The relationship between genes involved in Ca2+ signaling and mitochondria-associated membranes (MAM) was demonstrated through cytosolic calcium handling by means of an automated fluorescent plate reading system , and apoptosis and mitochondrial ROS production were examined by means of flow cytometry analysis. Altogether, our data suggest that the Ca2+ signaling pathway is altered in HGPS at least in part due to the overproduction of reactive oxygen species (ROS). Our results unravel a new therapeutic window for the treatment of this rare disease and open new strategies to study pathologies involving both accelerated and healthy aging.Hutchinson–Gilford progeria syndrome (HGPS) is a deadly childhood disorder, which is considered a very rare disease. It is caused by an autosomal dominant mutation on the LMNA gene, and it is characterized by accelerated aging. Human cell lines from HGPS patients and healthy parental controls were studied in parallel using next-generation sequencing (NGS) to unravel new non-previously altered molecular pathways. Nine hundred and eleven transcripts were differentially expressed when comparing healthy versus HGPS cell lines from a total of 21,872 transcripts; ITPR1, ITPR3, CACNA2D1, and CAMK2N1 stood out among them due to their links with calcium signaling, and these were validated by Western blot analysis. It was observed that the basal concentration of intracellular Ca Hutchinson–Gilford progeria syndrome (HGPS) is a deadly childhood genetic disorder caused by an autosomal dominant mutation on the LMNA gene and is a very rare disease . HGPS behttp://www.ncbi.nlm.nih.gov/geo/, accessed on 30 April 2018). The raw files are public and freely accessible . This is an analysis of a previously published at PLoS ONE [2+ signaling are indicated in Genomic datasets were deposited in the Gene Expression Omnibus (GEO-NCBI) repository  higher in HGPS vs. healthy cell lines and IP3R3 was significantly (p < 0.05) lower in HGPS vs. healthy cell lines. In addition, the level of GRP78 was statistically significantly lower in HGPS than in healthy cell lines  higher in HGPS vs. healthy cell lines and ITPR3, CAMK2N1, SULF2, and TENM2 expression were significantly (p < 0.05) lower in HGPS vs. healthy cell lines. All of these results validated the results obtained by shotgun analysis  (p < 0.05) increase in the Ca2+ release was observed in HGPS vs. control cell lines when 100 µM histamine, 10 µM Io, or 2 µM Tg were added to the medium  A. Howevee medium B–D. Thesp < 0.05) decrease in the GRP75 level. However, no differences were found in the levels of the rest of the proteins located in the MAM and involved in cytosolic calcium handling , followed by cells treated with Tg (70 ± 3%). The addition of NAC to the medium decreased the production of ROS statistically significantly (p < 0.05) in all cell types, regardless of the treatment used on them  more sensitive to apoptosis when treatments that affected Ca2+ pathways were added to the medium (65 ± 3%) compared to healthy cell lines (95 ± 2%), except with Tg. Surprisingly, H2O2 treatment produced a statistically significant increase in apoptosis in healthy cell lines versus HGPS  levels and reduced GRP78 expression in a Ca2+-dependent manner. Mitochondrial Ca2+ uniporter’s pore (MCU) is required to allow the Ca2+-dependent activation of oxidative signaling [2+ in HGPS cells.Glucose regulatory protein 78 (GRP78), a master ER chaperone, is present at the MAM, where it folds steroidogenic acute regulatory protein (StAR) for delivery to the outer mitochondrial membrane. Thus, GRP78 is an acute regulator of steroidogenesis at the MAM, regulating the intermediate folding of StAR, which is crucial for its activity . Accordiignaling . However2+ release through IP3Rs in the ER, we applied ATP to activate metabotropic purinergic receptors in our cell lines. To induce a Ca2+ release largely from sensitive ER stores, we added Io or Tg, producing a slow liberation of more than 90% of the ER Ca2+ content in the Ca2+-free extracellular solution. Our results demonstrate that Ca2+ transport is compromised in HGPS cells since its release from intracellular organelles was increased in a statistically significant way when Io or Tg was added to the system. Our observations could be explained by a decrease in the GRP78 level, together with the imbalance of IP3R1 and IP3R3 levels found in the HGPS cell lines versus healthy ones. Furthermore, ROS production affects several functions localized at the MAM, which are involved in local Ca2+ transport [To invoke a cytosolic Caransport .2+ flow in HGPS cell lines. GRP78 is an ER molecular chaperone that is a major UPR target with anti-apoptotic properties. GRP78 enhances the functional ability of the ER, promotes the survival and invasiveness of cancer cells, and confers chemoresistance [2+ flow in healthy cell lines was similar to that in HGPS cells pre-incubated with NAC. To explain these results, immunoblotting was performed for the receptors involved in cytosolic calcium handling in MAM. Only the GRP75 level decreased in a statistically significant way in HGPS cells treated with NAC. Lv et al. indicated that GRP75-mediated ER-mitochondrial Ca2+ transfer upregulation of GRP75 produced an enhancement of reactive oxygen species (ROS) [2+ flux observed in our study.The mitochondria-mediated route is an important apoptotic pathway in cells. To examine this route, NAC was added in our HGPS cell lines to alleviate the oxidative stress in mitochondria, and therefore in the MAM, and discern whether the ROS-scavenger agent could restore, at least partially, the normal intracellular Casistance . It was es (ROS) . Therefo2+ was the crucial factor in causing cells to undergo oxidative-induced apoptosis. Our results indicated that the production of ROS was statistically significantly higher in HGPS compared to healthy cells independently of treatments, and pre-incubation with NAC produced a statistically significant decrease in ROS production in all the groups. This is in agreement with Kim et al. [2+ signaling play roles in the disruption of mitochondrial homeostasis and the precedence of ROS production over the failure of Ca2+ flux homeostasis. Under physiological conditions, the Ca2+ transfer from the endoplasmic reticulum (ER) to mitochondria occurs at least in part at contact points between them in the MAM and involves the VDAC1/Grp75/IP3R1 complex. The ROS depletion from HGPS cells when NAC is added to the system could inhibit the GRP75 level, therefore decreasing protein interaction within the complex, attenuating the mitochondrial Ca2+ overload, and leading the normal flux of intracellular Ca2+. Paillard et al. [2+ in the mitochondrial matrix. The MAM is a complex matrix, composed of multiple proteins interacting between organelles to trigger the formation of functional channels that maintain cellular homeostasis.It is well known that HGPS cell lines show increased oxidative stress, which induces cell injury through mitochondrial-mediated cell apoptosis . In a stm et al.  and Richm et al. , who demd et al.  showed t2+ pathway. STS was used to induce a cytotoxicity response by inhibiting the PI3K/AKT signaling pathway [2O2 was used to directly activate IP3 receptors [2+ levels through oxidative stress [2O2.We performed an apoptosis study in our system by pre-treating the cell lines with NAC to decrease the oxidative stress, and at the same time they were incubated with different treatments involving the Ca pathway  and Tg w pathway . H2O2 waeceptors , and reae stress . Menadio2+ flow and reducing sarcoplasmic reticulum calcium loading and release. Similarly, when applying Tg to HGPS cells, there was an increase in the surviving cells, which was higher when NAC was present in the system. These results indicate that oxidative damage causes a selective downregulation of the neuronal stress response, activated under conditions of ER dysfunction. Paschen et al. proposed that oxidative stress was implicated in mechanisms leading to neuronal cell injury in various pathological states of the brain using Tg [Treatment with 2 µM Tg plus NAC produced a statistically significant increase in living cells. It is likely that NAC has a protective effect, inhibiting calcium depletion in the ER and consequently increasing cell survival . Rivera-using Tg .2O2, which could be explained by the fact that GRP78 and GRP75 are reduced in HGPS cell lines. The low glucose-induced apoptosis, targeting the endoplasmic reticulum and/or mitochondrial Ca2+, was explained by Younce et al. and Scrima et al. [Surprisingly, HGPS cells had a statistically significant higher survival than healthy ones when treated with Ha et al. ,34.2+ observed in our HGPS cell lines. However, the specific molecular mechanism to demonstrate that the basal intracellular accumulation of Ca2+ in HGPS is due to the imbalance of receptors from the MAM involved in Ca2+ signaling has to be establish through in vitro studies with HGPS mutant mice. The human HGPS cell lines from patients have a lot of genetic variability.Our results indicated that the MAM could be involved in the imbalance of flow CaHealthy  and HGPS  human skin fibroblasts, obtained from the National Institute of Aging (NIA) Aged Cell Repository , were maintained in DMEM, high glucose, GlutaMAX™ Supplement  containing 10% fetal bovine serum (FBS) and supplemented with antibiotics. Experiments were performed within four passages following the arrival of the cells.®  extraction method. The quality of 1 µL of each RNA sample was checked using the Agilent Bioanalyzer 2100 to determine the RNA integrity number (RIN) score using the Agilent 6000 Nanochip and reagents . Samples with a RIN score > 7 were retained and converted to cDNA using the SureSelect Strand Specific RNA library Prep for Illumina multiplexed sequencing method. One microgram of total RNA per sample was generated. The study was designed to screen the complete transcriptome sequence of normal (AG03257 and AG03512) and HGPS (AG03199 and AG03513) human skin fibroblasts. The sequencing data was generated on HiSeq 1500 on a rapid-mode flow cell from Illumina. Sample preparation and sequencing were conducted in duplicate. This procedure has been used and published previously by our group [RNA was isolated using the TRIzolur group .http://bowtie-bio.sourceforge.net/index.shtml, accessed on 12 November 2017) and Tophat2 , and the Cufflinks  of differentially expressed genes and transcripts were calculated with Cuffdiff. Groups were compared. The fold-change thresholds had to be higher than 1.2 and lower than 0.8. Identified genes with statistically significant changes were categorized according to their function, biological processes, and cellular components using the R/Bioconductor package RamiGO . This procedure has been used and published previously by our group [An average of 23 million paired-end 100-bp reads was obtained per sample. The raw RNA-Seq reads for each sample were aligned to the reference human genome browser (GRCh38.p12 assembly) using Bowtie2 (ur group .HPRT) using the 2−ΔΔCt method [The amplification program consisted of an initial denaturation at 92 °C for 2 min, followed by 40 cycles from 92 °C for 15 s, annealing at 55–62 °C, depending on the gene, for 30 s and extension at 72 °C for 15 s. PCR analysis was performed in triplicate, with each set of assays repeated three times. To minimize the effects of unequal quantities of starting RNA and to eliminate potential sources of inconsistency, relative expression levels of each gene were normalized to ribosomal protein  following the commercial protocol. Immunoblot analysis was performed on 40 μg of protein, which was resolved on NuPAGE 4–12% Bis-Tris gels , and the blots were probed with antibodies directed against human antigens involved in Cag of MAM .v/v), consisting of 1 × 106 cells in 2.5 mL of 1.5 mM Ca2+ Krebs glucose for 30 min at room temperature. Each cell line was measured at 340 nm excitation and 510 nm emission for 400 s. Digitonin was adding to check Rmax and EGTA to check Rmin. The formula used was:A Luminescence Spectrometer  was used for the basal calcium measurement of healthy and HGPS cell lines. In summary, de-esterification was carried out with Fura-2  (1/1000 2+ at room temperature (241 nM)Kd = dissociation constant of Fura-2 for CaR = basal ratioRmin = minimum ratio Rmax = maximum ratio Sf2 = F380 maxSb2 = FF380 min2+ release in cultured primary skin fibroblasts from healthy and HGPS patients was measured. Ca2+-free extracellular medium supplemented with 3 mM EGTA was used to exclude Ca2+ entry across the cell membrane. Then, 10 × 103 cells per well (96 wells/plate) were incubated with 2 μM Fura-2 for 30 min and washed twice with Ca2+ free standard external solution, including the following: 10 mM HEPES, 120 mM NaCl, 5.4 mM KCl, 1 mM MgCl2, 10 mM glucose, and pH 7.4 buffer. In some experiments, HGPS cell lines were pre-incubated with 10 μM of N-Acetyl-L-Cysteine (NAC) for one hour. The fluorescence intensity of fura-2-loaded cells was monitored using an automated fluorescent plate reading system . A monochromator dual-wavelength-enabled alternative excitation was at 340 and 380 nm, whereas the emission fluorescence was monitored at 510 nm. SoftMax Pro Software  was used to process the images of multiple cells collected at each excitation wavelength and to provide ratios of fura-2 fluorescence (F340/F380). The baseline calcium levels were normalized for WT and HGPS cells using GraphPad Prism6 .The intracellular Ca2+-free conditions to mobilize Ca2+ from the stores, and ATP  was applied to activate cell-surface purinergic receptors and induce subsequent IP3 production and Ca2+ release. Furthermore, 10 μM Ionomycin (Io) or 2 μM Thapsigargin (Tg) were applied to the cells to determine ER Ca2+ content.Cells were stimulated with Histamine at 100 μM in Ca2O2, 60 μM menadione, or 1 μM DMSO for 5 h prior to the apoptosis analysis. Incubation with FITC -7-AAD and PE-Cy5.5-annexin V was performed to check the potential apoptosis involved with mitochondrial membrane potential and oxidative stress. Then, 2 × 105 cells were analyzed using FlowJo software V8.6 . Mitochondrial ROS was measured after the incubation of cells with 5 mM of Mitosox-A. The stained cells were then washed twice with PBS, and 2 × 105 cells were analyzed using NovoExpress Software . For negative control staining, we used FITC-conjugated mouse IgG1K isotype, PE-conjugated mouse IgG1K isotype, PE-Cy5.5-conjugated mouse IgG1K isotype, and APC-conjugated mouse IgG1K isotype .Flow cytometry was performed using Attune NxT  equipment to study the apoptosis potential of HGPS cell lines and their healthy progenitors. Cells were washed twice in PBS  and incubated with 1 μM staurosporine (STS), 1 μM Tg, 200 μM Hp < 0.05 was considered statistically significant.All experiments were performed in duplicate, and one representative result is shown. Non-parametric statistical analysis was performed using the Mann–Whitney U and Kruskal–Wallis tests using GraphPad Prism6 . All results were presented as the standard error of the mean. 2+ homeostasis. The normal Ca2+ flux can be partially rescued through the scavenging of ROS by NAC. In summary, HGPS involves the unbalanced homeostasis of the cellular inter-compartmental calcium flux and these new pieces of evidence point to ER and/or mitochondrial calcium transporters as targets of a new therapeutic strategy.Our data show that HGPS, caused by an autosomal dominant mutation on the LMNA gene, produces a metabolic alteration, which could be associated with the MAM, destabilizing intracellular Ca


CCR2 and TNFRSF11B) were positively correlated with the invasive potential of the cell lines, whereas a negative correlation was observed for the TNFRSF14 gene expression. We also found a small set of receptor genes that exhibited a significantly decreased expression in association with a V600EBRAF mutation. Based on our results, we assume that the analyzed cytokine and chemokine receptor collection may provide potential to distinguish the different subtypes of melanomas, helping us to understand the biological behavior of V600EBRAF-mutated melanoma cells.Cytokine and chemokine receptors can promote tumor progression, invasion, and metastasis development by inducing different intracellular signaling pathways. The aim of this study was to determine the cytokine and chemokine receptor gene expression patterns in human melanoma cell lines. We found a large set of cytokine and chemokine receptor genes that were significantly differentially expressed between melanoma cell lines that originated from different subtypes of primary melanomas as well as cell lines that originated from melanoma metastases. The relative expressions of two receptor genes ( The signaling network between cells is essential for communication and regulation and can be facilitated by the interaction of cell surface receptors with secreted ligands . CytokinAmong the cytokines, chemokines are low-molecular-weight proteins that are able to regulate immune cell recruitment as well as tumor cell proliferation, invasiveness, and metastasis ,7. TheirThe role of chemokines and chemokine receptors in melanoma progression and metastasis formation has been described in several studies ,9,10,11.Different in vitro and in vivo studies have shed light on the importance of the chemokine/chemokine receptor expression of the endothelial cells at the surface of the melanoma cells involved in organ-specific metastasis formations . For insIt is now established that cells from the primary tumor can prepare a metastatic niche before invasion. In the metastatic niche, immune cells and stromal cells—in collaboration with chemokines, matrix-degrading factors, and growth factors—stimulate the development of the metastatic lesion ,21. The n = 7; nodular melanoma (NM), n = 3) and metastasis-originated  melanoma cell lines. The expression of 13 interleukin receptor genes, five tumor necrosis factor receptor genes, and five chemokine receptor genes was significantly different between the SSM- and NM-originated primary melanoma cell lines (p < 0.05). Hierarchical clustering of the 23 differentially expressed genes clearly showed distinct expression between the NM and SSM subtypes of the originated cell lines  and chemokine receptors of primary tumors , ll lines . p < 0.05).Most of the receptor genes were significantly downregulated in the cell lines that originated from the NM subtype. Only two genes (IL1RL2 and IL1RN) were significantly overexpressed in the NM-type cell lines compared with the SSM-derived cell lines . This indicated that the expression patterns of the cytokine and chemokine receptor genes in the NM-derived cell lines were more similar to the metastasis-originated cells than the SSM-originated cell lines. From these 26 significantly altered genes, 14 were differentially expressed between the SSM- and NM-originated cell lines, whereas only two of the 26 (TNFRSF17 and IL27RA) significantly altered genes were found after comparing the NM- and MM-originated cells (V600E mutant and three wild-type (BRAFWT) melanoma cell lines and found five receptor genes  that were significantly overexpressed in the WT cell lines compared with the BRAFV600E mutant cells . Correlation analysis was performed between the number of invaded cells/field and the qRT-PCR expression data, which revealed a significant positive correlation between the relative mRNA levels of the CCR2 and TNFRSF11B genes (p = 0.016). A significant negative correlation was observed between the relative expression of the TNFRSF14 gene and the invasive capacity of the melanoma cells (p = 0.041).The relative expression level of the TNFRSF11B gene was significantly increased in the invasive cells compared with the non-invasive cells  is one of the crucial unanswered questions in cancer research ,24. Recently, a large amount of evidence has accumulated on the important role of bioactive lipids/lipid signaling in the progression of malignant melanoma . Sphingon = 2), interleukin receptor genes (n = 17), and tumor necrosis factor receptor genes (n = 7). This group of receptors may therefore have potential to distinguish between the different subtypes of melanoma tissues. The two most common subtypes of primary melanomas are superficial spreading melanoma (SSM) and the nodular melanoma (NM) . AlthougDuring metastasis formation, there is a switch from a proliferative to an invasive state through transcriptional reprogramming, which is a crucial event in melanoma cells. Verfaillie et al. described a gene expression-based signature to predict melanoma invasion . Two genThe expression of the CCR2 receptor gene was positively correlated with the invasive potential of melanoma cells. The role of CCR2 (activated by its ligands) in the induction of migration and invasion in tumor cells including melanomas has been reported in recent studies ,38. The V600E mutation. A BRAF mutation is the most frequent type of mutation in melanomas (40–50%) as well as a resulting aberrant MAPK pathway activation, cell cycle deregulation, and apoptosis inhibition [We also observed a small set of receptor genes where a decreased expression was significantly associated with a BRAFhibition ,44. The hibition ,46,47. Thibition ,48,49,50In conclusion, the expression of cytokine and chemokine receptors may provide new approaches to distinguishing different melanoma cells based on clinicopathological parameters or to predict the proliferative and invasive characteristics of melanoma cells.Primary and metastatic tumor-derived melanoma cell lines were obtained from two different sources. Three cell lines  were purchased from the American Type Culture Collection , and 11 cell lines  were obtained from the Coriell Institute for Medical Research . The WM165−1, WM266−4 and cell lines were obtained from Rockland Immunochemicals, Inc. . The HT168, HT168-M1 were developed from the A2058 cell line . The cha2 according to the protocol of the supplier.The cells were cultured in RPMI 1640 medium  or MCDB153-L15 medium  supplemented with 5–10% fetal bovine serum  at 37 °C in 5% CO4 cells/well) whilst a medium containing 10% FBS was applied to the lower chamber as a chemoattractant. After the cells were incubated for 24 h at 37 °C, the cells in the lower layer were fixed and stained. The invaded cells were counted using a light microscope in seven different visual fields at 200× magnification; the data are presented as the means ±SD of three independent experiments.The invasive potential of the primary tumor-derived melanoma cell lines was examined using BD Biocoat Matrigel invasion chambers  as described before . BrieflyRNA was isolated using an RNeasy Plus Mini Kit  according to the manufacturer’s protocol. The concentration and quality of the RNA were assessed using a NanoDrop and Bioanalyzer . RNA samples with a 260/280 ratio ≥ 1.8 were included in further analyses. Reverse transcription was performed on the total RNA (1000 ng) using a High Capacity cDNA Archive Kit  according to the manufacturer’s protocol. ® 480 Real-Time PCR System . The primer sets (Human Cytokine and Chemokine Receptor Primer Library) were provided by RealTime Primers . The data are presented as the mean of 2−ΔCt values of two independent experiments.The relative expression level of 96 genes  was used for the statistical analyses. The Shapiro–Wilk test was used to evaluate the normality of the data. The Spearman’s correlation coefficient was calculated to correlate the qPCR data with the number of invasive cells. The Mann–Whitney–Wilcoxon test was used to compare the qPCR data, and the different clinicopathological parameters;


The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS–TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.Combinations of anti-cancer drugs can overcome resistance and provide new treatments A survey of potency and efficacy of 2,025 clinically relevant two-drug combinations against 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines identifies rare synergistic effects of anticancer drugs, informing rational combination treatments for specific cancer subtypes. Empirically developed combinations of chemotherapy drugs are used to treat cancer patients1. Combination therapies using targeted anti-cancer agents have the potential to overcome resistance, enhance the response to existing drugs, reduce dose-limiting single agent toxicity and expand the range of treatments2, as exemplified by triple combination therapy for patients with BRAF-mutant colorectal cancer3. However, our ability to predict effective combinations is limited4. Molecularly annotated cancer cell line panels5 are increasingly being used to identify active drug combinations8. Studies performed so far have limitations, including testing relatively few combinations, using few molecularly targeted drugs, or using a limited number and sub-optimal range of drug concentrations. Furthermore, previous studies have employed a limited number of cell lines4 (a maximum of 85), making it difficult to link combination activity and molecular context.Single-agent targeted therapies for patients with molecularly defined tumours are transforming cancer treatment. Nonetheless, many patients still lack effective treatments and pre-existing or acquired resistance limits the clinical benefit of even the most advanced medicines5 to measure the effects of 2,025 pairwise drug combinations (Supplementary Table n = 51), colorectal  and pancreatic (n = 29) cancer  cell line screening platformcer Fig. . We prodn = 20), such as ERBB2 inhibitors in breast and drugs targeting MAPK signalling in colon and pancreas , and (3) the estimated library drug concentration producing a 50% viability reduction (IC50) for the library and combination  or efficacy10   dose–response curve of the library compound ) on the 121 pan-tissue combinations showed moderate mixing of cell lines by tissue, indicating that tissue has some effect on combination response, but is not on its own a major driver of variation  broadly active, (2) minimally active, and (3) variable activity , then colon (5.4%) and breast (4.4%). Only 27.5% of synergistic combination–cell line pairs were observed at both high and low anchor concentrations, suggesting that synergy is detected within a specific range of concentrations Fig.  and poinr = 0.69–0.84, all P < 0.001), and synergy classifications were consistent for all tissues   in 30 cell lines per tissue resulted in a validation dataset of 4,881 combination–cell line pairs. Primary and validation datasets correlated well for single-agent and combination response metrics  or two (2.5%) chemotherapeutic agents. Chemotherapeutic–chemotherapeutic combinations had lower ΔICP ≤ 0.05). More than 76% of synergies between chemotherapeutics and cell cycle inhibitors per tissue occurred with AZD7762 (CHEK1/2), pairing well with 5-fluorouracil, gemcitabine, cisplatin and irinotecan  comprised 25.4% of all synergistic combination–cell line pairs , despite only representing 5.4% of combinations . Seventy-eight per cent (137 out of 175) of combination–tissue pairs with navitoclax were synergistic in at least three cell lines (average per combination–tissue: 19.6% synergy). Navitoclax showed a synergy rate of more than 50% when combined with TOP1 inhibitors (irinotecan in pancreas and camptothecin in breast and colon) or microtubule stabilisers (docetaxel or paclitaxel) and destabilisers (vinorelbine) . We calculated the shortest finite network distance between the drug target nodes and found that on average combinations whose targets are between one and two nodes away from each other yield the most synergy  ≤ 5%)  of each drug  Fig. . Five pa5%) Fig. . Despite5%) Fig. . This coOverall, we find that drug synergy, on the basis of complementary potency and efficacy metrics, is rare but frequent for a subset of combinations. Synergy most frequently enhances weak to modest single-agent drug response and rates are highest for combinations of targeted compounds. Combinations containing the apoptosis regulator navitoclax show high synergy rates. Whereas some trends are universal across all three tissues, such as high synergy rates for inter-pathway targeting of MAPK and PI3K pathways, other synergistic effects are tissue-specific, including navitoclax plus AURK inhibition in breast.15 to identify multi-omics biomarkers of combination  and single-agent (IC50) responses. Multiple analyses were performed, grouping cell lines in molecular contexts, including pan-tissue (3 tissues), individual tissues and seven molecular ‘baskets’ representing specific molecular subgroups ) ) Fig. . TP53, KP ≤ 0.001, FDR ≤ 5%, Glass deltas for positive and negative populations both ≥ 1)  sensitivity in PIK3CA-mutant cell lines , efficacy (ΔEmax) and combination activity (combination Emax), respectively  or ΔEmax (33.8%) but not with both, consistent with the complementary nature of these synergy metrics. Of the 2,025 combinations, 28.7% had at least one combination response biomarker  tests, out of which 8,078 associations were significant and of large effect size ( 1) Fig. . Biomark 1) Fig. . Some 3,ely Fig. . Of note50 and ΔEmax associations, we identified only three in pancreas-specific analyses: of these, low CDH1 gene expression, which is associated with epithelial-to-mesenchymal transition, sensitised cells to irinotecan (TOP1 inhibitor) plus AZD7762 (CHEK1/2 inhibitor)  paired with EGFR inhibitors afatinib and sapitinib screened in colon . Combinations of EGFR antibodies and BRAF inhibitors are a clinically approved regimen in BRAF-mutant metastatic colorectal cancer17. These examples show that our screen can identify known clinical biomarkers and candidate markers of combination response warranting further investigation.Of the 1,645 ΔIC18, an alternative interactome  of features onto protein nodes; mapping was impossible for certain features such as clinical subtyping. We calculated the shortest finite network distance between each of the drug target and feature nodes for 582 and 124 unique combination and single-agent biomarkers, respectively  combinations, and a copy number gain of CDK12, ERBB2 and MED24 (feature: ‘gain:cnaPANCAN301’) as predictor of sensitivity to ERBB2-targeting combinations such as lapatinib (targeting EGFR and ERBB2) with vorinostat (a histone deacetylase inhibitor) in breast. Low expression of NRAS predicted response to dabrafenib (BRAF inhibitor) plus trametinib (MEK1/2 inhibitor) in breast, consistent with NRAS expression modulating BRAF inhibitor sensitivity19. This represents a biomarker one node away from a combination drug target  + MK-2206 (AKT1/2 inhibitor) in a pan-tissue KRAS-mutant context, and is two nodes away from targets of both drugs  and MK-2206 (AKT1/2 inhibitor), and 74% of synergistic cell lines are KRAS mutants  have at least one associated ΔBB2 Fig. . Similarnts Fig. . These eOur comprehensive analysis identified drug combination response biomarkers, including for many synergistic combinations, which could be used for patient stratification in pre-clinical and clinical follow-up. Combination biomarkers are more likely than single agents to be emergent properties arising from combinatorial drug activity in the context of signalling networks.n = 22), MSS colon cancer (n = 31) or KRAS-mutant colon cancer (n = 25).We leveraged the number and molecular diversity of the cell lines to investigate synergy rate, biomarkers and clinical trials for combinations screened in three populations with unmet clinical need: patients with basal-like breast cancer (n = 107) of all combinations have synergy in at least 25% of cell lines from these populations , with some having exquisite specificity of synergy in the population of interest. Of these 75 unique combinations, we identified 11 combinations with matching trials on clinicaltrials.gov for basal-like breast  or MSS colon  cancer  plus MK-2206 (AKT1/2 inhibitor) , with significantly higher potency and efficacy in MSS cell lines than in MSI cell lines  and KRAS–TP53 double-mutant cell lines had significantly stronger combination responses than KRAS single-mutant cell lines  and TOP1 (TOP1i).One of the top synergistic combinations was camptothecin (TOP1 inhibitor) with AZD7762 (CHEK1/2 inhibitor). Camptothecin is an analogue of the standard-of-care chemotherapeutic irinotecan used to treat colon cancer, and CHEK1 inhibitors can potentiate responses of DNA-damaging compounds through abrogation of DNA damage-induced cell cycle arrest50: 8.5- to 10.5-fold shift) and efficacy (median ΔEmax: 0.22–0.24) in combination with camptothecin in 4 cell lines, whereas the CHEK2-selective inhibitor CCT-241533 did not  resulted in fewer colonies and increased cell death Fig.  than eitnse Fig. . The comt-test, P < 0.05) , suggesting a fitness disadvantage  in NOD/SCID mice and treated them for 24–35 days with irinotecan (TOP1 inhibitor), rabusertib (CHEK1 inhibitor), or with a combination of the two drugs. In LS-1034 and SW837, which showed more cell death with combined TOP1i and CHEK1 inhibition than SNU-81 in vitro Fig. , the eff05) Fig. . In SNU-age Fig. . Combinaage Fig. .KRAS–TP53 double-mutant colon cancer cells, and demonstrate the potential for follow-up of other synergistic drug combinations identified here.These data validated combined TOP1 and CHEK1 inhibition as a potent combination in MSS and 10, and could identify combinations leading to dose reduction, improved efficacy or both, relative to single agents. We demonstrate that synergy is rare, as has been described7, that it varies within tissues and between molecular backgrounds, and that combinations of targeted drugs are more likely to synergise than combinations involving chemotherapy. This indicates that combinations of targeted agents in molecularly defined patient populations are most likely to be synergistic. We identified many highly synergistic combinations, notably for cancers of unmet clinical need. We recommend detailed validation of promising drug combinations reported here. As proof of concept, we validated in vitro and in vivo a combined irinotecan and CHEK1 inhibition. Although combinations of CHEK1i with DNA-damaging agents have been linked to TP53- and KRAS-mutant cancer25, to our knowledge, this is the first report of notable activity in MSS and KRAS–TP53 double-mutant colon cancer. Clinical trials combining CHEKi with chemotherapy show variable toxicity and anti-tumour activity, particularly for unstratified patient populations. Since irinotecan is approved for the treatment of colon cancer, and rabusertib (a CHEK1-selective inhibitor) has an acceptable safety profile in phase 1 trials26, our data indicate that this combination—with appropriate consideration of potential toxicity—could be particularly effective for patients with MSS or KRAS–TP53 double-mutant colon cancer.The scale and breadth of our study provides insights into combination response. We establish that evaluating combination potency and efficacy are complementary, a recently introduced concept27. Future screens in additional cancer types, focused studies using more complex culture models, and screening of higher-order combinations, will support and extend our observations. The testing of combinations in non-cancer cell lines may help to estimate clinical toxicity. Our findings that drugs with weak single-agent activity, and those separated by one or two nodes in a protein–protein interaction network are most likely to yield a synergistic interaction could improve the design of future screens. Similarly, our data could improve computational approaches, which currently lack training datasets, for predicting effective drug combinations in different contexts29. In conclusion, the data and analyses presented here are fertile ground for catalysing new discoveries and a basis for effective rational combinatorial therapies.The data presented here are a rich resource and augment existing genomic, transcriptomic and functional datasets for cell lines available as part of the Cancer Dependency Mapt-tests unless otherwise specified. Enrichment analyses were performed using the phyper function for hypergeometric tests in R. Multiple testing correction was performed based on the Benjamini–Hochberg method. Biomarker analysis was performed using GDSCTools as described below. Box plots depict the median value as the centre bar, first and third quartiles as box boundaries, and whiskers extending to either first quartile minus 1.5 × the inter-quartile range (lower boundary), or third quartile plus 1.5 × the inter-quartile range (upper boundary). Points beyond this region are individually plotted.Statistical tests are two-sided Welch’s 2. To prevent cross-contamination or misidentification, all cell lines were profiled using a panel of 94 single nucleotide polymorphisms . Short tandem repeat (STR) analysis was also performed, and cell line profiles were matched to those generated by the cell line repository. All cell lines are routinely tested for mycoplasma and are negative for mycoplasma. Further information on the cell lines used in this study, including their source and molecular profiling datasets can be found in Supplementary Table 30 (https://cellmodelpassports.sanger.ac.uk).Cell lines were acquired from commercial cell banks. All cells were grown in RPMI medium  or DMEM/F12 medium  , was performed and concentrations optimized to give a range of sensitivities across the cell lines. Anchor drugs were screened at two fixed concentrations with a 2-, 4- or 10-fold difference between them to give moderate activity (50–90% viability) across the cell lines within each cancer type. Screening concentrations  and four replicates of the library alone (full dose response). A single replicate of the combination dose response was performed in the primary screen.Compounds were sourced from commercial vendors (Supplementary Table 2 for 24 h then dosed with the test compounds using an Echo555 (Labcyte). Final DMSO concentration was typically 0.2%. Following dosing with compounds assay plates were incubated, and the drug treatment duration was 72 h. To monitor cell growth over the duration of drug treatment, a parallel undrugged control plate was assayed at the time of drug treatment and referred to as a ‘day = 1’ plate. This was repeated each time that a cell line was screened. To measure cell viability, 2.5 μl of CellTiter-Glo 2.0 (Promega) was added to each well and incubated at room temperature for 10 min; quantification of luminescence was performed using a Paradigm (Molecular Devices) plate reader.Cells were transferred into 1,536-well plates in 7.5 μl of their respective growth medium using XRD384 (FluidX) dispensers. The seeding density was optimised prior to screening to ensure that each cell line was in the exponential growth phase at the end of the assay. For this, six seeding densities with a two-fold dilution step were each dispensed into 224 wells of a single 1,536-well assay plate (XRD384 (FluidX) dispenser) and cells were incubated for 96 h. Cell number was quantified using CellTiter-Glo 2.0 (Promega). The maximum density tested varied based on cell type, typically 5,000 cells per well for suspension cells and 1,250 cells per well for adherent cells  and positive control wells  distributed across the plate. We used these positive and negative control wells to test whether the plates meet defined quality control criteria. A maximum threshold of 0.18 was applied to the coefficient of variation (CV) of the DMSO-treated negative controls . Using the DMSO-treated negative control (NC1) and the two positive controls (PC1 and PC2), we determined Z-factors  / (|μP − μN|), where σN and σP are the s.d. of the negative and positive controls, respectively, and μN and μP are the mean of the negative and positive controls, respectively). The Z-factors were calculated for all plates that indicate sensitivity of the cell lines to the positive control (ratio of NC1:PC ≥4). In case a cell line is insensitive to both positive control drugs, the Z-factors were calculated based on blank wells instead. Z-factors were required to exceed a minimum threshold of 0.3 for individual plates and a mean of 0.4 across all plates within a screening set. Where a cell line was sensitive to both positive controls, it had to pass Z-factor thresholds for both positive controls. Plates that did not meet these requirements were excluded from the study. Overall, 3,106 (>70%) of 1,536-well microtitre screening plates passed coefficient of variation and Z-factor thresholds. Wherever possible, failed plates were repeated, leading to dataset completeness of more than 96% for all three tissues .All screening plates contained negative control wells  and negative control (NC) values (viability = (Fluorescence of treated cells − B)/(NC − B)). Anchor viability was determined from the mean across the five replicate wells screened on each plate. All library drug dose responses were fitted as a two-parameter sigmoid function33. The dose–response curves for the combinations were fitted similarly, butwith two notable differences: (1) the cell line parameters were obtained from the library drug fits; (2) the maximum viability was capped at the anchor viability (rather than from 0 to 1). We use the 50% (inflection) point of the sigmoidal curve between zero and the anchor viability for both the expected Bliss and the observed combination. We extended the model to nest each replicate within the drug or cell line to obtain stable estimates from the replicate experiments. To assess the quality of the fits, we computed the root mean square error (RMSE) and excluded curves with RMSE > 0.2 . The Emax and the IC50 are based on the fitted curves. Emax is reported at the highest tested concentration for the drug.For each plate, the raw fluorescent intensity values were normalised to a relative viability scale (ranging from 0 to 1) using the blank  and in efficacy (∆Emax) were calculated as the difference between the observed combination response and expected Bliss . ∆IC50 is reported on a log2 scale.To detect synergy we compared observed combination responses to expected combination responses. For the latter, we used Bliss independence50 was less than twice the highest screened library concentration and either the ∆IC50 or the ∆Emax was above a specific threshold: ∆IC50 ≥ 3  or the ∆Emax ≥ 0.2 (20% shift in viability). Replicate measurements of ‘anchor-library–cell line’ tuples were summarized as synergistic if half or more of the replicate measurements showed synergy. To summarize both anchor concentrations, we considered a ‘combination–cell line’ pair as synergistic if synergy was observed at either anchor concentration.A given measurement was synergistic if the combination ICTo assess the reproducibility within a screen, we generated 2–18 biological replicates for 4–5 cell lines per tissue ; colon: 4 ; pancreas: 5 ). Single-agent and combination responses were averaged across technical replicates  and correlated .F-score (F-score = TP/(TP + 0.5 × (FP + FN))), recall ), and precision (precision = TP/(TP + FP)) per tissue. To investigate the strength of effects of ΔEmax and ΔIC50 of FP and FN measurements, the distance to ΔEmax and ΔIC50 synergy thresholds was calculated for each ‘anchor concentration-library–cell line’ tuple based on combination responses averaged across replicates .To assess the reproducibility of the screen, we rescreened a subset of combinations in each tissue  and combination response  metrics were used as input for GDSCTools ANOVAs15. To obtain a single combination Emax, ∆IC50 and ∆Emax value per cell line–combination pair, responses were averaged across replicates for each anchor concentration-library–cell line tuple and the combination metrics were compared for the two anchor concentrations: the larger of the two ∆IC50 and ∆Emax values and the smaller of the two combination Emax values were used for biomarker discovery in order to capture the largest effects of the combination. A range of binary feature files were used, including multi-omics binary event matrices (MOBEMs) composed of genes known to be mutated, amplified or homozygously deleted in human cancers5 , CELLector signatures34 , RNA-seq gene expression35 , CRIS36 and PAM5038 classifications. Gene expression was limited to a curated panel of genes composed of targets of the drugs used, additional members of the BCL2 family and apoptosis-associated genes39, genes annotated as clinically relevant for cancer40, and genes whose mutations were listed as features in the MOBEMs40 and CELLector40 feature files. Continuous values of gene expression were binarized by z-scoring each variable across the subset of cell lines used for the molecular context tested, and substituting a z-score ≥ 2 for a binary value representing that feature being elevated , and a z-score ≤ −2 for a binary feature representing that feature being decreased . Overall significance thresholds were P ≤ 0.001 and FDR ≤ 5%.Matrices of single-agent (library IChttps://cran.r-project.org/web/packages/igraph/citation.html) using the Reactome18 human interactions file (accessed April 2021), and all human interactions reported in IntAct12 (accessed July 2021). All non-protein nodes and duplicated interactions were removed, resulting in a non-directed network of 5,556 Uniprot protein nodes and 25,731 edges for the Reactome interactome. For the IntAct interactome, an evidence filter of 0.5 was applied, and all non-protein nodes and duplicate interactions were removed, resulting in a non-directed network of 14,431 protein nodes and 110,118 edges. Drug targets and biomarkers features were manually mapped to their Uniprot proteome identifiers (UPID), with overall 57 out of 66 (86.3%) drug target profiles being mapped to one or more UPIDs, and 633 out of 1,501 (42.2%) biomarker features being mapped for one or more UPIDs. UPID mapping was not possible for chemotherapeutics, PAM50, CRIS, and not done for methylation sites not associated with a cancer driver gene. A distance matrix between all nodes was calculated using iGraph: infinite values were reported for nodes that did not exist in the same network. When calculating the shortest distance between drug targets or drug targets and biomarkers, distances were calculated for all target-target or target-biomarker pairs and the smallest distance was reported. For example, for a drug with two targets combined with a drug with three targets, the shortest of six target-target distances would be reported. To simulate false positive biomarker associations, the biomarker features used in the genuine distance plot were randomly shuffled without replacement, before re-calculating the shortest distance between the new, ‘false’ biomarker and drug targets.An interactome of binary, undirected interactions was built in the iGraph R package  using an R script and searches in the format ‘drug1 + drug2 + cancer + tissue’. Obtained lists of trials were manually curated to ensure that drugs were exact matches and to remove trials using radiotherapy alongside drug combination treatment. Searches were limited to 81 combinations with ≥ 25% synergy in populations of clinical need.Clinical trials data were extracted from the API at To test CHEK specificity we seeded SW620, SW837, SNU-81 or LS-1034 cells in 96-well plates  and treated them with camptothecin  in combination with six CHEK inhibitors : AZD7762 , prexasertib , MK-8776 , SAR-020106 (CHEK1), rabusertib (CHEK1) and CCT241533 . After 96 h of drug treatment viability was measured with CellTiter-Glo 2.0 . Drug response curves were fitted as described above.t-test.For siRNA experiments, SW837 and SNU-81 cells  were reverse transfected with siRNAs of a non-targeting pool as negative control , polo-like kinase 1 (PLK1) pool as positive control , CHEK1 pool  or CHEK1 individualsiRNAs (LQ-003255-00-0005), and CHEK2 pool  using lipofectamine RNAiMax (Thermofisher). After 30h, 0.025 μM or a dose range of 0.001–9 μM SN-38 or DMSO were added and viability was measured 72 h later with CTG. Signal was normalised to siNT+DMSO controls. Statistical significance between conditions was tested using a two-sided Welch’s Cells were seeded in 96-well plates . After 24 h drugs (0.125 μM staurosporine (positive control), 0.025 μM SN-38, 0.75 μM rabusertib, 0.75 μM CCT241533) or DMSO and real-time fluorescent reagents for detection of cell death  or caspase-3/7 activity  were added. Pictures were recorded every 2 h for 96 h using an Incucyte (Essen Bioscience). Recorded fluorescent signals were measured as mean intensity per cell area and normalised to time 0 h.Cells were seeded in 6-well plates at 50,000 cells per well. Drugs  or DMSO were added on day 1 and were refreshed through medium change on day 8. 14 days after drug treatment started the cells were fixed in 4% paraformaldehyde (Sigma-Aldrich) in PBS for 10 min at room temperature, and then stained with Giemsa  for at least 30 min at room temperature.SW837 (1 million) or SNU-81 (1.5 million) cells were seeded in 10 cm dishes and treated with drugs ) or DMSO the day after. After 72 h, live and dead cells were collected and lysed in RIPA buffer (Sigma-Aldrich) supplemented with 1 mM DTT  and protease and phosphatase inhibitors (Roche). Total protein content was determined using Bradford reagent (ThermoFisher) and around 20 μg of lysate were loaded onto a 4–12% Bis-Tris gel (Invitrogen) for SDS–PAGE followed by protein transfer from the gel onto a PVDF membrane. Membranes were blocked in 5% milk (in TBST) and incubated overnight with the appropriate antibodies. Blots were washed in TBST and incubated with secondary antibody for 1 h at room temperature. Blots were washed in TBST before the signal was enhanced with Super Signal Dura and visualised. The following primary antibodies were used for immunoblot analysis: anti-PARP , and anti-β-tubulin  as loading control.CHEK1 and CHEK2, SW837 or SNU-81 cells were reverse transfected with siNT, siCHEK1 or siCHEK2 as described above. Cells were collected 72 h after transfection and lysed in RIPA buffer  and protein concentrations were determined using the BCA assay  as per manufacturer’s instructions. SDS–PAGE and western blots were conducted as described above and the following primary antibodies were used for immunoblot analysis: anti-CHEK1 , anti-CHEK2 , and anti-β-actin  as a loading control. Anti-Mouse IgG  and anti-rabbit  HRP-linked secondary antibodies were used as secondary antibodies. PageRuler Plus Prestained Protein Ladder, 10–250 kDa  was used as a molecular weight marker.For experiments with knockdown of 6 LS-1034 cells, 5 × 106 SW837 cells or 2.5 × 106 SNU-81 cells in 30% Matrigel were injected subcutaneously into the right flank of male and female 6-week-old NOD/SCID mice. Once tumours reached an average volume of approximately 300–400 mm3, mice were randomized into treatment arms, with n = 12 (irinotecan and irinotecan + rabusertib) or n = 6 (vehicle and rabusertib) per group. Rabusertib was administered orally, 200 mg kg−1 daily ; irinotecan was administered intraperitoneally, 25 mg kg−1 twice a week . Tumour size was evaluated once weekly by calliper measurements, and the approximate volume of the mass was calculated using the formula (4π/3) × (d/2)2 × (D/2), where d is the minor tumour axis and D is the major tumour axis. Results were considered interpretable when a minimum of 4 mice per treatment group reached the prespecified endpoints . A major tumour axis of 20 mm is the endpoint permitted by the Italian Ministry of Health in authorization no. 806/2016-PR, in accordance with national guidelines and regulations. This endpoint was not exceeded in any experiment. Operators were blinded during measurements. In vivo procedures and related biobanking data were managed using the Laboratory Assistant Suite41. All animal procedures were approved by the Institutional Animal Care and Use Committee of the Candiolo Cancer Institute and by the Italian Ministry of Health.A total of 4.5 × 10t-test. Statistical analyses in the survival experiments were performed by log-rank (Mantel–Cox) test. For all tests, the level of statistical significance was set at P < 0.05. Graphs were generated and statistical analyses were performed using the GraphPad Prism (v9.0) statistical package.Statistical significance for tumour volume changes during treatment was calculated using a two-way ANOVA. For endpoint comparisons, statistical analysis was performed by two-tailed unpaired Welch’s 3) or the indicated compounds (after 72 h). Tumours (n = 1–3 for each treatment arm) were explanted and subjected to histological quality check and immunohistochemical analysis with the following antibodies: mouse anti-Ki-67(MIB-1) , rabbit anti-cleaved caspase-3 (Asp175)  and rabbit anti-phospho-histone H2AX (Ser139) (20E3) . After incubation with secondary antibodies, immunoreactivities were revealed by DAB chromogen (Dako). Images were captured with the Leica LAS EZ software using a Leica DM LB microscope. Morphometric quantitation was performed by ImageJ software using spectral image segmentation. Software outputs were manually verified by visual inspection of digital images. Each dot represents the value measured in one optical field , with 2–10 optical fields (Ki67 and phospho-H2AX) and 3–5 optical fields (active caspase-3) per tumour depending on the extent of section area . The plots show mean ± s.d. Statistical analysis by two-tailed unpaired Welch’s t-test.Morphometric quantitation of Ki67, active caspase-3, and phospho-H2AX immunoreactivity was performed in xenografts from mice treated with vehicle (until tumours reached an average volume of 1,500 mmFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-04437-2.Supplementary Fig. 1This file contains images of Western blots described in the paper.Reporting SummaryPeer Review FileSupplementary Table 12,575 drug combination-tissue pairs.Supplementary Table 2Detail of 125 cancer cell lines used in this study.Supplementary Table 3All significant and large effect biomarkers.Supplementary Table 4Synergy and biomarkers: all combinations.Supplementary Table 5Synergy and biomarkers: populations of unmet clinical need.


R132H plasmid treated cell line. The results showed that the wild-type IDH1 could significantly inhibit the proliferation, migration and promote the apoptosis of RCC cell lines, which were consistent with the IDH1's substrate α-KG. The mutant IDH1R132H was found to lose this biological function of IDH1. Moreover, we verified the proliferation inhibition of IDH1 in vivo. In addition, we verified the correlation between IDH1 and hypoxia signal-related proteins in vitro and in vivo, specifically, IDH1 overexpression could significantly reduce the expression of HIF-1α and HIF-2α proteins and its downstream proteins . Furthermore, we preliminarily verified the possibility of α-KG in the RCC's treatment by injecting α-KG into the xenograft model. α-KG significantly reduced tumor size and weight in tumor-bearing mice. This study provided a new therapeutic target and small molecule for the study of the treatment and mechanism of RCC.The purpose of our study was to explore the effect and intrinsic mechanism of wild-type IDH1 and its substrate α-KG on renal cell carcinoma (RCC). IDH1 was observed lower expression in RCC cell lines. Phenotype experiment was carried out in the wild-type IDH1 and mutant IDH1 Renal cell carcinoma (RCC) is a common urinary malignant tumor, accounting for about 2%-3% of adult malignant tumors. Currently, the incidence rate and the mortality rate were increasing in worldwide There are exact evidences that the von hippel-lindau (VHL) tumor suppressor gene is mutated or inactivated on chromosome 3p25 in more than 80% of ccRCC patients Under normal oxygen (>8%-10%), the hydroxylation of HIF-alpha by intracellular hydroxylase provides a binding site for VHL protein, which is a substrate-binding protein of ubiquitin protein complex CRL2, thus mediating the hydroxylation of HIF-alpha by polyubiquitination and degradation through proteasome degradation pathway in vivo, participates in the oxidative stress response of cells on the one hand, and also participates in the oxidation process of unsaturated fatty acids Isocitrate dehydrogenase (IDH) is a key rate-limiting enzyme in the tricarboxylic acid cycle. It catalyzes the oxidative decarboxylation of isocitrate to oxalosuccinic acid, an intermediate product, and then oxidizes decarboxylation to produce α-KG and carbon dioxide, providing energy and precursors for cell metabolism. IDH1 is one of three isomerases of IDH gene family which located in the cytoplasm and oxidase body and participates in the tricarboxylic acid cycle and provides energy. The products produced by IDH1 catalytic reaction include α-KG and reduced coenzyme II (NADPH). NADPH, as a donor of reductive hydrogen et al. R132H. Mutant IDH1R132H could lead to changes in enzyme activity and could not produce substrate molecule α-KG. Zhao et al. found that the mutated IDH1 was a dimer rather than a homodimer. The mutation results in a decrease in intracellular α-KG levels, and a series of reactions lead to an increase in the stability of HIF-1-alpha, which activates the HIF signaling pathway and may ultimately promote tumor growth Parsons In this study, we found that the expression of IDH1 in ccRCC was significantly lower than that in normal renal tissues adjacent to cancer. The decrease of IDH1 expression in ccRCC resulted in the decrease of intracellular α-KG production, and the up-regulation of IDH1 expression in ccRCC could inhibit the growth of ccRCC. The reduction of α-KG production in ccRCC cells resulted in the accumulation of HIF-alpha in ccRCC, promoting the growth and malignant transformation of tumors. It was proved that IDH1 and small molecule α-KG had inhibitory effect on ccRCC. This study is expected to further explore the pathogenesis of ccRCC and find new therapeutic targets.The tumor and paracancerous tissue samples (n = 39) in this study were obtained from radical nephrectomy patients at Zhongnan Hospital of Wuhan University. The clinical, and pathological data record of all patients were collected, and two pathologists were invited to confirm the histology diagnosis independently. The study using clinical information and surgical tissue specimens was approved by the Ethics Committee at Zhongnan Hospital of Wuhan University . All patients provided the informed consent. The procedures in this study were done in accordance with the ethical standards of the institutional and/or national research committee.RCC cell lines  and 293T cell line were cultured in RPMI1640 medium , ACHN cells were cultured in minimum essential medium , Caki-1 cells were maintained in McCoy's 5 A Medium  supplemented with 10% FBS. Human renal proximal tubular epithelial cell line (HK-2) was maintained in KSF medium with epidermal growth factor as well as bovine pituitary extract . All cell lines were purchased from the Stem Cell Bank, Chinese Academy of Sciences in Shanghai, China. These cell lines were grown at 5% CO2, 37 °C in a humidified incubator (Thermo Scientific).https://david.ncifcrf.gov) can help reveal the biological processes and signal pathways that IDH1 get involved in RCC tumorigenesis.In addition to the data from our center, we also accessed microarray expression profiles of The Cancer Genome Atlas (TCGA) to obtain the clinical and cytogenetic data of the external RCC patients. Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6.8 (IDH1-siRNA (si-IDH1) and control-siRNA (NC) were all synthesized by GenePharma Gene Co Ltd. in Suzhou, China. IDH1-LV (LV-IDH1) was synthesized by Genechem Co Ltd. In Shanghai, China. The sense sequence of IDH1-siRNA was 5′-AAAUGAUUCGUGUCAUUUCAUCUCC-3′, the sense sequence of control-siRNA (NC)/control-shRNA (NC) was 5′-UUCUCCGAACGUGUCACGUTT-3′. IDH1 cDNA (1245 bp) was polymerase chain reaction (PCR) amplified from cDNA library of human RCC cell lines, then was cloned into p3 × FLAG-CMV-14 empty vector. IDH1 forward primer sense EcoRI 5′-GCGAATTCAATGTCCAAAAAAAT-CAGTGGCG-3′, IDH1 reverse primer sense BamHI 5′-CCGGGATCCAAGTTTGG-CCTGAGCTAGTTTG-3′. Reference to the manufacturer's protocol, cells were transfected with plasmids or siRNA oligonucleotides using Lipofectamine 3000 transfection reagent. To select the stable cell line, ACHN cells were infected with IDH1-LV and control-LV. 24 h later, 5 μg/ml puromycin  was added into cell culture medium to last 14 days. The antibiotic-resistant cells were selected successfully.With the manufacturer's protocol, HiPure Total RNA Mini Kit  was used to isolate total RNA from cells and RCC tissues. We performed the reverse transcription with ReverTra Ace qPCR RT Kit . RT-PCR was conducted with iQTM SYBR® Green Supermix . Table The transfected RCC cells were gathered, centrifugated and washed with cold PBS once. Follow the manufacturer's protocol, cells were collected and stained by annexin V-fluorescence isothiocyanate (FITC)/PI Apoptosis Detection Kit  according to the manufacturer's instructions protocol and analyzed by the flow cytometry analysis.ACHN and 786O cell lines were first incubated in a 6-well plate, When the cells were adherent and the cell density reached 60%, α-KG  was used for treatment. After IC50 (50%inhibiting concentration) was calculated by cell proliferation assay, 0, 100, 300 μM was selected as the concentration gradient in subsequent experiments. We used this concentration gradient to incubate ccRCC lines for 48 hours and then collect the cells for subsequent experiments. α-KG used in all experiments was dissolved in 1% DMSO .After harvesting the RCC cells, the RCC cells were washed by PBS for 3 times, then lysed cells with mixed solution that contained RIPA buffer, phosphatase inhibitor and protease inhibitor , for 30 min on ice. Then centrifuging the cell lysates at 13,000 × g for 15 min at 4 ℃, the supernatant liquid was then collected. Bradford protein assay  was used to measure the protein concentration of the supernatant liquid. Western blot analysis was performed after the total protein samples were separated with 7.5-15% SDS-PAGE. When HIF-1α and HIF-2α were detected by western blot analysis, the RCC cells were treated with cobalt chloride for 12 hours. The immunoreactive bands were visualized with an enhanced chemiluminescence kit , and then detected with Molecular Imager Chemi Doc XRS + Imaging system . Table 6 cells were collected by counting and then dissolved in 100 μl PBS, and then injected subcutaneously into mice to construct the xenograft models. We then observed tumor volume every three days and recorded it until all the mice were sacrificed at 6 weeks.The BALB/c-nu mice  were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. . The xenograft mouse model related experiment was carried out in The Laboratory Animal facility of Zhongnan Hospital of Wuhan University. First, lentiviruses were used to construct stable transfected NC and sh-IDH1 ACHN cell lines, respectively. Then, 1 × 10All the results of the study were repeated at least three times. Two-tailed student's t test was used to calculate the significance between the two sets of data. The SPSS 16.0 and R was used to perform all statistical analyses. GO and KEGG analysis were performed by “clusterpofiler” R package under the R version 3.6.1, and a p value < 0.05 was considered statistically significant.To investigate the functional enrichment of IDH1 in ccRCC, relation analysis of IDH1 expression and top 500 correlation-genes in TCGA-KIRC database was performed. We identified 408 positive-related genes and 92 negative-related genes by the functional enrichment analyses . We filtered out the low-signal genes, and selected the top 500 genes with the highest correlation for functional annotation analysis by calculating the Pearson correlation coefficient between all genes and IDH1. Through GO analysis, we found that IDH1 was related to biological processes such as Oxidation-reduction process, Protein phosphorylation, Metabolic process (Supplementary We downloaded the RNA-seq data (n = 607) of ccRCC of TCGA database from the UCSC Xena  could lead to decreased proliferation of ACHN and 786-O cell lines (Figure 00 μM couR132H mutant plasmid on the level of IDH1 substrate α-KG (Figure R132H mutant group (Figure We first verified the effect of IDH1 overexpressed plasmid and IDH1G Figure A and theG Figure C. By wesp Figure B. We appp Figure D. We alsp Figure E-F. In sA xenograft mouse model was established to study the effect of IDH1 and IDH1 substrate α-KG on RCC cell growth. We carried out xenograft mouse model treat with lentivirus IDH1. We first verified the overexpression efficiency of lentivirus IDH1 with qRT-PCR and WB assays Figure A, and thFor the α-KG-related experiments in xenograft mice, after 1 week of adaptive feeding, mice were subcutaneously injected with the same amount of ACHN cells, and α-KG was injected 16 days later. After 12-40 days, tumor size and weight of mice were measured, and experiments such as H&E staining and IHC staining were carried out Figure A. Then tR132Hin vivo and in vitro strict verification. In addition, we discussed the substrate of IDH1 α-KG potential as a treatment for RCC.Several studies have confirmed the important role of IDH1 in tumors R132H has a weaker effect on them than wild-type IDH1, indicating that IDH1 can influence the development of RCC through hypoxia signal, and IDH1R132H mutation can weaken such influence. In addition, IDH1 plasmid and its substrate α-KG were used for in vivo experiments. We verified the inhibitory effect of IDH1 and its substrate α-KG on RCC by regularly measuring the tumor size and weight of tumor-bearing mice. Importantly, we verified the effect of IDH1 on HIF-related proteins through IHC in tumor-bearing mice. Finally, IDH1 substrate α-KG was used for in vivo experiments, and the results showed that α-KG also had a significant inhibitory effect on tumor size and weight in tumors bearing mice.We first found that IDH1 expression in normal kidney tissues and cell lines was significantly higher than that in kidney cancer tissues and cell lines, and so was its substrate α-KG. It indicates that wild-type IDH1 has some inhibitory effect on renal carcinoma. Then we explored the biological function of wild-type IDH1 on RCC cells through MTT, migration and flow cytometry apoptosis assays, and the results showed that IDH1 could significantly inhibit the proliferation and migration of RCC cells and increase cell apoptosis. Moreover, we found that it's substrate α-KG also has a consistent biological function. In 92% of RCC, the VHL is mutated and inactivated in vivo and in vitro.In summary, our study demonstrated that wild-type IDH1 can inhibit the proliferation, migration, and promote cell apoptosis of RCC. IDH1 was found to inhibit the development of RCC by inhibiting the expression of HIF and its downstream proteins VEGF and TGF-α. Moreover, IDH1's substrate α-KG has the same biological function, and we preliminarily verified the possibility of using α-KG as the treatment of RCC in vitro cell assay that wild-type IDH1 could inhibit the progression of RCC by inhibiting the proliferation and metastasis of RCC and promoting the apoptosis of RCC cell, and verified the inhibitory effect of IDH1 on RCC and the inhibitory effect of IDH1 substrate α-KG on tumor through in vivo experiments, preliminarily verified the possibility that α-KG could be used for the treatment of RCC.We proved through Supplementary figures and tables.Click here for additional data file.


While computed tomography (CT) exams are the major cause of medical exposure to ionising radiation, there is increasing evidence that the potential radiation-induced risks must be documented. We investigated the impact of cellular models and individual factor on the deoxyribonucleic acid double-strand breaks (DSB) recognition and repair in human fibroblasts and mammary epithelial cells exposed to current chest CT scan conditions.Twelve human primary fibroblasts and four primary human mammary epithelial cell lines with different levels of radiosensitivity/susceptibility were exposed to a standard chest CT scan exam using adapted phantoms. Cells were exposed to a single helical irradiation (14.4 mGy) or to a topogram followed, after 1 min, by one single helical examination (1.1 mGy + 14.4 mGy). DSB signalling and repair was assessed through anti-γH2AX and anti-pATM immunofluorescence.Chest CT scan induced a significant number of γH2AX and pATM foci. The kinetics of both biomarkers were found strongly dependent on the individual factor. The topogram may also influence the biological response of radiosensitive/susceptible fibroblasts to irradiation. Altogether, our findings show that a chest CT scan exam may result in 2 to 3 times more unrepaired DSB in cells from radiosensitive/susceptible patients.Both individual and tissue factors in the recognition and repair of DSB after current CT scan exams are important. Further investigations are needed to better define the radiosensitivity/susceptibility of individual humans.The online version contains supplementary material available at 10.1186/s41747-022-00266-0. Chest computed tomography (CT) scan exposure discriminates individuals with double-strand breaks (DSB) as endpoints.BRCA1/BRCA2 mutation carriers elicit more DSB after chest CT scan exposure.Cells from The justification of CT scans should take into account individual factor.BRCA1 and BRCA2 genes have been associated with a significant risk of breast cancer [BRCA1 and BRCA2 proteins are also involved in the radiation-induced deoxyribonucleic acid (DNA) damage repair and signalling pathway, BRCA1+/− and BRCA2+/− carriers are considered to be at high risk of IR-induced cancer, notably through chest CT scan exams [To date, computed tomography (CT) scan exams represent the largest cause of medical exposure to ionising radiation (IR). An accurate quantification of the IR-induced cancer risk is therefore becoming a societal, medical, and scientific issue. Furthermore, a reliable biological dosimetry is required to help clinicians in justifying a safe use of diagnostic imaging , 2. Whilt cancer . Since tan exams , 7, 8.in vitro or ex vivo lymphocytes [Nevertheless, no report has still focused on the biological response of cutaneous fibroblasts and mammary epithelial cells, which are the most relevant cellular models to address this question: the very great majority of studies dealing with the assessment of the DNA damage potentially caused by chest CT scan have been done with phocytes , 9.IR induces several types of DNA damage, base damage, DNA single-strand breaks, and double-strand breaks (DSB), which differentially participate in the molecular response to IR. Particularly, DSB have been shown to be predictive of radiosensitivity/toxicity if unrepaired, and cellular transformation and radiosusceptibility if misrepaired . While tBRCA1/BRCA2 mutations, are systematically associated with hyper-recombination [Recently, a mechanistic model of the response to IR, based on the DSB recognition and repair and on the radiation-induced nucleoshuttling (RIANS) of the ataxia-telangiectasia mutated (ATM) protein kinase was proposed –13. The bination . Hence, bination .in vitro by using a new generation optical scintillating fibre dosimeter [In this study, the response to DSB induced by current chest CT scan exams combined or not with topogram was examined by assessing nuclear γH2AX and pATM foci osimeter , twelve Human untransformed fibroblasts were cultured as monolayers in the conditions detailed elsewhere . The fibBRCA2-mutated patient and the 202CLB and 203CLB cell lines derived from BRCA1-mutated patients served as representative breast cancer–susceptible examples. All the last three patients underwent a prophylactic mastectomy. The 01HNG, 02HNA, and 13HNG cell lines were derived from patients who showed significant tissue reaction after radiotherapy [NF1+/− mutated patients. The 85MA cell line was derived from a Li-Fraumeni syndrome (p53+/− mutated) patient and was a kind gift from D. Scott . The GM03399 cell line was purchased from Coriell Institute  and derived from heterozygous ataxia telangiectasia (ATM+/− mutated) patients. These last 5 cell lines served as representative non-breast cancer susceptible examples. The origin and the major clinical features of the fibroblast cell lines have been gathered in Table Among the COPERNIC cell lines, the 200CLB cell line was derived from an apparently healthy patient and served as the radioresistant control. The 201CLB cell line derived from a otherapy  and servFour mammary epithelial cell lines derived from the 200CLB, 201CLB, 202CLB, and 203CLB patients were used in this study. These primary mammary epithelial cells were routinely cultured as monolayers with the specific mammary epithelial cell medium MEpiCM provided by Sciencell Research Laboratories  supplemented with the specific growth factor complement MEpiCGS  and penicillin and streptomycin cocktail  but not fungicide agents. To confirm the epithelial nature of cultured cells, immunofluorescence staining using antibodies against cytokeratin 18 ; dilution 1:100; Abcam SAS, Cambridge, UK) was performed , and the absorbed dose was measured with a scintillating fibre dosimeter developed by the Fibermetrix company , 22, 23 ser139 antibody  was used at 1:800, the monoclonal anti-mouse anti-pATMser1981  was used at 1:100 and the monoclonal anti-mouse anti-cytokeratin 18  was used at 1:100. Incubations with anti-mouse fluorescein secondary antibodies provided by Sigma-Aldrich  were performed at 1:100 at 37 °C for 20 min. Slides were mounted in 4′,6′-diamidino-2-phenyl-indole-stained Vectashield  and cells were counted using a × 100 objective with a fluorescence BX51 microscope . For each of the three independent experiments, 100 nuclei were analysed. The patented procedures of foci scoring have been detailed elsewhere [Immunofluorescence protocol for assessing DSB induction and repair was described elsewhere , 25. Brilsewhere .The data and statistical analyses were processed using MATLAB R2016a . With regard to the values described in the “Human fibroblasts derived from 12 patients showing different levels of individual radiosensitivity/susceptibility were submitted to one single helical (14.4 mGy) chest CT scan session. This set-up provided an average volumetric CT dose index (CTDIvol) of 8.1 ± 0.6 mGy. The average dose-length product (DLP) was 136.1 ± 11.9 mGy cm. The average absorbed dose at the surface of the phantom was 14.4 ± 1.91 mGy. Concerning the topogram, the CTDIvol was 0.13 ± 0.01 mGy. The average DLP was 6.7 ± 0.6 mGy cm. The average absorbed dose at the surface of the phantom was 1.1 ± 0.06 mGy.p < 0.001), suggesting a higher genomic instability. It is however noteworthy that the numbers of γH2AX foci never exceeded 1 focus per cell  showed significantly more spontaneous γH2AX foci   Fig. . This su01) Fig. a. In theion Fig. a. In theIn the radioresistant controls, the number of γH2AX foci significantly decreased with repair time and reached a number of residual γH2AX foci comparable to that assessed in non-irradiated cells. All the other fibroblast cell lines showed a different shape of γH2AX foci kinetic Fig. a and S1ap = 0.008 and p = 0.001, respectively), suggesting an impairment of DSB repair. All the other cell lines showed a number of residual γH2AX foci similar to that of radioresistant controls, suggesting a normal DSB repair. Again, similar conclusions were reached with the pATM data and with the percentage of cells with more than 2 γH2AX foci   and as a number of cells with more than two foci γH2AX per cell Fig. b, the saa and as The same experimental protocol with the same physical features described above was applied to the four mammary epithelial cell lines provided from the 200CLB, 201CLB, 202CLB, and 203CLB donors. In order to avoid any confusion, “epi” labels were added at the end of the name of each mammary epithelial cell line.versus 0.6 ± 0.3 γH2AX foci for fibroblasts and mammary epithelial cells, respectively) . For example, for the 201CLB cells, there was an 8-fold difference in the number of γH2AX foci between the two cell types   Fig. . Conversp < 0.001)  Fig. .p < 0.001)  Fig. .Along our observations, some cells with more than 20 γH2AX foci appeared in certain conditions of exposure Fig. . As alreThe anti-pATM immunofluorescence was also performed on mammary epithelial cells, but the number of pATM foci could not be accurately assessed, mainly because of the predominant cytoplasmic localisation of this protein specific to the epithelial cells , and/or to impairment in genome maintenance, which is correlated with the hyper-recombination process. This last phenomenon is generally observed in cancer-prone and radiosusceptible patients . The nump = 0.001; 85MA, p = 0.008). It is noteworthy that the 01HNG cell line is known to be hypersensitive to low doses [p53+/− mutations). All these data are therefore consistent with a significant impact of the individual factor on the final response to IR, even in current chest CT scan conditions. This observation is also confirmed by the high number of DSB observed in mammary epithelial cells , there is a significant difference between the biological and the physical dose Fig . The numow doses  and the lls Fig. .BRCA1-mutated 202CLB and 203CLB fibroblast cell lines  when a topogram was added to the irradiation protocol. Previous studies have reported a similar phenomenon after two successive low doses mimicking a two-view mammography screening  [The effect of the topogram corresponding to a dose delivery pattern of 1.1 mGy followed, after 1 min, by 14.4 mGy. In the frame of the RIANS model, our findings with topogram can be interpreted as the result of two antagonistic phenomena according to the cell lines considered: 1) the additional ATM monomers produced by the topogram may help a higher recognition of DSB; 2) a dose repetition like that induced by the topogram (1.1 mGy + 14.4 mGy) may exacerbate the hyper-recombination process and increases the number of misrepaired DSB. Indeed, the topogram data suggest a dose repetition effect in nes Fig. . Indeed,by 2mGy) . In our by 2mGy) .BRCA1-mutated patients. Interestingly, the 4', 6'-diamidino-2-phenyl-indole-staining indicated that the morphological shape of HDC was clearly different from that of apoptotic cells. The increase in HDC yields cannot correspond to the proportion of cells in the S phase since the percentage of HDC increased with dose. A more plausible interpretation would be that HDC contain several misrepaired DSB that, despite their number, do not lead to cell death. Again, this interpretation is consistent with the fact that there were more HDC in BRCA1-mutated cells.When radiosusceptible cells were subjected to these specific irradiation conditions, the hyper-recombination process was shown to be exacerbated. This trend was notably observed by the increase in HDC cells. This effect producing additional endogenous DNA breaks was called the “low-dose additional and dose-induced”, LADI, effect and was found specific to cells from cancer-prone patients . InteresAt any rate, further investigations are needed to better understand the consequences of such low-dose repetition effects and the potential link between HDC, cellular lethality, and carcinogenesis, notably in patients whose cells undergo hyper-recombination.i.e., cutaneous fibroblasts and mammary epithelial cells. Dosimetric monitoring was carried out with a new technology using a small waterproof scintillating fibre. These dosimeters allowed accurate measurement of the dose, providing a new useful tool for radiobiologists. Our data showed that the radiation response is influenced by the presence of genetic mutations associated with radiosensitivity and/or radiosusceptibility [The present study was performed by applying current chest CT scan conditions to relevant cellular models, tibility . Even ifThe RIANS model based on the nucleoshuttling of the ATM protein after irradiation was already shown to be relevant at a low dose and proposes a mechanistic interpretation of both radiosensitivity and radiosusceptibility , 39, 40.Altogether, these data provide an additional proof that the justification of the CT scan exam should take into account the individual factor. Additional studies are however needed to reliably quantify the risk for a given genetic status.Additional file 1. Electronic Supplementary Material.


Fentanyl was used in cell lines replicating HBV or HCV at concentrations of 1 ng, 100 ng, and 10 ug. Viral protein synthesis was quantified by ELISA, while apoptosis and cell death were measured by M30 or MTT assays, respectively. HCV replicative fitness was evaluated in a luciferase-based system. RNAseq was performed to evaluate cellular gene regulation in the presence of fentanyl. Low dose fentanyl had no impact on HCV replication in Huh7.5JFH1 hepatocytes; however, higher doses significantly enhanced HCV replication. Similarly, a dose-dependent increase in HCV replicative fitness was observed in the presence of fentanyl. In the HepG2.2.15 hepatocyte cell line, fentanyl caused a dose-dependent increase in HBV replication, although only a higher doses than for HCV. Addition of fentanyl resulted in significant apoptosis in both hepatocyte cell lines. Cell death was minimal at low drug concentrations. RNAseq identified a number of hepatocyte genes that were differentially regulated by fentanyl, including those related to apoptosis, the antiviral / interferon response, chemokine signaling, and NFκB signaling. Collectively, these data suggest that synthetic opioids promote viral replication but may have distinct effects depending on the drug dose and the viral target. As higher viral loads are associated with pathogenesis and virus transmission, additional research is essential to an enhanced understanding of opioid-virus pathogenesis and for the development of new and optimized treatment strategies.The US is in the midst of a major drug epidemic fueled in large part by the widespread recreational use of synthetic opioids such as fentanyl. Persons with opioid use disorder are at significant risk for transmission of injection-associated infections such as hepatitis B virus (HBV) and hepatitis C virus (HCV). Commonly abused substances may antagonize immune responses and promote viral replication. However, the impact of synthetic opioids on virus replication has not been well explored. Thus, we evaluated the impact of fentanyl and carfentanil using  The US is experience a major opioid crisis , and thein vitro.Opioids comprise both endogenous and exogenous or synthetic compounds that function by activating several opioid receptors. Opioid receptors are expressed on a variety of immune cells including lymphocytes, monocytes, macrophages, and neutrophils –9. They JFH1 cell line–which produces infectious HCV genotype 2a virions–was provided by Dr. Guangxiang Luo [JFH1 cells, filtered, spun at high speed to pellet cellular debris, and stored at −80°C prior to use as described previously [50 of virus. Virus was then removed, and cells were washed with PBS multiple times to remove unbound virus prior to drug exposure.HepG2.2.15 cell line contains two copies of stably integrated HBV and produces high levels of hepatitis B e antigen and hepatitis B surface antigen (HBsAg) . The Huhiang Luo  and mainiang Luo , 23 and eviously . Naïve Hin vitro identification, calibration, and quantification of analytes in analytical and R&D applications. Fentanyl represents a μ-opioid receptor agonist that was approved in the US as an intravenous anesthetic in 1972 and is approximately 50–100 times more potent than morphine [Fentanyl and carfentanil oxalate were obtained from Cerilliant . Per the manufacturer’s certificate of analysis, these reagents are suitable for the morphine . Carfentmorphine . Fentany6 cells were resuspended in lysis buffer and diluted 1:2. 100 uL of cell/lysis suspension was used in the human opioid receptor mu 1 (OPRM1) ELISA .Mu opioid receptor expression was quantified in several hepatocyte-derived cell lines, as well as the SH-SY5Y neuroblastoma cell line as a positive control. 2 x 10HBV HBsAg protein was measured in culture supernatants by the QuickTiter Hepatitis B Core Antigen ELISA Kit  with a lower limit of detection (LLD) of 1 ng/mL. HCV core protein was quantified in cell culture supernatants using the QuickTiter Hepatitis C Core Antigen ELISA Kit  with an LLD of 1 ng/mL.JFH1 and HepG2.2.15 cell lines were seeded at 500,000 cells per well. Fentanyl or carfentanil was added to culture medium after 24 hours. After 24 hours of incubation with drug, HCV core (ng/mL), or HBsAg (ng/mL) was quantified in culture supernatants.The Huh7.5500,000 cells were seeded per well in 96-well format. Drug was added to culture medium after 24 hours. After 24 hours of incubation with drug, the potential toxicity was evaluated by the MTT Cell Proliferation Assay Kit .HCV replicative fitness was measuring using patient-derived NS5B sequences inserted into an NS5B shuttle vector. As reported previously, the shuttle vector included the non-structural genes NS3, NS4A, NS5B and the 3′ untranslated region (UTR) derived from the genotype 1a strain H77, as well as several cell culture adapting mutations and a firefly luciferase reporter gene . Full-leCells were seeded at 500,000 per well. Fentanyl was added to culture medium after 24 hours. After 3 days of incubation with drug, M30 (units/mL) was quantified in culture supernatants using the M30 Apoptosense ELISA  with an LLD of 20 U/L.4 in dilution buffer , and qPCR was performed with the Kapa Library Quantification kit  using ABI’s 9700HT real-time PCR system (Thermo Fisher). Individually indexed and compatible libraries were proportionally pooled  for clustering in cBot system . Libraries at a final concentration of 15 pM were clustered onto a single read flow cell using the Illumina TruSeq SR Cluster kit v3, and sequenced to 50 bp using the TruSeq SBS kit and the Illumina HiSeq system.The NEBNext Poly (A) mRNA Magnetic Isolation Module  was utilized for polyA RNA purification with a total of 300 ng total RNA as input. A PrepX mRNA Library kit (WaferGen) combined with Apollo 324 NGS automated library prep system was used for library preparation. Sample-specific indices were added to each adaptor-ligated cDNA sample, using the universal and index-specific primer with a limited PCR cycle number, and the amplified library was cleaned by AMPure XP beads in the Apollo 324 system with a final elution volume of 16 μl. To confirm the quality and yield of the purified library, 1 μl was analyzed by Bioanalyzer  using a DNA high sensitivity chip. To accurately quantify the library concentration for clustering, the library was diluted 1:102-tranformed, normalized with a 75th percentile shift, and baselined to the median of all samples. Further, transcripts were filtered to include only those with TPM > 3 in 100% of samples in at least one experimental condition . Differential expression was assessed using moderated t-tests with a cutoff of p < 0.05 and fold change > 1.25. Transcripts meeting significance criteria were submitted to ToppGene and ToppCluster for ontological analyses.Following the removal of primers and barcodes, raw reads were aligned to the Hg38 genome using Kallisto, which employs pseudoalignment to rapidly and accurately determine compatibility of reads with genomic targets. Annotations were provided by UCSC, with output of transcripts per million (TPM). Raw data were loghttps://gataca.cchmc.org/gataca/) and ToppGene (https://toppgene.cchmc.org) and submitted to principal component analysis, which creates comprehensive variables through linear combinations of numerous related variables. Thus, expression of multiple candidate genes was distilled into principal components. Samples were plotted according to values of principal components 1 and 2 to determine sample distribution according to candidate gene expression. RNAseq data are available through GEO using accession number GSE167922.Candidate gene sets were generated using Gataca  compared to the “no drug” condition.JFH1 (HCV-infected), the parental / uninfected Huh7.5, and HepG2.2.15 (HBV-infected). The SH-SY5Y neuroblastoma cell line was included as a positive control. As shown in Mu opioid receptor expression was quantified by ELISA in several hepatocyte-derived cell lines, including Huh7.5in vitro systems that replicate infectious HCV or HBV. As shown in The impact of two synthetic opioids–fentanyl and carfentanil–were evaluated utilizing To determine if this proviral effect was virus-specific, the HepG2.2.15 hepatocyte cell line which constitutively expresses infectious HBV was evaluated in the presence/absence of fentanyl or carfentanil. As shown in Addition of carfentanil led to a modest increase in HBV replication at the low dose (112.8% compared to the no drug condition), although decreased HBV replication was observed at higher doses (51.9% and 41.6% of the no drug condition).JFH1 and HepG2.2.15 cell lines, respectively.The impact of fentanyl on hepatic apoptosis was then evaluated. As shown in JFH1 cells in the presence of fentanyl or carfentanil using the MTT Cell Proliferation Assay . For most gene families, the data also clustered based on fentanyl treatment status. As shown in JFH1 cells. Eight genes involved in chemokine signaling were statistically differentially regulated in the Huh7.5JFH1  and/or HepG2.2.15 cell lines  as shown in JFH1 (HLA-C and OAS1) or HepG2.2.15  cell lines, indicating that fentanyl augments different components of interferon signaling depending upon the cell type (JFH1 (CXCL16 and DDR1) or HepG2.2.15  cell lines, suggesting that fentanyl augments different elements of NFκB signaling depending upon the cell type  underlying the proviral effect of fentanyl are not yet known, the RNAseq analyses . To our knowledge, this is the first study to show an effect of fentanyl on replication of any virus. As well, despite its transmissibility by IDU, very little is known about the effects of any drugs of abuse–including synthetic opioids–on HBV replication analyses  Figs proand IL-4 . Others and IL-4 –39. Addiand IL-4 , 39, 40.JFH1 cells expressed higher levels of mu opioid receptor than the parental, uninfected Huh7.5 cells. Mu opioid receptor gene expression is dependent upon several transcription factors, including activator protein 1 (AP-1) and NFκB and can be altered by multiple cytokines [et al. reported lower mu opioid receptor mRNA expression in liver biopsies and peripheral blood from individuals with untreated chronic HCV infection compared to controls [In vitro studies suggest that the HIV proteins may upregulate mu opioid receptor expression [Of note, Huh7.5ytokines , 41–44. controls . In vitrpression –48. HoweInterestingly, carfentanil led to increased replication of HBV and HCV replication at the low dose but not at intermediate/high drug doses. Despite similar chemical structures, molecular docking models and radioligand displacement assays highlight distinct binding affinities of fentanyl and carfentanil for the mu opioid receptor , 50. Carin vitro drug exposure was short-term. While this may reflect acute drug exposure, it may not adequately reflect long-term drug exposure. Thus, the impact of opioids on cell viability, cell signaling, and/or apoptosis may vary be dose or timing of drug exposure. As well, recreational users likely encounter fentanyl on a more continuous, daily basis that could lead to sustained increases in viral replication. Fentanyl exposure may also co-occur with exposure to other opioids and/or other substances . As higher viral loads are often associated with increased likelihood of transmission to others and/or pathogenic consequences, in vivo studies conducted in individuals with viral hepatitis and opioid use are needed. Second, while physiologically relevant concentrations of fentanyl are poorly characterized, a limited number of studies suggest that concentrations of 10-12M to 10-4M are reasonable for the described experiments [in vitro interactions that have not be evaluated in clinically relevant populations yet and in vivo studies, as well as complementary evaluation in primary hepatocytes and/or liver organoid systems is warranted. Nonetheless, these findings highlight the need to carefully study virus-opioid interactions for the myriad of opioids and opioid derivatives that are now detected in IDU populations, as well as characterize the various mechanisms in a virus- and drug-dependent manner.Several caveats to the current study design should be considered. First, eriments –58. ThirIn summary, there is an urgent public health need to address the linked opioid and viral hepatitis epidemics, particularly in regions with high prevalence of opioid use disorder and synthetic opioids. Rigorous characterization of the interactions among HCV and its treatments, fentanyl, and host cells will improve clinical management paradigms for difficult-to-treat populations, facilitate rational public health policies given severely strained resources, and reveal additional pathways for novel target-specific therapeutic interventions.S1 FigJFH1 hepatocyte cell line in the presence / absence of fentanyl.RNAseq analysis of genes that are upregulated (red) or downregulated (blue) in the Huh7.5(TIFF)Click here for additional data file.S2 FigRNAseq analysis of genes that are upregulated (red) or downregulated (blue) in the HepG2.2.15 hepatocyte cell line in the presence / absence of fentanyl.(TIFF)Click here for additional data file.