, which, of course, is not restricted to advanced therapies . Measureherapies . FurtherThe number of PSC-based cell clinical studies has rapidly increased in the last 5 years. In the next decade, we expect to see more strategic collaborations within industry as big pharma seeks to acquire the expertise of smaller companies to enter the cell therapy arena. At the same time, smaller companies forge strategic alliances with one another through licensing or collaboration agreements . MergersS.K., collection and assembly of data, data analysis, and generation of figures; N.B., implementation of the database and collection and assembly of data; G.S. and A.K., conception, design, and writing of the manuscript; S.C.M., writing and revision of the manuscript; N.M., data analysis and interpretation, manuscript writing, and generation of figures and tables.