TRAF3 is an adapter protein that regulates signals through many receptors important for B cell differentiation and function. Loss-of-function mutations or deletions of TRAF3 are common in B cell malignancies. Glycogen Synthase Kinase 3 (GSK3) regulates signaling in growth, survival, and metabolism pathways. GSK3 inhibitors have been effective against many solid tumors; the inhibitor used in this work is currently being tested for efficacy against BCLs. We found that TRAF3 and GSK3 associate in multiple BCL cell lines, and that BCLs with low TRAF3 have a higher susceptibility to GSK3 inhibition. In contrast to BCL cell lines, GSK3 inhibition has little effect on TRAF3-sufficient and deficient resting primary B cells. These results suggest TRAF3 level as a predictor of BCL responsiveness to GSK3 inhibitor therapy.TNF receptor-associated factor 3 (TRAF3) is an adapter protein that inhibits many signals that promote B cell survival and activation. Mice with a B cell-specific TRAF3 deficiency and humans with a rare haploinsufficiency in TRAF3 have enhanced development of BCLs as they age. Loss-of-function mutations in TRAF3 are common in B cell malignancies. Recent studies show that pharmacological inhibition of the enzyme glycogen synthase kinase 3 (GSK3), which regulates cellular growth, survival, and metabolism, inhibits growth and survival of BCL-derived B cells. In this study, we found that TRAF3 and GSK3 associated in B cells. The relative levels of TRAF3 in BCL cell lines correlated positively with the ratio of inactive to total GSK3β, and negatively correlated with susceptibility to GSK3 inhibition by the GSK3 inhibitory drug 9-ING-41, currently in clinical trials. Uniquely in BCLs with low TRAF3, GSK3 inhibition caused increased loss of the TRAF3-regulated, anti-apoptotic protein Mcl-1. GSK3 inhibition also blocked hyperresponsiveness to IL-6 receptor signaling in TRAF3-deficient BCL cells. Together, these results support the utility of 9-ING-41 as a treatment for BCL, and suggest that a decrease or loss of TRAF3 in BCLs could act as a biomarker for increased susceptibility to GSK3 inhibitor treatment. Many of of BCLs ,9. This gnancies ,11,12,13gnancies ,15. ThisGlycogen Synthase Kinase 3 (GSK3) is a ubiquitously expressed serine/threonine kinase with both alpha and beta isoforms. GSK3 kinases have at least 40 substrates involved in essential cellular processes including growth, proliferation, apoptosis and metabolism ,17. GSK3GSK3 participates in several of the pathways modulated by TRAF3 in B cells, thus we investigated the interplay between TRAF3 and GSK3 in BCL-derived human cell lines and primary B cells. GSK3β, but not GSK3α, has a predicted TRAF3 binding site and indeed, we found that GSK3β associated with TRAF3 in multiple BCL lines. The relative amount of TRAF3 in BCL lines was positively correlated with the ratio of inactive to total GSK3β, and negatively correlated with susceptibility to GSK3 inhibition by CHIR-99021 and the drug 9-ING-41, currently in clinical cancer trials (NCT03678883). Susceptibility of TRAF3 low or negative BCL to 9-ING-41 was accompanied by increased loss of the anti-apoptotic molecule Mcl1, which is elevated in TRAF3-deficient primary B cells and contributes to their enhanced homeostatic survival . InteresCd19-Cre+/−flox/Traf3flox) was performed as previously described [Cd19-Cre+/−+/+Traf3 or Cd19-Cre−/−flox/floxTraf3 are referred to throughout as “WT”. Mice were maintained under specific pathogen free conditions and used in accordance with the National Institute of Health guidelines under animal protocols 1990801 and 1062397, approved by the Animal Care and Use Committee at the University of Iowa. Both male and female mice aged 2–6 mos were used in similar numbers in these studies. Generation of mice with a B cell-specific TRAF3 deficiency (escribed . B cells−/−Traf3 A20.2J [TRAF3 gene were prepared using the CRISPR design tool (crispr.mit.edu) maintained by Dr. Feng Zhang. The double-stranded synthetic oligonucleotides 5′ CACCGCCATCATATCCTCTCATGCA 3′, and 5′ AAACTGCATGAGAGGATATGATGGC 3′ were used to target intron one. Exon five was targeted with the following pairs: 5′ CACCGGTTCCGATGATCGCGCTGC 3′ and 5′ AAACGCAGCGCGATCATCGGAACC 3′. BJAB cells suspended in Optimem (Invitrogen) were transfected by electroporation at 225 V for 30 ms in the presence of: 2.5 μg of each of the two guide RNA/Cas9 vectors, 0.5 μg pEGFP-C1 , and 5 μg double-stranded filler DNA oligonucleotides (random sequence). Twenty-four hours later, GFP-expressing cells were sorted using a Becton Dickinson FACS Fusion and plated at 1 cell/well into 96-well plates. Clones were screened by PCR of genomic DNA using the following primers: TRAF3 targeting primers include 5′ CTGAAAGACAGCAGGTCTCAGGCAC 3′, and 5′ GAATGTATCATATAGGAATTGAGTGG 3′ (Int-5R3), and by Western blotting. GSK3 inhibitors CHIR-99021 (Sigma) and 9-ING-41  were reconstituted in DMSO. All experiments including inhibitor treatments also included control groups with equivalent concentrations (v/v) of DMSO only. Survival of primary B cells and BCL cell lines during GSK3 inhibitor treatment was quantified by propidium iodide exclusion. Where indicated, 20 μg/mL LPS  was added to primary mouse B cell cultures 2 h prior to addition of 9-ING-41 for survival analysis. For IL-6 receptor (IL-6R) signaling studies, cells were pre-treated with 5 μM 9-ING-41 for two hours followed by stimulation with 20 ng/mL IL-6  in the presence of 5 μM 9-ING-41. Splenic mouse B cells were enriched using a negative selection kit . Peripheral blood mononuclear cells were obtained from human whole blood leukocyte reduction cones obtained from DeGowin Blood Center at the University of Iowa. Healthy donors 17 to 70 years of age provided written consent for their blood to be used in research projects in compliance with the University of Iowa’s Institutional Review Board. Mononuclear cells were isolated by centrifugation in Histopaque 1077 , and B cells were enriched with a negative selection kit (Stemcell Technologies). Primary mouse B cells, mouse B cell lymphoma cell lines, and human B cell lymphoma cell lines were cultured in complete medium: RPMI 1640 (Life Technologies) containing 10% heat-inactivated fetal calf serum, 10 μM β-mercaptoethanol , 2 mM L-Glutamine  and 100 U/mL penicillin-streptomycin (Gibco). Provenance of cell lines is as follows: A20.2J  and Traf− A20.2J  were gen− A20.2J  was purc− A20.2J  and Karp− A20.2J  were pro− A20.2J  was prov− A20.2J , Ramos [− A20.2J , Ramos 2− A20.2J , and SKW− A20.2J  were pur− A20.2J  was purc− A20.2J . Briefly2, 1 mM CaCl2, 2 mM Na3VO4, and EDTA-free cOmplete mini protease inhibitor cocktail ) on ice for 30 min. Lysates were pre-cleared for 10 min with protein G Dynabeads (Invitrogen). Antibody (Ab) recognizing one of the following was added: TRAF3  #sc-1828, 1:1000, or Cell Signaling Technologies  Ab #4729, 1:100) or GSK3β . Lysates were incubated with IP Ab for 2 h at 4 °C, then Ab-bound targets were captured with protein G Dynabeads for 20 min at 4 °C. Dynabeads were washed 3X with IP washing buffer  then resuspended in IP lysis buffer for Western blotting analysis.Cells were lysed in Immunoprecipitation (IP) lysis buffer , Mcl1 (#94296), phospho-STAT3Y705 (#9145) STAT3 (#4904), TRAF3 (#4729); Santa Cruz Biotechnology: β-Actin (#sc-47778), GAPDH (#sc-47724). Following 1° Ab incubation, membranes were washed and incubated with HRP-tagged goat anti-mouse IgG  or HRP-tagged goat anti-rabbit IgG  for 2 h at room temperature. Blots were imaged using SuperSignal West Pico or Femto substrate  and a low-light imaging system . Densitometry of Western blots was performed in ImageStudio Lite software  or MultiGauge Imaging software . Density of bands for proteins of interest was divided by density for loading control (Actin or GAPDH) from the same sample to normalize protein levels.Cell lysates were prepared in 1X Radio Immunoprecipitation Assay  Buffer containing EDTA-free cOmplete mini protease inhibitor cocktail (Roche). Proteins were separated on a NuPAGE gel (Invitrogen) and transferred to a polyvinylidene difluoride membrane using the XCell II blotting system (Invitrogen), following manufacturer recommendations. Membranes were blocked with 5% nonfat milk and incubated with 1° Ab overnight at 4 °C. The following 1° Abs were used for Western blotting: Cell Signaling Technologies: GSK3β (#12456), phospho-GSK3βData were graphed and the indicated statistical tests performed using GraphPad Prism software.Traf3 deletion develop spontaneous BCLs [TRAF3 regulates many aspects of B cell biology. These include several pathways restraining homeostatic survival, as well as signaling by the B cell antigen receptor (BCR), various tumor necrosis factor receptor superfamily members, Toll-like receptors (TLRs) and cytokine receptors [ous BCLs . The pleous BCLs . We therous BCLs , we founSpontaneous genetic loss of TRAF3 in BCLs has been reported in both humans and dogs , but varRecent work showed efficacy of the GSK3 inhibitor 9-ING-41 in halting BCL cell cycle progression in vitro and in limiting growth of a lymphoma xenograft in vivo . Given tPrimary BCLs and BCL-derived cell lines exhibit a high degree of heterogeneity, so it was possible that factors besides TRAF3 status were affecting the responsiveness of these cell lines to 9-ING-41 treatment. We thus generated a TRAF3-deficient version of the human BCL line BJAB, which normally has a high level of TRAF3, to more specifically test the effect of TRAF3 level on susceptibility to GSK3 inhibition. Two distinct clones of TRAF3-deficient BJAB cells showed significantly lower tolerance for GSK3 inhibition by 9-ING-41, arguing that loss of TRAF3 alone is sufficient to increase BCL susceptibility to killing by GSK3 inhibition C.One of the mechanisms by which TRAF3 restrains homeostatic B cell survival is by targeting the transcription factor cycling AMP-responsive element binding protein (CREB) for degradation in the nucleus . In B ceY705 compared to TRAF3-sufficient cells , TLRs and the IL-6R . In suppThough long appreciated for its roles in cell growth and metabolism, the multi-functional kinase GSK3 has only recently been appreciated for its importance as a metabolic regulator in B cells , and sti50 of 9-ING-41 of two different subclones of TRAF3-deficient BJAB cells compared to the parent line. Among BCL cell lines with naturally varied levels of TRAF3, we observed a greater decline in the anti-apoptotic molecule Mcl1 in TRAF3 low or negative BCL cell lines compared to those with an intermediate or high level of TRAF3. Finally, we explored the possibility that GSK3 inhibition could differentially affect other TRAF3-regulated pathways based on TRAF3 status. TRAF3 regulates IL-6R signaling through its recruitment of the inhibitory phosphatase PTPN22, which dampens IL-6-induced JAK1 and STAT3 activation [In this study, we report an interesting negative correlation between the ratio of inactive to total GSK3β and the amount of TRAF3 cellular protein across several different types of BCL cell lines, but not in primary mouse B cells. Consistent with the reduced amount of inactive GSK3 in BCL cell lines with low or no TRAF3, these cell lines were more susceptible to killing with both the promising new GSK3 inhibitor 9-ING-41, and CHIR-99021, an older GSK3 inhibitor. Previous studies showed no effect of GSK3 inhibition on primary B cell survival, and indeed we saw little effect of 9-ING-41 treatment on survival of resting B cells. There was also only a minimal differential effect of TRAF3 status on survival following 9-ING-41 treatment in primary mouse B cells. We confirmed that the differential susceptibility of BCL cell lines was likely attributable to TRAF3 by using CRISPR/Cas9 to delete TRAF3 from the BCL cell line BJAB. TRAF3 deficiency significantly decreased the ICA high priority for future studies is assessment of how TRAF3 status can predict other therapies likely to synergize with GSK3 inhibition. The study that initially described the efficacy with which GSK3 inhibition kills BCL cells tested GSK3 inhibition in combination with other inhibitors but found no universally successful combination . This isThe enhanced loss of Mcl1 in 9-ING-41-treated BCL cell lines with low or no TRAF3 is of particular interest. GSK3 activity can increase or decrease cellular Mcl1 levels. GSK3 promotes Mcl1 translation via pathways that involve the kinases Akt and Erk, but it can also directly phosphorylate Mcl1 at key residues that target it for ubiquitination and degradation . This maWe here show a relationship between GSK3 activation status and TRAF3 protein level in BCL-derived cell lines that has the potential to allow more accurate prediction of responders to GSK3 inhibitor therapy. TRAF3 low or negative BCL cell lines showed much higher susceptibility to GSK3 inhibition, but GSK3 inhibition showed no effect on viability of non-transformed, resting B cells. These findings provide an important foundation for further studies of how TRAF3-regulated pathways exploited by malignant B cells for survival can be targeted indirectly for therapeutic benefit.


In Huang et alThe corrected Figure The corrected Figure 7 legend is given below:Figure 7 RGS16 played an oncogene role in glioma cell lines. (A) The expression of RGS16 protein in the human astrocytes (HA) cell line and glioma cell lines. (B) The real‐time quantitative PCR (qPCR) assay of RGS16 mRNA expression in LN229, U87, and U251 cell lines after infection with RGS16 siRNA or negative control. (C) The Western blot analysis of RGS16, TCF8, N‐cadherin, and β‐catenin protein expression in LN229, U87, and U251 cell lines after applying RGS16 siRNA or negative control.  Transwell assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.  Cell scratch assay of LN229 and U87 cell lines treated with RGS16 siRNA or negative control.  Clonogenic assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.It earlier reads as:RGS16 played an oncogene role in glioma cell lines. (A) The expression of RGS16 protein in the human astrocytes (HA) cell line and glioma cell lines. (B) The real‐time quantitative PCR (qPCR) assay of RGS16 mRNA expression in LN229, U87, and U251 cell lines after infection with RGS16 siRNA or negative control. (C) The Western blot analysis of RGS16, TCF8, N‐cadherin, and β‐catenin protein expression in LN229, U87, and U251 cell lines after applying RGS16 siRNA or negative control.  Transwell assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control. (D) Cell scratch assay of LN229 and U87 cell lines treated with RGS16 siRNA or negative control. (E) Clonogenic assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.The authors apologize for these errors.


Glioblastoma is the most common and lethal primary malignant brain tumor in adults. Glioblastoma stem cells (GSCs) promote and are responsible for glioblastoma intratumoral heterogeneity and therapy resistance, due to their two main features: self-renewal and differentiation. Lipids have important biological and physiological functions that are critical for understanding the regulation and control of stem cell fate; lipid metabolism and related unsaturation levels play a possible role as the target of therapeutics to overcome glioblastoma radioresistance. This paper aimed at an in-depth analysis of 13 GSC mesenchymal (MES) lines, two subclones, and a stabilized glioblastoma line (T98G) by magnetic resonance spectroscopy (MRS). Particularly, 2D MRS was used to investigate lipid unsaturation behavior during growth in culture and after treatment with etomoxir and photon beams. MES lines, although belonging to the same genetic and metabolic cluster, showed metabolic heterogeneity when observed by MRS, focusing on lipid signals. Nonetheless, the observed unsaturation level stability for two representative lines after stressful treatments suggests unusual robustness of the unsaturation levels for each line, as a peculiar and intrinsic characteristic of GSCs. Glioblastoma is the most common and lethal primary malignant brain tumor in adults; it is a heterogeneous tumor with multiple subclonal driver mutations (such as isocitrate dehydrogenase (IDH) mutation) that induce high adaptability and resistance to all therapeutic approaches ,2,3. ForMoreover, glioblastomas are complex systems that rapidly evolve in response to severe environmental conditions and can coopt stem-like features to survive and progress ; they acGlioblastoma stem-like cells (GSCs) reside in intratumoral hypoxic protective niches, where they are maintained in a slowly dividing state. They contribute, at least partly, to both glioblastoma intratumoral heterogeneity and resistance to pharmacology, radiation, and surgery, due to their two main features: self-renewal and differentiation ,8,9. GSCThe high heterogeneity of GSCs has been demonstrated in previous studies. Lines from a collection of forty-four patient-derived GSCs have been assigned to two GSC clusters, one characterized by a proneural-like phenotype (PRO) and the other showing a mesenchymal-like phenotype (MES), by magnetic resonance spectroscopy (MRS)  gene expParticularly, GSC lines with a MES phenotype were characterized by high lipid content present in cells as lipid droplets (LDs), dynamic and structured cellular organelles . LiteratLipids have important biological and physiological functions that are critical for understanding the regulation and control of stem cell fate. Lipid metabolism is also altered in rapidly proliferating cells and an increased lipogenesis has been considered as another metabolic hallmark of cancer cells ,23. MoreWithin the cell, FAs can be esterified with glycerol, leading to the formation of triglycerides which are then stored in LDs. LDs regulate the trafficking of polyunsaturated fatty acids (PUFAs) to prevent oxidative stress and cell death and the release of PUFAs for their conversion by cyclooxygenases and lipoxygenases into a whole range of oxygenated mediators of inflammation in cancer cells .Glioma tissues have been shown to have enhanced lipid synthesis and MR studies have revealed a correlation between spectroscopic lipid signals from spectra of glioblastoma extracts and the grade of malignancy . HoweverMobile lipids (ML) in LDs are visible by MRS and are present as saturated, monounsaturated, and polyunsaturated FAs in TG molecules; MRS offers the unique possibility of examining properties of LDs in their intact environment. ,30. In pThe present paper aimed at an in-depth analysis by MRS of lipid metabolism and lipid unsaturation levels in 13 GSC MES lines plus two subclones and a stabilized glioblastoma line (T98G), to evidence their possible role as the target of therapeutics and in glioblastoma radioresistance. Given the importance of the eicosanoid cascade starting from arachidonic acid and the protective effects of omega-3 lipids, we analyzed the MR data of the MES subgroup of GSCs bound to high content of LDs in terms of 2D cross-peak intensity ratios, suggestive of different FA prevalence in the different lines, to obtain more insights into the unsaturation content of triglyceride molecules in LDs.A possible link between lipid unsaturation levels and non-lipid metabolite concentrations was also investigated. Finally, changes in unsaturation level after treatment with external stressors etomoxir (an inhibitor of fatty acid oxidation (FAO)) and gamma radiation were studied in two MES lines, characterized by very high lipid content.Tumor tissue samples were harvested from patients undergoing craniotomy at the Institute of Neurosurgery, Università Cattolica del Sacro Cuore (UCSC), Rome, Italy. All the patients provided written informed consent according to the research proposals approved by the Ethical Committee of UCSC. Patients were eligible for the study if a diagnosis of glioblastoma mutant for IDHas tested by mutational analysis of IDH1 and IDH2 genes  was esta2O and 2mM sodium 3-(trimethylsilyl) propionate-2,2,3,3-d4 (TMSP) as a frequency standard. A 15 μL aliquot of the suspension was transferred into a 1 mm MR microtube and centrifuged to obtain a packed cell volume. All MRS reagents were purchased from Cambridge Isotope Laboratories, Inc. .For MRS sample preparation, cells were removed and washed in PBS and centrifuged at 162 rcf for 3 min. The pellet was suspended in PBS with 20% DAll GSC MES lines samples were prepared and measured on the 4th day in culture. GSC line #74 samples were prepared and measured as a function of time in culture on the 4th, 7th, 10th, and 14th day in culture.4 cells/mL were deposited in 75 cm2 flasks and incubated at 37 °C in a 5% CO2 atmosphere for MRS experiments. After one week of culture, cells were treated in triplicate with 200 μM Etomoxir  for 6 h. MRS experiments on control and treated samples were run 24 h later for both lines.For etomoxir treatments, 2000 cells (line #61 and line #74) in exponential growth phase at density of 2 × 10For irradiation treatments, line #61 and #74 cells were seeded as described previously and after 72 h, cells were irradiated in culture flasks at a single acute dose of 20 Gy with a cesium-137 gamma ray source  operating at a dose rate of 0.66 Gy/min at Istituto Superiore di Sanità, Rome, Italy. A single radiation dose of 20 Gy was used, comparable to total doses delivered during radiation therapy in a fractionated regimen and to a single high dose used in special therapeutic modalities, such as intraoperative radiotherapy and stereotactic radiosurgery . MRS exp1H MRS experiments were run on a digital Bruker Avance spectrometer at 400.14 MHz, equipped with a 1mm microprobe. Both one-dimensional (1D) and two-dimensional correlation spectroscopy (2D COSY) experiments were performed, at T = 298 K. The 1D 1H MRS spectra of GSCs were acquired with a 90° RF pulse, the number of scans (ns) was equal to 1000  for cell spectra while ns = 4000 was used for culture media spectra. When indicated, a Lorentzian–Gaussian function was applied in the time domain, before Fourier transformation. The 2D COSY spectra were acquired with a 90°–t1–90°–t2 pulse sequence and ns = 32 for cell or ns = 128 for culture media samples. Spectra were acquired as a matrix of 512 × 128 data points in time domain.MRS parameters were obtained in at least three independent experiments and data expressed as mean ± standard deviation (SD) values. TopSpin 4.0.9 software  was used to perform 2D cross-peak integration, as reported in . The 2D https://past.en.lo4d.com/windows, accessed on 15 July 2022) [Unsupervised agglomerative hierarchical clustering, principal component analysis, and Student’s t test were performed utilizing PAST 4.03 Software, version 2020 (ly 2022) .Typical MR 1D MRS and 2D-COSY spectra from representative MES cell line #74 are shown in Moreover, M/A and P/A ratios are related to mono- and polyunsaturation, respectively . Table SWe have examined intensities of these lipid signals from spectra of 13 MES lines, two subclones with MES signature, and one commercial glioblastoma line, namely T98G. Clinical parameters, patient demographics, and features of the 13 MES lines are reported in The intensity of the cross peak A calculated on the 4th day in culture is reported in Intensity of all mobile lipid (ML) signals increased during cell growth as a function of time in culture, as previously observed in GSCs ,40,41, aAverage values of ratios B/A, F/A, E/A, F/B, B/M, P/A, and M/A for all examined lines monitored between the 4th and 14th day after seeding are reported in a Box and Whisker Plot A and theTo have more insights on the anomalous behavior of the listed cell lines, an unsupervised cluster analysis was performed by taking into account all lipid signals evidencing an anomalous behavior only for line #112 C.To obtain more insight into the meaning of lipid unsaturation in these cells, we examined if a link existed between the lipid unsaturation level and that of other metabolites.Given that the literature reported a strict relation of low aspartate (Asp) and glutamine (Gln) levels with the proliferative status of cells ,44, we eTo highlight the unsaturation level sensitivity to external stressors, we examined the response to chemical and radiation treatments of two MES lines, namely lines #61 and #74, among those of cell groups here studied with higher lipid content. Both cell lines have been treated with a sublethal dose of etomoxir and observed after 24 h. A cytotoxic effect was observed only for line #74 A. After The two lines were then treated with photon beams with a dose of 20 Gy B. The cyMesenchymal stem cells (MSCs) are a type of multipotent stem cell that plays important roles in regeneration and wound healing . In the Metabolic targeting has long been advocated as a therapy against many tumors including glioblastoma, but how lipid metabolism is altered to suit different microenvironmental conditions and whether CSCs have altered lipid metabolism are still yet to be solved ,53. FAs In previous studies, GSCs lines have been classified according with their metabolic and genomic profiles as PRO and MES ,17,18. PIn the present work, we tried to quantify by 2D MRS the unsaturation content of TG molecules in LDs from 13 different MES GSCs and observed their behavior during cell growth in culture and after treatment with etomoxir and radiation. In fact, 2D MRS is a powerful tool to investigate the FA chemical composition of the TG, particularly the fraction of saturated, monosaturated, and polyunsaturated fatty acids, allowing a better resolution of metabolic signals that are overlapping in 1D spectra ; ratios The lipid in LD concentration, measured as A, B, E, and F peak intensity, together with MUFA (M) and PUFA (P) signals, increase as a function of days in culture, in agreement with previous work ,41. On tOn the other hand, cross-peak ratios B/A, E/A, F/A, M/A, and P/A seem likely not to be influenced by culture conditions, as hypoxia induced by increased cell density, thus suggesting they represent a specific feature of the cell line mainly related to the different FA composition.Analysis of the FA cross-peak ratios suggests differences in PUFA, in omega-3, and in omega-6 levels in these patient-derived cells, though comprised in the same MES group. Furthermore, parameters of subclones of MES line #83 and of T98G with similar lipid features fit the distribution of the MES lines.Recent studies suggest a role of omega-3 in prevention and therapy of brain cancer, as omega-3 and omega-6 play a role in brain health . PUFAs aOne recent newsworthy question is the role played by desaturation in tumor cell stemness. Besides FAO, lipid desaturation to modify FA chain seems implicated in CSC regulation in many kinds of tumors . StearoyAlthough unsaturation levels are similar in all examined lines, it is worth noting that significantly lower levels in some GSC MES lines can be related to anomalous intensity values of no lipid metabolites. In fact, line #220, that presented low values of B/A and sum of P/A and M/A, was characterized by a very high value of the neurotransmitter GABA signal, while in the other lines GABA signals were barely detectable. Furthermore, this line was characterized by the lowest value of A peak intensity even on the 14th day in culture (not shown). Both GABA and A signal intensity are similar to those observed for PRO cluster lines , suggestA different explanation could be hypothesized for the low unsaturation level observed in lines #112 and #196, as well as for line #275 to a minor extent. The absence of P signal in the spectra of lines #112 and #196 and of relative ratios for these two lines could be a consequence of a high presence of saturated oleic chains . On the On the opposite side, lines #148 and #242 were characterized by the highest values of both M/A and P/A together with low values of B/A ratio; this may derive from a consistent presence of EPA and DHA . AdditioMoreover, in line #112, low levels of unsaturation were concomitant to low aspartate and glutamine concentration. Both types of metabolism are closely involved in glioblastoma chemoresistance. The literature reported a relationship between low Asp and Gln levels and the proliferative status of cells and cellular redox homeostasis; in particular, insufficient cytosolic aspartate delivery leads to cell death when TCA cycle carbon is reduced following glutamine withdrawal and/or glutaminase inhibition ,44. In fIn previous studies, we observed the response of GSCs to drug and radiation treatments, addressing mainly the energetic metabolism and, indirectly, the lipid and glutamine involvement as energy sources. Particularly, oligomycin, a mitochondrial ATPase inhibitor, induced high lactate extrusion, producing neutral lipid accumulation . MoreoveThe present work added further information to what was observed in the past. An increase in mobile lipid cross peak A and B/A signal ratio after irradiation, paralleled by cytotoxic effects, was observed only in line #74. Therefore, this allows us to infer that effects due to radiation are different in different lines, and even if lines #61 and #74 belong to MES clusters, their response to stressors are different, with the behavior of line #74 more closely resembling what was observed in PRO line #1 ,41. On the other hand, etomoxir, a widely used small-molecule inhibitor of FAO through its irreversible inhibitory effects on CPT1a, induced a cytotoxic effect in both MES lines, more evident in line #74, associated to a decrease in lipid content measured as cross peak A, as expected. This is in agreement with a recent study, in which lowered glycolysis and a remarkably low level of cytoplasmic lipids were detected in radioresistant glioblastoma cells after FAO inhibition by etomoxir . The levThese data may indicate that lipid storage may or may not be used as cell fuel for membrane synthesis, concomitant to a proliferative arrest induced in these lines. This behavior may not be, therefore, attributable to the MES profile of these GSCs, as they behave differently: the contribution of membrane lipid changes to oncogenic signaling appears to be complex and multifactorial.Analysis of the FA cross-peak ratios allowed us to detect differences in unsaturation levels in patient-derived GSCs, even if belonging to the same MES group: monitoring of changes in the equilibrium between different PUFAs may give information that is hopefully useful for patient treatment. The high level of saturated FAs in LDs of the analyzed MES lines could be related to the promotion of tumor survival under stress conditions. However, a definite explanation cannot be drawn by present results: probably, different contributions can be ascribed.The concomitance of low levels of unsaturation with low aspartate and glutamine concentration may play a relevant role, with both types of metabolism closely involved in glioblastoma chemoresistance; in our experiments the low levels of unsaturation, Gln, and Asp in line #112 could, therefore, indicate an anomaly of this line and deserves future investigations in terms of response to therapy.Finally, the unsaturation level stability in all observed lines either after etomoxir and irradiation treatment suggest a robustness of the unsaturation level of the GSC lines to be likely considered as a specific feature of the cell line mainly related to different FA composition.These results contribute to the rapidly growing interest in lipid targeting in cancer that represents a central hallmark of cancer with a promising strategy for clinical application. The link with diet, including dietary lipids, will also create unique opportunities for preventive strategies and therapy enhancement, especially for personalized medicine.


Weighted Correlation Network Analysis (WGCNA) of serial whole-transcriptome data of cell lines treated with nimesulide revealed that the sets of genes upregulated after treatment with nimesulide were enriched for genes associated with autophagy and apoptosis, and on the other hand, the sets of downregulated genes were associated with cell cycle and RNA splicing. Serial transcriptome of bone marrow patient sample confirmed the upregulation of genes associated with autophagy after the response to nimesulide. Lastly, we demonstrated that nimesulide potentiates the cytotoxic in vitro effect of several Food and Drug Administration (FDA)-approved chemotherapy drugs used in AML, including cytarabine.Acute myeloid leukemia (AML) is a hematologic malignancy associated with high morbidity and mortality. Here we describe a case of a patient with AML who presented a partial response after utilization of the non-steroidal anti-inflammatory drug nimesulide. The response was characterized by complete clearance of peripheral blood blasts and an 82% decrease of bone marrow blasts associated with myeloblast differentiation. We have then shown that nimesulide induces  Despite therapeutic advances in recent years, acute myeloid leukemia (AML) is still associated with high morbidity and mortality, with most patients succumbing to the disease , 2. AML Drug repurposing, that is, the identification of new uses for existing drugs, is a strategy with the potential to improve treatment outcomes with significantly cheaper, faster, and safer preclinical and clinical development protocols . Severalin vitro leukemic cell death, also induces myeloid differentiation  have anti-proliferative and pro-apoptotic effects in AML cell lines and xenograft models –13. In antiation . There intiation .In this article, we report the first-in-human clinical response to nimesulide in AML and provide insights into the mechanisms of action involved in this effect through whole exome, transcriptome, and cell culture experiments.Peripheral blood and bone marrow samples were obtained from an AML patient after the informed consent form was signed. Blood count was performed on an XP-300 hematology analyzer , and the bone marrow smear was processed according to the standards of the diagnostic medical laboratory. Bone marrow mononuclear cells (BMMC) were obtained using Ficoll-Paque  and used for immunophenotyping, DNA, and total RNA extraction for whole-exome and transcriptome sequencing  at 37°C in a 5% CO2-humidified atmosphere.The leukemic cell lines HL-60, THP-1, OCI-AML2, and OCI-AML3 were obtained from American Type Culture Collection  or Leibniz Institute DSMZ (German Collection of Microorganisms and Cell Cultures GmbH) and cultured in RPMI-1640 medium containing 10% fetal bovine serum, 2 mM of The drugs nimesulide , prednisolone , and cytarabine  were used in the cellular experiments. Nimesulide was diluted in dimethyl sulfoxide (DMSO) in a 10-mM stock solution, and prednisolone and cytarabine, presented in liquid form, were diluted in RPMI medium immediately before being applied to the cells. Food and Drug Administration (FDA)-approved drug library  was used for drug screening in combination with nimesulide.The eBioscience™ Annexin V Apoptosis Detection Kit FITC  was used for the detection of early and late apoptosis in treated cell lines, following the manufacturer’s instructions. For the cell cycle analysis, treated cells were washed with cold phosphate-buffered saline (PBS) twice and fixed with 70% cold ethanol at 4°C overnight. After cells were washed again with PBS twice, they were incubated with PBS containing 10 mg/ml of RNase at 37°C for 30 min, followed by DNA staining with 1 mg/ml of propidium iodide . After being left in a dark place at 4°C for 10 min, the cell DNA content was evaluated using a flow cytometer to analyze the distribution of DNA content in cell cycle phases. All experiments were performed using flow cytometer FACScan Fortessa  and recorded at least 10,000 events/sample. The analysis of the experiments was performed by FlowJo software.® Luminescent Cell Viability Assay  according to the manufacturer’s instructions. After 24 h of incubation of cell line with drugs alone or in combination with nimesulide, the cells were incubated for 10 min with Cell Titer-Glo reagent and then assayed by a luminescence plate reader. The ATP content of cells was calculated using the ATP standard curve in a preconfigured CellTiter-Glo protocol on GloMax® Discover Microplate Reader (Promega).To identify potential effects of small molecules in combination with nimesulide, an FDA-approved drug library was purchased from Selleck Chemicals for a high-throughput drug screening with selected drugs to assess the roles of drugs in cells lines. The following drugs approved for the treatment of AML were selected: azacitidine (1 μM), gilteritinib fumarate (0.1 μM), cladribine (10 μM), doxorubicin hydrochloride (6.7 μM), mitoxantrone hydrochloride (0.33 μM), fludarabine (0.09 μM), thioguanine (1.78 μM), and decitabine (0.5 μM). All drugs were diluted in RPMI 1640 culture medium (Gibco) to obtain all the above concentrations, based on the lowest concentration found in the literature. Cell viability was analyzed using the CellTiter-GloRNA was extracted with RNeasy Mini Kit , and the library preparation and sequencing were performed at the Oklahoma Medical Research Foundation Genomics Core (USA)  package from R  was emplThe strategy used to compare the molecular findings from cell line transcriptomes with the patient’s transcriptomes was the CAMERA  package.For flow cytometry assays, ANOVA statistical test was used, followed by Tukey’s test for multiple comparisons. The statistical computations were conducted using GraphPad Prism 7.0, and the significance level adopted was 5% (p < 0.05). For the bioinformatics analysis, statistical methods specific to each computer program were used.3  and a platelet count of 139,000/mm3. Bone marrow evaluation was compatible with the diagnosis of AML with 56% blasts on bone marrow smear. Blast immunophenotyping was positive for CD117, CD64, CD36, CD33, HLA-DR, and negative for CD34. Cytogenetics analysis revealed a diploid karyotype , and whole-exome sequencing revealed the following driver mutations NPM1:NM_002520:c.861_862insTGCT:p.L287fs, DNMT3A:NM_175629:c.1658delA:p.N553fs, IDH1:NM_005896:c.G395A:p.R132H, NRAS : NM_002524:c.G38T:p.G13V, and PTPN11:NM_080601:c.G794A:p.R265Q with wild-type FLT3 and CEBPA. Her final diagnosis was AML with mutated NPM1, and the European LeukemiaNet risk stratification was a favorable risk  count of 4,740/mmThe analysis of bone marrow whole exome at diagnosis suggests an ancestral clone harboring the DNMT3A mutation with an allele frequency of 34.5%; a second subclone with IDH1 and NRAS with allele frequencies of 27% and 26%, respectively; and the third subclone with NPM1 and PTPN11 with allele frequencies approximately 5% , G0/G1, S, and G2/M phases Figure 2To characterize the mechanism of cell death, the proportions of early and late apoptotic cells were evaluated by flow cytometry after treatments for 24 h Figure 3In order to find a common mechanism behind the induction of cell death in AML cell lines after treatment with nimesulide alone and in combination with prednisolone, we decided to perform whole-transcriptome sequencing for each condition. All samples had at least 30 million reads and a Phred Score above Q30. We then performed principal component analysis (PCA) on raw gene expression data, normalized by FPKM  with death levels: downregulated Lightyellow module (p = 0.00052), whose expression levels are inversely correlated with death levels, and upregulated modules Tan (p = 0.000038) and Lightcyan (p = 0.00025), which showed a positive correlation , we created two gene expression ratios based on the FPKM ratio for each gene. Ratio 1 was FPKM from D+21 after nimesulide treatment (early) divided by FPKM at diagnosis, and ratio 2 was FPKM from D+63 after nimesulide treatment (late) divided by FPKM at diagnosis. Then, both ratios were ranked from the highest to lowest in order to show at the top of the rank the genes with the highest fold-change and at the bottom of the rank the genes with the lowest fold-change (including downregulated genes). Afterward, we performed an enrichment test to see if the genes from the modules of experiment 1  generated by the WGCNA had a non-random distribution along with this ranked list, that is, if upregulated genes tended to occur more on the top or the bottom of the ranks and vice versa.We verified that when comparing the patient’s early rank with the genes from modules from experiment 1, related to autophagy (Lightcyan) and RNA splicing and cell cycle (Lightyellow), those genes presented a non-random distribution, being concentrated among the genes with the highest fold-change, with p-values of 0.003 and 0.004, respectively Table 2.We have also evaluated changes in gene expression induced by nimesulide in mutated driver genes in the cell lines. The change in expression of DNMT3A was heterogeneous. While it was slightly reduced in THP1, it was marginally increased in HL60 and OCI-AML3 and significantly increased in OCI-AML3  patients , 20. Whiin vitro  and a xein vitro , 22, makin vitro induction of cell death and cell cycle arrest in AML cell lines treated with COX-2 inhibitors, including nimesulide .The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI's Sequence Read Archive (SRA) with BioProject ID: PRJNA817613  and The Applebaum Foundation.Author RP was employed by company Grupo Pardini.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that  As important regulatory elements, enhancers collaborate with promoters to regulate the transcription of genes in a k-mer frequencies of a DNA fragment with a support vector machine (SVM) to classify regulatory elements k-mer and improved the prediction performance Enhancers are usually identified by high-throughput biological experiments. For example, Heintzman and Ren Over the past five years, deep learning has been incorporated into bioinformatics studies. For example, DeepBind used a convolutional neural network (CNN) to predict binding proteins and showed higher prediction power than traditional classifiers Chromatin accessibility of the genome has received more and more attention in the recent years. It is known that putative accessible regions in the genome often work with TFs, RNA polymerases, and other cellular machines to regulate gene expression With the aforementioned understanding, we propose in this study DeepCAPE, a deep CNN for the accurate prediction of enhancers, using DNA sequences and DNase-seq data. Through comprehensive experiments, we show that our model is not only superior to existing methods in the prediction of enhancers, but also able to predict enhancers across cell lines. With a visualization strategy, we show that sequence motifs discovered by our method successfully match known motifs. Through joint analysis of prediction results with genome-wide association study (GWAS) data, we show the potential ability of our method to identify genetic variants associated with liver cancer and discriminate enhancers related to lymphoma.i.e., non-enhancer fragments that do not overlap with enhancers. First, we randomly sample DNA fragments of variable length from the background genome, with the constraint that the length and GC content of negative samples should be identically distributed as those of known enhancers. The background genome is defined as the entire human reference genome (GRCh37), excluding known enhancers, promoters for coding and non-coding genes, and exonic regions for coding and non-coding genes. Second, we discard the constraint on the GC content to demonstrate the adaptability of our method to different genome contexts. The first model is more stringent and is used throughout this study. We set the ratios of positive to negative samples to 1:10 and 1:20, i.e., for each positive sample, we generate 10 and 20 negative samples, respectively.We use the promoter enhancer slider selector tool (PrESSTo) to download experimentally verified enhancers specific to 9 different cell lines from the FANTOM project, including epithelial cell of esophagus, melanocyte, cardiac fibroblast, keratinocyte, myoblast, stromal cell, mesenchymal cell, natural killer cell, and monocyte. We use two strategies to generate negative samples, S) of a DNA position as the number of reads (N) starting at this position divided by the average number of reads  model where k replicates) into a single channel. After another batch-normalization layer, a decoder component, which is also a feedforward neural network, transfers the data back to k channels. With the module well trained, the decoder is able to produce output similar to the original input, and results of the encoder component can then be used as features extracted from the original data and fed to the successive DNase module. Such an auto-encoder module benefits our model in two aspects. First, regardless of the number of replicates for different cell lines, output of the module is of the same dimension, and thus makes cross-cell line prediction possible. Second, effective dimensionality reduction significantly alleviates the computational burden of the successive prediction model.A DNase-seq experiment usually has a small number of replicates, and this number varies between cell lines, making the dimensionalities of input data variable between cell lines and preventing the use of a CNN in cross-cell line prediction. To solve this problem, we adopt auto-encoder, a neural network designed for unsupervised learning of efficient encodings The DNase module extracts multi-level features from chromatin accessibility scores and is essentially identical to the DNA module in structure, except for the number of input channels. The DNA module is fed with one-hot encoded DNA sequence and has 4 channels, while the DNase module is fed with chromatin accessibility data produced by the encoder component of the auto-encoder module and has a single channel. A statistical analysis on a total of 43,011 experimentally verified enhancers in FANTOM shows that the median and mean lengths of these enhancers are 275 and 288 bp, respectively. We therefore select 300 as the dimensionality of the auto-encoder latent space, and thus the shapes of outputs of subsequent layers in DNA and DNase modules are symmetrical.The joint module integrates multi-level features from both the DNA and DNase modules to predict the probability that the input DNA fragment is an enhancer. Drawing on the idea of skip connection in ResNet On the top of the architecture, a softmax layer predicts the probability that an input DNA fragment is an enhancer based on the integrated features, as.i.e., 1 for enhancer and 0 for non-enhancer).where We carry out 5-fold cross-validation experiments to validate the performance of our method for each cell line. Particularly, in order to avoid information leakage, we partition both positive (known enhancers) and negative (non-enhancers) samples into 5 subsets of nearly equal size before converting sequences of variable length to sequences of fixed length by the data augmentation strategy. In each fold of the experiment, we take 4 subsets to train the model and test its performance using the remaining subset.Considering that the positive and negative samples are highly imbalanced, we adopt a two-stage training strategy. First, we train an initial model using all positive samples and an equal number of negative samples sampled from the training set. After this stage, the DNA and DNase modules obtain the ability to extract features. Then, the joint module is further trained as usual using all the imbalanced samples on the training set, with learning rates of DNA and DNase modules setting to 0 With a well-trained model, we score all samples augmented from an original sequence on a test set, and then average over these scores to obtain the final probability that the sequence is an enhancer. We also used another strategy that takes the maximum of these scores as the final probability to study the effects of different statistics on the results.https://keras.io) with Tensorflow as the backend, while the Theano backend also generated very close results according to our test. The NVIDIA GeForce GTX 1080Ti GPU is used to accelerate the computation. We have released our code in Github (https://github.com/ShengquanChen/DeepCAPE).We implement DeepCAPE in Python using Keras  by counting nucleotide occurrences in the set of sequences that activate the kernels. Briefly, each kernel of the first convolutional layer is converted into a PWM by scanning along input sequences for activated positions and then calculating the PWM by pooling corresponding regions where q-value threshold 0.1 to match PWMs identified by our method to the JASPAR database We set length of kernels in the first convolutional layer to 8 and To verify the performance of DeepCAPE, we conducted a series of 5-fold cross-validation experiments using enhancers collected from FANTOM and negative data generated by the background model with the consideration of GC content (see Method). We compared the performance of our method with several baseline methods, including gkmSVM P < 2.2E − 16 for all the three baseline methods). In terms of AUROC scores, our method is on average 0.121 and 0.151 higher than gkmSVM, 0.169 and 0.151 higher than DeepSEA, and 0.150 and 0.150 higher than DeepEnhancer, when the ratios are 1:10 and 1:20, respectively. One-sided paired-sample Wilcoxon signed rank tests similar also consistently report significant results . All these results suggest the superior performance of our method over existing sequence-based approaches in predicting enhancers.The performance at different ratios of positive to negative samples (1:10 and 1:20) with augmentation stride 1 is shown in P < 2.2E − 16 for AUPRCs, P < 2.2E − 16 for AUROCs; one-sided paired-sample Wilcoxon signed rank test). Similarly, our method significantly outperforms CENTIPEDE when the ratio of positive to negative samples is 1:20 .We also compared the performance of our method to that of CENTIPEDE (with default parameters), which integrates DNA sequences and chromatin accessibility information to predict TFBSs We further validated the performance of our method on an independent test set with the following experiment P = 4.019E − 4 against gkmSVM, P = 7.908E − 4 against DeepSEA, and P = 4.571E − 3 against DeepEnhancer), suggesting that our method is not sensitive to the partition of training and test samples. Besides, our method consistently performs well in all the cell lines, while the performance of the other three methods shows significant fluctuation across cell lines, suggesting that they are sensitive to the number of training samples.Our method demonstrates much higher robustness than the baseline methods. With the variance of AUPRCs in the 5-fold experiments calculated for each cell line, one-sided Wilcoxon rank sum tests consistently show that our method achieves smaller variance than a baseline method ” model is also superior to the three baseline methods in most cases, suggesting that our model has the advantage in the case of predicting only with sequences. Secondly, taking the maximum of the scores of samples augmented from an original test sequence as the final probability generates slightly worse performance, and this may be due to the outliers with high scores in the augmented negative samples. Finally, the performance on datasets without considering GC content is slightly superior to that on datasets under the GC content constraint .In terms of model training, benefiting from the usage of dropout layers and the early stop strategy, the performance on the test set is fairly close to that on the training set, indicating that DeepCAPE is able to avoid overfitting. In addition, with regard to the efficiency of model training, DeepCAPE is superior to other deep learning models due to the zero-learning-rate strategy in the second training stage. Take the dataset with augmentation stride 1 of myoblast as an example, when the ratio of positive to negative samples is 1:20, the training time for an epoch is about 126 s for DeepCAPE, 301 s for DeepSEA, and 237 s for DeepEnhancer.A, the auto-encoder module not only makes cross-cell line prediction possible, but also maintains the superior performance of our method even if the dimensionality of DNase-seq data is reduced .To illustrate the contribution of auto-encoder module, we compared the performance of DeepCAPE with auto-encoder to that of DeepCAPE without auto-encoder as well as other two strategies that average the replicates or randomly select a single replicate. As shown in Fig. 3To evaluate contributions of the DNA and DNase modules, we performed a model ablation analysis. As shown in There are more than 100 million parameters in the whole neural network of DeepCAPE, and most of them are concentrated on the merge-layer of the joint module. As shown in In order to explore the effect of the number of training samples to the final performance, we repeated the cross-validation experiments on datasets of different numbers of augmentation strides for each cell line. The results show that although the performance is decreasing overall with the increasing augmentation strides C, the peAll the aforementioned observations suggest that DeepCAPE can not only achieve superior performance with limited known enhancers, but also achieve satisfactory performance with longer augmentation strides to effectively save computational time when there are massive enhancers.Experimental approaches are expensive and time-consuming for large-scale identification of enhancers across a variety of human cell lines. For a cell line whose enhancers have not been identified yet, predicting potential enhancers has great significance in guiding biological experiments for novel enhancer identification.To accurately predict enhancers across cell lines, we employed a collective scoring strategy. Given a cell line of interest and a DNA fragment, we used models trained on other cell lines to predict the probability that the fragment is an enhancer, and then averaged over these predictions to obtain a final prediction probability. The basic idea of this strategy is that a DNA sequence may be an enhancer in the new cell line if it plays a role of enhancer in some other cell lines. To better support the idea, we calculated the overlap rates between regions of enhancers of different cell lines. As shown in Table S1, the mean overlap rates of enhancers of most cell lines range from 15.4% to 21.8%, although those of natural killer cell and monocyte are 8.0% and 5.9%, respectively, due to the large number of enhancer samples in these two cell lines. In addition, we calculated the overlap rates between called DNase-seq peaks of different cell lines and found that the mean overlap rates range from 34.5% to 50.0% (Table S2). The results indicate that there are common enhancers and chromatin accessibility features in different cell lines, and thus we can utilize information from other cell lines to predict enhancers in a new cell line. To avoid introducing extra prior information, we directly averaged over the predictions from models of other cell lines to obtain a final prediction probability without other operation such as weighting different cell lines, thus making it easier to generalize the model.We used the datasets of 9 cell lines from FANTOM to demonstrate the ability of DeepCAPE to predict enhancers in a cross-cell line manner. For each cell line, we first excluded the samples that overlap with samples in other cell lines to make sure that there are not common samples with other cell lines, thus making the task more challenging. On average, 35.2% and 37.6% of samples are left in the datasets of 9 cell lines when the ratios of positive to negative samples are 1:10 and 1:20, respectively, and the corresponding ratios become 1:8.3 and 1:18.1 averagely. We next used the models of other 8 cell lines to make predictions for the filtered samples of the cell line of interest, and then averaged over the resulting 8 probabilities to obtain the final prediction probability. We also used other three baseline models to predict enhancers in this cross-cell line manner.As shown in i.e., PWMs) learned from the first convolutional layer of the DNA module. We further identified putative motifs by using the tool TomTom To interpret features extracted by DeepCAPE, we used a motif visualization strategy (see Method) to obtain sequence signatures  in ENCODE and trained a DeepCAPE model using enhancers and DNase-seq data specific to this cell line. We then calculated a probability that indicates whether a DNA fragment of 300 bp surrounding a SNP is an enhancer for each of the 334 SNPs. We finally classified the SNPs into 5 groups according to log10-transformed P values of the SNPs and drew box plots of the predicted probabilities for each group. As shown in Fig. 6A, the predicted probabilities for SNPs with smaller P values are relatively higher than those with larger P values. This observation suggests that predictions given by our method using genomic and epigenomic data are potentially correlated with P values obtained from genetic studies.To demonstrate potential applications of DeepCAPE, we collected 334 single nucleotide polymorphisms (SNPs) that were possibly associated with liver cancer from GRASP P = 7.915E − 6 for adjacent 1 kb, P = 9.032E − 7 for adjacent 3 kb; one-sided paired-sample Wilcoxon signed rank test). These results suggest that our method has the potential ability to discriminant enhancers related to lymphoma from their nearby DNA fragments.We further collected 14 enhancers that are shown to be associated with lymphoma from the literature We have introduced a deep learning framework named DeepCAPE to integrate DNA sequence information and chromatin accessibility data for predicting enhancers. Benefitting from the integration of DNase-seq data, the well-designed feature extraction modules, the skip connection strategy, and the adoption of auto-encoder, DeepCAPE is superior to existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, self-adaptable to different sizes of datasets, capable of making cross-cell line predictions, and interpretable in extracted features. We have further demonstrated the potential ability of DeepCAPE to explain functional implications of genetic variants and discriminate disease-related enhancers. Our method has two main application scenarios. Firstly, one can use our method to establish a landscape of potential enhancers specific to a cell line that still lacks systematic exploration of enhancers, thereby promoting the deciphering of regulatory mechanisms for the cell line. Secondly, one can use our method to explore functional implications of genetic variants or DNA fragments specific to a cell line, thereby bridging genomic and genetic studies toward the understanding of disease development.Certainly, our work can further be improved in several aspects. Firstly, the incorporation of the long short-term memory (LSTM) network, a kind of recurrent neural network architectures, into our framework may further improve the performance, because LSTM may be able to capture very long-range interaction in the sequence. In addition, the adaptation of an embedding representation of DNA sequences instead of the use of the one-hot encoding may also benefit the prediction accuracy https://github.com/ShengquanChen/DeepCAPE.The source code and detailed tutorial of DeepCAPE are freely available at The authors have declared no competing interests.Shengquan Chen: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. Mingxin Gan: Conceptualization, Funding acquisition, Project administration. Hairong Lv: Data curation, Validation, Methodology. Rui Jiang: Conceptualization, Funding acquisition, Project administration, Writing – review & editing.


KRAS) and tumor protein p53 (TP53), three and eight variants were identified, respectively. In conclusion, both inhibitors demonstrated in vitro efficacy on the PDAC cell lines. However, aberrations and expression of inhibitor target genes did not appear to affect the efficacy of the corresponding inhibitors. In addition, specific aberrations in TP53 and KRAS affected the efficacy of both inhibitors.Casein kinase II (CK2) and cyclin-dependent kinases (CDKs) frequently interact within multiple pathways in pancreatic ductal adenocarcinoma (PDAC). Application of CK2- and CDK-inhibitors have been considered as a therapeutic option, but are currently not part of routine chemotherapy regimens. We investigated ten PDAC cell lines exposed to increasing concentrations of silmitasertib and dinaciclib. Cell proliferation, metabolic activity, biomass, and apoptosis/necrosis were evaluated, and bioinformatic clustering was used to classify cell lines into sensitive groups based on their response to inhibitors. Furthermore, whole exome sequencing (WES) and RNA sequencing (RNA-Seq) was conducted to assess recurrent mutations and the expression profile of inhibitor targets and genes frequently mutated in PDAC, respectively. Dinaciclib and silmitasertib demonstrated pronounced and limited cell line specific effects in cell death induction, respectively. WES revealed no genomic variants causing changes in the primary structure of the corresponding inhibitor target proteins. RNA-Seq demonstrated that the expression of all inhibitor target genes was higher in the PDAC cell lines compared to non-neoplastic pancreatic tissue. The observed differences in PDAC cell line sensitivity to silmitasertib or dinaciclib did not depend on target gene expression or the identified gene variants. For the PDAC hotspot genes kirsten rat sarcoma virus ( Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignancies and ranks fourth among all cancer-related deaths in both men and women . Due to Casein kinase II (CK2) is a highly conserved serine/threonine kinase, which is constitutively active and ubiquitously expressed in mammalian cells. CK2 has a wide range of candidate physiological targets and is involved in a series of complex cellular functions . For exaCK2 is not the only protein kinase that plays a critical role in PDAC. Cyclin-dependent protein kinases (CDKs) are critical regulators of cell cycle progression. This dysregulation of the cell cycle is the fundamental process of cancer growth and spread . Within KRAS and TP53 are two of the hotspot genes frequently mutated in PDAC. It has been reported that KRAS and TP53 mutations can be found in approximately 92% and 70% of PDAC patients, respectively [KRAS mutations showed a bad response to first-line gemcitabine-based therapy and represented a poor prognosis [TP53 expression were reported to show a significant improvement in progression-free survival when compared to complete loss. Interestingly, cases showing as many as two TP53 somatic variants are reported to have a better prognosis than when compared to cases exceeding accumulation of more than three somatic variants [KRAS and TP53 on the prognosis and drug efficacy of PDAC, we explored the influence of the somatic variants of these two genes on the response of PDAC cell lines to CK2 and CDK inhibitors.Different genetic aberrations affecting direct drug target genes, downstream pathways, or key oncogenic regulators also have an impact on drug efficacy. ectively ,34. Morerognosis . PDAC pavariants ,37. TherKRAS oncogene and TP53 tumor suppressor genes.Akin to the above-mentioned studies, several other publications have demonstrated the influence of CK2 and CDKs on the pathophysiology of PDAC. However, for these drug target genes, it is still poorly understood if and which somatic variants affect sensitivity to the respective inhibitors ,23. In g50 values for proliferation and biomass showed a range from 2.131 μM to 16.20 μM for proliferation and a matching range from 1.691 μM to 14.32 μM for biomass  initiated significant reductions at 5 μM . The IC5 biomass .50 values of cell proliferation and biomass were applied in the following bioinformatic clustering  for sensitivity classification of cell lines. Ten PDAC cell lines were separated into three groups with low , moderate , and high sensitivity (AsPc-1 and BxPc-3) (IC BxPc-3) .50 values ranged from 0.001253 μM to 0.01111 μM (proliferation) and 0.002146 μM to 0.01390 μM (biomass) , but responses varied between cell lines . At the biomass) .50 values of cell proliferation and biomass were applied in bioinformatics clustering  for sensitivity classification of the cell lines. Ten PDAC cell lines were separated into three groups with low (Panc-1 and SU.86.86), moderate , and high  sensitivity . Significant increases in apoptotic/necrotic cells were observed starting at a concentration of 0.0075 μM . InteresCSNK2A1, CSNK2A2, and CSNK2B; for dinaciclib: CDK1, CDK2, CDK5, and CDK9) was evaluated in all cell lines by RNA-Seq. The expression level was estimated as Log2 (transcripts per kilobase million (TPM) + 1) and compared to the expression data of non-neoplastic pancreatic tissue, which was chosen as a control. All target genes were expressed higher in the PDAC cell lines than in normal pancreatic tissue. The inhibitor target gene expression in PDAC compared with the control are as follows (PDAC Minimum–Maximum vs. control): CSNK2A1 (5.83–7.69 vs. 3.63), CSNK2A2 (5.41–6.47 vs. 4.43), CSNK2B (6.53–7.52 vs. 6.00), CDK1 (5.73–8.51 vs. 0.41), CDK2 (4.37–6.95 vs. 2.83), CDK5 (3.51–5.32 vs. 1.98), and CDK9 (4.57–6.63 vs. 4.50) (The expression of target genes for each inhibitor (for silmitasertib: s. 4.50) .CSNK2A1, CSNK2A2, CSNK2B) and dinaciclib  were selected to analyze transcript variants by WES.The target genes for silmitasertib , KRAS c.35G>T , and KRAS c.183A>C (p.Gln61His), all of them were missense variants. KRAS c.35G>A were observed in AsPc-1 : 100), Colo357 (VAF: 23.8), Panc-1 (VAF: 62.1), and SU.86.86 (VAF: 83.7). KRAS c.35G>T were identified in Capan-1 (VAF: 97.1), PaTu8902 (VAF: 100), PaTu8988S (VAF: 96.9), and PaTu8988T (VAF: 98). KRAS c.183A>C was identified in T3M4 (VAF: 32.6).WES identified ll lines . Three dKRAS in all PDAC cell lines were higher than those compared to non-neoplastic pancreas tissue (4.16–7.09 vs. 2.14)  and SU.86.86 (7.09), respectively. The expressions of all KRAS c.35G>T variants, which were identified in Capan-1, PaTu8988S, PaTu8988T, and PaTu8902 were similar to wild type BxPc-3 , the expression of KRAS c.183A>C in T3M4 and KRAS c.35G>A in AsPc-1, and Panc-1 and SU.86.86 were higher than wild type BxPc-3 (4.79–7.09 vs. 4.53).The expressions of s. 2.14) . Both thKRAS status revealed that PDAC cell lines carrying the KRAS variant appeared to be less sensitive to silmitasertib and the high sensitive group contained only the wild-type and one KRAS mutant cell line, while the rest of the KRAS mutant carrying cell lines were all classified into the moderate or low sensitivity groups  were classified into each of the three sensitivity groups, while wild-type (BxPc-3) was in the moderate sensitivity group. Interestingly, the sensitivity of the KRAS c.183A>C mutant cell line (T3M4) was higher than BxPc-3  and TP53 c.267delC (p.Ser90fs), were identified in AsPc-1 : 96.4) and Colo357 (VAF: 100), respectively. Missense variants, TP53 c.476C>T  and TP53 c.818G>A (p.Arg273His), were identified in Capan-1 (VAF: 100) and Panc-1 (VAF: 98.8), respectively. Double missense mutation including TP53 c.733G>A (p.Gly245Ser) and TP53 c.1079G>T  were identified in SU.86.86 . TP53 c.659A>G (p.Tyr220Cys) was identified in BxPC-3 (VAF: 99) and T3M4 (VAF: 100). TP53 c.844C>T (p.Arg282Trp) was identified in PaTu8902 (VAF: 100), PaTu8988S (VAF: 100), and PaTu8988T (VAF: 100). The expressions of TP53 with frameshift variants were lower than that of the missense variants (1.24–2.13 vs. 4.39–5.42) and control (2.83) (Two different types of variants including frameshift (fs) variant and missense variant of ll lines . Fs varil (2.83) .TP53 status demonstrated that the two cell lines carrying fs variants (Colo357 and AsPc-1) were in the dinaciclib high sensitive group, while cell lines carrying point mutations were distributed in the three sensitivity groups  in all of the tested PDAC cell lines were higher than in non-neoplastic pancreatic tissue. This result suggests that these cell lines could be sensitive to silmitasertib. Indeed, the inhibition of CK2 by silmitasertib significantly affected cell proliferation of all cell lines except Panc-1, and significantly reduced the cell biomass in all PDAC cell lines. However, silmitasertib did not perform well in reducing the metabolic activity of the PDAC cell lines, and the effects of silmitasertib in inducing apoptosis were also very weak, with significant effects only observed in AsPc-1 and T3M4, which indicates that silmitasertib may inhibit the proliferation of PDAC cells by inducing cell-cycle arrest or cell autophagy rather than apoptosis [This study demonstrated that the expression levels of silmitasertib target genes  of PDAC patients, and only 0.2% (3/1228) involved protein structural changes, while the majority involved gene amplification [CDK1, 0.3%, CDK2, 1.1%, CDK5, 2%, CDK9, 1%) of PDAC patients, and only 0.5% (6/1228) involved protein structural changes, while the majority involved gene amplification [CK2 gene aberrations were detected in 2.9% (34/1228: fication . Similarfication . TherefoKRAS in nine of ten PDAC cell lines including KRAS p.Gly12Asp (c.35G>A), KRAS p.Gly12Val (c.35G>T), and KRAS p.Gln61His (c.183A>C). It was reported that patients with KRAS mutations showed a weak response to first-line gemcitabine-based therapy and had a poor prognosis [KRAS c.35G point mutation and wild-type cell lines, suggesting the inhibitory effect of dinaciclib is not affected by the KRAS c.35G point mutation. Interestingly, T3M4 cells, which carry a KRAS c.183A>C variant are more sensitive to dinaciclib than wild-type BxPc-3 cells, suggesting that dinaciclib may improve the efficacy of patients with specific KRAS c.183A>C mutation, but due to the limited number of cell lines, further experiments are still needed to verify the relationship between this KRAS mutation and the efficacy of dinaciclib. However, a comprehensive analysis of silmitasertib efficacy and KRAS mutations suggests that carrying the KRAS variants reduced the PDAC sensitivity to silmitasertib. Since AKT is an important effector kinase of CK2, inhibition of CK2 causes a reduced activation of AKT, whereas mutant KRAS directly activates the PI3K/AKT pathway [KRAS-mutated cell lines to ssilmitasertib. Overall, blocking CDKs with dinaciclib in monotherapy may be beneficial to patients with the specific KRAS c.183A>C mutation, whereas silmitasertib monotherapy in patients with KRAS point mutations may not be a good option.We identified three different amino acid substitution variants of y12Asp c.G>A, KRAS61His c.13A>C. It  pathway . This anTP53 mutation that causes amino acids to change. Our sequencing data revealed that the expression of TP53 with fs mutations were lower than those of TP53 with point mutations. Fs variants resulted in a strong disruption of TP53 function, and low TP53 mRNA expression was associated with a poor prognosis in PDAC patients [TP53 in PDAC cell lines was higher than in the control, and it has been reported that some specific point mutations inactivate TP53  and confer an advantage in tumor growth [TP53 p.Ala159Val, p.Tyr220Cys, and p.Gly360Val variants, which demonstrated similar expression properties. In addition, combined with the results of the PDAC inhibitory assays, cell lines carrying specific TP53 variants  were less sensitive to silmitasertib. These cell lines were all in the low  sensitivity group. Knockdown of CK2 causes the enhanced transactivation of p53, thereby increasing apoptosis [TP53, inhibition of CK2 did not transactivate these proteins. This may be the reason for the reduced efficacy of silmitasertib in cell lines with specific mutations in TP53. AsPc-1 and Colo357, which carry the fs variant, are both in the dinaciclib high sensitivity group, suggesting that dinaciclib may be able to improve the poor prognosis of the TP53 fs mutation. However, the expression level of TP53 cannot fully explain the observed responses of all cell lines to silmitasertib or dinaciclib. These results indicate that TP53 variants are an indicator of an inhibitory response, while the expression level of TP53 is not. Furthermore, the results of the PDAC inhibitory assays indicate that patients with TP53 mutations may benefit from a potential application of dinaciclib and silmitasertib.We identified that all tested PDAC cell lines contained at least one patients ,46. On tr growth ,48. The Our study focused on univariate genetic variants and did not evaluate the potential effect of complex variant landscapes. Thus, our conclusions are limited to direct genetic variants observed in the respective target genes of the evaluated inhibitors. Interactions between different gene aberrations, influence on downstream signaling as well as expression deregulations can also have significant influence. Accordingly, a bioinformatical complex analysis allowing drug target, target downstream signaling as well as bioinformatical modeling is needed. Furthermore, the complex validation of predicted mechanistic targets on cell biological level should be performed in the future to further evaluate factors influencing drug response.Kinase inhibitors, silmitasertib (CK2 inhibitor) and dinaciclib (CDK1/2/5/9 inhibitor) were purchased from Selleck Chemicals . According to the manufacturer’s instructions, silmitasertib and dinaciclib were separately dissolved in dimethyl sulfoxide (DMSO)  as a stock solution at a final concentration of 10 mM. The stock solutions were stored at −80 °C and diluted into corresponding working concentrations before each experiment.2 incubator at 37 °C with a humidified atmosphere.PDAC cell lines AsPc-1, BxPc-3, Capan-1, Colo357, Panc-1, PaTu8902, PaTu8988T, PaTu8988S, SU.86.86, and T3M4 were kindly provided by the University of Greifswald. AsPc-1, BxPc-3, Colo357, Panc-1, SU.86.86, and T3M4 were cultured in RPMI1640 medium  supplemented with 10% heat-inactivated fetal calf serum (FCS) (PAN-Biotech) and 1% penicillin-streptomycin solution  (PAN-Biotech). PaTu8902, PaTu8988T, PaTu8988S were cultured in DMEM/F12 medium (PAN-Biotech) supplemented with 10% heated-inactivated FCS and 1% penicillin-streptomycin solution. Capan-1 was cultured in RPMI1640 medium supplemented with 15% heat-inactivated FCS and 1% penicillin-streptomycin solution. After verifying that all cell lines were not contaminated by mycoplasma, these PDAC cell lines were maintained in a 5% CO4 cells per milliliter in a 6-well plate , 24-well plate , or 96-well plate . For viability assays, after 24 h, the supernatant was discarded and media containing increasing concentrations (range from 1–10 μM for silmitasertib and 0.001–1 μM for dinaciclib) of inhibitors or vehicle  were added to the corresponding PDAC cell lines. For the apoptosis/necrosis analysis, the inhibitor concentrations were selected according to the results of the cell viability assays. The inhibitor concentrations were adjusted according to the response observed in the viability assays for further analysis of the induced apoptotic and necrotic events. The treated cells were incubated for 72 h at 37 °C with 5% CO2. At the indicated time points, cell proliferation, metabolic activity, cell biomass, or apoptosis/necrosis was evaluated in at least three biologically independent replicates.For all assays, the PDAC cell lines were seeded at the density of 3.3 × 10Cell proliferation was evaluated by absolute counting and Trypan blue  staining. After drug exposure in 24-well plates, the cells were harvested and washed by 1× PBS (PAN-Biotech). Following the cells being stained with Trypan blue, the number of viable cells was determined by counting with a hemocytometer. Proliferation was expressed as a percentage of viable cells treated with the inhibitor to the vehicle-treated control (control = 100%).®-Multi Microplate Multimode Reader  and the metabolic activity was calculated as recommended by the manufacturer. Metabolic activity was expressed as a percentage of the inhibitor-treated group to the vehicle-treated controls (control = 100%).Metabolic activity was tested by using the Water Soluble Tetrazolium—1 (WST-1) assay . After exposure to the corresponding inhibitor, the cells were incubated with 15 μL WST-1 for 2 h in 96-well plates. Absorbances at 450 nm and the reference wavelength of 620 nm were measured by Promega GloMax®-Multi Microplate Multimode Reader for background normalization. CV cell biomass estimation result was expressed as a percentage of the inhibitor-treated group to vehicle-treated controls (control = 100%).Biomass quantification was carried out by Crystal Violet (CV) (Sigma-Aldrich Chemie GmbH) staining. After exposure to the corresponding inhibitors in 96-well plates, the cells were washed once with PBS and stained with 50 μL 0.2% CV solution on a shaker at room temperature for 10 min. Following this, the plates were washed twice with PBS. To elute bound CV, 100 μL 1% sodium dodecyl sulfate (SDS)  was added to each well and incubated on a shaker at room temperature for 10 min. Finally, absorbances at 570 nm and reference wavelength at 620 nm were measured by a Promega GloMax50 values were calculated based on cell proliferation, metabolic activity, and biomass after 72 h of inhibitor exposure. GraphPad Prism Version 8.0.2  was used to evaluate IC50. Briefly, after transforming concentrations and normalizing the results of the three vitality assays, nonlinear regression model  was used to evaluate the IC50 values. Calculate the IC50 corresponding to the three vitality assays, and apply these results to the response-based clustering analysis in order to evaluate the sensitivity of cell lines to inhibitors.IC−/PI− cells are considered to be viable cells, YO-PRO-1+/PI− cells are considered to be apoptotic cells, and PI+ cells are considered to be necrotic cells. Flow cytometry measurement was performed on FACSverse , Heidelberg, Germany) and all data were analyzed by BD FlowJo software (BD).Apoptosis and necrosis were evaluated by YO-PRO-1  and propidium iodide (PI) (Sigma-Aldrich Chemie GmbH) double staining by flow cytometry. After exposure to the corresponding inhibitor, supernatants were collected and cells were harvested and washed twice with cold PBS. Following this, cells were resuspended in 200 μL YO-PRO-1  solution. After incubating at room temperature for 20 min in the dark, cells were washed twice in cold PBS and resuspended in 400 μL cold PBS. Then, cells were stained with PI  straightway before measurement. Unstained and single-stained cells were used as controls and measured in every single experiment. YO-PRO-1® Tissue Kit  according to the manufacturers’ instructions. In brief, 5 × 106 cells were harvested from each continuous cultural cell line and washed twice with cold sterile PBS. Cell pellets were lysed, then the lysis that contained genomic DNA were extracted and purified by a silica membrane of the NucleoSpin column. Finally, genomic DNA was eluted by 30 μL of nuclease-free water.Genomic DNA was extracted by the NucleoSpin6 cells were harvested from each continuous cultural cell line and washed twice with cold sterile PBS. Cell pellets were resuspended in 700 μL QIAzol Lysis Reagent (QIAGEN GmbH), then the aqueous phase that contained the total RNA of the lysed cells were extracted and purified by a silica membrane of RNeasy Mini spin columns. Finally, total RNA was eluted by 30 μL of nuclease-free water.Total RNAs were extracted by miRNeasy Mini Kit  according to the manufacturers’ instructions. In brief, 5 × 10After extraction, nucleic acid concentrations as well as OD 260/280 and OD 260/230 ratios were measured with a NanoDrop 1000 Spectrophotometer .Barcoded sequencing libraries were generated after enrichment with the SureSelect Human All Exon Kit , pooled and sequenced on a HiSeq4000  instrument using 150 paired-end protocol to yield at least 20× coverage for >98% of the target region and an overall average depth of coverage above 100×. An in-house bioinformatics pipeline including read alignment to human genome reference hg 19, variant calling , and variant annotation with publicly available data based was used.After WES, the sequencing data from ten PDAC cell lines were obtained and filtered in order to select variants with the expected highest impact on gene function. Briefly, variants were filtered based on quality , VAF, depth of coverage (DP), and variant type. In order to exclude false positive variants, only variants with qual > 100, VAF > 20, and DP > 9 were included in our analysis. Germline mutations were excluded through a comparison with the COSMIC and dbSNP databases. Then, variant types were excluded that were not able to cause amino acid substitution, RNA structure change, or base insertions/deletions (indels). These variant types include synonymous variants, intronic variants, upstream or downstream variants, 3 prime or 5 prime UTR variants. After this filtering procedure, missense variants, splice region variants, inframe indels, frameshift variants, gene fusion, start/stop gain, or lost were kept for further analysis .Barcoded sequencing libraries were prepared with the TruSeq Stranded mRNA Kit (Illumina), pooled, and sequenced on a NextSeq 500 System (Illumina) using the 75 bp paired-end protocol. At least 30 million reads were obtained for each sample. The reads were aligned to reference genome GRCh37/Release 38 with STAR V.2.7.6a using the two-pass mode .The expression data of non-neoplastic pancreatic tissue from The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas Program (TCGA) were chosen as the control. Non-inhibitor target genes were analyzed to exclude the tumor-induced upregulation of all genes.50 values of cell proliferation and biomass. Then, these IC50 results were applied to the Sci-kit learn package using Python programming language to predict optimal clusters. The Silhouette score was used to detect the clustering density and the separation between clusters [The cell sensitivity grouping was performed by the k-means++ clustering method based on an unsupervised machine learning algorithm. Briefly, after performing viability assays on all ten PDAC cell lines, we obtained the ICclusters . Ten celp < 0.033, ** p < 0.002, *** p < 0.001 versus the control group.Data were replicated with at least three biologically independent experiments. Results of proliferation, metabolic activity, biomass quantification, and apoptosis/necrosis analysis were expressed as mean ±  standard deviation (SD). Statistical significance was determined by one-way ANOVA (after proving the data within each group conformed to the Gaussian distribution) or Kruskal–Wallis test (the data within each group conformed to non-Gaussian distribution) and displayed as * KRAS variants may reduce the sensitivity of PDAC cell lines to silmitasertib. Specific TP53 variants including c.267delC, c.818G>A, and c.844C>T, reduced the sensitivity of silmitasertib to the PDAC cell lines. Interestingly, cell lines carrying TP53 frameshift variants are highly sensitive to dinaciclib compared to cell lines carrying TP53 point mutations. Thus, both inhibitors displayed excellent in vitro efficacy on PDAC cell lines, and further experiments are still needed to verify the in vivo efficacy and the effects of the target genes and hotspot genes on the efficacy of the inhibitors.Our present study revealed distinct sensitivities of the PDAC cell lines when treated with dinaciclib or silmitasertib. Neither the expression level of the inhibitor target genes nor gene variants could affect the differences in the observed sensitivity to these drugs. For PDAC hotspot genes, the


Anopheles cell lines are used in a variety of ways to better understand the major vectors of malaria in sub-Saharan Africa. Despite this, commonly used cell lines are not well characterized, and no tools are available for cell line identification and authentication.Utilizing whole genome sequencing, genomes of 4a-3A and 4a-3B ‘hemocyte-like’ cell lines were characterized for insertions and deletions (indels) and SNP variation. Genomic locations of distinguishing sequence variation and species origin of the cell lines were also examined. Unique indels were targeted to develop a PCR-based cell line authentication assay. Mitotic chromosomes were examined to survey the cytogenetic landscape for chromosome structure and copy number in the cell lines.Anopheles coluzzii ancestry. Cytogenetic analysis indicates that the two cell lines are essentially diploid, with some relatively minor chromosome structural rearrangements. Whole-genome sequence was generated, and analysis indicated that SNPs and indels which differentiate the cell lines are clustered on the 2R chromosome in the regions of the 2Rb, 2Rc and 2Ru chromosomal inversions. A PCR-based authentication assay was developed to fingerprint three indels unique to each cell line. The assay distinguishes between 4a-3A and 4a-3B cells and also uniquely identifies two additional An. coluzzii cell lines tested, Ag55 and Sua4.0. The assay has the specificity to distinguish four cell lines and also has the sensitivity to detect cellular contamination within a sample of cultured cells.The 4a-3A and 4a-3B cell lines are female in origin and primarily of Anopheles cell lines was used to develop a simple diagnostic assay that can distinguish these cell lines within and across research laboratories. A cytogenetic survey indicated that the 4a-3A and Sua4.0 cell lines carry essentially normal diploid chromosomes, which makes them amenable to CRISPR/Cas9 genome editing. The presented simple authentication assay, coupled with screening for mycoplasma, will allow validation of the integrity of experimental resources and will promote greater experimental reproducibility of results.Genomic characterization of the 4a-3A and 4a-3B The online version contains supplementary material available at 10.1186/s13071-022-05590-3. Anopheles mosquitoes are major vectors of malaria parasites across sub-Saharan Africa. Vector control tools such as insecticide-treated bed nets and indoor residual spraying have long been the cornerstone of vector control strategies aimed at curbing malaria, arboviral and other vector-borne disease transmission. However, given increased insecticide resistance within mosquito populations, other control strategies are needed  and the variants fixed similarly in both cell lines, but having a genotype distinct relative to the AgamP4 genome assembly (Same GT), were significantly different across the genome from the expectation if SNPs and indels occurred randomly across the chromosome arms  and 4a-3B as 2Rb , consistent with both the molecular karyotyping results and the whole genome sequence analysis  predicted 4a-3A as 2R + c  and 4a-3B as 2Rc , again consistent with the whole genome analysis , with 3 An. gambiae SNPs and 17 An. coluzzii SNPs  were diploid. Visual examination of the cytological preparations indicated that the homologous chromosomes in the majority of 4a-3A cells have similar lengths, suggesting that there is little autosomal rearrangement  were diploid. In Sua4.0 cells, all homologous chromosomes appear to have different lengths consistent with the presence of some rearrangements. For both cell lines, 5% of observed cells were tetraploid, which can be an artifactual result of cell treatment with colcemid [Cells from the 4a-3A and Sua4.0 cell lines were cytologically examined for ploidy levels. 4a-3A is a female cell line, as shown by the presence of a pair of X chromosomes, and of 331 4a-3A cells examined, 95%  and Sua4.0. Thus, with three PCR reactions using identical cycling conditions, an investigator can distinguish among four Anopheles cell lines. This indel-based PCR assay allows for clear differentiation on an agarose gel, is easy to execute and requires no specialty laboratory equipment or reagents. This assay serves not only to identify cell lines but also as a tool to detect cell line contamination.Whole genome sequencing of two An. gambiae. The current genomic assessment clearly demonstrates an origin more closely rooted in An. coluzzii, with fewer than 20% of species diagnostic SNPs consistent with An. gambiae origin.The initial report of the 4a-3A and 4a-3B cell lines (5), as well as many subsequent publications utilizing them, refers to them as originating from Anopheles colonies [Similar identification and authentication tools should be developed and utilized for commonly used anopheline mosquito colonies, as contamination and mislabeling of colonies can lead to spurious results. The Malaria Research and Reference Reagent Resource Center used an authentication matrix to distinguish among genetically similar colonies . Frequencolonies . Experimcolonies .One notable difference between the 4a-3A and 4a-3B cell lines is their expression of six prophenoloxidase genes, PPO1-6, which 4a-3A cells do not express, while 4a-3B cells express all six PPO genes constitutively . CurrentCytogenetic analysis demonstrates that both the 4a-3A and Sua4.0 cell lines are diploid. Knowledge of ploidy is a relevant and important consideration for experiments involving genetic modification via CRISPR or similar technologies. While greater ploidy does not preclude genetic modification, using diploid cell lines simplifies the experimental problem.Anopheles cell lines 4a-3A and 4a-3B reveals that both are diploid cell lines of female origin with the bulk of the genetic variation between them resulting from a fixation of the 2Rb, c and u inversions within the 4a-3B cell line. Contrary to previous reports of an An. gambiae species origin, these cell lines are primarily of An. coluzzii ancestry. An authentication assay based on PCR amplification of three variable indels is capable of distinguishing not only 4a-3A and 4a-3B, but also Ag55 and Sua4.0 cell lines.Genome characterization of Additional file 1: Table S1. Primer sequences for indels selected for cell line diagnostic. Fasta file of indel region sequences. Sequence of the three diagnostic indel regions, 2R_25670547, 3R_11788474 and 3R_11809836, in each of the four tested cell lines. 2La inversion. Figure S1. Molecular karyotyping of the 2La inversion in four cell lines. Sua4.0 cells are fixed for the 2La inversion, and 4a-3A, 4a-3B and Ag55 cells are fixed for 2L + a. Lane 1: 100-bp ladder, lane 2: 4a-3A, lane 3: 4a-3B, lane 4: Ag55, lane 5: Sua.40 and lane 6: GeneRuler 1-kb Plus DNA Ladder. Expected band sizes are 492 bp for 2La and 207 bp for 2L + a (46). Figure S2. Passage number does not affect the molecular assay to differentiate cell lines. Molecular fingerprints for three PCRs amplifying indel regions in gDNA isolated from 4a-3A cells at passage 4  and passage 29  and gDNA isolated from Sua4.0 cells at passage 5  and passage 57 . Indel 2R.25670547 (lanes 2–5), indel 3R.11788474 (7–10), indel 3R.11809836 (lanes 12–15) and 1-kb Plus DNA Ladder . Band sizes are as expected; for indel 2R.25670547 282 bp in 4a-3A and 361 in Sua4.0, for indel 3R.11788474 735 bp for 4a-3A cells and 736 bp for Sua4.0 cells with the single nucleotide difference not resolvable on an agarose gel or important for cell line authentication and for indel 3R.11809836 263 bp in 4a-3A cells and 262 bp in Sua4.0 cells. As 4a-3A cells originate from the 4a r/r strain of mosquitoes and Sua4.0 cells from the Suakoko 2La strain of mosquitoes, minor differences in sequence are expected [