In vitro drug combination screens have not been performed on NF1-associated HGG, hindering our ability to develop informed clinical trials. Here we present the first in vitro drug combination screen  on the only human NF1 patient derived HGG cell line available and on three mouse glioma cell lines derived from the NF1-P53 genetically engineered mouse model, which sporadically develop HGG. These mouse glioma cell lines were never exposed to serum, grow as spheres and express markers that are consistent with an Oligodendrocyte Precursor Cell (OPC) lineage origin. Importantly, even though the true cell of origin for HGG remains elusive, they are thought to arise from the OPC lineage. We evaluated drug sensitivities of the three murine glioma cell lines in a 3D spheroid growth assay, which more accurately reflects drug sensitivities in vivo. Excitingly, we identified six compounds targeting HDACs, BRD4, CHEK1, BMI-1, CDK1/2/5/9, and the proteasome that potently induced cell death in our NF1-associated HGG. Moreover, several of these inhibitors work synergistically with either MEK or PI3K inhibitors. This study forms the basis for further pre-clinical evaluation of promising targets, with an eventual hope to translate these to the clinic.Neurofibromatosis Type 1 (NF1) patients develop an array of benign and malignant tumors, of which Malignant Peripheral Nerve Sheath Tumors (MPNST) and High Grade Gliomas (HGG) have a dismal prognosis. About 15–20% of individuals with NF1 develop brain tumors and one third of these occur outside of the optic pathway. These non-optic pathway gliomas are more likely to progress to malignancy, especially in adults. Despite their low frequency, high grade gliomas have a disproportional effect on the morbidity of NF1 patients.  Neurofibromatosis 1 (NF1) is an autosomal dominant tumor predisposition syndrome impacting approximately one in every 3000 births , 5. AbouNF1 tumor suppressor gene due to inactivating mutations or deletions [NF1 individuals have a germline mutation in the NF1 gene that encodes neurofibromin, a GTPase activating protein that negatively regulates the RAS pathway. Mechanistically, neurofibromin accelerates the conversion from the active GTP-bound RAS to its inactive GDP-bound form. NF1-HGG have complete functional loss of the eletions , leadingeletions . Recentleletions , 10.https://cellmodelpassports.sanger.ac.uk/passports/SIDM01460). To our knowledge, this is the first drug sensitivity study performed on a HGG cell line derived from an NF1 patient.To date, limited drug sensitivity studies have been performed on NF1 associated High Grade Gliomas. This is in part due to the lack of HGG cell lines derived from NF1 patients; only a single human NF1-HGG is publicly available (TM-31). This cell line has inactivating mutations of NF1 , ATRX , CDKN2A (homozygous deletion) and TP53 (C238G)  sporadi- in cis . Signali- in cis –15. Our In our study, we sought to identify NF1-HGG sensitivities against agents targeting downstream RAS effector pathways (MAPK and AKT pathways) in combination with a broad array of approved and investigational anti-cancer drugs. Although NF1-HGG arises from aberrant RAS pathway hyperactivation, it is well established that monotherapy targeting a single pathway often renders limited therapeutic benefit especially against malignant, high-grade tumors. In recent years, combinatorial drug therapies have significantly altered the prognosis of many cancers in children and adults . ConsideThis research was determined to be none-human subject research by the Children’s Hospital Philadelphia Committees for the Protection of Human Subjects (IRB). The need for consent was waived.The NF1 patient-derived HGG cell line TM-31 (RRID:CVCL_6735) , purchasNf1 and Tp53; mutant alleles are located in cis. The 17, 5653, and 5746 glioma cell lines are grown in NeuroCult™ Mouse and Rat Basal Medium  supplemented with NeuroCult™ Mouse and Rat Proliferation Supplement , 1% Penicillin Streptomycin , 20 ng/mL human recombinant epidermal growth factor  and 10 ng/mL human recombinant fibroblast growth factor .Mouse NF1-HGG cell lines 17, 5653, and 5746 were derived from three distinct high-grade murine brain tumors sporadically developed by the NPcis genetically engineered mouse model. The germline of these mice is heterozygous for ® Nuclight Red Lentivirus  such that they will produce an EF1α promoter driven nuclear restricted red fluorescent label (nuclear RFP) and flow sorted to select for successfully transduced cells .All cells are cultured at 37°C in a humidified 5% CO2 incubator. Cell lines were transduced with IncucyteThe following antibodies were purchased from Cell signaling Technologies: pERK , total ERK , pAKT , total AKT , Ubiquitin , MYC , pCHEK1 S296 , BMI1 , H3K27Ac , total H3 , CDK9 , Vinculin , CPNase  and MAG . Beta-Actin  was purchased from Novus, pS2 RNApol II  from EMD Millipore, SOX2  from R&D systems and NG2  from Invitrogen.All drugs and compounds were purchased from Selleckchem . A complete list of compound names, sources, catalogue numbers, are listed in ® S3 Live-Cell Analysis System  and serve as a proxy for the number of cells present. Analysis parameters are listed in Two thousand cells/well of 17, 5653, or 5746 glioma cell lines are plated in 96-well round-bottomed ultra-low attachment plates  and used for spheroid drug screening. Cells are plated in 100uL of complete culture media, centrifuged  and incubated overnight to stimulate a single colony from forming at the bottom of the round well. Twenty-one drugs were screened, using three concentrations . We used the Incucyte® S3 Live-Cell Analysis software to identify and count the number of individual cells, as defined by the number of RFP+ nuclei, across the 72 hours. Analysis parameters are listed in The TM-31 cells were plated  in 96-well flat-bottom tissue culture treated plates (Corning 3596). Cells were plated in 100uL culture media and incubated overnight before adding drugs on Day 0. Twenty-one drugs were screened, using three concentrations  for each time point relative to 0 hours was calculated. Drugs were considered for a broader follow-up combinatorial evaluation if our initial treatment caused more than 50% cell death in 72h  at concentrations below the maximum serum concentration that can be safely attained during pharmacokinetic drug studies for clinical trials (Cmax). These Cmax obtained from the literature (Table 2), and values were converted from mg/ml to nM.https://synergyfinder.fimm.fi/; parameters: LL4 curve fit, ZIP method for synergy score calculation).The synergy of drug combinations was evaluated in the mouse 5746 and TM31 glioma cell lines. The 17 glioma cell line was used for the PTC596/Trametinib combination as this combination showed limited sensitivity than the 5746 line . Cell liOur initial 21 compound drug screen was performed in triplicates, the synergy study was performed in duplicates. IC50 and Lethal Dose 50 (LD50) values and 95% confidence intervals were calculated to assess drug sensitivity (GraphPad Prism 9 software) and tabulated in Nf1  and Tp53  was performed [GAACATGATTCTCTTTTGGACCAC and ACCTGTTTCAAATGTGACCCTTT and sequencing primer GGATACCATACTTGCAGAGC) and NF1 mutation  in the TM-31 cell line was confirmed by sequencing. Western blot was performed to evaluate the expression of OPC lineage markers and for the expression of TP53 and CDKN2A in TM-31. Target inhibition of the drugs that caused potent cell death in our glioma lines inhibited was evaluated by western blot as well. Glioma cell line 17 was treated for 24h with 20nM Trametinib, 1μM GDC-0941, 5nM bortezomib, 25nM Dinaciclib, 500nM JQ1, 100nM LY2606368, 500nM PTC596, or 1μM Vorinostat and target inhibition was evaluated by comparing the expression levels of pERK, pAKT, Ubiquitin, pS2 RNApol II and CDK9, MYC, pCHEK1, BMI1 and H3K27Ac respectively.To characterize our mouse glioma cell lines, a genotyping PCR for erformed . The repNf1 and Tp53, in cis on mouse chromosome 11. Tumors form when a cell loses the wildtype copy of Nf1 and TP53, usually due to whole chromosome loss. Importantly, the loss of the wildtype chromosome occurs sporadically, each tumor thus is unique. About 10% of these NPcis mice form high grade gliomas [Nf1 and TP53 as evidenced by the loss of the wildtype Nf1 and Tp53 alleles of the genotyping PCRs  and the MEK inhibitor Trametinib (GSK1120212) were selected. Both agents are currently in active or recently completed clinical trials for solid and brain malignancies. https://www.cancerrxgene.org/compounds) or in the literature [The sensitivity to 21 compounds as single agents and in combination with Trametinib or GDC-0941 was determined. terature , 24 , culture media (serum versus stem cell media), culture method (2D vs 3D assays) and/or species (human versus mouse) and will require the evaluation of other human NF1 HGG once they become available. As mentioned before, it is believed that 3D stem cell cultures represent more accurately the biology of tumors than 2D serum grown lines. Our screen provides valuable insight into potential drug targets for NF1 associated HGG. Follow-up in vivo pre-clinical studies will have to confirm if the identified drug targets and drug combinations are able to shrink tumors in an in vivo setting at well tolerated doses and enhance survival in preclinical models of NF1-HGG.Six drugs combined with MEK or PI3K and the combination of MEK and PI3K inhibitors worked synergistically in our mouse NF1-HGG stem cell line. None of the combinations worked synergistically in the human TM-31 line (12 additive and 1 antagonistic combination). Crucially however, baseline sensitivity to these compounds remains high in our human NF1 HGG line. Moreover, while synergistic effects of drug combinations point to a mechanistic interaction between both compounds, the fact that a combination therapy work additively should not exclude them from clinical trials or further investigation. It is unclear if the observed differences between our mouse and human line are due to differences in driver mutations Click here for additional data file.S1 TableOverview of the drugs used in this study including targets and catalogue numbers.(XLSX)Click here for additional data file.S2 TableDrug sensitivity data expressed in log2 fold change in the different cell lines. A value <0 denotes that less cells are present compared to day 0.(XLSX)Click here for additional data file.S3 TableIncucyte analysis settings and parameters used to analyze our 3D spheroid assays.(XLSX)Click here for additional data file.S4 TableIncucyte analysis settings and parameters used to analyze our 3D spheroid assays.(XLSX)Click here for additional data file.


Despite its ubiquity and essential nature across the plant and animal kingdoms, PRPP synthetase displays species-specific characteristics regarding the number of gene copies and architecture permitting interaction with other areas of cellular metabolism. The impact of mutated PRS genes in the model eukaryote Saccharomyces cerevisiae on cell signalling and metabolism may be relevant to the human neuropathies associated with PRPS mutations. Human PRPS1 and PRPS2 gene products are implicated in drug resistance associated with recurrent acute lymphoblastic leukaemia and progression of colorectal cancer and hepatocellular carcinoma. The investigation of PRPP metabolism in accepted model organisms, e.g., yeast and zebrafish, has the potential to reveal novel drug targets for treating at least some of the diseases, often characterized by overlapping symptoms, such as Arts syndrome and respiratory infections, and uncover the significance and relevance of human PRPS in disease diagnosis, management, and treatment.Phosphoribosyl pyrophosphate synthetase (PRS EC 2.7.6.1) is a rate-limiting enzyme that irreversibly catalyzes the formation of phosphoribosyl pyrophosphate (PRPP) from ribose-5-phosphate and adenosine triphosphate (ATP). This key metabolite is required for the synthesis of purine and pyrimidine nucleotides, the two aromatic amino acids histidine and tryptophan, the cofactors nicotinamide adenine dinucleotide (NAD T. TPRPS1 ectively b, and bopression ,147. Thehionine) . We havemely low ,76,80, onthetase . Neuronsnthetase ,148—a fuetase  fn levels . The com pathway . MYC proto-oncogene, thus implicating PRPS2 in CRC metastasis. Previously, Qiu et al. (2015) [PRPS1 are upregulated in CRC. Knockdown of hPRPS1 or upregulation of the micro-RNA, miR-124, a tumour suppressor, leads to reduced DNA synthesis and cell proliferation in CRC cell lines, revealing potential therapeutic targets. There are miRNA integrated signatures for hPRPS1 mRNA and hPRPS2 mRNA interactions, which may represent potential biomarkers and therapeutic targets for CRC [Interestingly, in a recent study conducted by Miao and Wang (2019) , it has . (2015)  demonstr for CRC .PRPS1 predicted to be disease-causing mutations [PRPS1 missense mutation c.335T > C (p.V112A) spans the reference SNP (rs11541075) for the BOR  syndrome. This syndrome, which is associated with hearing loss and other hearing abnormalities, in addition to abnormalities in kidney structure and function, may affect approximately 1 in 40,000 people (MedlinePlus). These data show the far-reaching effects of missense mutations in the hPRPS1 gene which, on reflection, are not unexpected in a gene whose product is required to produce essential metabolites involved in nucleotide synthesis as well as contributing to cellular signalling and energy metabolism [MD simulation of four missense mutations p.D52H, p.M115T, p.L152P and c.607G > C (p.D203H) in hutations , revealeutations . Interestabolism ,205.225via the inhibition of ADP, AMP, and GDP by sterical blocking allosteric site I to ADP, thus leading to an increase in nucleotide synthesis and progression of HCC. The oncogenic transcription factor c-Myc upregulates hnRNPH1/2 expression, thereby favouring splicing from KHK-C to KHK-A which then phosphorylates PRPS1 at T225, increasing PRPP synthesis. By in vitro phosphorylation assays it was shown that KHK-A functions as a protein kinase rather than phosphorylating fructose, thus constitutively activating PRPP production in HCC which has been linked to a considerable risk of poor prognosis in HCC [+ cells by reduction in PRPS1 expression is a result of increased cell apoptosis [PRPS1 mRNA as being negatively regulated by the expression of miR-154 in glioma CD133+ cells [KHK-A (ketohexokinase-A), a protein kinase that is a splicing variant of KHK-C, an enzyme involved in fructose metabolism, phosphorylates and activates hPRPS1 at Ts in HCC . Clinicas in HCC . The uprs in HCC . Furtherpoptosis . Screeni3+ cells . This re3+ cells , adding 103. CDK1 (cyclin-dependent kinase 1)-driven cell proliferation is dependent on the phosphorylation of PRPS1 at S103, possibly by blocking the binding of ADP to the allosteric sites of PRPS1. Upregulation of PRPS1 synthetase activity in the S-phase allows CRC cells to overcome cell cycle progression. Manipulation of this residue may be a promising strategy to combat CRC, especially since the mutations p.S103N (c.308G > A) and p.S103T have been shown to affect PRPS1 synthetase activity in ALL [103 for therapeutic purposes. Jing et al. (2019)  observedy in ALL . PRPS1 sy in ALL . HoweverNotably, PRPS2 can be arginylated on Asn3 (asparagine), distinguishing it from PRPS1 by conferring differential degradation for the two proteins. Arginylation affects both the activity and stability of PRPS2 since different molecular mechanisms are responsible for its degradation . The disPRPS2 blocks the progress of prostate cancer via the suppression of the cell cycle and induction of apoptosis. They revealed that PRPS2 upregulates prostate adenocarcinoma tissues, whereas knockdown of hPRPS2 suppresses cell growth in both in vitro and in vivo studies. Li et al. (2019) [PRPS1 in vitro and in vivo blocks neuroblastoma cell proliferation and tumour growth. Qian et al. (2018) [180 and PRPS2 at S183, which leads to brain tumorigenesis. Interestingly, under these conditions, the collapse of the hexameric structures of PRPS1 and PRPS2 to the constituent monomeric states occurs, along with concomitant loss of PRPS1 and PRPS2 synthetase activities, nucleic acid synthesis and NAD+ synthesis. Qiao et al. (2020)  postulat. (2019)  reported. (2018)  reportedde novo purine synthesis [PRPS1 mutants elevate intracellular PRPP levels, which potentially enhances the transformation of 5-fluorouracil to fluorodeoxyuridine monophosphate and fluordeoxyuridine triphosphate, which are known metabolites that can induce DNA damage and apoptosis. The study suggests that ALL patients with hPRPS1 mutations are likely to benefit from 5-fluorouracil-based chemotherapy.The study conducted by Lei et al. (2015)  demonstrynthesis . Wang etynthesis  reportedde novo purine synthesis. Supplementation of the cell culture media with SAM improved cell growth and reduced the proportion of senescent cells. These observations are consistent with premature ageing being associated with reduced purine metabolism and open a new therapeutic opportunity for the treatment of progeria [Hutchinson–Gilford progeria syndrome (HGPS), also known as progeria, arises from a mutation in the gene encoding lamin A, which is a protein scaffold at the inner edge of the nuclear membrane. It is a very rare fatal disease that causes accelerating ageing in infancy ,220. Celprogeria .+ and NADP+ and the aa histidine and tryptophan, all essential in many life processes. However, PRPP-synthesizing enzymes are not found in certain intracellular parasites. Mutations of PRS in different model organisms result in the disruption of cellular processes and metabolic and neurological disorders. In humans, it has been established that mutations in hPRPS1 lead to a loss or gain-of-function. Gain-of-function in hPRPS1 causes PRPS1 superactivity via the alteration of allosteric sites I and II, affecting either the trimer or the dimer interfaces, whereas the loss-of-function mutations in hPRPS1 result in the following neurological disorders: Arts syndrome, CMTX5 and DFNX1/DFN2. This occurs by destabilizing the ATP binding sites and disturbing allosteric site I and the trimer interface in some instances. Furthermore, hPRPS1 and hPRPS2 have been implicated in cancer; for example, hPRPS2 knockout causes c-Myc-driven tumourigenesis and promotes the metastasis of neuroblastoma cancer, while overexpression of hPRPS1 elevates cancer cell proliferation and inhibits apoptosis in B-ALL cell lines. The two PRPS isoforms have distinct roles in normal physiology and disease; however, the molecular mechanisms responsible for this differentiation are not yet wholly understood, although the kinetic parameters of the two isoforms have been known for over 20 years [PRS plays a vital role in the biosynthesis of purine and pyrimidine nucleotides, NAD20 years . PRPS2 i20 years . As desc20 years  PRPS act20 years ,140. The20 years .S. cerevisiae, negative and positive genetic interactions established that one of these three functional entities, Prs1/Prs3, Prs2/Prs5 and Prs4/Prs5, must be present for survival. S. cerevisiae cannot survive with only a single ScPRS gene, indicative of the importance of ScPRS genes not only in one aspect of yeast cellular metabolism but also by exploiting genome duplication to allow the acquisition of insertions which link PRPP synthesis with the maintenance of CWI. The simultaneous deletion of ScPRS1/ScPRS5 or ScPRS3/ScPRS5 causes synthetic lethality, whereas the knockdown of ScPRS1/ScPRS3 in the absence of ScPRS2 or ScPRS4 leads to a reduction in enzymatic activity and decreased capacity to synthesize PRPP as well as severe growth retardation in S. cerevisiae. Y2H results were confirmed by co-immunoprecipitation, revealing that Prs1 interacts with Slt2, the MAPK of CWI, indicating that PRS enzymes are involved in PRPP synthesis and also play an essential role in the maintenance of CWI in S. cerevisiae. The CWI pathway does not stand alone in yeast metabolism but has crosstalk to other MAPK signalling pathways, e.g., the HOG (high-osmolarity glycerol) pathway [In  pathway ,225. Pbs pathway  under coPRPS1 paralogues, prps1a and prps1b, with a high sequence similarity to hPPRS1. It was observed that the deletion of Drprps1a and Drprps1b resulted in smaller otic vesicles, otoliths, and loss of inner ear hair cells, indicating that the absence of Drprps1a or Drprps1b in zebrafish results in sensorineural hearing impairment. In mice, mPRPS1 and mPRPS2 knockouts have been created. Murine PRPS1 was identified in a high-throughput screen for skeletal phenotypes as one of the four X-linked genes responsible for hemizygous lethality [PRPS mutations is scarce . Nevertheless, the mouse model may be relevant since it has been shown that miRNA, specifically miR-376a-5p, represses the mPRPS1 transcript at multiple binding sites within its 3′-UTR in a tissue-specific manner. These binding sites are not present in the 3′-UTR of mPRPS2. The expression of mouse and human miR-376 RNAs has been documented in both developing embryos and adult tissues, including the cochlea [PRPS1 transcript complex, thereby reducing PRPS1 activity, which in turn reduces cell proliferation, metastasis, and glycolysis [hPRPS1 transcripts and DLEU1—lnc RNA deleted in lymphocytic leukaemia 1 [PRPS1 mRNA via ‘mopping up’ the miRNA with a concomitant reduction in hPRPS1 activity. Each of these models has the potential to provide useful information on the role of PRPP synthetase in neurodegenerative diseases and cancer. Zebrafish have two Drethality . Notably cochlea ,228. Speycolysis . A similkaemia 1 —or lncRNkaemia 1 , which aPRPS1 or hPRPS2 in combination with sh/siRNA in selected cell lines may provide an ideal tool to screen and test for potential drug targets. In a human neuroblastoma cell line with hPRPS1 knockouts, Li et al. (2019) [PRPS1 resulted in poor neuroblastoma patient prognosis and a low patient survival rate. However, down-regulation of hPRPS1 inhibited neuroblastoma cell proliferation and tumour growth, thus resulting in good neuroblastoma patient prognosis and an improved patient survival rate. The use of the cell lines Sup-B15 and Raji indicated that overexpression of hPRPS1 accelerates growth and inhibits apoptosis and may be responsible for an adverse/poor prognosis in Chinese children with B-ALL [PRPS1 and hPRPS2. Each of the model organisms discussed has the potential to contribute useful information on the roles of PRPS1 and PRPS2 across the spectrum of eukaryotic PRPP synthetase.Overexpression of h. (2019)  reportedth B-ALL ,192,200.PRS mutations demonstrating unexpected phenotypes and protein/protein interactions could prove useful in drug testing and uncover upstream regulators of PRPP in yeast, thus contributing to the role of PRPP in basic metabolism.In this respect, our collection of ScPRPS mutations cause neurological disorders and resistance to cancer therapy in humans. It should be noted that, in spite of D. rerio’s ability to grow continuously, the zebrafish model has the potential to be used in studying nonsyndromic X-linked sensorineural deafness, retinopathies and neurological disorders associated with hPRPS1 mutations. There is now compelling evidence that hS. cerevisiae. However, the connections uncovered in yeast (The elucidation of the signalling pathways in the model organisms described is essential for their exploitation. In the higher eukaryotes, the connection between genotype and phenotype is more complex than in in yeast  may info


Here, we asked whether the distribution of AKTIP is altered in cancer cells and whether this is associated with alterations of lamins.Lamins, key nuclear lamina components, have been proposed as candidate risk biomarkers in different types of cancer but their accuracy is still debated. AKTIP is a telomeric protein with the property of being enriched at the nuclear lamina. AKTIP has similarity with the tumor susceptibility gene TSG101. AKTIP deficiency generates genome instability and, in p53We performed super-resolution imaging, quantification of lamin expression and nuclear morphology on HeLa, MCF7, and A549 tumor cells, and on non-transformed fibroblasts from healthy donor and HGPS (LMNA c.1824C > T p.Gly608Gly) and EDMD2 (LMNA c.775 T > G) patients. As proof of principle model combining a defined lamin alteration with a tumor cell setting, we produced HeLa cells exogenously expressing the HGPS lamin mutant progerin that alters nuclear morphology.In HeLa cells, AKTIP locates at less than 0.5 µm from the nuclear rim and co-localizes with lamin A/C. As compared to HeLa, there is a reduced co-localization of AKTIP with lamin A/C in both MCF7 and A549. Additionally, MCF7 display lower amounts of AKTIP at the rim. The analyses in non-transformed fibroblasts show that AKTIP mislocalizes in HGPS cells but not in EDMD2. The integrated analysis of lamin expression, nuclear morphology, and AKTIP topology shows that positioning of AKTIP is influenced not only by lamin expression, but also by nuclear morphology. This conclusion is validated by progerin-expressing HeLa cells in which nuclei are morphologically altered and AKTIP is mislocalized.Our data show that the combined alteration of lamin and nuclear morphology influences the localization of the tumor-associated factor AKTIP. The results also point to the fact that lamin alterations per se are not predictive of AKTIP mislocalization, in both non-transformed and tumor cells. In more general terms, this study supports the thesis that a combined analytical approach should be preferred to predict lamin-associated changes in tumor cells. This paves the way of next translational evaluation to validate the use of this combined analytical approach as risk biomarker.The online version contains supplementary material available at 10.1186/s13046-022-02480-5. When we consider the association between the presence of AKTIP at the nuclear rim and the level of nuclear aberration, we observe a significant negative correlation . We firstly monitored the expression of progerin by immunofluorescence using an anti-progerin antibody Fig. A, B and Taken together these experiments show that when aberrant HGPS lamin is expressed in tumor cells it can induce alteration of nuclear morphology and a robust mislocalization of AKTIP.Lamins are key nuclear components, which control many cell functions and shape nuclear morphology. Lamin alterations and nuclear morphology have long been studied in cancer –15. LamiConcerning the relation of nuclear morphology with cancer, the clinical data indicate that nuclear morphometrics is a relatively accurate approach for the diagnosis of late-stage cancer . HoweverLMNA. The involvement of different tissues and the different clinical symptoms in laminopathies suggest that the pathomolecular events downstream to LMNA mutations are complex and heterogeneous. For example, patients with HGPS suffer from premature aging, growth retardation, loss of subcutaneous fat, reduction in bone density, poor muscle development, and succumb to the disease at an early age [Another way for the use of nuclear morphology and lamin expression for cancer prognosis is to perform a refined integrative analysis of these two parameters. This approach is being implemented in laminopathies; rare genetic conditions due to genetic mutations in the lamin encoding gene arly age , 61. In arly age . To resparly age –67. SuchAKTIP is a protein enriched at the nuclear rim  associatWe report that AKTIP is in close proximity with lamin B1, but specifically co-localizes with lamin A. This is reinforced by the observation that lamin A/C overexpression impacts on AKTIP recruitment at the rim. This association suggests a putative functional link of AKTIP with lamin A. This aspect could be of help to mechanistically interpret the partial phenotype overlap between progeroid mice and mice depleted of Ft1, the mouse orthologue of AKTIP. It could be speculated that this overlap is due to a functional association between AKTIP and lamin A , 6.NPCs are intertwined with the organization of the lamina , 69. ForWe next focused on cancer cells MCF7, HeLa and A549. We selected these three types because of their different origin and of their extensive previous characterization, including their lamin and p53 status , 48–52. www.cbioportal.org) and Oncomine [www.proteinatlas.org) indicates AKTIP as a prognostic marker of survival in renal cancers. This information taken together, suggest that AKTIP delocalization linked to lamin alterations could be further investigated as a mechanistic path to cancer disease.AKTIP is implicated in essential cellular processes including telomere metabolism and cell division , 2, 6, aOncomine  data seti.e., their altered solidity and circularity. It is to note that HGPS cell abnormalities have also been associated to permanent protein farnesylation [This study also opens new insights for laminopathies. Indeed, consistently with the close association of AKTIP with the nuclear rim and with lamin A, the analysis of nuclei with either exogenous expression of progerin, or with the endogenous HGPS mutation, reveals that progerin profoundly impinges on AKTIP distribution, which suggests that AKTIP dysfunction could be a co-element in the progeroid disease. On the other hand, we do not see significant alterations of AKTIP in EDMD2 cells. Our data also highlight that the mislocalization of AKTIP in HGPS cells is not related to progerin sequestration, but rather to the specific morphological alterations of HGPS nuclei, sylation , 74. We In conclusion, this work, on one hand, contributes to defining the relationship between AKTIP, lamin, and nuclear alterations in cancer cells. On the other hand, it points to AKTIP dissociation from the nuclear rim as being one of the consequences of nuclear morphological changes caused by the HGPS LMNA mutation. Given that AKTIP is required for correct telomere function and genome integrity , its abeAdditional file 1: Supplementary movie 1. Reconstruction of AKTIP localization in the nucleus labelled for lamin B1. 3D volume rendering of an HeLa nucleus stained with anti-lamin B1 (red) and AKTIP (green) and imaged with 3D-SIM. The volume rendering and the movie generation were performed with IMARIS software .Additional file 2: Supplementary movie 2. Reconstruction of AKTIP localization in the nucleus labelled for lamin A/C. 3D volume rendering of an HeLa nucleus stained with anti-lamin A/C (red) and AKTIP (green) and imaged with 3D-SIM. In gray the AKTIP-lamin A/C co-localization surfaces are shown. The volume rendering and the movie generation were performed with IMARIS software .Additional file 3: Supplementary Figure S1. Distribution of AKTIP-lamin A/C co-localizing foci and AKTIP localization in lamin A overexpressing cells. (A) Percentage of AKTIP foci that co-localizes with lamin A/C signal at rim and at nucleoplasm calculated on the total of AKTIP foci at rim and nucleoplasm respectively in HeLa cells. Each dot represents a nucleus. Images from 10 HeLa nuclei were analyzed. Mean values ± SEM are shown. *** p<0.01, in unpaired Student t-test. (B) Representative images and magnification from control and Flag-lamin A transfected AKTIP-GFP (green) 293T cells stained with anti-Flag antibody (red). DNA was stained with DAPI.Additional file 4: Figure S2. Correlation analysis of AKTIP localization with lamin B1 expression and with proliferation rate of tumor cells. (A-B) Western Blotting and relative quantification showing lamin B1 levels. Actin was used as loading control. In (B) for each sample, relative fold change with respect to non-transformed wild type fibroblasts is shown. Mean values of two independent experiments ± SEM are shown. *p<0.05, ** p<0.01, in unpaired Student t-test. (C-D) Correlation between the percentage of AKTIP positive rim and lamin B1 (C), and between the ratio between lamin A  and lamin B1 protein levels (D) quantified by Western Blotting. Mean values ± SEM are shown. Linear regression values are shown. (E) Cumulative population doubling of tumor cells. Mean values of three independent experiments ± SEM are shown (F) Correlation between the percentage of AKTIP positive rim and population doubling of HeLa, A549, MCF7 cells. Mean values ± SEM are shown. Linear regression values are shown.


Protein–protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those proteins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using 12 topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest classifiers (DependANT) to predict novel gene dependencies.We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent.https://bitbucket.org/bioinformatics_lab_sussex/dependant2Software available at Bioinformatics Advances online. An essential gene is one on which a cell depends for cellular survival. However, the set of essential genes is context specific, depending on the cell type, genetic and epigenetic aberrations, and the environment the cell finds itself in. Different definitions and measurements of essentiality often have considerable overlap, but there are also large areas of disagreement .et al., 2009; During the process of carcinogenesis, the cell becomes addicted to oncogenes and tumour suppressor genes become inactivated. Consequently, the cell develops new gene dependencies as different genes become transiently essential to that cell as it evolves  screens , which iet al. developed the computational method, CERES, to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for copy number–specific effects. A gene dependency score was assigned to each gene in each cell-line, reflecting the probability that the cell-line depends on the gene  represents a protein and each edge (∼60 000 edges) a known physical protein interaction. The PPI data was sourced from the STRING database (v.10)  was usedet al. via DepMap Public 18Q3 ; page rank  or nonsense substitutions were labelled as LOF. For missense mutations the pathogenic mutations were identified using the FATHMM algorithm  tends to 0 the weight (w) tends to 1, and as g tends to infinity, w tends to 0. For genes subject to a gain of function mutation we multiplied the gene expression by 10 before calculating the weight (w) as a function of the gene expression g. To provide the properties described above we used the functiong is the expression in transcripts per million. To focus on the differences in gene dependency between tissue types we removed genes that are either almost always essential or almost never essential . Features for the classification algorithm were recalculated for each individual cell-line weighted by the edge scores for each node. To improve performance in cross cell-line classification each cell-line’s feature set was normalized, i.e. each feature was independently scaled between 0 and 1  in black 96 well plates. Plates were incubated at 37°C, 5% CO2 for 72 h. CellTitre-Blue was added to determine cell viability, plates were analysed using a plate reader at 560/590 nm.We validated our predictions experimentally using the unlabelled breast cancer cell-line, MCF7 as it is not featured in our training data, readily available and has a good class balance for predictions on genes featured as part of the available DDR gene library. We performed a high-throughput siRNA screen. MCF7 cells  were grown in minimal essential medium (MEM) supplemented with 10% foetal calf serum, penicillin/streptomycin and L-glutamine at 37°C and 5% CODruggability annotation was performed using CanSAR Black’s cancer protein annotation tools . Significance scores below are based on this baseline distribution.We first predicted genes which are cell-essential in all the cell-lines, i.e. pan-cell-line essential genes. To create a baseline score, we performed the prediction on a base PPI model with randomized edges. With a balanced class distribution of positive and negative labelled DepMap genes, we achieved an AUC ROC score of 0.520  when the classifiers were tested on genes from the same cell-line. This dropped slightly to an average of AUC ROC 0.758  when they were tested on genes from other cell-lines. Differences between the tissue types are shown in On average the baseline PPI networks predicted pan-cell-line essential genes with an AUC ROC score of 0.765  when predicting pan-cell-line essential genes compared to the base model’s performance of AUC ROC 0.765  that measure the connectivity of a gene, perform better than degree centrality. Eccentricity, which measures how close a node is to the centre of the network, performs badly across all models.As expected, the difference in mean value for each feature across cell-essential and non-cell-essential genes is fairly well mirrored in these importance scores: features with values that vary more between cell-essential and non-cell-essential genes provided more predictive power. Pagerank and constraint showed a noticeable differentiation between classes, while the differentiation between classes for eigen centrality and hubscore features were not as prominent see .Current PPI network models are both incomplete and suffer from ascertainment bias in that some proteins are better studied than others , a further improvement on the mean individual cell AUC ROC score of 0.801 (SD 0.006). This model provided the most predictive power and as such represents the most suitable available for predicting gene dependencies in cell-lines with no prior labelling as discussed below.The MCF7 breast cell line was chosen to experimentally validate the DependANT classifier due to the availability of its mutational and expression data. We used our pan-tissue training set to train our classifiers and produced a full set of predictions for the MCF7 breast cell-line.Survival screens focusing on a library of 240 genes involved in the DNA damage response (DDR) were repeated in triplicate for the MCF7 breast cell-line. Cell viability for knockdown of each of the genes was reported using a z-score: a positive number indicates that viability increased with the knockdown of the gene, whilst a negative z-score indicates a decrease in viability.The variance of results across all three repeats was high, which may have been due to the choice of library see . The losIn addition, although only 7 of the 240 genes screened and classified in the MCF7 cell-line had a mean z-score of less than –1 in all 3 repeats, two of these, MEN1 and CHEK1 were predicted as cell-essential with a score of over 0.85 see .Being able to predict cell-specific dependency genes from genetic analysis of clinical samples allows the direct identification of promising points of therapeutic intervention specific to that sample. Targeting the protein products of tumour-specific dependent genes should kill the cancer cells, whist leaving the normal cells which are not dependent on these genes for viability, relatively unharmed. To explore whether this is a feasible approach we undertook a druggabilty analysis on a set of cell line samples.Using Cansar’s cancer protein annotation tools (Next, we identified 35 cell-lines previously unlabelled for gene dependency, 15 for breast, 12 for kidney and 8 for pancreas. Each of these cell-lines were chosen based on the amount of mutation and expression training data available. We used our pan-tissue training set to train our classifiers and produced a full set of dependency predictions for each of these cell-lines see .A druggability analysis of the genes predicted to be cell-essential identified 0.7% whose protein products were known drug targets. The proportion of predicted druggable genes was higher at 45.1% compared to 34.2% in our training set. We found therapeutic opportunities in almost every cell-line in both our training data and prediction set both in the form of genes with known drugs and genes those who exhibit druggable traits .PPI maps provide us with a robust model of how the proteome is organised. Here we find that the topological relationships across these maps tends to be different for essential genes and non-essential genes, opening up the opportunity for predicting which genes are essential. However, the real therapeutic benefit would come from using the mutational and expression data associated with a specific cancer cell to predict which genes acquire cell-essentiality as a result of the cancer. The proteins associated with these genes could then be inhibited leading to death of the cancer cells.S.cerevisiae (E.coli (A standard PPI map cannot predict whether a gene which is not normally essential will become so under the specific combinations of mutations and changes in gene expression that occur in cancer cells. To address this problem, we first ‘personalized’ the PPI to incorporate information about mutations and gene expression before using resulting topological features to train a random-forest classifier, DependANT. We find that these alterations to the PPI can improve predictions for cell essentiality in human cell-lines with ROC AUC scores of up to 0.84. This is an improvement of 10.8% when compared to the baseline PPI classifier, and an improvement on the accuracy reported by previous studies that use PPI network models to predict essential genes in revisiae  and E.coDespite the relatively high performance of our classifiers, we are aware that the association between gene expression and protein expression is only partial and so it is likely that further improvements will be possible for this type of model when it is possible to modify the PPI network as a result of protein expression as well as existing ‘omic data. It is possible that improvements to the completeness of our source PPI networks could also lead to significant improvements in this type of study. In particular our source PPI network provides only non-directional, binary information about interactions between proteins rather than the inhibitory or excitatory nature of the interaction. Although we report that our models are relatively robust to incompleteness in the source networks, we expect that as the completeness and sophistication of PPI models improves so will the effectiveness of this type of model. However, it is possible that such improvements may be limited, perhaps because genes with high dependency scores tend to be better studied than genes with lower scores.The ability to identify therapeutic vulnerabilities for cancer cells using readily available omic data from cancer samples may lead to novel treatment protocols for hard to treat tumours.vbac084_Supplementary_DataClick here for additional data file.


Given that eATP is toxic to cancer cells, we hypothesized that augmenting the release of eATP through P2RX channels and inhibiting extracellular ATPases would sensitize TNBC cells to chemotherapy.Breast cancer affects two million patients worldwide every year and is the most common cause of cancer-related death among women. The triple-negative breast cancer (TNBC) sub-type is associated with an especially poor prognosis because currently available therapies fail to induce long-lasting responses. Therefore, there is an urgent need to develop novel therapies that result in durable responses. One universal characteristic of the tumor microenvironment is a markedly elevated concentration of extracellular adenosine triphosphate (eATP). Chemotherapy exposure results in further increases in eATP through its release into the extracellular space of cancer cells via qRT-PCR, western blot, and flow cytometry analyses, respectively.TNBC cell lines MDA-MB 231, Hs 578t and MDA-MB 468 and non-tumorigenic immortal mammary epithelial MCF-10A cells were treated with increasing concentrations the chemotherapeutic agent paclitaxel in the presence of eATPases or specific antagonists of P2RXs with cell viability and eATP content being measured. Additionally, the mRNA, protein and cell surface expressions of the purinergic receptors P2RX4 and P2RX7 were evaluated in all examined cell lines In the present study, we observed dose-dependent declines of cell viability and increases in eATP of paclitaxel-treated TNBC cell lines in the presence of inhibitors of eATPases, but not of the MCF-10A cell line. These effects were reversed by specific antagonists of P2RXs. Similar results, as those observed with eATPase inhibitors, were seen with P2RX activators. All examined cell lines expressed both P2RX4 and P2RX7 at the mRNA, protein and cell surface levels.These results reveal that eATP modulates the chemotherapeutic response in TNBC cell lines, which could be exploited to enhance the efficacy of chemotherapy regimens for TNBC. Breast cancer affects millions of women every year. At 47.8 new cases and 13.6 deaths per 100000 per year, it has the highest global incidence rate and is the most common cause of cancer-related mortality among women in 2020  –7. Under6-fold . However6-fold .+ efflux; Na+ and Ca2+ influx; membrane phosphatidylserine-flip; membrane blebbing and cell death (Purinergic P2 receptors (P2Rs), integral plasma membrane receptors activated by ATP, are divided into the ionotropic P2 (P2RX) and metabotropic P2 (P2RY) sub-types . P2XRs, ll death –14. Mostll death –17. Howell death .via ATP-binding cassette subfamily C member 6 (ABCC6) and the ankylosis gene product ANK and diffusion via P2RX7 and Pannexin1 channels , MDA-MB 468 , Hs 578t , and HEK-293T ATCC Cat# CRL-3216, RRID : CVCL_0063) were maintained in DMEM (Corning) and supplemented with 10% FBS (Cytiva), 1% MEM non-essential amino acids (Gibco), 1 mM sodium pyruvate (Gibco), 4 mM L-glutamine (Gibco) and antimicrobial agents  (Gibco). Non-tumorigenic immortal mammary epithelial MCF-10A cells  were maintained in DMEM/F12 (Gibco) supplemented with 5% horse serum (Gibco), hydrocortisone (Sigma), epidermal growth factor (Sigma), cholera toxin (Sigma), insulin (Sigma) and antimicrobial agents. All cell lines were authenticated and were maintained in a humidified incubator at 37°C and 5% COin vitro system.The following drugs and chemicals were used: dimethyl sulfoxide/DMSO (Sigma), ATP (Sigma), UTP (Sigma), paclitaxel , sodium polyoxotungstate or POM-1 (Tocris), PSB 069 (Tocris), levamisole hydrochloride (Abcam), A438079 (Tocris), 5-BDBD (Tocris), ENPP1 inhibitor C , SBI-425 (MedChemExpress), etidronate disodium and ivermectin (Sigma). ATP and POM-1 were dissolved in nuclease-free water (Invitrogen); paclitaxel, A438079, Iso-PPADS, 5-BDBD, SBI-425, ENPP1 inhibitor C, levamisole hydrochloride, etidronate disodium (Sigma), and ivermectin were dissolved in DMSO. TNBC cell lines, MDA-MB 231, Hs 578t, MDA-MB 468 cells and non-tumorigenic immortal mammary epithelial MCF-10A cells were plated on 96 well plates (Costar) at a high density of 25,000 cells/well and after 24 hours treated with paclitaxel, inhibitors, or ATP for 6 or 48 hours. PrestoBlue™ HS cell viability reagent (Invitrogen) was added to each well according to the manufacturer’s instructions. Fluorescence readings (excitation and emission ranges: 540–570 nm and 580–610 nm) were obtained using a Bioteck Synergy HT plate reader. ATP was measured in supernatants according to the protocol described by the ATPlite 1 step Luminescence Assay System (PerkinElmer). Luminescence readings were measured on a Bioteck Synergy HT plate reader. The student’s t-test was applied to the applicable assays (via the TRIzol method (Invitrogen), and qRT-PCR was performed on a Bio-Rad T100 thermal cycler using the following exon-exon junction-spanning primers: for P2RX4, the forward primer TGGCGGATTATGTGATACCAGC and the reverse primer GTCGCATCTGGAATCTCGGG; for P2RX7, the forward primer GTGTCCCGAGTATCCCACC and the reverse primer GGCACTGTTCAAGAGAGCAG; and for GAPDH forward primer GTCGTATTGGGCGCCTGGTC and the reverse primer TTTGGAGGGATCTCGCTCCT. The student’s t-test was applied to the applicable assays to ascertain significance. * represents p<0.05 and ** represents p<0.01 relative to MCF-10A; + represents p<0.05 and ++ represents p<0.01 relative to HEK293-empty vector transfected.MDA-MB 231, Hs 578t, MDA-MB 468, cell lines and MCF-10A cells were maintained under standard conditions in subconfluent cultures. RNA was extracted Total cell lysates were prepared in lysis buffer  with a protease inhibitor cocktail (Thermo Scientific). The lysates were sonicated, placed on ice for 30 minutes, and spun at 10,000 rpm for 10 minutes at 4°C to collect the cleared supernatants for analysis. Protein quantification was performed using the Pierce BCA Protein Assay (Thermo Scientific) and absorbance readings taken at 595 nm. Protein samples  using Lipofectamine 3000 (Thermo Fisher Scientific). Cells were detached with accutase (Thermo Fisher Scientific). One million cells were washed in PBS with 0.05% BSA, stained with P2RX7 –FITC  antibodies or stained with rabbit IgG Isotype Control-FITC , or with anti-P2RX4  plus goat anti-rabbit IgG (H+L) secondary antibody-FITC  or IgG isotype control plus secondary antibody-FITC in Flow Cytometry Staining Buffer . Analysis was performed on BD FACS Fortessa using the FITC channel (530/30 nm) and Flowjo software (RRID: SCR_008520). The student’s t-test was applied to the applicable assays to ascertain significance. * represents p<0.05 and ** represents p<0.01 relative to MFI difference in MCF-10A cells; + represents p<0.05 and ++ represents p<0.01 relative to MFI difference in HEK293-empty vector transfected. O/E represents overexpressed.We examined the toxic effects of ATP using UTP as a control. We observed a dose-dependent loss of viability in TNBC MDA-MB 231, MDA-MB 468 and Hs 578t cell lines upon exposure to ATP but not UTP and not in non-tumorigenic immortal mammary epithelial MCF-10A cells (We next treated the cells with various purinergic receptor antagonists to determine whether P2RX receptors mediate the effects of eATP on cell viability (We next studied the effects of combinations of eATPase inhibitors with chemotherapy (paclitaxel) to determine their effects on the efficacy of chemotherapy. For these experiments, all the cell lines were treated for six hours to simulate the duration of systemic exposure in patients  could augment the effects on cell viability and eATP release in paclitaxel-treated cells while using a protein tyrosine phosphatase inhibitor, etidronate disodium, as a control. Although we observed substantial changes in eATP upon treatment of the TNBC cell lines, there was no significant change in cell viability Previous research had shown that ivermectin is a P2RX4 and P2RX7 activator , 45. HenIn the same experiments, we also looked at eATP release upon exposure to the combined treatment of ivermectin and paclitaxel. For paclitaxel-treated cells, there were increases in eATP release in the presence of ivermectin when compared to the vehicle addition. These increases were much more dramatic in the TNBC cell lines than immortal mammary epithelial cells ; whereas Hs 578t cells expressed levels that were not significantly different (p>0.05). MDA-MB 231 and Hs 578t cells expressed significantly less P2RX7 mRNA when compared to MCF-10A cells (p=0.0001 for both); whereas, MDA-MB 468 cells expressed 25-fold more P2RX7 mRNA when compared to MCF-10A cells (p=0.0006).We next sought to assess the expression of P2RX4 and P2RX7 mRNA and protein. qRT-PCR was performed on TNBC and MCF-10A cells with specific exon-exon junction-spanning primers for Western blot analysis was performed on TNBC cell lines, MCF-10A cells and HEK 293T cells transfected with either a P2RX4 or P2RX7 expression plasmid as positive controls, probing for P2RX4 and P2RX7 with GAPDH as the internal loading control (Given that unglycosylated form may not represent the plasma membrane-localized fraction of a protein, we used flow cytometry to quantitate the expression of P2RX7 and P2RX4 at the cell surface. Flow cytometry analysis was performed on TNBC, MCF-10A, and HEK 293T cells transfected with either vector control or P2RX4 expression plasmid as a positive control, probing for cell surface expression P2RX4 (We performed flow cytometry analysis on TNBC, MCF-10A, and HEK 293T cells transfected with either empty vector or P2RX7 expression plasmid as a positive control, probing for cell surface expression P2RX7 (Additionally, all cell lines were treated with 100 µmol/L paclitaxel and the cell surface expressions of P2RX4 and P2RX7 were examined with the calculated difference in MFI values between cells stained with P2RX4 or P2RX7 specific antibodies to cells stained with the corresponding isotype control (Chemotherapy by itself fails to ablate metastatic TNBC. Extracellular ATP, in the high micromolar to millimolar range, induces cytotoxicity in cancer cell lines. Chemotherapy is known to induce increases in eATP. We hypothesized that interventions that augment chemotherapy-induced increases in eATP would increase cancer cell death.Our results show that inhibitors of E-NTPDases, ENPPases and TNAP all significantly increased the release of eATP with chemotherapy exposure. However, only the E-NTPDase inhibitor PSB 069, a sulfonated tetracyclic compound, but not POM-1, another E-NTPDase inhibitor, consistently and significantly increased chemotherapy-induced cell death. Both are inhibitors of multiple E-NTPDase isoforms. Some reports suggest that POM-1 also blocks several P2XRs. This may interfere with cell death and may explain their differing effects on chemotherapy-induced cell death. ENPPase and TNAP substrates are not limited to ATP and can affect other nucleotides and cyclic nucleotides, and therefore, inhibition of these enzymes may have ATP non-specific effects –48. EachWe also showed that the addition of exogenous eATP in the absence of chemotherapy significantly reduced TNBC cell viability. Specific inhibitors of P2RX4 and P2RX7, but not a non-specific P2RX inhibitor, attenuated the effects of ATP on cell viability and the effects of E-NTPDase inhibitors on eATP levels and their positive effects on chemotherapy-induced cell death. These data show that ATP-induced cell death and E-NTPDase-inhibitor induced augmentation of chemotherapy-induced cell death are mediated through P2RX4 and P2RX7 channels. However, two observations suggest that P2RX4 and P2RX7 activation alone may not be sufficient for the loss of cell viability observed. Firstly, the addition of eATP to cells was toxic to MDA-MB 231 and Hs 578t cells but minimally affected MDA-MB 468 cells. However, upon chemotherapy treatment, an E-NTPDase inhibitor significantly increased eATP levels and augmented chemotherapy-induced cell death in all the TNBC cell lines. Secondly, the addition of eATP induced loss of TNBC cell viability at concentrations that were higher than those that were observed upon chemotherapy treatment in the presence of eATPase inhibitors. Thus, although P2RX4 and P2RX7 activation are necessary for the augmentation of chemotherapy-induced cell death by eATP, other factors may be required in parallel. For example, the NLRP3 inflammasome is one such death pathway whose activation is dependent on eATP but must also be primed by other factors such as NF-κB activation. Also, it is important to consider previous research which suggests that eATP concentrations in the immediate pericellular region may far exceed those in the bulk interstitial fluid . Thus, oThe effects of eATP on cell viability and the effects of extracellular ATPase inhibitors on eATP levels were reversed by specific P2RX4 and P2RX7 inhibitors suggesting that these receptors are not only necessary for the accentuated cell death downstream of increased eATP but also necessary for increased eATP release. The fact that P2RX4 and P2RX7 antagonists significantly attenuated eATP release even at concentrations of paclitaxel at which cytotoxicity was similar between the treatment groups, suggests that their attenuation of eATP was not due to attenuation of cell death. Given that the P2RX4 and P2RX7 antagonists but not a non-specific P2RX blocker reversed these effects, they are likely specific to these two receptor types.We aimed to identify clinically approved compounds that modulate eATP levels. The antiparasitic drug ivermectin is an activator of P2RX4 and P2RX7 , 45. We Concerning expression levels, our western blot data show that P2RX4 is highly expressed in some TNBC cell lines as compared to immortal mammary epithelial cells. However, our flow cytometry data revealed significantly decreased cell surface expression of P2RX4 in the TNBC cell lines as compared to between MCF-10A cells. Previous publications suggest that the majority of P2RX4 is expressed on lysosomal membranes and that cell surface expression can be increased by stimuli that induce lysosomal exocytosis such as calcium ions . This maOn western blot analysis of mammary cells, we detected a specific band corresponding to the full-length glycosylated form of P2RX7 in the MDA-MB 231 and MCF-10A cells and lower molecular weight bands that may represent unglycosylated forms of P2RX7 in the MDA-MB 468 and Hs 578t cells. Although expression levels may be low, given that a specific inhibitor of P2RX7 markedly attenuated the cytotoxic effects of eATP and attenuated the positive effects of E-NTPDase inhibitors on eATP levels and loss of cell viability upon chemotherapy exposure, it is possible that even low levels of expression P2RX7 may have functional consequences due to the formation of non-selective macropores in the cell membrane.On the other hand, our flow cytometry data shows that P2RX7 is expressed at the cell surface of all the TNBC cell lines at higher levels than MCF-10A cells and in the presence of paclitaxel some TNBC cell lines expressed more P2RX7. This suggests there may be selection pressure for higher expression of P2RX7 in TNBC cell lines. Several published data support the facilitator role played by extracellular adenosine, derived from the metabolism of eATP, for the survival of cancer cells by inducing cell-autonomous effects on proliferation and cancer stem cell-like properties as well as paracrine effects on angiogenesis and immunoevasion –53. Addi. Although only E-NTPDase inhibitors consistently increased chemotherapy-induced loss of cell viability, all the different classes of extracellular ATPase inhibitors increased eATP levels in the setting of chemotherapy exposure. Thus, to maximally augment eATP levels and reduce adenosine in the tumor microenvironment, inhibitors that have broad inhibitory effects on multiple classes of extracellular ATPases may be necessary. This is in contrast to current monoclonal antibody-based strategies that narrowly focus on E-NTPDase1/CD39 (in vivo in the context of an intact tumor microenvironment and functional immune system. These preclinical experiments may lead to therapeutic strategies for TNBC that modulate purinergic signaling in the tumor microenvironment.In summary, our data indicate that eATP is toxic to several TNBC cell lines and P2RX4 and P2RX7 purinergic channels are necessary for this effect. Chemotherapy exposure induces the release of ATP from TNBC cell lines and inhibitors of eATP metabolism augment chemotherapy-induced loss of TNBC cell viability, and these effects are reversed by specific inhibitors of P2RX4 and P2RX7, suggesting that both eATP release and eATP induced loss of cell viability are mediated by these channels. A heterocyclic-sulphonate inhibitor of multiple E-NTPDases, PSB 069, was most effective at accentuating chemotherapy-induced cell death. A P2RX4 and P2RX7 activator, ivermectin, also accentuated chemotherapy-induced increases in eATP and loss of TNBC cell viability se1/CD39 . Our futThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.All authors contributed to review and analysis. JMM performed a majority of the assays with NW executing the RNA analysis and JD carrying out the Western blot analysis. JM and MAC conceived of and designed the experiments, reviewed the data, authored and edited the manuscript. All authors contributed to the article and approved the submitted version.Research reported in this publication was supported by The Ohio State University Comprehensive Cancer Center. Institutions that provided funding support had no role in the design or conduct of this study or the preparation of the manuscript. This publication was also supported, in part, by the National Center for Advancing Translational Sciences of the National Institutes of Health under Grant Numbers KL2TR002734. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Epigenetic therapies have been difficult to translate to solid tumors, in part because a lack of a full mechanistic understanding has not allowed the selection of tumors most likely to benefit from the therapies. Here we identified a RAS-phenotype that can be targeted by the histone deacetylase inhibitor (HDACi) romidepsin. We showed that the hyperacetylation induced by romidepsin depletes acetyl-CoA, the cell donor substrate for acetylation, and it leads to metabolic stress and death in KRAS-activated cells. Transcriptomic analysis confirmed that perturbation of two acetyl-CoA generation pathways (fatty acid metabolism and branched-chain amino acid (BCAA) metabolism) were correlated with HDACi sensitivity in a 608-cell line panel and in patients treated with belinostat. Our analysis highlights the potential utility of an acetyl-CoA phenotype to sharpen treatment choices for RAS-activated tumors, and it suggests that acetyl-CoA depletion could be a key effect underlying the myriad cellular responses that follow HDAC inhibition. Background: The mechanism of action of romidepsin and other histone deacetylase inhibitors is still not fully explained. Our goal was to gain a mechanistic understanding of the RAS-linked phenotype associated with romidepsin sensitivity. Methods: The NCI60 dataset was screened for molecular clues to romidepsin sensitivity. Histone acetylation, DNA damage, ROS production, metabolic state , and gene expression alterations (transcriptomics) were determined in KRAS-WT versus KRAS-mutant cell groups. The search for biomarkers in response to HDACi was implemented by supervised machine learning analysis on a 608-cell transcriptomic dataset and validated in a clinical dataset. Results: Romidepsin treatment induced depletion in acetyl-CoA in all tested cell lines, which led to oxidative stress, metabolic stress, and increased death—particularly in KRAS-mutant cell lines. Romidepsin-induced stresses and death were rescued by acetyl-CoA replenishment. Two acetyl-CoA gene expression signatures associated with HDACi sensitivity were derived from machine learning analysis in the CCLE (Cancer Cell Line Encyclopedia) cell panel. Signatures were then validated in the training cohort for seven HDACi, and in an independent 13-patient cohort treated with belinostat. Conclusions: Our study reveals the importance of acetyl-CoA metabolism in HDAC sensitivity, and it highlights acetyl-CoA generation pathways as potential targets to combine with HDACi. RAS-mutated tumors are the most common and the deadliest of all human cancers, and they remain among the most refractory to available treatments ,2. In thInterestingly, our group and others ,4,5 haveApart from new G12C genotype targeted therapies, strategies to kill RAS-mutant cells by directly targeting RAS or its downstream effectors have been mostly unsuccessful, mainly due to pathway redundancy and heterogeneities in RAS-induced phenotypes. One therapeutic strategy that has emerged involves the exploitation of tumor metabolic vulnerabilities induced by RAS activation . In factIn this study, we show that romidepsin-induced hyperacetylation caused depletion of stores of acetyl-CoA, the cell donor substrate for acetylation, and leads to lethal metabolic stress in KRAS-activated cells but not in KRAS-WT. We also confirmed that two acetyl-CoA metabolism pathways are hallmarks of sensitivity in a cell panel and in patients treated with an HDACi, validating not only these drugs as metabolism-targeting drugs, but also these pathways as a potential tool to identify a RAS-linked metabolic phenotype in a personalized medicine strategy.The complete NCI-60 panel was obtained from National Cancer Institute (NCI) Anticancer Drug Screen . Pancreatic cell lines were obtained from ATCC . All cells were cultivated in RPMI including 2 mM glutamine and 11.11 mM glucose supplemented by 10% FBS and penicillin/streptomycin .Romidepsin  was provided by the Cancer Therapy Evaluation Program, NCI. Tariquidar was provided by the NCI Anticancer Drug Screen. PD-0325901 (MEK inhibitor), MK-2206 (AKT inhibitor) were purchased from ChemieTek . Glutaminase inhibitor 968 compound was obtained from Millipore ; oligomycinA, NAC, glutathione, dimethyl-α-ketoglutarate, dimethyl-glutamate, sodium citrate, and sodium acetate were obtained from Sigma-Aldrich ; DPI, ML171, and etomoxir was obtained from Selleckchem . MitoQ was provided by Dan L. Sackett . t-tests were performed to evaluate significance of results, and asterisks were added to graphs when the p-value was lower than 0.05. Unless otherwise specified, ® Human Genome U133 Plus 2.0 Array. Microarray data have been deposited in the NCBI Gene Expression Omnibus (accession number GSE133120). Detailed methods are provided in related figure legends.Gene expression basal levels for the NCI-60 and CCLE (Cancer Cell Line Encyclopedia) panels were downloaded via the cBio Cancer Genomics Portal . Gene ext-test t < 0) subtracted from the expression sum of up-regulated genes (t-test t > 0) and divided by the total number of up- and down- regulated genes).Gene expression signature scores were calculated using weighted average expression  were purchased from the Jackson Laboratory . Tumors were generated by injecting 10 million cells subcutaneously into the left flank. Treatment started once the tumor size reached an average of 100 mmCell death was measured by flow cytometry after annexin V-FITC/ propidium iodide staining, according to manufacturer’s instructions . Cell lines with a high expression of the multidrug efflux transporter P-glycoprotein (P-gp) according to the cellMiner database  were co-A western blot was performed as previously described . HistoneOxygen consumption rates (OCR) and extracellular acidification rates (ECAR) were measured using the XF96 or the XFp Extracellular Flux analyzer . The protein amount per well was quantified using the BCA assay.t-test.Metabolic profiling of A549 and ACHN cell lines was performed by Metabolon Inc. . After treatment, cells were pelleted, frozen, and sent for analysis (quadruplicates). All samples were randomly distributed across a single day platform run on GC/MS and LC/MS/MS instrumentation, and they were analyzed using Welch’s two-sample Cells were harvested and sent frozen to the Metabolomics Core at the Translational Research Institute for Metabolism and Diabetes at Florida Hospital for estimation of acetyl-CoA and malonyl-CoA by LC-MS/MS.p-value > 0.0001) between romidepsin alone and romidepsin in combination with the MEK inhibitor , and a 0.92 correlation  for the same analysis with the AKT inhibitor . All but two of the 65 lines were insensitive to AKTi or MEKi alone. These results indicate that RAS downstream inhibitors did not trigger cell death, but amplified romidepsin-induced cell death. We queried mutations in well-known key cancer genes carried by these cell lines from the Cosmic Cancer Database. Eleven relevant genes were grouped in four categories with notable mutation: tyrosine kinase receptor (TKR)/RAS/BRAF pathway , PI3K pathway , CDKN2A, and p53 . Cell doubling time, mutations in the PI3K pathway, CDKN2A, or p53 did not correlate with treatment efficacy  in the two romidepsin-sensitive cell clusters , we screened the NCI-60 panel (58 human cell lines from seven cancer tumor types), adding seven pancreatic cancer cell lines because they have a high prevalence of RAS mutation (97%) . For thi and p53 a. Noticeefficacy . Basal eclusters c. Gene eclusters d. Again,To assess the efficacy of the combination drug therapy in a preclinical model, we injected A549 cells  subcutaneously in athymic mice and treated them with romidepsin and/or MEKi . From 15To understand why RTK/RAS/BRAF mutated cells were sensitive to romidepsin, we focused on the KRAS-mutant cells as a model. Progression in signaling events during romidepsin treatment using clinical dosing was evaluated in at least three KRAS-mutant and three KRAS-WT cell lines. Histone acetylation, the main target of romidepsin, and DNA damage (using the commonly accepted marker γH2AX) were measured by western blot after 6 h combination treatment in six cell lines a. No difNext, total and mitochondrial reactive oxygen species (ROS) production were measured over time in the A549 cell line treated with romidepsin by flow cytometry. Total ROS increased approximately 10–12 h, whereas no increase was detected in mitochondrial ROS over 18 h b. Total N-acetyl-cysteine (NAC) significantly rescued all KRAS-mutant cells from death, whereas neither mitochondrial ROS scavenger (mitoQ) nor NADPH oxidase (DPI) inhibitor did  or itor did . We alsoitor did . These sTaken together, these results indicated that romidepsin induces early cytoplasmic ROS production in KRAS-mutant cells. Mitochondrial ROS production and mitochondrial membrane depolarization appeared late, suggesting that they were apoptotic by-products rather than drivers of apoptosis. Having eliminated the main known causes of ROS production (NADPH oxidase and mitochondria), we therefore explored other possible sources of cytoplasmic ROS imbalance after romidepsin treatment. Four enzymes in metabolic pathways produce cytoplasmic NADPH, an intrinsic ROS scavenger that maintains a cytoplasmic redox balance a. We hypTo assay whether glutamine was a dominant OXPHOS fuel source rather than glucose, the same experiment was performed in 1 h-glucose/glutamine-deprived cells, and glutamine was subsequently added e. In allTogether, these results indicated that cancer cell lines used β-oxidation and glutamine rather than glucose as the main source of fuel for the TCA cycle, independently of KRAS status. Romidepsin impaired OXPHOS via inhibition of β-oxidation in almost all tested cell lines, but impaired glutamine use for OXPHOS only in cells bearing KRAS mutations.To understand the disturbances in β-oxidation and glutamine metabolism induced by romidepsin, a metabolomic analysis was performed in A549 (KRAS-mutant) and ACHN (WT) cell lines. Cells were treated 12 h or 18 h with romidepsin using clinical dosing, and 629 metabolites were quantified. Romidepsin-altered pathways were determined by Metabolync pathway enrichment analysis. Fatty acid metabolism and TCA cycle pathways were significantly modified in both cell lines at 12 h and/or 18 h , as wellMetabolites in the TCA cycle and in its two fueling pathways (glutaminolysis and β-oxidation) are reported in Notably, among all the quantified metabolites in the TCA fueling pathway, only acetyl-CoA and its two direct precursors downstream of β-oxidation (C2- and C4-acylcarnitine) were depleted, whereas almost all other TCA-fueling metabolites were accumulated b. To valTo explore this hypothesis, we returned to the Seahorse experiments. Cells were placed for 1 h in glucose/glutamine-deprived medium, then glutamine, DM-glutamate (cell permeable glutamate), DM-glutarate (cell permeable αKG) or pyruvate were added while OCR was measured d. After When looking at the cytoplasmic NADPH-producing pathways downstream of TCA cycle and/or glutaminolysis , because acetyl-CoA itself cannot cross the cytoplasmic membrane. Various precursor concentrations were tested on three KRAS-mutant cell lines and we selected the optimal conditions for cell death rescue . AdditioWe then investigated the coupling between acetyl-CoA depletion and cell death. In the presence of acetyl-CoA precursors, romidepsin did not induce a decrease in basal OCR levels in the KRAS-mutant cells, contrary to what was observed in the absence of citrate/acetate c. We alsThese results showed that the decrease in acetyl-CoA levels was a leading cause of romidepsin-induced cell death in KRAS-mutant cells. Romidepsin-induced acetyl-CoA decrease caused a reduction in TCA cycle flux and an increase in cytoplasmic ROS production that triggered cell death in KRAS-mutant cells only. Notably, romidepsin-induced damage was reversed by just replenishing acetyl-CoA stocks, and it was not caused by romidepsin-induced inhibition of acetyl-CoA generation pathways, suggesting that it was hyperacetylation-induced acetyl-CoA overconsumption that led to the decrease in acetyl-CoA.Because romidepsin-induced acetyl-CoA decrease was observed in all tested cell lines, whereas only KRAS-mutant cell lines showed a decrease in acetyl-CoA-linked cell death after HDAC inhibition, we hypothesized that KRAS-mutant cells were more acetyl-CoA-dependent than KRAS-WT cell lines. p-values of less significance. To determine whether HDACi-sensitive cells had an acetyl-CoA altered metabolism at baseline, we derived an acetyl-CoA-related signature from transcriptomic datasets. We used the CCLE panel (608 cell lines) with a known response to eight HDACis to gather enough samples to gain a sufficient statistical power . A machip-value = 0.0045 and 0.0004, respectively, while the FA signature alone could not differentiate sensitive and resistant (p = 0.07). These results confirmed that samples with an altered expression profile for acetyl-CoA metabolism at baseline have the strongest sensitivity to HDACi.To validate the importance of acetyl-CoA for HDACi sensitivity in clinical samples, we tested the performance of the three GES in samples obtained from 13 acute myeloid leukemia (AML) patients for whom microarray data prior to treatment and clinical response to belinostat are publicly available d. The coTogether, these results confirmed that alteration in two acetyl-CoA metabolism pathways (FA metabolism and BCAA metabolism) were a hallmark of sensitivity to HDACi in patients. It also validated the two acetyl-CoA derived GES as possible tools to further select patients for whom HDACi could be beneficial.In this study, we discovered that the class I HDAC inhibitor romidepsin caused a depletion in acetyl-CoA, most probably through an overconsumption of acetyl-CoA itself triggered by induced hyperacetylation of histones. We also uncovered a strategy for treating KRAS pathway modified tumors. Indeed, in RAS-activated cells, the depletion in acetyl-CoA triggered a decrease in TCA cycle flux combined with an increase in cytoplasmic ROS that led to cell death. Computational analysis of transcriptomic profiles in a 608-cell panel highlighted inherent alterations in acetyl-CoA generation pathways that could cause a higher dependency on acetyl-CoA depleting drugs, and we confirmed our results in samples from patients treated with belinostat. Together, these findings provide a promising strategy for improving treatments for the RAS pathway-modified tumors, where acetyl-CoA metabolism signatures could be exploited to select patients who are most likely to benefit from HDACi treatment.The tight link between histone acetylation and acetyl-CoA levels is well known but has been described in only one direction: acetyl-CoA levels determine histone acetylation levels. In fact, the abundance of the nucleo-cytosolic pool has a direct impact on the enzymatic activity of HATs . Here, wThis is consistent with the fact that acetyl-CoA is the unique donor of acetyl groups for acetylation, and that even after 6 h treatment followed by 12 h washout, the acetylated histone signal did not fade , implyinIn normal physiology, fluctuations in acetyl-CoA concentration reflect the metabolic state of the cell. Due to this key role, its regulation is tightly controlled by three different mechanisms: the expression of its metabolic enzymes or their regulators, its cellular and subcellular compartmentalization, and the post-translational regulation of the enzymes involved in its production (by acetylation) ,18,20. SAnother point to discuss is the link between TCA cycle and cytoplasmic ROS production. Gansemer et al.  found thIn sum, our results suggest that a dominant effect of HDAC inhibition is the depletion of acetyl-CoA. While the canonical mechanism of opening chromatin to facilitate gene transcription is undoubtedly important in some settings, the rapid cell death in T-cell lymphoma cells is more consistent with a metabolic impact. One future direction will be to determine what role acetyl-CoA could play in the myriad other effects observed following HDAC inhibition, including R-loop persistence, kinetochore assembly and DNA damage.Another direction will be to determine whether this metabolic vulnerability could be exploited in the clinic. Acetyl-CoA metabolism signatures were performant in a small validation cohort of patients, and will have to be tested in a prognostic study in order to confirm their strength for future clinical use as biomarkers to help stratifying patients that should receive HDACi. Our results also open doors to new combinations to treat patients with solid cancer using HDACi with FA or BCCA metabolism inhibitors.


Recombinant DNA technology is widely used for different applications in biology, medicine and bio-technology. Viral transduction and plasmid transfection are among the most frequently used techniques to generate recombinant cell lines. Many of these methods result in the random integration of the plasmid into the host genome. Rapid identification of the integration sites is highly desirable in order to characterize these engineered cell lines.We developed detectIS: a pipeline specifically designed to identify genomic integration sites of exogenous DNA, either a plasmid containing one or more transgenes or a virus. The pipeline is based on a Nextflow workflow combined with a Singularity image containing all the necessary software, ensuring high reproducibility and scalability of the analysis. We tested it on simulated datasets and RNA-seq data from a human sample infected with Hepatitis B virus. Comparisons with other state of the art tools show that our method can identify the integration site in different recombinant cell lines, with accurate results, lower computational demand and shorter execution times.https://github.com/AstraZeneca/detectIS.The Nextflow workflow, the Singularity image and a test dataset are available at Bioinformatics online. Recombinant DNA technology can be used to generate transgenic animals, plants and cell lines, widely used for different applications in biology, medicine and biotechnology . Viral tWe present detectIS, a pipeline to detect the ISs in paired end (PE) HTS experiments (either DNA or RNA sequencing data). It can be directly used with different host and exogenous DNA references, without the need of creating a specific index. Consequently, it is suitable for different applications, for example detecting ISs of plasmids in stable cell lines, either clones or pools, as well as locating viruses integrated in any host genome. The speed of execution makes the detectIS pipeline well-suited for quickly screening HTS data from panels of different cell lines generated during the cell line development process for therapeutic protein manufacture, enabling the detection of cell lines with undesirable transgene fusion sequences.Perl script integrates the four alignment results looking for potential ISs. ISs can be identified by split reads—read pairs in which at least one read has a part mapping to the host genome and the remaining part mapping to the plasmid/transgene, and chimeric reads, read pairs in which one of the two reads is mapped to the host genome and the other one to the plasmid/transgene. The pseudocode of the subroutines used by the Perl script is reported in DetectIS  consistsIn order to use the workflow, the user has to create a configuration file specifying the reference host genome and exogenous sequence references, the directory containing the raw data and the output directory. The analysis can be executed locally or in an HPC environment, in the latter scenario the user also has to specify the cluster executor. A configuration file is provided to analyze a test dataset and can be used as a template for other analyses.The recipe of the Singularity image with all the necessary software is also supplied. A bash script is also given to analyze a test dataset without Nextflow and can be used as a template for analysis in local environments.In order to test the functionality of detectIS and the accuracy of its results, we simulated random integrations of a plasmid in a Chinese hamster ovary (CHO) scaffold, exploring different modalities of transgene size, depth of sequencing coverage and read length. We compared the results of detectIS with the ones derived by other tools for viral detection, that are able to use host references different from human. SeekSV  is a proSupplementary Supplementary Supplementary Supplementary We extended the comparison to publicly available RNA-seq experiments of four hepatitis B virus (HBV) positive hepatocellular carcinoma cell lines with verified chimeric viral-human transcripts . In thisSupplementary Supplementary Financial Support: none declared.Conflict of Interest: none declared.btab366_Supplementary_DataClick here for additional data file.