Background: Exploiting synthetic lethality (SL) relationships between protein pairs has emerged as an important avenue for the development of anti-cancer drugs. Nicotinamide phosphoribosyltransferase (NAMPT) is the rate-limiting enzyme of the NAD+ salvage pathway, having an SL relationship with nicotinic acid phosphoribosyltransferase (NAPRT), the key enzyme in the NAD+ Preiss-Handler pathway. NAMPT inhibitor holds clinical potential not only as a promising cancer treatment but also as a means of protection against chemotherapy-induced-peripheral-neuropathy (CIPN). However, as NAD+ is essential for normal cells, the clinical use of NAMPT inhibitors is challenging. This study aimed to identify a novel NAMPT inhibitor with enhanced selective cytotoxicity against NAPRT-deficient cancer cells as well as prominent efficacy in alleviating CIPN.Methods: We began by conducting drug derivatives screening in a panel of lung cancer cell lines to select an agent with the broadest therapeutic window between the NAPRT-negative and-positive cancer cell lines. Both in vitro and In vivo comparative analyses were conducted between A4276 and other NAMPT inhibitors to evaluate the NAPRT-negative cancer cell selectivity and the underlying distinct NAMPT inhibition mechanism of A4276. Patient-derived tumor transcriptomic data and protein levels in various cancer cell lines were analyzed to confirm the correlation between NAPRT depletion and epithelial-to-mesenchymal transition (EMT)-like features in various cancer types. Finally, the efficacy of A4276 for axonal protection and CIPN remedy was examined in vitro and in vivo.Results: The biomarker-driven phenotypic screening led to a discovery of A4276 with prominent selectivity against NAPRT-negative cancer cells compared with NAPRT-positive cancer cells and normal cells. The cytotoxic effect of A4276 on NAPRT-negative cells is achieved through its direct binding to NAMPT, inhibiting its enzymatic function at an optimal and balanced level allowing NAPRT-positive cells to survive through NAPRT-dependent NAD+ synthesis. NAPRT deficiency serves as a biomarker for the response to A4276 as well as an indicator of EMT-subtype cancer in various tumor types. Notably, A4276 protects axons from Wallerian degeneration more effectively than other NAMPT inhibitors by decreasing NMN-to-NAD+ ratio.Conclusion: This study demonstrates that A4276 selectively targets NAPRT-deficient EMT-subtype cancer cells and prevents chemotherapy-induced peripheral neuropathy, highlighting its potential as a promising anti-cancer agent for use in cancer monotherapy or combination therapy with conventional chemotherapeutics. Cancer cells exhibiting accelerated proliferation as their acquired hallmark often show a high demand for NAD+ to support their survival Nicotinamide adenine dinucleotide (NAD+) is an essential metabolite for cell survival; it not only serves as an oxidizing agent for energy production, fatty acid oxidization (FAO), and serine biosynthesis but also participates in numerous cellular processes as a substrate for various enzymes, such as SARM1 Despite the growing progress in identifying NAMPT inhibitors, their therapeutic application is limited. Tumors can restore the NAD+ pool via alternative pathways, as shown in Figure The two hallmarks of cancer, EMT-like features and metabolic reprogramming, are not distinct cellular processes but rather interconnected phenomena Neuronal NAD+ levels are closely linked to axonal survival and death In this study, we identified a novel, orally bioavailable NAMPT inhibitor, A4276, using phenotypic screening to emphasize its selective cytotoxicity against NAPRT-negative cancer cells. We confirmed that NAPRT deficiency can serve as an indicator of EMT-like cancers across various tumor types, underscoring the clinical significance of our compound. Furthermore, considering the broad role of the NAD+ biosynthetic pathway in governing diverse cellular processes, we extended the potential applications of our compound to CIPN, a condition for which manipulating the NMN-to-NAD+ ratio has reported relevance as a potential treatment. Overall, our study identified a novel compound, A4276, with great potential as a specific treatment for NAPRT-deficient EMT-like cancers. Additionally, A4276 shows promise as a protective agent against axonal death, potentially alleviating adverse effects when used in combination with chemotherapy.NAPRT expression as a single biomarker As NAMPT and NAPRT are the rate-limiting enzymes of the two NAD+ biosynthetic pathways Figure A, neopla50 of 2.27 μM, Figure We hypothesized that A4276 has high selectivity for NAPRT-deficient cancer cells due to it inhibiting NAMPT enzymatic activity, as these cancer cells are likely dependent on NAMPT for NAD+ supply. To test this hypothesis, we treated the purified NAMPT with various concentrations of A4276 and measured its activity  s Figure A. To furs Figure B. As exps Figure B. These These data imply the clinical potential of A4276 for the treatment for various tumor types of malignant EMT-like cancer characterized by downregulated NAPRT. To further demonstrate this potential, we evaluated the responses of diverse GC, CRC, and PDAC cell lines to A4276 treatment. Regardless of the cancer type, NAPRT-deficient cancer cell lines were more susceptible to A4276 than the NAPRT-positive cancer cell lines Figure C. The inThe specific cytotoxicity of A4276 sparing NAPRT-positive cell lines in multiple cancer types and normal cells Figure C, and 3ETo test the first hypothesis, we examined whether certain NAPRT-positive cancer cell lines sensitive to FK866 are subjected to NAM-competitive NAMPT inhibition and NAD+ depletion by FK866. We observed that NUGC3 cells harbor NAPRT-positive and non-EMT features but were resistant only to A4276 Figure SA-B.After excluding the first hypothesis, we proceeded to assess the potential involvement of a secondary target in the A4276's selectivity. To verify whether the A4276's toxicity depends on the perturbation of the NAD+ synthetic pathway, we supplemented NAD+, NAM, and NA with A4276 in NAPRT-negative EMT-cancer cell lines exhibiting sensitivity to A4276, SNU484 Figure C and 4A,Additionally, we conducted a comparative analysis of A4276 and FK866's mode of NAMPT inhibition. We treated purified NAMPT with different concentrations of each of the inhibitors, and monitored its enzymatic activity over 60 min  to enhance its water solubility. Furthermore, we explored the potential benefits of co-administration with nicotinic acid (NA) to extend the therapeutic window of the agent. Previous reports have suggested that NA supplementation can prevent normal cell toxicity associated with NAMPT inhibition by restoring a sufficient NAD+ in normal cells while rarely affecting NAPRT-deficient tumor cells in vivo therapeutic potential of A4276, we assessed its cardiac toxicity and pharmacokinetics (PK). To determine if A4276 is associated with cardiac dysfunction, we conducted an in vitro human ether-à-go-go-related gene (hERG) cardiac potassium channel inhibition assay, with testing concentrations ranging from 0.001 μM to 100 μM. The positive control, E-4031, inhibited hERG currents by 92.8% (± 6.11%) at 10 μM, while A4276H inhibited hERG currents by only 14.6% (± 1.84%) at 10 μM (max) and total drug exposure (AUClast) increased in a dose-proportional manner. Interestingly, the AUClast at a dose of 160 mg/kg and when co-administered with NA were comparable in both plasma and tumor tissues (max) ranged from two to eight hours, followed by a gradual decline over 24 h in all groups . When combined with NA, A4276H suppressed tumor growth by 58.27%, whereas KPT-9274 only inhibited it by 18.45%    is a significant clinical application of drugs that attenuate Wallerian degeneration. CIPN is a common adverse effect of chemotherapy, characterized by the development of painful neuropathy during drug administration and the degeneration of highly myelinated primary sensory axons, leading to a permanent loss of tactile sensation. As we observed a strong effect of A4276 in delaying Wallerian degeneration in DRG neuronal axons and CIPN, we investigated whether it could also prevent CIPN. The dissociated DRG neurons were cultured in a microfluidic device with fluidically separated somal and axonal compartments  in vivo, even at a lower dose than that which hampered the growth of HGC27-derived xenograft tumors  is still an active area of ongoing research, as they are responsible for most cancer-related mortality s Figure B. Suppors Figure  and studIn the present study, we comprehensively analyzed and compared the efficacy of NAMPT inhibitors as anti-neoplastic agents and axonal protectors. In particular, comparative analyses with FK866, a relatively well-studied competitive NAMPT inhibitor in cancer and axonopathy, revealed the mechanism behind the A4276 efficacy. Excluding the possible involvement of secondary targets in the distinct response of certain NAPRT-positive cancer cell lines to FK866 and A4276 Figure B, we proConclusively, the findings of this study have important translational implications. A4276 has not only demonstrated promise in the treatment of cancers with a clear biomarker, but also in preventing chemotherapy-induced axonal degradation. These results may help overcome major obstacles in the clinical management of cancer, such as targeting EMT-like cancers and preventing the development of CIPN.2. Colorectal and pancreatic cancer cell lines were grown in RPMI-1640 medium supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin-streptomycin (Invitrogen) at 37°C in 5% CO2. All normal cell lines were cultured according to standard protocols.Human NSCLC cell lines  were kindly provided by Michael A. White and John D. Minna . Gastric cancer cell lines, except for SK4 and Yonsei Cancer Center (YCC)-series cell lines, were purchased from the Korea Cell Line Bank . The SK4 cell line was generously provided by Dr. Julie Izzo , and the YCC-series cell lines were obtained from the Song-Dang Institute for Cancer Research, Yonsei University College of Medicine. All colorectal cancer cell lines except HGC27 were purchased from the Korea Cell Line Bank. The HGC27 cell line was obtained from the Riken cell line bank . Normal cell lines including THLE-2, primary cardiomyocytes, and primary retinal epithelial cells were purchased from ATCC , Promo Cell , and Lonza , respectively. All pancreatic cancer cell lines were obtained from the Korea Cell Line Bank. NSCLC cell lines were cultured in RPMI-1640 medium supplemented with fetal bovine serum  and 1% penicillin-streptomycin  at 37°C in 5% COCancer cell lines were classified into NAPRT-positive and NAPRT-negative groups by confirming NAPRT expression via immunoblot analysis. Ten colorectal cancer cell lines and eight pancreatic cancer cell lines were designated as EMT-like or non-EMT like based on the analysis of EMT signature genes as described in our previous report Cell viability assays were conducted using the CellTiter-Glo assay system , according to the manufacturer's instructions. Briefly, cells were treated with compounds, including A4276, FK866 , and KPT-9274  for 72 h. Next, 10 μL of assay solution was added to each well, and the mixture was dispensed using a Multidrop dispenser . After 15 min of incubation at room temperature, luminescence was measured using a SpectraMax Paradigm microplate reader . All values were normalized to those of DMSO controls. Drug-response curves were generated using the normalized viability of each agent, and the area under the curve (AUC) was calculated using the trapezoidal method. The 50% effective dose (ED50) was obtained by fitting a four-parameter log-based non-linear dose-response curve. In the 39 gastric cancer cell lines, 14 colorectal cancer cell lines, and 6 pancreatic cancer cell lines indicated in Figure For the experiments to test the NAD+-biosynthetic pathway perturbation-dependent cytotoxicity of FK866 and A4276, cells were treated with the indicated compounds for 72 h and were subjected to the CellTiter-Glo assay. NAD+ , NAM , NA , or 2-Hydroxynicotinic acid (2-HNA)  were utilized. The relative cell viability values were obtained by normalizing to the DMSO control group.For the A4276-paclitaxel combination experiments, COLO320-HSR and HGC27 cell lines were treated with the drugs for 72 h, and a cell viability assay was conducted using the CellTiter-Glo assay system, as described above. The relative cell viability values were normalized to those of the DMSO control.The cells were harvested, washed with PBS, and lysed on ice for 15 min in radioimmunoprecipitation assay (RIPA) buffer  containing a protease and phosphatase inhibitor cocktail . The lysates were centrifuged at 15,000 rpm for 20 min at 4°C, and the protein concentrations were determined using a Bradford assay . Equal amounts of total protein were subjected to SDS gel electrophoresis and transferred to PVDF membranes (Bio-Rad). The membranes were blocked for one hour at room temperature (RT) before overnight incubation at 4°C with a primary antibody in buffer containing 0.1% Tween 20. The membranes were washed three times with Tween-PBS buffer and then incubated for an hour at RT with a secondary antibody diluted in blocking buffer containing 0.1% Tween 20. The membranes were washed three times with Tween-TBS for 10 min each. Immunoreactive bands were visualized using Pierce enhanced chemiluminescence (ECL) western blotting substrate , SuperSignal West Pico PLUS chemiluminescent substrate , and X-ray films . The following antibodies were used in this study: rabbit polyclonal anti-NAPRT antibody , rabbit polyclonal anti-GRHL2 , rabbit monoclonal anti-PBEF/NAMPT antibody , rabbit monoclonal anti-HSP90 antibody , rabbit monoclonal anti-zinc finger E-box-binding homeobox 1 (ZEB1) , rabbit monoclonal anti-Snail antibody , monoclonal anti-Vimentin antibodies , rabbit polyclonal anti-E-cadherin antibody , mouse monoclonal anti-EpCAM antibody , and mouse monoclonal anti-beta-Actin antibody .To investigate the effect of A4276 on NAMPT activity, an NAMPT Activity Assay Kit  was used according to the manufacturer's instructions. In brief, NAMPT was incubated with various concentrations of A4276 in the presence of ATP, nicotinamide, nicotinamide mononucleotide adenylyltransferase 1 (NMNAT1), and phosphoribosyl pyrophosphate (PRPP) at 30°C for 60 minutes. After incubation, a mixture of water-soluble tetrazolium salts (WST-1), alcohol dehydrogenase (ADH), diaphorase, and ethanol was added to each sample and incubated for 30 min. The activity of NAMPT was then measured by determining the optical density (OD) at 450 nm on a microplate reader every 5 min for at least 30 min in the dark.The reactivation of inactivated NAMPT was monitored through the jump dilution method. At room temperature, NAMPT (200 nM) was incubated with excess A4276 (14 μM) for 60 minutes and then the concentration of A4276 was diluted to 70 nM by using assay buffer in the NAMPT Activity Assay Kit . The recovered NAMPT activity was measured as described above. NAMPT (200 nM) was incubated with DMSO (1.5 %) for 60 minutes and then the NAMPT activity was monitored.2-scale expression value of the 161 downregulated genes from that of the 149 upregulated genes included in the EMT gene signature. To enable an analysis involving multiple datasets with varying experimental designs, the calculated EMT scores were further subjected to Z-score normalization.The EMT scores were calculated by subtracting the average log2- normalized. To avoid duplicate entries per sample, we calculated the sums of the raw counts and mean expression values for other quantification methods.The gene expression profiles of colorectal and pancreatic cancers were collected from multiple public cohorts. Variance-stabilizing transformations were conducted with raw counts using the R package DESeq2 (version 1.36.0) for TCGA-COAD, TCGA-READ, TCGA-PAAD, Severance colorectal cancer cohort, and GSE190826. The dataset GSE92921 was normalized in the CEL format with the robust multichip average method using the R package affy (version 1.74.0). For all other datasets, the expression values were downloaded as logThe transcriptomics dataset (v.23Q2) of lung, gastric, colorectal and pancreatic cancer cell lines were downloaded from DepMap portal (www.depmap.org). For the analysis, 19 cancer cell lines without gene expression data in the dataset were excluded from the 63 cancer cell lines where A4276 response was monitored. 44 cancer cell lines included 7 lung cancer cell lines , 23 gastric cancer cell lines , 8 colorectal cancer cell lines  and 6 pancreatic cancer cell lines .The NAMPT Activity Assay Kit  was used to monitor NAMPT activity, following a similar procedure as described above. In detail, NAMPT was incubated with various concentrations of A4276 or FK866, along with ATP, nicotinamide, nicotinamide mononucleotide adenylyltransferase 1 (NMNAT1), and phosphoribosyl pyrophosphate (PRPP) at 30°C for 60 minutes. Subsequently, a mixture of water-soluble tetrazolium salts (WST-1), alcohol dehydrogenase (ADH), diaphorase, and ethanol was added to the samples and incubated for 30 minutes. NAMPT activity was then monitored every 5 minutes.6 HGC27 cells. Once the tumor volume reached 150 mm3, the mice were randomly divided into eight groups, and 6-7 mice per group were orally administered with various doses of A4276H , KPT-9274, or the vehicle for 21 d. For NA co-administrated groups, 100 mg/kg of NA was added together with the compounds. Tumors were measured (length × width) every 2-3 d, and the weights of the mice were measured every 1-2 d from the first day of drug treatment. Tumor volume was calculated using the following formula: 0.5 × major axis × (minor axis)2. Further analyses were conducted using the data obtained from mice with a tumor volume of at least 100 mm3 on the first day of drug administration.All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Yonsei University. Five-week-old female BALB/c-nu mice were purchased from SLC, Inc. . Mice were housed in an individual ventilation cage system (IVCS) with a computerized environmental control system . The room temperature was maintained at 22±2 °C with a relative humidity of 50±10%. The mice were allowed to adapt to their laboratory housing environment for at least one week before the experiments. Five-week-old mice were subcutaneously injected with 5x10TM assay kit  was used. After 48 h of seeding 0.5 x 104 cells per well into a 96-well culture plate, each compound was treated for 24 h. Subsequently, the media was replaced to 50 µl of PBS, followed by the addition of 50 µl NAD/NADH-GloTM Detection reagent. The total amount of NAD present in resulting 100 µl of solution in each well was directly measured according to the manufacturer's instructions.To monitor the NAD(H) levels  in cell lines, the NAD/NADH-GLONAD(H) levels in the tumor xenografts were quantified using an NAD/NADH colorimetric quantification kit  according to the manufacturer's instructions. Briefly, 20 mg of tissue sample was washed with ice-cold PBS and extracted with 400 μl of NAD/NADH extraction buffer. The total NAD+ absorbance was measured at 450 nm using a SpectraMax paradigm microplate reader and normalized to the total protein concentrations, which was determined using Bradford assay.Combination index (CI) was calculated based on the Loewe additivity model and median-effect principle, which refers to the Chou-Talalay method using the SiCoDEA tool Xenopus retinal axons and imaging of their degeneration were performed as previously described Xenopus embryos were anesthetized and a plasmid encoding EGFP was transfected into the optic vesicle via targeted electroporation. At stage 41, when the retinal ganglion cell axons had reached the optic tectum, the optic nerve originating from the electroporated retina was severed. The drug was administered at the time of axotomy throughout the experiments. The EGFP-positive retinal axons terminating at the contralateral optic tectum were imaged using a laser scanning confocal microscope .Labeling of the After axotomy, retinal axons undergo stereotypical steps of synchronous degeneration, which can be clearly distinguished at 24-h intervals, with approximately 50%, 15%, and 0% axonal fragments remaining at 24, 48, and 72 h post-axotomy, respectively Xenopus embryos, and the drug  was administered at 10 μM for 48 h. NAD+ concentration was measured using NAD/NADH-Glo Assays  and a microplate reader . All experiments were conducted with at least three independent biological replicates.Axotomy was performed on stage 41 g and 4 °C. Afterward, 500 µL of chloroform was added to the supernatants and centrifugation was performed for 10 min at 16,000 ×g and 4 °C. Chloroform extraction was repeated, and supernatants were dried using a speed vacuum concentrator with a cold trap . Each dried sample was reconstituted in 100 µL of HPLC grade water and centrifuged for 10 min at 16,000 ×g and 4 °C. The supernatants were transferred to vials for liquid chromatography-mass spectrometry analysis.Drug  was administered at 10 μM for 48 h. Tadpole samples were collected in three groups , 50 mg each in a 1.5 ml tube (n=4). Nicotinamide mononucleotide (NMN), Nicotinamide adenine dinucleotide (NAD+) were extracted from 500 µL of pre-cooled (-20 °C) 80% methanol with internal standard (ISTD) 2-chloroadenosine. ISTD was added to all samples at the concentration of 40 ng/mL for quality control Extracted samples were injected into ACQUITY UPLC BEH Amide 1.6 μm C18 130Å, 100 mm × 2.1 mm column  and separated over 6 min (0.6 mL/min) using a gradient of solvent B at 40 °C. Solvent B was 0 min, 80% B; 1 min, 80% B; 2 min, 5% B; 3.5 min, 5% B; 3.51 min, 80% B; 6 min, 80% B. Solvent A was composed of 20mM ammonium acetate in water (pH 3.2 with formic acid) and B was acetonitrile (100%). Injection volume was 2 µL. Polarity was set at positive mode. Liquid chromatography system was coupled to a SCIEX QTRAP 5500+  with Nexera series LC-40 .Single reaction monitoring (SRM) analysis was performed. Declustering potential (DP), collision energy (CE), and collision cell exit potential (CXP) were optimized for the target compound. Each target was optimized with a total of two transitions. One transition was used for quantification and the other for qualification. MRM transitions, DP, CE, CXP are summarized in The dorsal root ganglia (DRG) were aseptically removed from postnatal mice, and each ganglion was cut into several pieces. Cell culture inserts , also known as the Boyden chamber, adequate for a 6-well plate, were coated with poly-L-lysine and Laminin (both from Sigma). The inserts were placed in a 6-well plate and each well was filled with 2 mL of Neurobasal A medium supplemented with 2% B-27, 0.5% glutamine, and 1% antibiotic antimycotic  with 50 ng/mL of NGF (Sigma). DRG explants were placed on the insert and allowed to grow axons through the pores on the bottom surface of the insert, which was facilitated by the NGF in the lower compartment  were aseptically removed from postnatal mice and treated with 0.25% trypsin at 37℃ for 15 min, after which the trypsin was neutralized with fetal bovine serum. The DRG were centrifuged at 3000 rpm for 5 min and resuspended in Neurobasal A medium supplemented with 2% B-27, 0.5% glutamine, and 1% antibiotic antimycotic  with 10 ng/mL of NGF (Sigma). The DRG was then dissociated into single cells through trituration with flame-polished glass pipettes and pre-plated on an uncoated Petri dish to remove adherent cells. After 2 h, loosely attached neurons were dislodged by gentle pipetting and plated into one compartment  of a microfluidic device that had been precoated with poly-L-lysine and Laminin (Sigma)  and an Alexa Fluor 488-conjugated secondary antibody . Samples were immuno-stained and imaged under identical settings for quantitative analyses. The axon fragmentation index was defined as the proportion of the lost axonal length. Specifically, traces of axons in an image were manually delineated as segmented lines. The channel for acetylated alpha-tubulin was thresholded to generate a binary copy of the same image on which traces of axons were superimposed. Then axon fragmentation index for the entire image was calculated as [the sum of the lengths of axons not stained with acetylated alpha-tubulin] divided by [the sum of all axonal traces]. All experiments were performed with at least three independent biological replicates.Mice were anesthetized using Zoletil, the sciatic nerve was transected, and the wound was sutured. After surgery, the mice were euthanized at 1, 3, or 7 d, and serum and sciatic nerve fragments distal to the injury were collected for analysis.Paclitaxel (2 mg/kg) dissolved in 80% saline, 10% Kolliphor EL, and 10% ethanol were injected intraperitoneally for four alternating days The sciatic nerve was fixed, processed, and imaged using transmission electron microscopy (TEM) at the Zeiss-Yonsei imaging facility. Myelinated axons, both healthy and degenerated, were manually counted for quantitative analysis.Serum NFL concentration was measured using an NF-Light ELISA kits .The von Frey test was performed on post-injection days 3, 7, and 14, according to the protocol provided by the Jackson Laboratory Mouse Neurobehavioral Phenotyping facility. Prior to the test, the mice were habituated to a wire grid for 10 min, and then each hind paw was stimulated twice for each trial.An oral administration of A4276 (80 mg/kg) dissolved in 0.5% methylcellulose and 0.2% Tween 80 was given to 7-week-old mice. All experimental procedures were performed in accordance with protocols approved by the Yonsei University College of Medicine IACUC.P212121  with four molecules in the asymmetric unit (PhaserPhenixCootwork/Rfree = 20.41/23.13 %) consisted of residues 8 - 42 and 53 - 483 of NAMPT, four A4276 molecules, 710 water molecules, and 12 phosphate molecules  were purified after buffer exchange. The PBS buffer (pH 7.4) containing 10% glycerol was changed to S-buffer consisting of 25 mM Tris pH 8.0 and 2 mM DTT. Next, NAMPT in S-buffer was loaded onto a HiTrap Q FF column  pre-equilibrated with S-buffer and eluted using a 0 - 1 M NaCl gradient in the same buffer. Eluted fractions were concentrated and loaded onto Superdex 75 HR 16/60 column (Cytiva). Purified NAMPT in a buffer containing 25 mM Tris-HCl pH 8.0 and 2 mM DTT was concentrated to approximately 10 mg/mL for crystallization. The microbatch crystallization set up ric unit . Moleculolecules . Atomic P-values are denoted as follows: * < 0.05, ** < 0.01, *** < 0.001, and not significant (ns) ≥ 0.05. R version 4.0.3 .Graphs were obtained using GraphPad Prism and R. All statistical tests were performed using the R statistical software (ver. 4.0.3). We performed an unpaired t-test, one-way ANOVA test with Tukey's multiple comparison test, and two-way ANOVA, as stated in the corresponding legends of the article. Supplementary figures and tables.Click here for additional data file.


DPP8/9 inhibition induces either pyroptotic or apoptotic cell death in hematological malignancies. We previously reported that treatment with the DPP8/9 inhibitor 1G244 resulted in apoptotic cell death in myeloma, and our current study further evaluates the mechanism of action of 1G244 in different blood cancer cell lines. Specifically, 1G244 inhibited DPP9 to induce GSDMD-mediated-pyroptosis at low concentrations and inhibited DPP8 to cause caspase-3-mediated-apoptosis at high concentrations. HCK expression is necessary to induce susceptibility to pyroptosis but does not participate in the induction of apoptosis. To further characterize this DPP8-dependent broad-spectrum apoptosis induction effect, we evaluated the potential antineoplastic role for an analog of 1G244 with higher DPP8 selectivity, tominostat . In vitro studies demonstrated that the cytotoxic effect of 1G244 at high concentrations was enhanced in tominostat. Meanwhile, in vivo work showed tominostat exhibited antitumor activity that was more effective on a cell line sensitive to 1G244, and at higher doses, it was also effective on a cell line resistant to 1G244. Importantly, the weight loss morbidity associated with increasing doses of 1G244 was not observed with tominostat. These results suggest the possible development of novel drugs with antineoplastic activity against selected hematological malignancies by refining and increasing the DPP8 selectivity of tominostat. The S9b serine protease family has the ability to cleave Xaa-Pro dipeptides from N-termini of their substrates, and these enzymatic members include dipeptidyl peptidase-4 (DPP4), DPP8, DPP9, and fibroblast activation protein (FAP) .For example, DPP4 cleaves the pancreatic polypeptide family, including neuropeptide Y and peptide YY, several members of the glucagon family, and certain chemokines as natural substrates ,3. Of thSeveral proteins have been identified as substrates of DPP8 and DPP9 ,7,8,9,10DPP8 and DPP9 localize intracellularly in contrast to DPP4, which has an extracellular catalytic domain. With significant homology among these enzymes (79% amino acid similarity and 61% amino acid identity) , no smalThe DPP family nonselective inhibitor Val-boroPro  triggers a lytic form of programmed cell death known as pyroptosis in human acute myeloid leukemia (AML) cell lines and primary AML samples, an effect that is dependent on DPP9 inhibition . The inhAlternatively, 1G244 is a specific inhibitor of DPP8 and DPP9 . We haveOn the other hand, 12 m, a 1G244 methylpiperazine analog, has been reported to be highly selective for DPP8 . Based oIn this study, we found that high concentrations of 1G244 induce caspase-3-activation-mediated apoptosis that is dependent on DPP8 inhibition. We also demonstrated that the higher level of selectivity for DPP8 by tominostat enhances its apoptotic effect and that it may be a more potent anticancer drug with an improved in vivo weight loss toxicity profile.T cell non-Hodgkin’s lymphoma cell line KARPAS299 was supplied by the European Collection of Authenticated Cell Cultures (ECACC). Multiple myeloma cell lines, MM.1S and RPMI8226; Burkitt’s lymphoma cell lines, Daudi, Raji, and NAMALWA; the acute myelogenous leukemia cell line, KG1; the acute T cell leukemia cell line, Jurkat; and the chronic myelogenous leukemia cell line, K562, were supplied by American Type Culture Collection (ATCC). Acute monocytic leukemia cell lines, THP-1, MOLM-13, and NOMO-1; and the myelodysplastic syndrome cell line, SKM-1, were supplied by Japanese Collection of Research Bioresources Cell Bank (JCRB). All these cell lines were maintained in RPMI 1640 (Gibco BRL) supplemented with 10% heat-inactivated fetal bovine serum (Sigma), 100 µg/mL streptomycin, and 100 U/mL penicillin.The shRNA lentivirus, MISSION Lentiviral Transduction Particles, SHCLNV was purchased from Sigma-Aldrich. The lentivirus vector system is composed of the vector pLKO.1-Puro-CMV-tGFP. The control is MISSION pLKO.1-Puro-CMV-tGFP Positive Control Transduction Particles, SHC003V. DPP8 KD1, TRCN0000300778; DPP8 KD2, TRCN0000300777. DPP9 KD1, TRCN0000075265; DPP9 KD2, TRCN0000075264. HCK KD1, TRCN0000320535: HCK KD2, TRCN0000381826. The human HCK gene overexpressing lentivirus, HCK (NM_002110) Human Tagged ORF Clone Lentiviral Particle, RC217022L4V was purchased from OriGene. The lentivirus vector system is composed of the vector pLenti-C-mGFP-P2A-Puro. The control is Lenti ORF control particles of pLenti-C-mGFP-P2A-Puro, PS100093V. Lentivirus transduction and stable cell line selection were performed according to the manufacturer’s instructions.Puromycin and 1G244 were purchased from Sigma-Aldrich. Talabostat, Disulfiram, and Necrostatin-1 were purchased from MedChemexpress. Necrosulfonamide was purchased from Santa Cruz Biotechnology. Z-DEVD-FMK and Z-VAD-FMK were purchased from Medical & Biological Laboratories. Tominostat (12 m) was prepared according to previously published work .The viable cell number was quantified using a Premix WST-1 Cell Proliferation Assay System  according to the manufacturer’s instructions. The level of cytotoxicity was also quantified by measuring the level of lactate dehydrogenase (LDH) released from damaged cells using a Cytotoxicity LDH Assay Kit-WST  according to the manufacturer’s instructions.Cells were lysed in a buffer containing 1% sodium dodecyl sulfate (SDS); 20 mM Tris-HCl, pH 7.4; 5 μg/mL pepstatin A; 10 μg/mL leupeptin; 5 μg/mL aprotinin; and 1 mM phenyl-methylsulfonyl fluoride and then heated for 5 min. After passage through a 20-gauge needle ten times and centrifugation at 15,000 rpm at 4 °C for 30 min, the aliquot was boiled in a standard reducing sample buffer for 3 min and subjected to SDS-polyacrylamide gel electrophoresis. This was followed by transfer to an Immobilon-P membrane  and hybridization with an anti-DPP8 antibody (OTI1D2), monoclonal, mouse, NBP2-01830 , an anti-DPP9 antibody (OTI2E3), monoclonal, mouse, NBP2-01521 , an anti-GSDMD antibody, monoclonal, rabbit, ab210070 , an anti-Cleaved Caspase-3 antibody , monoclonal, rabbit, #9664S , an anti-DFNA5/GSDME antibody , monoclonal, rabbit, ab215191 (Abcam), an anti-Caspase-1 antibody, polyclonal, rabbit, #2225S , an anti-CARD8 antibody (2108C2a), monoclonal, mouse, sc-81213 , an anti-Caspase-3 antibody, polyclonal, rabbit, #9662S , an anti-HCK antibody (E1I7F), monoclonal, rabbit, #14643 , an anti-LCK antibody (3A5), monoclonal, mouse, sc-433 (Santa Cruz Biotechnology), an anti-Fyn antibody (15), monoclonal, mouse, sc-434 (Santa Cruz Biotechnology), an anti-c-Fgr antibody (B-8), monoclonal, mouse, sc-166079 (Santa Cruz Biotechnology), an anti-Lyn antibody (H-6), monoclonal, mouse, sc-7274 (Santa Cruz Biotechnology), an anti-Blk antibody (9D10D1), monoclonal, mouse, sc-65980 (Santa Cruz Biotechnology), an anti-β-actin antibody (8H10D10), monoclonal, mouse, #3700S , an anti-AK2 antibody, polyclonal, rabbit, ab37594 (Abcam), an anti-FADD antibody (A66-2), monoclonal, mouse, 556402 , an anti-phospho-FADD (Ser194) antibody, polyclonal, rabbit, #2781S , or an anti-DUSP26 antibody, polyclonal, rabbit, GTX109283 . Proteins detected by these antibodies were visualized with horseradish-peroxidase-conjugated anti-mouse or rabbit antibody (Santa Cruz Biotechnology) followed by the use of enhanced chemiluminescence , as we described previously [RNA was isolated from cells using an RNeasy Mini Kit . Microarray analysis was performed using the 3D-Gene human oligo chip 25k , which permits the detection of 24,460 mRNAs. After hybridization, the DNA microarray was washed according to the manufacturer’s instructions, followed by image scanning using 3D-Gene Scanner 3000 (TORAY Industries) and data processing using 3D-Gene Extraction 2.0.0.4 (TORAY Industries).scidIl2rgtm1Wjl/SzJ (NSG) female mice of age 6–7 weeks and weight 19–21 g were obtained from Charles River Japan Inc. . The mice were kept under specific pathogen-free conditions with a 12 h day and night cycle with free access to food and water, and they received humane care in compliance with Institutional Guidelines. All experiments were approved by the Animal Care and Use Committee of Toyama University (reference number A2020UH-5). In order to evaluate for lethal toxicity, NSG mice were administered with 1G244 or tominostat subcutaneously once a week, with body weights being measured at the same time. For the evaluation of antitumor activity, 5 × 106 of MM.1S or Daudi cells were subcutaneously inoculated on the left side on the backs of NSG mice. Three days after the inoculation, 1G244 or tominostat was administered subcutaneously once a week. Tumor measurements were obtained at the same time with a caliper, and tumor volume was calculated according to the following formula: MD × TL2 × 1/2, with MD and TL being the maximum diameter and transverse length, respectively.In vivo experiments were performed as we previously described . NOD.Cg-We compared the cytotoxic effect of the DPP8/9 inhibitors 1G244 and talabostat on hematological cancer cell lines using water-soluble tetrazolium (WST) to detect the respiratory chain metabolic activity in viable cells. As shown in To determine whether the DPP8/9 inhibitors 1G244 and talabostat exerted their cytotoxic effect through DPP8 or DPP9, we conducted knockdown studies on DPP8 or DPP9 expression in MM.1S and KARPAS299 cells, which are highly sensitive cell lines. As shown in As shown in These results were consistent with our hypothesis that the strong anticancer effect exhibited by high concentrations of 1G244 is caspase-3-dependent apoptosis. At the same time, Z-DEVD also weakly inhibited 10 and 100 µM of talabostat, suggesting that caspase-3-mediated pyroptotic pathways may also be involved. While a potential candidate may involve the cleavage of Gasdermin-E (GSDME) , our worMicroarray analysis was then performed as a comprehensive evaluation to compare sensitive cell lines with resistant cell lines. Factors with high expression in the former and low expression in the latter are shown in Our initial studies employed the Western blotting method to examine the HCK protein level in each cell line, as shown in Meanwhile, MOLM-13, NOMO-1, and SKM-1 were sensitive to 1G244, while Jurkat, K562, RPMI8226, and Raji were resistant to 1G244 D, with HTo further confirm the dependence on HCK for DPP8/9 inhibitor sensitivity, we conducted HCK knockdown studies in highly sensitive MM.1S cells F. The knHigh concentrations of 1G244 induced apoptotic cell death via DPP8, even in blood cancer cell lines resistant to pyroptosis induced by talabostat and low concentrations of 1G244 via DPP8/9. We therefore attempted to determine whether increasing the DPP8 selectivity of 1G244 could result in a greater antineoplastic effect. The selectivity for DPP8 compared to DPP9 is expressed as the selectivity index (SI), i.e., IC50 for DPP9 divided by IC50 for DPP8. The SI of 1G244 is 3.8 , whereasThe antitumor effect of tominostat was compared to that of 1G244 in the sensitive cell lines MM.1S and KARPAS299 and in the resistant cell line Daudi in in vitro studies A. For alFor in vivo studies, we first conducted experiments examining the toxicity profile of the pertinent inhibitors. There was no death of the tested animals from acute toxicity from the administration of either 1G244 A or tomiThe next in vivo experiment examined the antitumor effect of the inhibitors. Although the treatment of mice implanted subcutaneously with the sensitive cell line MM.1S cells with 30 mg/kg of 1G244 or tominostat resulted in tumor growth inhibition, tominostat exhibited significantly superior antitumor activity B. MeanwhAn initial finding of our current work is the dual nature of 1G244. At low concentrations, 1G244 induces pyroptosis mainly through the inhibition of DPP9, while it induces apoptosis through the inhibition of DPP8 at high concentrations. A potential explanation for this duality is that 1G244 inhibits DPP8 and DPP9 by different mechanisms. Indeed, it has been suggested that 1G244’s inhibition of DPP8 is based on binding that is slow and tight , i.e., iThe dependence of cell death caused by high concentrations of 1G244 on DPP8 inhibition is precisely demonstrated by DPP8 knockdown experiments C. FurtheIn our previous and present studies, no such toxicity was observed with 1G244 treatment, likely due to the fact it was administered subcutaneously once a week in mice . HoweverA reason for the observed reduction in toxicity may be due to differences in the tissue expression of DPP8 and DPP9. Specifically, DPP9 is expressed throughout all organs in the body , with its absence in mice causing impairment in tongue development, suckling defects, and subsequent neonatal lethality ,24. On tImportantly, the low-sensitivity cell line Daudi was resistant to 100 µM of 1G244, but the same dose of tominostat exhibited effective in vitro antitumor activity A, and 15An issue that needs to be clarified involves the specific signaling events induced by DPP8 inhibition. Comparing high- and low-susceptible cell lines, we identified HCK as one factor defining differences in susceptibility. However, HCK knockdown suppressed pyroptosis induced by DPP9 inhibition, while apoptosis induced by DPP8 inhibition was unaffected .Recent work by others suggested a mechanism for HCK involvement in signaling events of pyroptosis . These aOn the other hand, assuming that DPP8 inhibition induces apoptosis by inhibiting the cleavage of relevant substrates, adenylate kinase 2 (AK2), which is involved in energy metabolism, can be considered as a potential candidate substrate. AK2 forms a complex with dual-specificity phosphatase 26 (DUSP26) to dephosphorylate fas-associated protein with death domain (FADD) . AK2 is Our preliminary examination showed that AK2, FADD, phosphorylated FADD, and DUSP26 were all equally expressed in the three high-sensitive and three low-sensitive cell lines used in In this paper, we show that the DPP8/9 inhibitor 1G244 exhibits a dual role: at low concentrations, it induces DPP9 inhibition-dependent pyroptosis, and at high concentrations, it induces DPP8 inhibition-dependent apoptosis. Importantly, we identified HCK expression as a necessary determinant of susceptibility to pyroptosis, but with no effect on the induction of apoptosis . Further


Soft-tissue sarcoma (STS) are a heterogeneous group of rare tumors with different biological behavior that are fatal in more than 40% of cases, due to their metastatic evolution and inadequate treatment options. ATR inhibition already showed an activity, even if modest, in broad pre-clinical models of STS. By using genome-wide CRISPR/Cas9 library screening, we identified ATM signaling network genes as critical drivers for resistance to the specific ATR inhibitor AZD6738. The role of such genes in resistance to AZD6738 was confirmed by using CRISPR/Cas9 knockout models. More strikingly, the ATM inhibitor AZD0156 works synergistically with AZD6738 in vitro and abolishes STS growth in vivo in our models of most frequent histotypes . Moreover, the combination of AZD6738 and AZD0156 induced significantly higher levels of DNA damage than either drug used as single agent alone. In summary, our results demonstrate that targeting ATM is an effective approach to overcome resistance to ATR inhibition in different STS subtypes, including the most frequent histologies.The online version contains supplementary material available at 10.1186/s40164-023-00416-z. Soft tissue sarcoma (STS) are a heterogeneous group of rare tumors including more than 70 subtypes with distinct clinical and biological features . SurgicaGene expression profiling of a large cohort of STS allowed the identification and validation of a 67-gene signature of chromosome instability named CINSARC (for genome Complexity INdex in SARComas), which is the most significant predictor of metastasis-free survival in these tumors . InteresMoreover, a recent international study has shown that germline variants in several genes encoding proteins involved in DNA repair, such as BRCA2, ATM, ATR, and ERCC2, contributed significantly to sarcoma risk .ATR plays a crucial role in maintaining genomic integrity by responding to a large spectrum of DNA damages, including double strand breaks (DSBs) that interfere with replication. We have previously shown that ATR inhibition has an activity, even if modest, in a large number of pre-clinical models of STS . To maxiHere, we propose to investigate the mechanisms of resistance to ATR inhibition in STS by using a genome-wide negative CRISPR screening of a resistant STS cell line, and to validate treatment strategies to restore sensitivity .50 values ranging from 1.03 to 4.6µM . However, the two leiomyosarcoma cell lines displayed primary resistance to AZD7638: IB112: IC50 12.5µM; IB136: IC50 not determined .To examine the antitumor effect of ATR inhibition on STS, a panel of 7 STS cell lines  were plated and treated with increasing concentrations of the specific ATRi AZD7638 for 72 h. We showed that AZD7638 suppressed the viability of 5 of them, encompassing several histological subtypes, with ICTherefore, we decided to perform a CRISPR/Cas9-negative selection screening on one of the two resistant cell lines to identify genes involved in ATRi resistance. For this purpose, we performed the CRISPR/Cas9 screening on the IB112, one of the most resistant cell lines to AZD6738. We applied a negative selection screening, treating cells with suboptimal dose of AZD6738, to select the knockout cells most sensitive to the treatment, . Thus, among the significant genes identified, we found genes involved in DNA Damage Response (DDR) pathway and specifically Ataxia Teleangectasia Mutated (ATM) pathway (such as THRAP3 and HUS1), mitosis, apoptosis, and cell cycle   AZD0156 in combination with AZD6738. The Chou-Talalay score showed that the ATRi/ATMi combination is synergistic or additive in all seven STS cell lines . Increased apoptosis was also observed with the combination treatment in comparison with each drug used as single agent . Having found that targeting ATM with AZD0156 can sensitize STS cells to AZD6738, we next investigated the DDR signaling pathways activated by AZD6738 in the presence or absence of ATM function. We found that AZD6738 reduces the level of phosphorylated CHK1 (pCHK1) and upregulates pATM. On the opposite, AZD0156 upregulated pCHK1 and reduced the level of pATM . These results are suggestive of a compensatory effect and cross-talk between the ATM and ATR-dependent checkpoint response pathways. The combination of AZD6738 and AZD0156 synergistically reduced the levels of pCHK1 and of pATM in all the cell lines analyzed including the cell lines resistant to AZD6738 (IB112 and IB136) .Finally, we assessed the impact of combined AZD6738/AZD0156 treatment in two in vivo models representing the two most frequent histological sarcoma subtypes: dedifferentiated liposarcoma (IB115) and undifferentiated pleomorphic sarcoma (JR588). Combined treatment reduced tumour growth, whereas single-agent treatments provided little benefit over the control Fig. . MoreoveExploiting synthetic lethality is a recurring theme in the field of anti-cancer drug development efforts. CRISPR-Cas9 knockout screens represent invaluable tools to identify genes whose loss sensitize tumor cells to a specific inhibitor. To identify biomarkers associated with sensitivity to AZD6738, Wang et al. used a CRISPR screen approach in the 293 A, HCT116, MCF10A cell lines [As observed in our previous study, the effects of ATR inhibition were modest both in vitro and in vivo . HoweverBelow is the link to the electronic supplementary material.Supplementary Material 1: Materials and Methods, and Supplementary data


Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker’s intensity distribution is presented as a mixture of normal distributions , and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms  utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution. Single-cell technologies have undergone significant advances in recent years, driven by the development of new computational methods. These technological advances have paved the way for investigating the heterogeneity of cell populations, providing valuable insights into the underlying mechanisms behind various diseases. Knowledge of cell states, dynamics, and regulatory mechanisms holds immense value in inventing new treatments and improving existing ones. However, analyzing the data provided by high-throughput technologies is challenging , mainly Mass Cytometry by Time-of-Flight (CyTOF) is one of the single-cell technologies. In opposite to flow cytometry, it uses rare stable isotopes to label the antibodies for cell type identification and can measure from tens to dozens of markers simultaneously. Data from mass cytometry may contain millions of cells, making the analysis difficult .The main objective in mass cytometry data analysis is to identify cell subpopulations after performing critical preprocessing steps like bead normalization, debarcoding, pre-gating, and batch effect correction . AlthougOne of the major challenges faced by cell subpopulation identification methods is their limited sensitivity to rare cell types. Existing solutions often rely on manual expert annotations for evaluation, where algorithms are compared against these annotations. Manual annotations are typically based on selected markers that are characteristic of specific cell types, overlooking other relevant relationships in the feature space. This practice can introduce bias into the manual annotations. Consequently, solutions that do not perfectly match the known cell labels are often perceived as inferior, leading to a tendency to underrate the results.Despite the availability of numerous methods for identifying cell populations, none can claim to be without limitations. The main challenge lies in scalability, as most methods struggle to handle large datasets containing millions of cells. For instance, methods that compute pairwise distances between observations often lead to a considerable matrix size that may exceed memory constraints, making it unfeasible to execute such computations. Therefore, some of the methods use downsampling, which may lead to rare subpopulations being overlooked.Identifying rare populations is another challenge. While the large cell populations with easily distinguishable marker profiles are relatively easy to identify, the rare ones may have marker profiles too weak to be noticed. For example, they may express marker values in the middle of the marker’s range, making the information harder to extract. The existing solutions for identification often use dimensionality reduction techniques, like t-SNE ,9, whichIn this study, a proposed modification of the feature space through its expansion is designed to help overcome the problem of low sensitivity in finding well-defined and separated groups of cells, including the rare ones. The idea is based on the assumption that the marker profile of one cell may overlap with another type, making it difficult to separate them into two clusters. Additionally, if the situation applies to the rare cell subpopulation, the group of cells is almost impossible to identify. Since the marker’s distribution is a mixture of Gaussians, each Gaussian potentially describes a different set of characteristic observations. Marker values can be assigned to the appropriate component of the Gaussian Mixture Model by calculating the conditional probability. Each cell then has a set of conditional probabilities that determine the components that most characterize it. Consequently, the calculated conditional probabilities create new features with values bounded to a range , which also acts as a normalization technique for the machine learning algorithms. The idea is visualized in Some clustering techniques are dedicated to identifying cell subpopulations in mass cytometry data. They usually adapt classical machine learning clustering techniques with data preprocessing steps, such as hierarchical clustering, combined with density-based downsampling of the observations. One of the best methods  in termsFlowSOM  is basedThe SPADE method  compriseIn contrast, the DensVM  approachAnother algorithm is based on k-Nearest-Neighbor (k-NN): ClusterX  reduces PhenoGraph  employs X-Shift  is an alThe publicly available dataset  was prepAfter preprocessing , the original feature space was expanded with the proposed algorithm. Each marker’s intensity values were decomposed with Gaussian Mixture Model and Bayesian Information Criterion (BIC) to find the optimal number of mixture components. The number of components found for the markers varied from 2 to 13.For each GMM, conditional probability values were calculated and visualized with a line plot. The existing artifacts were then corrected, resulting in the membership function. Visualization of the effect of the correction is presented in The final number of features increased from 38 (regular domain) to 318 (expanded domain). For the expanded feature space, the mISO plot with expert annotations (ground truth) was also created B. An intThe dataset in the regular and expanded feature space was applied to clustering with three chosen algorithms: FlowSOM, ClusterX, and PARC. The results are presented in The evaluation metrics, Calinski-Harabasz (CHI) and Davies–Bouldin Indices (DBI), are collected in The analysis of single-cell mass cytometry data is a challenging task, that requires thorough investigation and methodological implementation to account for the high-dimensional feature space and data heterogeneity. The presence of data heterogeneity can have a significant impact on clustering results, potentially leading algorithms to discover a larger number of cell groups than those identified by experts. Effective identification of rare cell populations is hindered by the complexity introduced by high-dimensional data, especially when there is a notable discrepancy in the cell counts between specific and rare cell groups. These challenges remain an active area of research as many researchers strive to find the most optimal solution to overcome these intricacies.Although many clustering algorithms are dedicated to mass cytometry ,14,15,16The study uses a publicly available dataset from Samusik et al. (2016) , which iThe transformed lines no longer represent conditional probabilities, leading to the adoption of the term ‘membership function’. Nevertheless, it is essential to note that the lines retain their original shape at the peaks’ summits and the points of intersection, making them still applicable for determining cut-off values—a prevalent practice in diverse domains. The algorithm will undergo several improvements to enhance efficiency and achieve superior results, selectively targeting lines that necessitate correction. Rigorous testing of the correction algorithm has been carried out on diverse datasets and its availability to the public is assured, with regular updates being provided.Among the various methods proposed for cell type identification thus far, only a few can handle large datasets and effectively identify rare subpopulations. To investigate their performance, this study compared the three best methods using Samusik’s dataset in both the regular and expanded feature space domains. Notably, the clustering of the dataset in the expanded domain yielded better results than in the regular domain, confirming the usefulness of dimensionality expansion. The best results were obtained for the PARC algorithm (both domains), with the best values of CHI = 95,594.57 and DBI = 1.3168 for the expanded feature space domain. Most manual clusters, including the rare ones, were identified, which is 20 out of 24 for PARC.The introduction of an expanded feature space may potentially have a negative impact on the clustering results, primarily due to the phenomenon known as the curse of dimensionality. Although the new features carry valuable information capable of distinguishing various cell types, the model may struggle to use this information effectively when making clustering decisions based on all available features. Consequently, the use of the expanded domain may result in fewer cell populations being identified. Additionally, certain algorithms may not perform optimally when feature distributions deviate significantly from Gaussian distributions. The expanded feature space includes observations at two ends of the feature range: values near zero for cells not expressing the particular feature and values near one for cells expressing the feature.The distribution of these values differs significantly from the original markers’ distributions, as shown in This study addressed the challenge of inefficient rare cell subpopulation identification in large mass cytometry data. To overcome this limitation, we proposed to modify the feature space to extract meaningful information that can aid cell type identification. The proposed expanded feature space comprises GMM components as separate features derived from the decomposition of each marker’s expression values. Remarkably, when existing techniques were applied using the expanded feature space, superior results were obtained compared to the regular space, underscoring its intrinsic value. Most manual clusters, including the rare ones, were identified. Our approach represents a novel contribution to the field, demonstrating significant potential. However, the existing solutions for determining cell types are not optimized to handle such a specific feature space. Therefore, new methods and feature selection algorithms need to be developed to achieve an optimal solution.The publicly available dataset from Samusik et al.  was usedThe dataset was preprocessed (ArcSinh transformation with a co-factor of 5) and corrected for batch effect with cyCombine .A mISO plot  was geneIn order to expand the feature space, the dataset underwent processing using Gaussian Mixture Models (GMMs) for each marker. Selecting the most suitable number of components was achieved through the Expectation Maximization algorithm, supplemented by the Bayesian Information Criterion . UtiliziCertain artifacts must be corrected for the results to be reliable and to enable the effective use of conditional probabilities as new features. Each Gaussian Mixture Model component is characterized by a specific set of parameters, and an expression value holds different probabilities of belonging to each component. Notably, the sum of probabilities for any expression value always amounts to 1. Visualizing these probabilities as lines makes the data more interpretable and analyzable. The visualization is similar to the probability density function for a given GMM, with some differences. The first and last of the conditional probability lines should start and end at a level equal to one, respectively, since the points at both ends of the marker distribution have the smallest distance to the first and last of the components.Due to the calculation of conditional probability lines from GMM components, certain artifacts may arise as a result of the inherent nature of GMMs. When two Gaussians partially overlap, determined by their mean and standard deviation values, the Gaussian with a higher standard deviation will exhibit a broader shape and, eventually, higher values on its ends compared to the other Gaussian. This behavior can cause one conditional line to have two “peaks”, leading it to dominate in more than one range of expression values. Consequently, this may also alter the order of the components. Furthermore, inactive components that fail to dominate in any range of values pose another challenge. x-axis. In that case, it may lead to cells with similar marker values being assigned to different Gaussian Mixture Model components while another cell displaying a more substantial difference in the expression value is included in the same component. In the presented case in Suppose there exist conditional probability lines dominating multiple segments of the In Correct the conditional probability lines to exhibit only one maximum (a single peak) throughout the entire range of marker values.Identify the first and last components’ lines and restore their dominance at the beginning and end of the distribution.Ensure that the first and last conditional probability lines commence and terminate (respectively) at a probability value of 1, resulting in a distinct shape modeled differently from the rest of the lines.Identify components with excessively low component proportion (weight), rendering them non-dominant in any marker value range. Consequently, remove these components from the expanded feature space.While correcting the conditional probability lines, preserving the points of intersection among the verified lines is crucial. These intersections often serve as thresholds in various applications. As a result, meticulous modeling of the lines above the intersection points is essential to reflect their original shape accurately.To establish an appropriate expanded feature space, a series of steps must be implemented as follows:The final new features after correction represent a membership function. The correction steps are visualized in x-axis value no longer sum up to 1, thereby rendering the term ‘conditional probability’ inappropriate. In light of this, we suggest adopting the term ‘membership function’, as the values now signify the membership of observations to the respective components. The main parts of the pipeline are presented in The correction algorithm implemented in MATLAB2022b satisfactorily fulfills all the above-prescribed criteria, operating directly on the conditional probability lines and their shapes. Nonetheless, due to the modification of the lines during the correction process, the resulting probabilities for each Initially, the algorithm verifies the presence of a peak near the mean value parameter for each component’s line, signifying its authenticity. Subsequently, it examines whether each component has a distinct range of values where it exhibits dominance over the other lines. If a component is deemed inactive, it is eliminated from the model. Following this removal, the probabilities are recalculated to ensure they sum up to 1.In the following phase, the algorithm focuses on correcting the line associated with the first component. The correction process involves transforming the line’s shape to commence from the point , representing the maximum membership value for the marker expression of 0. To accurately depict the line’s behavior for higher expression values, the algorithm samples a set of points from the right side of the peak. Eventually, the line concludes with a membership value of 0, which aligns with the maximum expression value. Upon achieving the necessary improvements to the first component, a similar strategy is implemented to model the middle components. However, during this process, the lines’ initiation and termination points are both set to a membership level of 0. The last component is then subjected to correction, ensuring it concludes with a membership level of 1 for the maximum marker value. The lines are effectively modeled by employing the ‘pchip’ function on a set of points within the interval of m ± 3 s  and the maximum and minimum values of the entire expression range.x-axis value (marker expression) to the closest component, indicated by the highest membership value, and to neighboring components with smaller membership values.The resulting membership function assigns each The dataset in the regular and expanded feature spaces was applied to clustering to identify cell subpopulations with the three best methods: FlowSOM, ClusterX, and PARC. The algorithms were chosen because of their adaptation to millions of cells, fast computation, and high accuracy compared to other methods. The algorithms were run with the default parameters. Because FlowSOM requires knowledge about the final number of clusters, it was set to 24 (as the number of cell types identified by experts). The results were then compared in terms of the Calinski–Harabasz Index , Davies–The analysis was conducted using MATLAB R2022b, Python 3.9, and R 4.2.2 programming environments. The calculations were carried out on GeCONiI servers .


Drosophila olfactory receptor as a biosensing element along with GCaMP6, a fluorescent calcium indicator protein. An automated 384-well microplate reader was used to characterize the olfactory receptor cell lines for their sensitivity to reference VOCs. Subsequently, we profiled a set of 66 VOCs on all cell lines, covering a concentration range from 1 to 100 μM. Results showed that 49 VOCs (74.2%) elicited a response in at least one olfactory receptor cell line. Some VOCs activated the cell lines even at nanomolar (ppb) concentrations. The interaction profiles obtained here will support the development of biosensors for agricultural applications. Additionally, the olfactory receptor proteins can be purified from these cell lines with sufficient yields for further processing, such as structure determination or integration with sensor devices. Agriculturally important crop plants emit a multitude of volatile organic compounds (VOCs), which are excellent indicators of their health status and their interactions with pathogens and pests. In this study, we have developed a novel cellular olfactory panel for detecting fungal pathogen-related VOCs we had identified in the field, as well as during controlled inoculations of several crop plants. The olfactory panel consists of seven stable HEK293 cell lines each expressing a functional  Numerous research programs have focused on the development of biosensors based on such mammalian receptors. However, due to their action mechanism, the receptors themselves are not sufficient for odorant perception, and additional signal transduction molecules are required for their proper function4.It is due to these receptors that animals, such as drug-sniffing dogs, can detect and trace marker volatiles with unprecedented sensitivity. Mammals have several hundred to a thousand ORs, which are G-protein-coupled receptors (GPCRs)5; therefore, insects are also able to sense relevant odorants at minute concentrations. Moreover, they possess a much narrower receptor repertoire compared to mammals, and—unlike GPCRs—do not need additional components for odorant recognition6.In contrast, insect ORs have a substantially different structure and mode of operation. For insects, too, odor perception is critical for feeding, oviposition, mate recognition, and predator avoidance7) which is essential for proper OR folding and function. While the amino acid sequence of OR proteins is very diverse, that of ORCO proteins is highly conserved even in diverse species. In many cases OR and ORCO protein combinations from different species can form functional channels. Cryo-EM structure determination on the ORCO protein from a parasitic fig wasp (Apocrypta bakeri) revealed a homotetrameric structure in the resulting autonomous cation channel8.Insect ORs are typically assembled from two types of proteins: the tuning OR, which is responsible for VOC binding and ligand-specificity, and the Olfactory Receptor Co-receptor  than for divalent (Ca2+ and Mg2+) cations, and permeability ratios also depend on the identity of the OR8. Under physiological conditions, ORs are localized in the dendrites of olfactory sensory neurons, where they depolarize the membrane upon ligand binding. This depolarization activates voltage-gated ion channels and changes the firing rate of the neuron15.Regarding their action mechanism insect ORs predominantly work as ligand-gated ion channels16. The receptor repertoire was mapped earlier for Drosophila melanogaster17. In vivo measurements, however, are not compatible with a sensor for the analysis of multiple samples. An olfactory panel consisting of several ORs selected for the specific sensing of the target molecules could work effectively as a biosensor.Using the antenna of the fruit fly it was possible to distinguish between the smell of healthy and cancer cell lines based on their different OR activation patternsSeveral research groups conducted experiments with heterologously expressed or purified ORs linked to sensor devices, but a complex insect receptor panel has not yet been developed. As a first step to this direction the ligand profiles of the sensory proteins must be characterized.Drosophila OR, ORCO, and GCaMP6 as a genetically encoded calcium ion sensor protein. Using these cell lines as an olfactory panel, the concentration–response profile of a large set of odorants was mapped here for the first time. The set of 66 tested VOC molecules was compiled in relation to our research on plant- and plant pathogen-derived VOCs18. VOC-specific cell responses were determined with a high-throughput microplate reader assay over a concentration range of three orders of magnitude. The new OR cell lines and hitherto untested plant disease-related volatiles represent original contributions to the present panel and ligand list of the ORs examined.Here we present an improved method for the generation of OR-expressing cell lines in which odorant binding is easy to follow by GFP fluorescence change without the need of loading the cells with a dye. As an example of the methodology, we generated seven stable cell lines in HEK293 cells all expressing a different http://neuro.uni-konstanz.de/DoOR/default.html)20 were used to select 12 ORs, which can be appropriate to detect the pathogen-related VOCs relevant for our study. To assemble a novel cell-based olfactory panel, 11 stable cell lines were successfully generated which expressed different Drosophila ORs and the fluorescent calcium ion indicator protein GCaMP621 in HEK293 cells. Out of these cell lines, seven were responsive to the respective OR-specific VOCs. Using these functional lines we constructed an olfactory assay in which binding of the examined odorants can be quantified by a fluorescence signal.In vivo measured ligand profile data derived from the DoOR database (Rosa26) were used to deliver the transgenes into the cells.Four different proteins were expressed in each of the cell lines: the respective ORs, the ORCO universal co-receptor, the mCherry fluorescent protein as a marker for OR expression, and the GCaMP6 fluorescent calcium ion indicator protein. To ensure stable and high-level expression, bacterial artificial chromosome (BAC) vectors containing an approximately 70-kb long open chromatin region  cells were nucleoporated with a plasmid, original 210 kb BACRosa26 and truncated 70 kb BACRosa26 vector all containing CAG promoter, GFP and Neomycin resistance gene as a selection marker. The ratio of GFP positive cells were approximately 3-times higher 24 h post transfection for truncated 70 kb BACRosa26 transfected cells (12% GFP positive of living cells) than for original 210 kb BACRosa26 transfected ones (4.1%) while it was less compared to plasmid transfected cells (17.1%)  none of the tested VOCs could activate the ORs despite the fact that OR7a, OR19a and OR69a showed strong responses to multiple VOCs according to the DoOR database. Similarly to the seven responding cell lines, the mCherry fluorescence signal was strongly visible indicating the presence of at least the OR transcripts as mCherry was transcribed from the same mRNA as the ORs. All cell lines responded to VUAA1 indicating the presence of functional GCaMP6-ORCO fusion proteins. Lack of response of some ORs in heterologous expression systems was reported in several previous studies32 It was shown that translation can be a bottleneck in these cases. Functional OR protein expression can be improved by codon optimization of OR genes instead of using wild type sequences33. In our study, we used a codon-optimized version of ORCO, however, we did not optimize any of the OR genes. OR release from the ER and trafficking to the plasma membrane could have been hindered in non-responding cell lines, too. The use of signal peptide tags might help in these cell lines as it was previously shown that their use could significantly enhance the intensity of the Ca2+ response of the Drodophila OR47a/ORCO transfected cells in transient expression studies34.In these cell lines, the Ptet-T6 promoter had a substantial baseline activity which was further increased upon doxycycline induction http://neuro.uni-konstanz.de/DoOR/default.html)20 and tested at 100 μM final concentration. The compound that triggered the highest response was chosen as the reference ligand for the given OR. For four cell lines —from our plant disease-related ligand list—the strongest agonists were not the ones which were suggested by the DoOR database. We used styrene instead of 2-methylphenol for OR49b, 6-methyl-5-hepten-2-one instead of 4-ethylguaiacol for OR71a and instead of ethyl benzoate for OR98a, and 2-heptanone instead of butyl acetate for OR85b as reference ligands ORCO, with assay buffer lacking Ca2+ and assay buffer lacking Ca2+ and containing EGTA. Neither of the cell lines responded to the ORCO agonist ligand . It was possible to measure 16 samples within approximately 3.5 h in a whole 384-well microplate.The concentration-dependent effect of the reference ligands on their cognate cell lines was examined in more detail. Twelve different ligand concentrations were measured on each cell line in an overall 0.01 μM to 100 μM concentration range with 5 repetitions with three technical replicates each time. The half-maximal effective concentration (EC50), and the maximal response compared to 50 μM VUAA1 were calculated Fig. B.46. In these studies, typical EC50 values and ligand sensitivity of the ORs were very similar to our results, but our system shows a much higher fluorescence intensity change (ΔF) in response to VOC ligands.Based on these parameters, a comparative evaluation of the OR cell lines was performed. There are a number of studies in which HEK293 cells have been used for functional characterization of insect ORs2+ signals with an EC50 of 4.33 μM35. The effect of the same ligand has been investigated recently on heterologously expressed OR13a receptors of a different fruit fly species  in HEK293 cells. An EC50 of 11.28 μM in fluorescent measurements, and 1.268 μM in electrophysiology was detected36. Our OR13a expressing cells had similar sensitivity to 1-octen-3-ol with an average EC50 of 1.44 μM , too. With 13.95 μM  of important crop plants . To this end, we had compiled a pathogen-related VOC catalog primarily based on our GC–MS analyses of infected samples (published results18 and unpublished data of our laboratory) and also systematically retrieved from published literature data50.Once responsive OR cell lines were selected, we tested them for sensing fungal pathogens  gave positive odorant responses. As the reference ligand (6-methyl-5-hepten-2-one) with the lowest overall VUAA1-normalized response elicited a 0.2 VUAA1-normalized response  of the 462 VOC-OR combinations tested, the OR cell lines responded over the whole concentration range measured. High sensitivity to a ligand at the lowest concentration did not always guarantee the typical logistic relationship between ligand concentration and response intensity. Several odorants only activated ORs at the medium concentration, with moderate increase in response at higher concentrations. As an example, isopentyl acetate first activated the OR47a cell line at 10 μM and increasing the ligand concentration to 100 μM increased the response intensity from 12 to 18% -octa-1,5-dien-3-ol, and 3-octanone were in the detectable range of two OR cell lines in samples from diseased wheat plants  for these ORs according to the DoOR database  according to the manufacturer’s instructions. Total RNA (1 μg) was treated with DNase I, then reverse transcribed with oligo(dT)12–18 primers and SuperScript IV enzyme (Invitrogen). The OR genes were amplified with gene-specific primers containing an AscI recognition sequence on both primers (except SwaI for OR19a) and a Kozak consensus sequence on the forward primers (Supplementary Table The OR genes OR7a (FlyBase ID FBtr0071186), OR47a (FBtr0088111), and a codon-optimized version of ORCO (FBtr0113193) were synthesized (Thermo Fisher Scientific) as double-stranded linear gene fragments. The cDNAs of the OR10a, OR13a, OR19a, OR47b, OR49b, OR67b, OR69a, OR71a, OR85b, OR98a genes were cloned from the wild-type Canton S strain of IRES) and the mCherry fluorescent protein gene52 were inserted into a modified pBlueScript KS (Agilent/Stratagene) called plasmid “L” 22. The OR genes were inserted into this vector at the AscI (SwaI) restriction site, while IRES-mCherry at the PmeI restriction site and the resulting plasmids were named as MIO(receptor name)p. The N-terminus of ORCO was fused to GCaMP621 via a Gly2SerGly3SerGly linker. Subsequently, this fusion gene was cloned into a modified plasmid “L” at the AscI restriction site, in which the CAG promoter24 was replaced by a Doxycycline-inducible Ptet-T6 bidirectional promoter26 and its cognate transactivator protein (3G) gene . Cloning was performed in the E. coli DH10B strain. All plasmid sequences are available upon request.The above 12 OR genes linked to a wild-type internal ribosome entry site from the encephalomyocarditis virus  was used as the vector backbone. Briefly, the original 210-kb BAC was shortened by removing approximately 70–70 kb DNA sequences from both ends of the genomic insert. First, two targeting constructs carrying 50 bp long homologous sequence regions (HR) on both ends  were generated by PCR  and by shifting the temperature to 37 °C. After one additional hour, cells were harvested and electrocompetent cells were prepared by a double wash with ice cold distilled water. Cells were resuspended in 10% ice cold glycerol solution and aliquoted to Eppendorf tubes (100 μl) prior snap freezing in liquid N2 or directly used for electroporation. 100 ng of linear DNA was electroporated to these cells with a Gene Pulser Xcell (BioRad) electroporator. Cells were incubated for 70 min at 37 °C without antibiotics in LB medium and plated on LB-agar containing the respective antibiotic (15 μg/ml kanamycin after 5′ targeting construct recombination and 100 μg/ml ampicillin after 3′ targeting construct recombination) at 37 °C overnight.For ET-cloning DH10B 56 to excise the antibiotic cassette flanked by frt/frt3 sites in the 5′/3′ targeting constructs. Cells were resuspended in LB medium without antibiotics after electroporation and incubated for 70 min at 30 °C. 100 μl of cell suspension was plated into LB-agar containing tetracycline (3 μg/ml) and the appropriate antibiotics for the BAC (chloramphenicol 12.5 µg/ml) and incubated overnight at 30 °C. The plate was incubated at 37 °C for 4–6 h. 10 single colonies were picked and streaked out to another LB agar/chloramphenicol plate from the left to the right, each in one row and cultured 37 °C overnight. Single colonies were picked from this plate and we plate them on replica plates either containing only chloramphenicol or chloramphenicol and the antibiotic derived from 5′/3′ targeting constructs (ampicillin/kanamycin). Some of the colonies will not grow on ampicillin/kanamycin plates anymore, here the recombination was successful.In the next step, bacterial cells derived from one single positive colony were cultured overnight at 37 °C in LB/antibiotic (ampicillin/kanamycin). The next day electrocompetent cells were prepared the same way as for ET-cloning and were electroporated with the expression plasmid 706 FLP which expresses the FLPe recombinaseAfter recombination and cassette excision was complete with the 5′ targeting construct it was repeated the same way with the 3′ targeting construct . Cells were kept at 5% CO2 and 37 °C in a humidified incubator and were passaged two–three times a week. Transfection-grade BAC DNA was purified with the NucleoBond Xtra BAC DNA purification kit (Macherey–Nagel) and was stored at 4 °C. Plasmid DNA was purified with Genopure Plasmid Midi Kit (Roche). In all cases, a total amount of 9.375 µg DNA was transfected into HEK293 cells reaching 60–70% confluency, in T-25 flasks with FuGene HD transfection reagent (Promega) at a 1:3 DNA:FuGene HD ratio. G418 antibiotic (Thermo Fisher Scientific) was added to the cells 48 h after transfection at 100 μg/mL concentration. For stable cell pool generation, cells were cultured for two weeks, then the antibiotic concentration was increased to 150 μg/mL and cells were cultured for one more week or until they divided vigorously without any visible sign of cell death.HEK293 cells (ATCC CRL-1573™) were cultured in growth medium  into 96-well cell culture microplates. Clonal cell lines were cultured in growth medium supplemented with 150 μg/mL G418 and observed under an Eclipse Ti2 inverted fluorescence microscope (Nikon) when starting to grow in the 96-well microplates. Four to six actively growing mCherry positive cell clones were selected and expanded for testing their VOC response. Odorant-responsive clonal cell lines were selected and cryopreserved for subsequent experiments.2, 1 mM MgCl2, 5 mM HEPES-Na, 20 mM glucose, pH 7.3, 340 mOsm). The fluorescence was excited at 485 nm and the emission was recorded at 535 nm with a SpectraMax iD3 multimode microplate reader (Molecular Devices) mounted with a two-channel injector. Baseline fluorescence (F0) was recorded for 5 s, after which 7 µL VOC buffer  was injected into each well in one row and the fluorescence response was recorded for an additional 30 s, at a rate of one read/sec. VOC solutions  were measured on the same wells in a second round with the same protocol. Each individual concentration of a specific VOC sample was recorded in three wells for the same cell line on the same microplate. The raw data were exported in xls file format, F0 and FMAX values, and the fluorescence intensity changes (∆F) were calculated in Excel (Microsoft)  48 h or 72 h before the measurements and were simultaneously treated with 1 μg/mL doxycycline. Typically, three columns were filled with one OR expressing cell line, each row was treated with the same compound , and each responsive VOC-OR combination was measured at least three times on different days. The plate has 24 columns, thus we could record responses in up to eight different cell lines in each plate. On the day of the experiment, the growth medium was replaced with 20 µL assay buffer/well , in order to test if all cell lines are responsive. The remaining rows were used to test VOCs with unknown effect.The recorded FMAX, F0, ∆F, VUAA1-normalized ∆F values, as well as the OR name, the name of the tested sample, and the final concentration for each well were copied into supplementary file Data summary.xlsx, that was used to summarize all the plates recorded. Here, the averages of the measurements were calculated for each receptor-VOC-concentration combination. Responses where the VUAA1-normalized ∆F values did not exceed 0.05 were considered as 0.All VOCs were purchased from Sigma-Aldrich (except for (5Z)-octa-1,5-dien-3-ol from Toronto Research Chemicals) in the highest available purity. VOCs were diluted either directly in the VOC dilution buffer or first in DMSO for water-insoluble compounds.Supplementary Figures.Supplementary Table 1.Supplementary Table 2.Supplementary Information.