The AKT kinases have emerged as promising therapeutic targets in oncology and both allosteric and ATP-competitive AKT inhibitors have entered clinical investigation. However, long-term efficacy of such inhibitors will likely be challenged by the development of resistance. We have established prostate cancer models of acquired resistance to the allosteric inhibitor MK-2206 or the ATP-competitive inhibitor ipatasertib following prolonged exposure. While alterations in AKT are associated with acquired resistance to MK-2206, ipatasertib resistance is driven by rewired compensatory activity of parallel signaling pathways. Importantly, MK-2206 resistance can be overcome by treatment with ipatasertib, while ipatasertib resistance can be reversed by co-treatment with inhibitors of pathways including PIM signaling. These findings demonstrate that distinct resistance mechanisms arise to the two classes of AKT inhibitors and that combination approaches may reverse resistance to ATP-competitive inhibition. How resistance to different classes of AKT inhibitors can emerge is unclear. Here, the authors show that resistance to allosteric inhibitors is mainly due to mutation of AKT1 while the ATP competitive resistance is driven by activation of PIM kinases in prostate cancer models. The AKT/PKB serine/threonine kinase functions as a central node in this pathway and is being investigated as a therapeutic target in oncology2. Three isoforms of AKT  exist in humans that each contain a Plekstrin homology (PH) domain, a kinase domain, and a C-terminal hydrophobic regulatory region3. Activation of these isoforms is mediated by recruitment to PtdIns-3,4-P2  and PtdIns-3,4,5-P3 (PIP3) at the plasma membrane and subsequent phosphorylation of T308 and S473 by 3-phosphoinositide-dependent protein kinase 1 (PDPK1) and mTOR complex 2 (mTORC2), respectively. Aberrant activation of AKT in cancer may occur via several mechanisms including mutational activation of the catalytic subunit of PI3K, which generates PIP3 and, indirectly, PI3,4P2; loss of the PIP3 phosphatase PTEN; and, albeit less frequently, activating mutations in AKT4. Upon activation, AKT mediates various cellular processes including cell survival, metabolism and proliferation by regulating the activity of downstream proteins including proline-rich AKT substrate of 40 kDa (PRAS40), glycogen synthase kinase 3 (GSK-3), Forkhead box class O (FoxO) transcription factors, tuberous sclerosis complex 2 (TSC2), Bcl-2 associated death promoter (BAD), mTOR complex 1 (mTORC1), eukaryotic translation inhibition factor 4E-binding protein 1 (4EBP1), and the S6 ribosomal protein kinase4.Enhanced activity of the phosphoinositide 3-kinase (PI3K)/AKT/mechanistic target of rapamycin (mTOR) signaling pathway is among the most frequently observed changes in cancer and is associated with tumor invasiveness, survival, and proliferation2. Importantly, these inhibitors differentially exploit the on-off activity cycle of AKT. In its inactive state, AKT adopts a closed conformation in which the PH domain interacts with the kinase domain, also referred to as the PH-in state6. Upon recruitment to the membrane and phosphorylation at T308 and S473, the interaction between the PH and kinase domains is released, resulting in an open PH-out conformation conducive to ATP-binding5. Allosteric inhibitors preferentially bind to the inactive PH-in conformation at a cavity formed between the PH and kinase domains, preventing phosphorylation and activation of AKT6. In contrast, ATP-competitive inhibitors selectively target the PH-out conformation, protecting AKT from dephosphorylation at T308 and S473 while simultaneously blocking ATP binding and kinase activity7. As a result, decreased AKT phosphorylation at both T308 and S473 is typically observed in allosteric inhibitor-treated cells while increased or sustained pAKT at both sites is characteristic of the ATP-competitive inhibitors.Two main classes of AKT inhibitors (AKTis) have entered clinical investigation in oncology: allosteric inhibitors such as MK-2206 and adenosine 5′-triphosphate (ATP)-competitive inhibitors such as ipatasertib/GDC-00688, the therapeutic potential of AKTis is likely to be the greatest in indications associated with PI3K/AKT pathway activating alterations. One such indication is prostate cancer. Activation of the PI3K/AKT pathway is thought to comprise roughly 50% of metastatic castration-resistant prostate cancer (mCRPC), frequently via PTEN loss11. In fact, a randomized phase II study evaluating combined inhibition of AKT via ipatasertib and androgen signaling via abiraterone in patients with mCRPC showed superior antitumor activity of the combination compared to abiraterone alone, especially in patients with PTEN-loss tumors12. Additionally, in metastatic triple-negative breast cancer (mTNBC), another indication associated with frequent PI3K/AKT pathway activating alterations, the combination of ipatasertib and paclitaxel also improved progression-free survival compared to paclitaxel alone in a randomized phase II trial, with a more pronounced effect observed in patients with PIK3CA/AKT1/PTEN-altered tumors13. Phase III clinical trials are currently underway to further evaluate ipatasertib as a therapeutic agent in these indications.Given that intrinsic sensitivity to AKTis such as ipatasertib correlates with AKT pathway activation14. Although some mechanisms of intrinsic resistance to PI3K/AKT/mTOR pathway inhibitors have been described, including SGK1 signaling in breast cancer15, Wnt-β-catenin signaling in colon cancer16, androgen receptor signaling in prostate cancer17 and RAS/RAF pathway signaling across multiple cancers8, few studies have explored mechanisms of acquired resistance to AKTis following prolonged treatment. Further, it remains unknown whether overlapping or distinct mechanisms of resistance will arise to long-term treatment with allosteric vs. ATP-competitive inhibitors. Here, we aim to identify mechanisms of acquired resistance to both allosteric and ATP-competitive AKTis using an unbiased approach. We performed systematic analysis of cell lines with acquired AKTi-resistance (AKTi-R) using methods including RNA sequencing (RNA-seq) and whole exome sequencing (exome-seq) and explored potential functional dependencies and combination strategies using a chemical genetics screen. Our findings indicate that distinct mechanisms do arise to the two different classes of AKTis and that combination approaches may be taken to reverse this resistance.While treatment of tumors with targeted therapies can initially result in impressive clinical outcomes, resistance is likely to emerge over extended treatment times50s  or W80C AKT1 , pPRAS40 (T246) and pS6 (S235/236) levels comparable to that observed following overexpression of WT AKT1   Fig. . In cont50) Fig. . We alsoAKT1S1)/PRAS40 were detected in multiple G-R clones  Given that a high number of alterations in both gene expression and exome sequence were detected in G-R cells compared to parental LNCaP cells  kinases  models, respectivelylls Fig. . Given tlls Fig. . Consist36. Interestingly, while PIM3 transcript levels are not increased in G-R cells compared with parental cells by RNA-seq analysis , each potently targeted by the pan-PIM kinase inhibitors included in this study, LNCaP cells primarily express PIM3 protein , suggesting that the low level PIM3 expression in the parental cells can antagonize the effect of AKT inhibition to some extent, the impact of PIM3 knockdown on ipatasertib sensitivity was more pronounced in G-R cells  -inducible overexpression experiments. While siRNA-mediated depletion of PIM3 resulted in some increase in sensitivity of parental cells to ipatasertib (~2.9-fold decrease in ICed) Fig. . Therefoed) Fig. . Furthered) Fig. . AltogetAmong the prostate cancer cell lines characterized in Supplementary Fig. 39), or a combination of ipatasertib and GDC-0339. Tumor growth inhibition was evident following treatment with ipatasertib in Par X1.6 but not G-R3 X1.2 tumor-bearing mice, demonstrating that G-R3 X1.2 retained AKTi resistance in vivo  mediating resistance appear to be context-dependent. For example, in PC3-LN4 prostate cancer cells, intrinsic resistance to both allosteric and ATP-competitive AKTis has been demonstrated to involve AKTi-induced PIM1 upregulation followed by a PIM1-dependent increase in receptor tyrosine kinase expression via cap-independent translation51. In various breast cancer cell lines, intrinsic resistance to the PI3Kα or AKT inhibitors has been demonstrated to involve PIM1, with PIM3 likely playing a less prominent role in this setting52. PIM2 expression has been linked to the resistance of breast cancer and MM cell lines to PI3K inhibitors GDC-0941 or BKM12053. Interestingly, PIM3 upregulation has been reported as a feedback mechanism in response to mTORC1 inhibition by rapamycin through miR-33 mediated suppression encoded by the SREBP loci54. In the current study, we did not observe increased PIM3 transcripts in the LNCaP parental or G-R cells in the presence of ipatasertib , AKT1 , AKT2 , AKT3 , pAKT(T308) , pAKT(S473) , pPRAS40(T246) , PRAS40 , pS6(S235/236) , S6 , p4EBP1 (T37/46) , p4EBP1 (S65) (#9456 1:1000), 4EBP1 , PARP (#9532 1:1000), Cleaved PARP , PTEN , PIM2 , PIM3 , pGSK-3β (S9) , GSK-3β , pBAD (S112) , and BAD  were obtained from Cell Signaling Technology. The PIM1 antibody was obtained from Abnova . An additional antibody to total PRAS40 was obtained from Invitrogen/ThermoFisher . Protein loading was assessed using antibodies to β-actin , β-Tubulin  or glyceraldehyde-3-phosphate dehydrogenase (GAPDH) .Further information and requests for reagents may be directed to, and will be fulfilled by, the corresponding author, Kui Lin (lin.kui@gene.com).56. LNCaP cells harbor a frameshift mutation (K6fs*4) (COSMIC # COSM4929) and loss of heterozygosity (LOH) in PTEN58. The presence of both alterations was confirmed in the LNCaP line in-house via exome sequencing and SNP array. AKTi-resistant (AKTi-R) cell lines were established by treating cells with gradually escalating doses of ipatasertib or MK-2206 until reaching a maximum dose of 5 μM of AKTi. ipatasertib-resistant (G-R) and MK-2206-resistant (M-R) cell pools were then subjected to single cell sorting using FACSAria instrumentation and software (BD Biosciences) and surviving clones were expanded in the presence of AKTi at the maximum doses indicated above. MK-2206 or ipatasertib-resistant cell pools are denoted as M-Rpool or G-Rpool, respectively. Individual AKTi-R clones were assigned numbers, which are indicated following the M-R or G-R prefix. LNCaP G-RB cell lines were established following long-term exposure of LNCaP cells to 5 μM ipatasertib, single cell sorting, and expansion of surviving clones in the presence of the AKTi. The Par X1.6 and G-R3 X1.2 selected lines used for in vivo studies were derived from LNCaP and LNCaP G-R clone 3 (G-R3) tumors that displayed growth in untreated male SCID.bg C.B-17 mice (Charles River Labs). The R0068 X1.2 line was established from mice bearing LNCaP tumors that had been treated with 50 mg/kg ipatasertib over the course of 106 days. Whole tumors from individual mice were excised, minced in complete media, and plated in tissue culture flasks. Cell lines were established following one to two passages. After establishment in culture, G-R3X1.2 and R0068 X1.2 cells were maintained in the presence of 5 μM ipatasertib. All cell lines were maintained at 37 °C/5% CO2 in Roswell Park Memorial Institute medium (RPMI) 1640 supplemented with 10% fetal bovine serum (FBS) (Sigma), 2 mM L-Glutamine, and 0.01 M HEPES, pH 7.2. Growth medium for AKTi-R cell lines was additionally supplemented with AKTi at the indicated concentration for cell line maintenance.Cell lines were originally obtained from the American Type Culture Collection (ATCC) and genotyped by Genentech’s cell banking facility. LNCaP is an approximately tetraploid epithelial line derived from a prostate adenocarcinoma metastasis6 cells into male NOD scid gamma (NSG) mice . Testosterone pellets  were implanted into the dorsal shoulder 5 days prior to cell inoculation. Animals were distributed into treatment groups (n = 9/group) when the tumors reached a mean volume of approximately 230 to 350 mm3. GDC-0068 and GDC-0339 were formulated in 0.5% methylcellulose/0.2% Tween-80 (MCT) and were administered once daily (QD) via oral formulation  at 25 and 100 mg/kg, respectively for 21 days. Tumor volumes were determined using digital calipers  using the formula (L x W x W)/2. Tumor volumes and body weights were recorded twice weekly over the course of the study. Mice with tumor volumes >2000 mm3 or recorded body weight loss of >20% from their start of treatment were euthanized per Institutional Animal Care and Use Committee guidelines.All in vivo efficacy studies were approved by Genentech’s Institutional Animal Care and Use Committee and adhere to the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Tumor xenografts derived from the LNCaP Par and LNCaP G-R3 X1.2 cell lines were established by subcutaneous injection of 10 × 1059. In brief, as tumors generally exhibit exponential growth, tumor volumes were subjected to natural log transformation before analysis. A generalized additive mixed model (GAMM) was then applied to describe the change of transformed tumor volumes over time using regression splines with auto-generated spline bases as this approach addresses both repeated measurements from the same study subjects and moderate dropouts before study end. Estimates of group-level efficacy were obtained by calculating a growth contrast, the difference in AUC-based growth rates  between the treatment and reference group fits. To calculate AUC-based growth rates, group AUC values are corrected for starting tumor burden and then subjected to slope equivalence “normalization”. Slope equivalence “normalization” of AUC results in the actual slope of a fit on the natural log (LN) scale in cases of log-linear growth. In the cases of non-log-linear growth, such “normalization” results in the constant log-linear growth rate that would have been needed to yield the baseline-corrected AUC that was actually observed for a fit on the natural log scale. Mathematically, this “normalization” is attained by dividing each estimated baseline-corrected AUC value by half of the square of the common study period resulting in units of natural log units per day. The more negative the Growth Contrast value, the greater the anti-tumor effect. The 95% confidence intervals are based on the fitted model and variability measures of the data.Analysis and comparison of tumor growth was performed as detailed previously using a package of customized functions in R v3.6.2  which integrate software from open source packages including lme4, mgcv, gamm4, multcomp, settings, plyr, and several packages from the tidyverse such as magrittr, dplyr, tidyr, and ggplot2A generalized additive mixed model (GAMM) was also employed to describe the change in raw body weights  over time with regression splines. After data fitting, raw body weight data at each time point from all individual animals and all group fits were normalized to the starting weight and reported as a percentage to yield % body weight change.8. Briefly, cells were plated in black/clear bottom 384 well plates  and incubated at 37 °C under 5% CO2. The following day, cells were treated with a 9-point dose titration of indicated inhibitors or with DMSO control. All conditions were tested in quadruplicate within each experiment. Treated cells were then incubated for 4 days and viability was assessed using the CellTiter-Glo® (Promega) luminescent assay according to the manufacturer’s instructions. Total luminescence was measured on a Wallac Multilabel Reader (PerkinElmer) and was considered to represent cellular viability. Dose response curves  generated with Prism (GraphPad) depict mean % viability (% DMSO control), with error bars representing standard error of the mean (SEM), from quadruplicate samples (y-axis) versus concentration of inhibitor (x-axis) from a single representative experiment. The inhibitor concentration resulting in the half maximum inhibitor effect (IC50) was calculated from % viability values from quadruplicate wells using a 4-parameter curve analysis . Scatter plots depict absolute IC50 values from independent biological repeats, with bars denoting mean absolute IC50 values and standard error of the mean (SEM). Mean IC50 values are denoted below x-axis and sample size (n) is indicated for each cell line/condition above scatter plot data. Unless otherwise stated, at least 3 independent experiments were performed to assess reproducibility. Where applicable, statistical significance is indicated above scatter plot data used to make comparisons.Cellular viability was assessed 4 days after addition of inhibitors using the CellTiter-Glo® (Promega) luminescent assay as described previouslyCells were washed with cold 1X phosphate-buffered saline (PBS) and lysed in Cell Extraction Buffer (CEB) (Biosource/Thermo Fisher Scientific) supplemented with a protease inhibitor cocktail (Sigma) and phosphatase inhibitor cocktail (Roche). Protein concentrations were determined using the Lowry-based RC DC protein assay (Bio-Rad) and normalized for equal protein loading. Lysates were loaded onto Tris–glycine gels (Invitrogen) and proteins were separated by electrophoresis. Proteins were then transferred onto nitrocellulose membranes using the iBlot® dry blotting system (Invitrogen), and membranes were blocked with blocking buffer for fluorescent Western blotting (Rockland or LI-COR). Primary antibodies (see below) were detected using IR Dye 800-conjugated (Rockland or LI-COR) or Alexa Fluor 680 (Invitrogen) or IR Dye 680 (LI-COR) species-selective secondary antibodies. Detection and quantification were conducted using an Odyssey infrared scanner (LI-COR) using the manufacturer’s software. Protein loading was assessed using antibodies to β-actin, β-Tubulin, or GAPDH. Raw immunoblot images for cropped blots are provided in the Supplementary Information.Par or AKTi-R cells were plated in duplicate in complete RPMI medium and, the following day, treated with DMSO, 5 μM ipatasertib, or MK-2206 for 14 h. Cells were then subjected to total RNA extraction using the RNeasy kit (Qiagen) and RNA concentrations were read using a NanoDrop 8000 spectrophotometer (Thermo Scientific). Following confirmation of RNA integrity with the 2200 TapeStation system (Agilent Technologies), RNA-seq libraries were prepared using the TruSeq RNA Sample Preparation Kit v2 (Illumina) from 1 μg of total RNA. Library size was determined using 2200 TapeStation and High Sensitivity D1000 screen tape (Agilent Technologies) and concentration was assessed by qPCR-based methodology . The libraries were multiplexed and sequenced on Illumina HiSeq2500 (Illumina) to generate 50 million paired-end 75 base pair reads. RNA-seq reads containing 30% or more bases with a Phred quality score of 23 or lower were excluded. The remaining high quality reads were then mapped to NCBI GRCh37 (hg19) using GSNAP and default settings. Multimapping reads were discarded. Gene expression levels were summarized into count and RPKM . Differential expression analysis was performed using limma. Genes with adjusted p-value < 0.05 and absolute value of log2FC > = 1 were considered to be differentially expressed. For the hierarchical clustering and heatmap of RNA-seq transcriptome analysis, the RPKM values for the top 100 most variably expressed genes were z-scored and clustered using Euclidean distance.Par or AKTi-R cells were plated in complete RPMI medium and the following day, DNA extraction was performed using the DNeasy kit (Qiagen). Prior to processing by whole exome sequencing, the concentration and integrity of DNA samples was determined using NanoDrop 8000 (Thermo Fisher Scientific) and 2200 TapeStation (Agilent Technologies), respectively. Exome capture was performed using 0.5 μg of genomic DNA and SureSelectXT Human All Exon v5 kit (50 megabases [Mb]) according to manufacturer’s protocol . Fragment size distribution of post-capture amplified libraries was determined with 2200 TapeStation using high sensitivity D1000 screen tape . Concentration of the libraries was measured by Qubit (Thermo Fisher Scientific). Exome capture libraries were sequenced on HiSeq 2500  to generate 75 million paired-end 75 base pair reads. High quality exome-seq reads were mapped to NCBI GRCh38 using GSNAP. Somatic SNVs and INDELs were called by comparing the treatment resistant clones against the parental clones using LoFreq with its default setting. Highly-confident variants were annotated using Ensembl Variant Effect Predictor and filtered with dbSNP 138, ExAC 0.3.1 and RepeatMasker 4.0.5. The functional consequences of somatic variants were annotated using SIFT, PolyPhen and Condel.60.DNA from parental or AKTi-R cells was extracted and assessed for quality and quantity as described above. Illumina HumanOnmi2.5-8 arrays were then used to assay genotype, DNA copy number, and loss of heterozygosity as described previouslyThe Lipofectamine RNAi Max (Life Technologies) transfection reagent was used to transfect cells with siRNA oligonucleotides (see table below) according to manufacturer’s instructions. Briefly, siRNA oligonucleotides and Lipofectamine RNAi Max were each mixed with Opti-MEM® (Invitrogen) in separate microcentrifuge tubes or wells in multiwell plates. These mixtures were combined and incubated for 15 min prior to mixing with cells in suspension. Cells were transfected with a final concentration of 25 nM siRNA duplexes. ON-TARGETplus SMART pool and individual siRNA oligonucleotides are reported to be highly specific, as verified by microarray analysis, as a result of unique dual-strand modification patterns used to synthesize the reagents.LNCaP cells were transfected with a pCMV6-AC-GFP vector containing the human AKT3 sequence (NM_005465) (Origene) or with EV using Fugene HD FuGENE® HD (Promega Corporation). 48 h later, cells were subjected to selection with 0.4 mg/l G418/Geneticin (Invitrogen/Gibco) and surviving cells were expanded. Cells were then sorted FACSAria instrumentation and associated software (BD Biosciences) for positive GFP expression and were maintained in the presence of 0.4 mg/l G418/Geneticin.The piggyBac transposon-based system (System Biosciences) was used to introduce a cumate-inducible version of AKT1 WT or W80C to LNCaP Par or M-R7 cells. Using NheI and BstBI, the following sequences were cloned into the B-Cuo-MCS-IRES-GFP-EF1-CymR-Puro Inducible cDNA Cloning and Expression Vector (System Biosciences #PBQM812A-1):AKT1 WTGGCGCCACCATGAGCGACGTGGCTATTGTGAAGGAGGGTTGGCTGCACAAACGAGGGGAGTACATCAAGACCTGGCGGCCACGCTACTTCCTCCTCAAGAATGATGGCACCTTCATTGGCTACAAGGAGCGGCCGCAGGATGTGGACCAACGTGAGGCTCCCCTCAACAACTTCTCTGTGGCGCAGTGCCAGCTGATGAAGACGGAGCGGCCCCGGCCCAACACCTTCATCATCCGCTGCCTGCAGTGGACCACTGTCATCGAACGCACCTTCCATGTGGAGACTCCTGAGGAGCGGGAGGAGTGGACAACCGCCATCCAGACTGTGGCTGACGGCCTCAAGAAGCAGGAGGAGGAGGAGATGGACTTCCGGTCGGGCTCACCCAGTGACAACTCAGGGGCTGAAGAGATGGAGGTGTCCCTGGCCAAGCCCAAGCACCGCGTGACCATGAACGAGTTTGAGTACCTGAAGCTGCTGGGCAAGGGCACTTTCGGCAAGGTGATCCTGGTGAAGGAGAAGGCCACAGGCCGCTACTACGCCATGAAGATCCTCAAGAAGGAAGTCATCGTGGCCAAGGACGAGGTGGCCCACACACTCACCGAGAACCGCGTCCTGCAGAACTCCAGGCACCCCTTCCTCACAGCCCTGAAGTACTCTTTCCAGACCCACGACCGCCTCTGCTTTGTCATGGAGTACGCCAACGGGGGCGAGCTGTTCTTCCACCTGTCCCGGGAGCGTGTGTTCTCCGAGGACCGGGCCCGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTACCTGCACTCGGAGAAGAACGTGGTGTACCGGGACCTCAAGCTGGAGAACCTCATGCTGGACAAGGACGGGCACATTAAGATCACAGACTTCGGGCTGTGCAAGGAGGGGATCAAGGACGGTGCCACCATGAAGACCTTTTGCGGCACACCTGAGTACCTGGCCCCCGAGGTGCTGGAGGACAATGACTACGGCCGTGCAGTGGACTGGTGGGGGCTGGGCGTGGTCATGTACGAGATGATGTGCGGTCGCCTGCCCTTCTACAACCAGGACCATGAGAAGCTTTTTGAGCTCATCCTCATGGAGGAGATCCGCTTCCCGCGCACGCTTGGTCCCGAGGCCAAGTCCTTGCTTTCAGGGCTGCTCAAGAAGGACCCCAAGCAGAGGCTTGGCGGGGGCTCCGAGGACGCCAAGGAGATCATGCAGCATCGCTTCTTTGCCGGTATCGTGTGGCAGCACGTGTACGAGAAGAAGCTCAGCCCACCCTTCAAGCCCCAGGTCACGTCGGAGACTGACACCAGGTATTTTGATGAGGAGTTCACGGCCCAGATGATCACCATCACACCGCCTGACCAAGATGACAGCATGGAGTGTGTGGACAGCGAGCGCAGGCCCCACTTCCCCCAGTTCTCCTACTCGGCCAGCGGCACGGCCTGAAKT1 W80CGGCGCCACCATGAGCGACGTGGCTATTGTGAAGGAGGGTTGGCTGCACAAACGAGGGGAGTACATCAAGACCTGGCGGCCACGCTACTTCCTCCTCAAGAATGATGGCACCTTCATTGGCTACAAGGAGCGGCCGCAGGATGTGGACCAACGTGAGGCTCCCCTCAACAACTTCTCTGTGGCGCAGTGCCAGCTGATGAAGACGGAGCGGCCCCGGCCCAACACCTTCATCATCCGCTGCCTGCAGTGTACCACTGTCATCGAACGCACCTTCCATGTGGAGACTCCTGAGGAGCGGGAGGAGTGGACAACCGCCATCCAGACTGTGGCTGACGGCCTCAAGAAGCAGGAGGAGGAGGAGATGGACTTCCGGTCGGGCTCACCCAGTGACAACTCAGGGGCTGAAGAGATGGAGGTGTCCCTGGCCAAGCCCAAGCACCGCGTGACCATGAACGAGTTTGAGTACCTGAAGCTGCTGGGCAAGGGCACTTTCGGCAAGGTGATCCTGGTGAAGGAGAAGGCCACAGGCCGCTACTACGCCATGAAGATCCTCAAGAAGGAAGTCATCGTGGCCAAGGACGAGGTGGCCCACACACTCACCGAGAACCGCGTCCTGCAGAACTCCAGGCACCCCTTCCTCACAGCCCTGAAGTACTCTTTCCAGACCCACGACCGCCTCTGCTTTGTCATGGAGTACGCCAACGGGGGCGAGCTGTTCTTCCACCTGTCCCGGGAGCGTGTGTTCTCCGAGGACCGGGCCCGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTACCTGCACTCGGAGAAGAACGTGGTGTACCGGGACCTCAAGCTGGAGAACCTCATGCTGGACAAGGACGGGCACATTAAGATCACAGACTTCGGGCTGTGCAAGGAGGGGATCAAGGACGGTGCCACCATGAAGACCTTTTGCGGCACACCTGAGTACCTGGCCCCCGAGGTGCTGGAGGACAATGACTACGGCCGTGCAGTGGACTGGTGGGGGCTGGGCGTGGTCATGTACGAGATGATGTGCGGTCGCCTGCCCTTCTACAACCAGGACCATGAGAAGCTTTTTGAGCTCATCCTCATGGAGGAGATCCGCTTCCCGCGCACGCTTGGTCCCGAGGCCAAGTCCTTGCTTTCAGGGCTGCTCAAGAAGGACCCCAAGCAGAGGCTTGGCGGGGGCTCCGAGGACGCCAAGGAGATCATGCAGCATCGCTTCTTTGCCGGTATCGTGTGGCAGCACGTGTACGAGAAGAAGCTCAGCCCACCCTTCAAGCCCCAGGTCACGTCGGAGACTGACACCAGGTATTTTGATGAGGAGTTCACGGCCCAGATGATCACCATCACACCGCCTGACCAAGATGACAGCATGGAGTGTGTGGACAGCGAGCGCAGGCCCCACTTCCCCCAGTTCTCCTACTCGGCCAGCGGCACGGCCTGAThese sequences include a silent mutation to confer resistance to the ON-TARGET plus siRNA oligonucleotide (Dharmacon) targeting AKT1 with the sequence CAUCACACCACCUGACCAA. The piggyBac expression vector includes a GFP expression cassette separated from the AKT1 sequence by an internal ribosome entry site (IRES), enabling independent expression of both genes from a single transcript. Using either the PureFection™ (System Biosciences) or FuGENE® HD (Promega Corporation) transfection reagents, LNCaP Par or M-R7 cells were transfected with the piggyBac transposase (System Biosciences) combined with the empty piggyBac expression vector, AKT1 WT piggyBac vector, or AKT1 W80C piggyBac vector. Transfected cells were then incubated for 48 h prior to selection with 1 μg/ml puromycin and subsequently expanded. Leaky expression was minimized by using FACSAria instrumentation and associated software (BD Biosciences) to select for cells that do not express GFP in the absence of cumate. In an effort to obtain populations that express similar levels of AKT1 WT vs. AKT1 W80C, cells were treated with 10 μg/ml cumate for 5 days  and were subjected to sorting for specific GFP expression levels using FACSAria instrumentation and associated software (BD Biosciences).Cell sorting was performed on multiple FACSAria™ Fusions running DIVASoftware v8.0.1 equipped with 5 lasers  (BD Biosciences). The instruments were set up with a nozzle size of 100 micron at a frequency of 32 kHz and pressure of 20 psi. The “Four-Way Purity” sort mode was used for coincident discrimination.http://www.cbioportal.org/index.do?session_id=5b5e1288498eb8b3d5672636). All AKT1 mutation information reported in those selected studies was then retrieved. The frequency of each AKT1 mutation detected within the same indication was calculated from these studies . Data bases used include TCGA: The Cancer Genome Atlas, https://portal.gdc.cancer.gov; METABRIC: Molecular Taxonomy of Breast Cancer International Consortium (Nature 2012 & Nat Commun 2016), Pierra et al., 2016 https://www.ncbi.nlm.nih.gov/pubmed/27161491; MSK-IMPACT: Memorial Sloan Kettering Cancer Center’s Integrated Mutation Profiling of Actionable Cancer Targets .Cancer genomics studies in which AKT1 W80 alterations were detected in patients were first identified using cBioPortal  was constructed using standard PCR techniques and mutants were generated using the QuikChange Site-Directed Mutagenesis Kit (Stratagene/Agilent Technologies). Mutant or WT AKT1 was cloned into the pRetro-internal ribosome entry site (IRES)-GFP vector (Clontech). MEK1 N3 was constructed as previously described61 and cloned into the pMXs-puro retroviral vector (Cell Biolabs). Retroviral constructs expressing the WT or mutant protein were transfected into the Phoenix amphoteric packaging cell line using Fugene6 (Roche). Viral supernatant was harvested 48 h after transfection and filtered using a 0.45-μM syringe filter. Ba/F3 cells were then infected with virus by spinoculation , and cells infected with WT or mutant AKT1 were sorted by flow cytometry based on GFP fluorescence. Infected cells were selected with 2 μg/mL puromycin for 7 days. Pools of these cells were used for subsequent studies. All Ba/F3-derived cell lines were maintained in culture in the presence of 2 ng/mL recombinant murine IL-3 (R&D Systems). For analysis of the impact of WT vs. mutant AKT1 on the response to AKT inhibition, Ba/F3 cells which stably co-express MEK1 N3 and WT or mutant AKT1 were washed three times with 1X PBS and plated in the absence of IL-3 in complete RPMI in 384 well plates (1000 cells per well). The next day, cells were treated with a 9-point dose-titration of MK-2206 or ipatasertib using a maximum dose of 20 μM. DMSO controls were included and all conditions were tested in 4 replicate wells. Cellular viability was assessed 4 days after addition of inhibitors using the CellTiter-Glo® (Promega) luminescent assay as described previously8. For analysis of protein levels, lysates were prepared from cells cultured in the absence of IL-3 for 1 day and subjected to Western blotting as described above.The impact of WT or mutant AKT1 on sensitivity to MK-2206 or ipatasertib was assessed using the IL-3 independent viability assay in the Ba/F3 model. Survival of the Ba/F3 murine pro-B cell line is constitutively growth-factor dependent but can be rendered IL-3 independent via co-expression of AKT1 and an activated form of the MAP2 kinase mitogen-activated protein kinase (MAPK)/extracellular-signal- regulated kinase (ERK) kinase (MEK1) , termed MEK1 N362. The data was processed using Genedata Screener, Version 14 , with a four‑parameter Hill equation using compound dose-response data normalized to the median of 42 vehicle‑treated wells on each plate. A “Robust Fit” strategy was also employed by Genedata Screener, which is based on Tukey’s biweight and is resistant to outlier data. The reported absolute IC50 is the dose at which cross‑run estimated inhibition is 50% relative to DMSO control wells. In addition to absolute IC50, mean fitted viability across the nine tested doses  was also computed.A library consisting of 426 compounds including targeted agents, chemotherapeutics, and tool compounds was used to screen for inhibitors associated with enhanced sensitivity in AKTi-R cells compared with Par cell lines. Compounds were obtained from in-house synthesis or purchased from commercial vendors. Cells were plated in 384 well plates  using seeding densities previous determined to achieve approximately 70–80% confluence at the final time point of the assay. AKTi-R cells were plated in the presence of inhibitor at the dose used to maintain the resistance and Par cells were plated in DMSO control media. The following day, cells were treated with a 9-point dose titration of each inhibitor or DMSO control and 4 days later, cell viability was assessed as described above. Screening drug management, quality control, and the calculation of drug response statistics were performed as described previously63 as well as Highest single agent (HSA) analysis64, as described previously65. Briefly, a Bliss expectation for a combined response (C) of drugs A and B was calculated by the equation: C = (A + B) − (A × B) where A and B are the fractional growth inhibitions of each respective drug at a given dose. The difference between the Bliss expectation and the observed growth inhibition of the combination of drugs A and B at the same dose is the “Delta.Bliss.” Delta.Bliss scores were summed across the dose matrix to generate a Bliss sum. Bliss sum = 0 indicates that the combination treatment is additive (as expected for independent pathway effects); Bliss sum > 0 indicates activity greater than additive (synergy); and Bliss sum <0 indicates the combination is less than additive (antagonism). Using the Highest single agent (HSA) model64, scores were calculated at each dose matrix point based on the excess loss of viability in the combination in comparison to the highest single drug response. At least three independent experiments were performed to assess reproducibility unless otherwise indicated. Heatmaps depict % viability inhibition, Bliss score, or HSA score associated with each dose combination point following treatment of cells with the two agents. Mean Bliss sum values from three independent biological replicates are depicted in scatter plots, with mean Bliss sum values denoted below the x-axis and sample size (n) indicated for each cell line/condition above scatter plot data.Par or G-R cells were plated in 384 well plates and 24 h later were treated with a 9-point dose titration of GDC-0068/ipatasertib, a second inhibitor, or a combination of the two. DMSO controls were also included. After a further 4 days, cell viability was assessed using the CellTiter-Glo® assay as described above. The combination effect of ipatasertib and other inhibitors was assessed by Bliss independence analysis66 were cloned into the BH1.4 Dox-inducible piggyBac vector with an IRES-turboGFP-nuclear expression marker . LNCaP Par cells were transfected with the piggyBac transposase combined with the empty BH1.4 vector or the PIM expression constructs. Transfected cells were then subjected to selection with puromycin and subsequently expanded. Leaky expression was minimized by FACS sorting to select for cells that do not express GFP in the absence of Dox. In an effort to obtain populations that express similar levels of PIM proteins, cells were treated with 100 ng/ml Dox for 3 days and subjected to sorting for specific GFP expression levels.Sequences encoding the full length forms of human PIM1 (NP_001230115.1), PIM2 (NP_006866.2), PIM3 (NP_001001852.2), or a kinase-deficient PIM3 K69M mutantFlash frozen tumors were pulverized to powder by mechanical force (Covaris cryoPREP). Tumor powder was lysed with RIPA lysis buffer (Sigma) supplemented with a cocktail of phosphatase and protease inhibitors (Thermo Fisher Scientific) and 1 mM PMSF (Sigma). Tumor lysates were homogenized by mechanical disruption (SPEX SamplePrep) and cleared by centrifugation at 14,000 rpm for 10 min at 4 °C. Protein levels were quantified by the BCA Protein Assay Kit (Pierce Biotechnology) and normalized to equal concentrations. Equal amounts of protein lysates were resolved using NuPAGE Bis-Tris precast gels (Thermo Fisher Scientific) and transferred onto nitrocellulose membranes using the iBlot® dry blotting system (Thermo Fisher Scientific). Membranes were blocked with blocking buffer for fluorescent Western blotting (LI-COR). Primary antibodies were detected using IR Dye 800-conjugated (LI-COR) or IR Dye 680-conjuated (LI-COR) species-selective secondary antibodies. Detection and quantification were conducted using an Odyssey infrared scanner (LI-COR) using the manufacturer’s software. Protein loading was assessed using antibodies to β-actin or GAPDH.Immunohistochemisty (IHC) for xenograft tumors were carried out using 5-μm paraffin sections of formalin-fixed tissue on a VentanaBenchmark XT instrument (VMSI) by deparaffinization, treatment with antigen retrieval buffer (VMSI) and incubation with primary antibodies against PTEN (#9559), pAKT (S473) (#4060), pPRAS40 (T246) (#2997), pS6 (S235/236) (#2211), cleaved caspase 3 (Asp175) (#9661) , or cyclin D1  at 37 °C. Bound antibody was detected using DABMap technology (VMSI) and sections were counterstained with hematoxylin. IHC stained slides were scanned on a NanoZoomer XR whole slide imager  at 200x magnification, and were used to quantify the tumor and IHC positive staining areas. Segmentation of tumor regions and DAB positive pixels was performed by a custom algorithm using standard morphological operations and global RGB color thresholds running on Matlab 2019a .A two-tailed Student’s t-test or a one-way analysis of variance (ANOVA) was performed when comparing two groups or more than two groups, respectively. Statistical analysis was carried out using Prism v9.3.1 (GraphPad) and Microsoft Excel. Data are expressed as the mean ± standard deviation (SD) or standard error of means (SEM) as described in the figure legends. Each experiment was repeated at least three times unless otherwise indicated.Further information on research design is available in the Supplementary InformationInventory of Supporting InformationSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Reporting Summary


Ferroptosis has been identified to play a crucial role in the progression and treatment of cancers. KAT6B, as a histone acetyltransferase, is involved in multiple cancer development. However, the function of KAT6B in glioma is still elusive. Here, we aimed to evaluate the effect of KAT6B on ferroptosis in glioma cells and explored the potential mechanisms. We observed that the expression of KAT6B was enhanced in clinical glioma samples. The viability of glioma cells was repressed by erastin and the overexpression of KAT6B rescued the phenotype in the cells. Meanwhile, the apoptosis of glioma cells was induced by the treatment of erastin, while the overexpression of KAT6B blocked the effect in the cells. The levels of lipid ROS and iron were promoted by the treatment of erastin and the overexpression of KAT6B could reverse the effect in the cells. Mechanically, we identified that the expression of STAT3 was repressed by the KAT6B knockdown in glioma cells. The KAT6B was able to enrich on the promoter of STAT3 in glioma cells. Meanwhile, ChIP assay showed that the knockdown of KAT6B inhibited the enrichment of histone H3 lysine 23 acetylation (H3K23ac) and RNA polymerase II (RNA pol II) on STAT3 promoter in the cells. Depletion of STAT3 reversed KAT6B-regulated viability, apoptosis, and ferroptosis of glioma cells. Thus, we concluded that KAT6B contributes to glioma progression by repressing ferroptosis  Glioma serves as a prevalent malignancy among brain tumors in adults, which is also highly aggressive and with grave prognosis . Cancer KAT6B is a major histone acetyltransferase, widely reported during the progression of various diseases, including cancer, cardiovascular diseases, and neurodegenerative diseases . KAT6B eIn this work, we studied the function of KAT6B in glioma cells. We aimed to evaluate the effect of KAT6B on ferroptosis in glioma cells and explored the potential mechanisms.n = 35) were collected from glioma patients hospitalized, who received no chemotherapy and radiotherapy before surgery. All tumor samples were clinicopathologically confirmed as glioma . All of glioma tissues were astrocytoma and IDH wildtype. Normal brain tissues were collected from patients undergoing brain tissue resection due to craniocerebral injury. All samples were immediately stored in liquid nitrogen with RNAhold (TransGen). All patients have signed the informed consent forms. All experiments were approved by the Clinical Ethics Committee of our hospital.The glioma samples , maintained in DMEM medium  added with 10% FBS  and 1% penicillin/streptomycin , and were incubated in a 37°C atmosphere with 5% CO5 cells per well and transfected with 50 nmol oligonucleotides or their corresponding negative controls [The small interference RNA targeting KAT6B and STAT3 was designed and synthesized by Ribobio (China). Recombinant plasmid pcDNA-KAT6B was constructed by cloning KAT6B cDNA into pcDNA3.1 vectors . The U251 and LN229 cells were seeded in a 6-well plate with 4 × 10controls  by usingμl/well) was added to each well and incubated for another 2 h at 37°C. The absorbance values were measured at 450 nm using a microplate detector .Cell viability was evaluated by the cell counting kit-8  in accordance with the manufacturer's description. The cells were planted in a 96-well plate, incubated overnight, and treated with corresponding oligonucleotides. At indicated time point, the CCK-8 solution  was added, and samples were incubated for 30 min at 37°C, 5% CO2 and protected from light. Excess C11-BODIPY was removed by washing the cells twice with PBS. Oxidation of the polyunsaturated butadienyl portion of the dye results in a shift of the fluorescence emission peak from 590 nm to 510 nm in a manner proportional to lipid ROS generation. This shift was analyzed using a flow cytometer [2+ or total iron in each cell line. 2 × 106 of cells were rapidly homogenized in 4-10 volumes of Iron Assay buffer. Samples were centrifuged at 13,000 g for 10 minutes at 4°C to remove insoluble material. To measure ferrous iron, 5 μL of iron assay buffer was added to each well. To measure ferric iron, two sets of wells were set up. Then, 5 μL of assay buffer was added to the samples in one set of wells and 5 μL of iron reducer was added to the other set of wells. To measure total iron, add 5 μL of Iron Reducer to each of the sample wells to reduce Fe3+ to Fe2+. Samples were mixed well using a horizontal shaker or by pipetting, and the reactions were incubated for 30 minutes at room temperature, protected from light. Then, 100 μL of Iron Probe was added to each well-containing standard or test samples. Samples were mixed well using a horizontal shaker or by pipetting, and the reactions were incubated for 60 minutes at room temperature, protected from light. Finally, the absorbance was measured at 593 nm (A593) [The lipid ROS was measured by flow cytometry analysis in the cells. For Lipid ROS, cells were treated as indicated, and then typsinezed and resuspended in medium plus 10% FBS. Then, 10 ytometer . The levΔΔCt- method.Total RNAs were isolated from tissues or cells using Trizol reagent , reversely transcribed into cDNA by using the High Capacity cDNA Reverse Transcription Kit . The relative RNA levels were quantified by quantitative real-time PCR using the SuperScript III Platinum SYBR Green One-Step qRT-PCR Kit . GAPDH was adopted as internal control for normalization following the 2The primer sequences are as follows: KAT6B forward: 5′- ATACGAGCGAATGGGTCAGAGTGATTTTGG-3′, reverse: 5′- GTTCACAGAGTTGACATTGTAGGCTGGCG-3′; STAT3 forward: 5′- AACTCTCACGGACGAGGAGCT-3′, reverse: 5′-AGTAGTGAACTGGACGCCGG-3′; GAPDH forward: 5′-AACGGATTTGGTCGTATTGGG-3′, reverse: 5′-CCTGGAAGATGGTGATGGGAT-3′.6 cells were fixed with formaldehyde  for 10 min at room temperature. Fixation was stopped with the addition of 1/10 volume 1.25 M glycine, and the samples were incubated for 5 min at room temperature. The sonication step was performed in a Qsonica sonicator , and 200 μg of the protein-chromatin complex was used in each immunoprecipitation. Antibody-protein complexes were captured with preblocked dynabeads protein G . ChIP DNA was analyzed by qPCR with SYBR Green (Biorad) on an ABI-7500 (Applied Biosystems) using the primers specified. The antibodies used are as follows: KAT6B , H3K23ac (Active motif), RNA pol II , and normal mouse IgG . The promoter sequence  was shown as follows:ChIP assay was performed by using Pierce Magnetic ChIP Kit  in accordance with the manufacturer's protocols. Antibodies against KAT6B, H3K23ac, and RNA polymerase II were used to precipitate the DNA-protein complex. The level of immunoprecipitated DNA was evaluated by qRT-PCR assay. Briefly, ChIP assays were essentially performed as previously described  with sliTGCCCTGTAGATGCCTCTGTCCCTCATTGGATATGAGGTGGTACGAGCGGTCTGAATCTGTAGACTTAGACAGGCTTCAGGTATCTGGGGAACCATGTGAGTAAAACCTTGTATTGTTTCTGGGGCTAGAATCTAGTCTCCTTCAAACACTCAGAAGGATCTGTGACTGCCTTCAAAGGTTAAGAGTCATTGATTTTTCTCACTAGAAATTAATAAGAACAGCTATCCTTGGGGAGAAGGAATGATGGGGGTAGGGAAAGATTTTCATTTAATATTTTTGACCTTTCTGAACGGTCTGCATTTTCTAAACAGGAACATGTATTAGGTTCATTGAAAAAAAATATGTCAGGGGTTAGCTGAGCAGTGAGATAATGGGTCCTTTTTAGATTTTTGTCTTTATACCTGTTCGTATTAAACAAACACAAATATTAATAACATATTTTTAAAGACCCAAAAAAATAAAAATTAAAAACCCTGATAGTATCAGCACATACACAGAAATCACTCCATTATGCAAAGTTCATCCTCTATTATGAAAGGCAAAATGTCTACATTTCCTATCAACCACTGGCTTCAATTCAGTAAAACTTGCATACCAAGTAGGCAAGGTGGAAAAGAAAAAGGCAGAACATTTCATGTATTTCAATTCAGACGCATAAAAATGTCAAGCCCTACACGTTATCAGCTTTCGTATACACCGTCTTCTGCATTCGCCTGTACGGGCCAATGGGCTAGCTGGTCGGCGTTTGATGCTTGAAGTGATGGAACGGAGTACGGGGTTAAATCCACTACCCTCTCCCCACGCACTCTAGTAATTACTCTATTTCCACGTCATGTTTCCGGGTGTGTGTGTCCCTGCTCACGCAGAAACTGAAGTTCAAAGCAGGCGGAGTCACCCATGTTCTTTTTGTTGTCCCCAGAACCCAATTCAGGAGTTGGGTCCCCAGAGGATCTGGAGATACCTGGGGACTATCTAACTAGCTGATTCCCGCGTGGTAAGAGGCTCTCAACCTCGCCACCACGTGGTGCCAAGGGCCGGGAAAAGGGAGAGCGGGCAGGAGGGAGCTGTATCAGGGGCATTTAAAGTGCCTTGACGTCACGCACTGCCAGGAACTCAGCTGAGTTTTCAGCAGGACATTCCGGTCATCTTCCCTCCCTCCCCCCGGGCTTCTGTGCCCAAGTCCTCGGCTCTTCCCTCGCTGTGGCGGAGGGAGGAGCACCGAACTGTCGGAACAGCCAGCACAGGGGCGTATCAGTCTCCTCTTGGCTCCGCCCTTTCTCCTAGCTGCTCTCCTCATTGGTCAGTGGGCGGGGCTTCGGCTGTACCGCACACGCACTGGGACCTCTGGGTGGCCGAACGAGCTGGCCTTTCATGAATTATGCATGACGGCGTGCCTCGGCCAGGCTGGGGCTGGGCGAGGATTGGCTGAAGGGGCTGTAATTCAGCGGTTTCCGGAGCTGCGGCGGCGCAGACTGGGAGGGGGAGCCGGGGGTTCCGACGTCGCAGCCGAGGGAACAAGCCCCAACCGGATCCTGGACAGGCACCCCGGCTTGGCGCTGTCTCTCCCCCTCGGCTCGGAGAGGCCCTTCGGCCTGAGGGAGCCTCGCCGCCCGTCCCCGGCACACGCGCAGCCCCGGCCTCTCGGCCTCTGCCGGAGAAACAGGTGAAGGGGGTGCAGGGTGGGGCC.t test or one-way ANOVA test. A p value less than 0.05 was considered to be statistically significant.Data in this work are presented as mean ± SD. Statistical analysis was performed by using SPSS software (version 17.0). Statistical comparison between two or more groups was determined by student's To analyze the correlation of KAT2B with glioma, the expression of KAT2B was analyzed in the clinical glioma samples. We observed that the expression of KAT2B was enhanced in clinical glioma samples . To evalNext, we were interested in the correlation of KAT2B with ferroptosis in glioma cells. To this end, the U251 and LN229 cells were treated with ferroptosis activator erastin or cotreated with erastin and KAT6B overexpressing plasmid. We found that the viability and colony formation numbers of U251 and LN229 cells were repressed by erastin and the overexpression of KAT6B rescued the phenotype in the cells Figures . MeanwhiThen, we evaluated the effect of KAT6B on the levels of ferroptosis markers, including lipid ROS and iron, in the glioma cells. We observed that the levels of lipid ROS and iron were promoted by the treatment of erastin and the overexpression of KAT6B could reverse the effect in the cells Figures .We next tried to explore the underlying mechanism by which KAT6B regulated glioma. We observed that the expression of STAT3 was repressed by the KAT6B knockdown in U251 and LN229 cells . We idenNext, we validated the correlation of KAT6B and STAT3 in the regulation of glioma cells. The effectiveness of STAT3 depletion by shRNA was validated in U251 and LN229 cells Figure . We obseGlioma is a prevalent malignancy among brain tumors, and ferroptosis plays crucial roles in the progression and treatment of cancers. Histone acetyltransferase KAT6B is involved in multiple cancer development but the function of KAT6B in glioma remains unclear. In this study, we uncovered the effect and the potential mechanism of KAT6B on ferroptosis in glioma cells.κB signaling in tongue squamous cell carcinoma [Previous studies have identified the function of KAT6B in cancer development. The inhibition of histone acetyltransferases KAT6A/B contributes to senescence and regulates tumor growth . KAT6B farcinoma . These rMoreover, it has been reported that STAT3 inhibits ferroptosis-mediated IIR-ALI by targeting SLC7A11 . miR-519via epigenetically inducing STAT3 (Therefore, we concluded that KAT6B contributes to glioma progression by repressing ferroptosis ng STAT3 . KAT6B m


In the mouse embryo, the sensors responded to cytoskeletal relaxation and stretch applied by micro-aspiration. They reported organ-specific differences and a spatiotemporal tension gradient along the proximodistal axis of the limb bud, raising the possibility that mechanical mechanisms coregulate pattern formation. These mouse strains and software are potentially valuable tools for testing and refining mechanotransduction hypotheses in vivo.Nuclear mechanotransduction is a growing field with exciting implications for the regulation of gene expression and cellular function. Mechanical signals may be transduced to the nuclear interior biochemically or physically through connections between the cell surface and chromatin. To define mechanical stresses upon the nucleus in physiological settings, we generated transgenic mouse strains that harbour FRET-based tension sensors or control constructs in the outer and inner aspects of the nuclear envelope. We knocked-in a published esprin-2G sensor to measure tensions across the LINC complex and generated a new sensor that links the inner nuclear membrane to chromatin. To mitigate challenges inherent to fluorescence lifetime analysis  Summary: We introduce transgenic and software tools to measure tensions across the outer and inner nuclear envelope in mice. These tools facilitate our ability to test mechanotransduction hypotheses in vivo. The cell nucleus experiences physical forces in a manner that has the potential to alter chromatin conformation , gene exin vitro studies  complex that links f-actin, microtubules, and intermediate filaments to the nuclear lamina . Mammaliin vitro  and the INM protein NEMP1 in mice. The former was previously generated for use in vitro , whereasWe wished to generate knock-in mouse strains that harbour tension sensors within the outer and inner aspects of the nuclear envelope. To measure force transmission across the LINC complex, we generated a mouse strain capable of conditionally expressing a previously published tension sensor (NespTS) . The conin vitro. Super resolution stimulated emission-depletion (STED) microscopy revealed appropriate co-localisation of NmpTS and controls with laminB1 (reflecting INM location), and that of NespTS external to laminB1   . We chosLIMvivo) . To maxiLIMvivo) . DiffereLIMvivo) . The proLIMvivo) .There are two principal sources of noise in our setting. A quick fluorescence decay that is likely due to autofluorescence can artificially decrease the measured fluorescent lifetime, and background, or scattered, light can artificially increase the fit value for measured lifetime unless addressed appropriately . In addi We tested individual sensor responses in vivo using E9.5 wild-type (+/+Nemp1) distal limb bud mesoderm as a model system due to its accessibility and well-studied patterning characteristics in intact embryos under live conditions. Relative to NmpTS, NmpDO exhibited higher mean lifetime values, as expected. NmpTL and NespHL reported low lifetime values relative to NmpDO and previously published donor only mTFP measurements  was a barrier to reliable FLIM analysis when we applied commercially available fitting software. Similar hurdles were encountered previously with an E-cadherin tension sensor in Drosophila, resulting in apparently poor sensitivity . Testing of constructs' proper subcellular localisation was done by Lipofectamine 2000  transient transfection in 293T cells. EBFP and IRES were removed from the pR26-CAG/BFP-Dest plasmid  and each construct was cloned into this ROSA26 targeting vector via gateway cloning  through pDONOR  for mouseline generation. These constructs were sequenced to select reliable candidates for mouseline generation.NmpTS/DO/TL were generated by PCR amplification of the tension sensor module from the Vinculin Tension Sensor  and ligaChimeras were generated by traditional homologous recombination through aggregation and implantation of electroporated embryonic stem cells (ESCs) in wild-type embryos . Final ESCs were selected by PCR genotyping and copy number analysis (TCAG at the Peter Gilgan Centre for Research and Learning). Founders were confirmed by PCR genotyping and fluorescence imaging of crossings with various Cre lines. Constitutively active lines were generated by crossings with pCX-NLS-Cre 1Nagy) or CMV-Cre  and littermates were compared visually and by weight. These lines were then inbred or outcrossed with CD1 to assess fertility.in vitro. For immunofluorescence of embryonic tissue sections, 550 nm or 488 nm excitation wavelength with 592 nm or 660 nm depletion wavelength were used on a Leica SP8 Lightning Confocal. Huygens software was employed for deconvolution. Confocal images were captured on a Nikon A1R confocal microscope using Nikon NIS software for acquisition and Volocity for image processing. The FRET donor of the sensors is mTFP, so all FLIM data were acquired on a Nikon A1R confocal microscope connected to a PicoQuant pulse system with a 440 nm laser and 520/35 nm detector channel in Picoquant SymPhoTime 64 software. All FLIM images were acquired using transgenic mice in a wild-type background.All imaging was performed in the Sickkids Research Institute Imaging Facility. Super resolution STED microscopy was performed on a Leica SP8 confocal microscope using 458 nm or 514 nm excitation wavelength with 592 nm or 660 nm depletion wavelength for sensor constructs 2 held in place with cheese cloth. In roller culture experiments, embryos were first placed in roller culture consisting of DMEM with 50% rat serum, 5% DMSO, and specified final drug concentrations and rolled for 1 h at 37°C in a 20% O2, 5% CO2 chamber. Aspiration experiments were performed on the A1R using a 90 µm diameter glass needle in a previously published device  fixation allowed for imaging of mTFP or Venus within sensor constructs, but longer fixation  of in vivo samples required labelling of sensor constructs with mouse monoclonal α-GFP .In fixed sample imaging, α-aminB1 rabbit polyclonal antibody was used to label LaminB1  or α-O-GlcNAc mouse monoclonal antibody was used to label nuclear pores . In fixed FLIM data were segmented into isolated nuclear membrane regions. Unless clarified otherwise in the figure legends, each dataset presented is nuclei from at least three separate embryos. With the exception of VinTFP which was fitted to entire fields of view, 15 nuclei, or as many clear nuclei as were available, per image were segmented for each region of each embryo. Further, segments with low quality data  were rejected prior to statistical analysis.P-values were calculated using the t-test, applying Welch's correction to comparisons with significantly different standard deviations for datasets expected to be normally distributed and using the Mann–Whitney test for non-normally distributed datasets. For t-tests, four key assumptions  were considered prior to test application. Pregnancy rate comparison P-values were calculated using the χ-square test. Variance comparisons were performed using F-tests. P-values greater than 0.05 were considered insignificant. In all experiments where tissues from the same embryos were compared, the values were normalised to the mean of all embryos and tissues for that experiment.Statistical analysis was done in Prism. With the exception of pregnancy rate comparisons, regression analyses, and variance comparisons, https://www.github.com/HopyanLab/FLIMvivo. The basic FLIMvivo.py script takes a CSV data file and can perform either convolution or tail fitting applying either a mono-exponential or bi-exponential model. Segmentation masks for analysed FLIM images were generated using either the FLIMvivo or the FLIMfit , then calls upon FLIMvivo to fit the extracted data. The script uses FLIMvivo first to fit the full field with a model of a Gaussian instrument response convoluted with a bi-exponential in order to establish an autofluorescence lifetime C. It cheWe model the FLIM-FRET decay as a bi-exponential, with autofluorescent and signal components,For our fitting algorithms we use a maximum likelihood fitting scheme assuming Poisson statistics, minimizing the ‘negative log-likelihood’ (NLL),Finally, in fitting, we treat the end point (where the data is cut off) as fit variable, chosen by maximizing the ratio of best fit NLL to the square root of the number of data points. This way the data range is uniformly chosen by maximizing a measure of data contribution to our fit metric, thereby eliminating any potential bias introduced by choosing manually.All constructs were genotyped using these primers: Forward: CTCTGCTGCCTCCTGGCTTCT, WT Reverse: CGAGGCGGATCACAAGCAATA, Mutant Reverse: CCGCGAGCTGTGGAAAAAAAAGGG.ATGGCGGGAGGAATGAAAGTGGCGGTCTCGCCGGCAGTTGGTC CCGGGCCCTGGGGCTCGGGAGTCGGGGGC GGTGGGACAGTGC GGC TACTCTTGATCCTCTCCGGCTGCTTGGTCTACGGCACAGCT GAAACTGATGTAAATGTGGTCATGCTTCAGGAATCCCAA GTTTGTGAAAAGCGTGCCAGCCAACAATTCTGTTACACAAATGTGCTTATCCCAAAATGGCATGATATATGGACACGGATACAGATCCGAGTAAATAGTTCCAGATTGGTTCGAGTCACCCAGGTGGAGAATGAGGAGAAACTGAAGGAGCTAGAGCAGTTTAGTATCTGGAACTTTTTTTCCTCCTTTTTAAAAGAGAAATTGAATGACACCTATGTTAACGTGGGTCTATACAGCACAAAAACCTGCCTCAAAGTTGAGATTATAGAGAAGGACACCAAGTACAGTGTCATTGTGATCCGGAGATTTGATCCCAAACTCTTTCTTGTTTTCCTTCTTGGACTTATGCTATTTTTTTGTGGAGACTTGCTGAGCAGAAGTCAAATTTTCTACTACTCTACTGGGATGACTGTGGGAATTGTGGCCTCTCTGCTAATCATCATTTTTATACTATCTAAGTTTATGCCTAAGAAAAGTCCCATTTACGTCATCCTGGTGGGAGGCTGGTCTTTTTCTCTGTACCTCATTCAACTAGTTTTTAAAAATTTACAAGAGATCTGGAGGTGTTACTGGCAGTATCTTTTAAGTTATGTCCTCACAGTTGGATTCATGAGTTTTGCAGTATGTTACAAGTATGGGCCCTTGGAGAATGAACGAAGTATCAACCTGCTGACCTGGACCTTGCAGCTGATGGGCCTGTGTTTCATGTATTCTGGCATCCAGATACCACATATTGCCCTTGCCATTATCATCATTGCTCTTTGTACTAAGAACCTGGAACACGGATCCATGGTGAGCAAGGGCGAGGAGACCACAATGGGCGTAATCAAGCCCGACATGAAGATCAAGCTGAAGATGGAGGGCAACGTGAATGGCCACGCCTTCGTGATCGAGGGCGAGGGCGAGGGCAAGCCCTACGACGGCACCAACACCATCAACCTGGAGGTGAAGGAGGGAGCCCCCCTGCCCTTCTCCTACGACATTCTGACCACCGCGTTCGCCTACGGCAACAGGGCCTTCACCAAGTACCCCGACGACATCCCCAACTACTTCAAGCAGTCCTTCCCCGAGGGCTACTCTTGGGAGCGCACCATGACCTTCGAGGACAAGGGCATCGTGAAGGTGAAGTCCGACATCTCCATGGAGGAGGACTCCTTCATCTACGAGATACACCTCAAGGGCGAGAACTTCCCCCCCAACGGCCCCGTGATGCAGAAGAAGACCACCGGCTGGGACGCCTCCACCGAGAGGATGTACGTGCGCGACGGCGTGCTGAAGGGCGACGTCAAGCACAAGCTGCTGCTGGAGGGCGGCGGCCACCACCGCGTTGACTTCAAGACCATCTACAGGGCCAAGAAGGCGGTGAAGCTGCCCGACTATCACTTTGTGGACCACCGCATCGAGATCCTGAACCACGACAAGGACTACAACAAGGTGACCGTTTACGAGAGCGCCGTGGCCCGCAACTCCACCGACGGCATGGACGAGCTGTACAAGGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCCGGTGGTGCAGGTCCAGGTGGTGCAGGTCCAGGTGGTGCAGGTCCAGGTGGTGCTATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGCTGATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGGGCTACGGCCTGCAGTGCTTCGCCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCACCGCCGACAAGCAGAAGAACGGCATCAAGGCCAACTTCAAGATCCGCCACAACATCGAGGACGGCGGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCTACCAGTCCAAGCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAGGAATTCCCTATTCAGTGGCTGTACATCACCTGCAGAAAGGTGTGTAAGGGAGCAGAAAAGCCTGTTCCCCCTCGTCTCCTGACAGAAGAAGAATATCGGATACAAGGAGAGGTAGAAACCCGAAAGGCTTTAGAGGAGCTCCGAGAATTTTGTAACAGTCCAGACTGCTCTGCTTGGAAGACTGTTTCTCGAATCCAGTCTCCAAAAAGATTTGCTGACTTTGTGGAAGGCTCTTCCCACCTCACGCCAAATGAAGTTTCTGTCCATGAGCAGGAGTATGGATTAGGGAGCATTATTGCCCAGGATGAAATCTATGAGGAAGCATCCTCTGAGGAGGAGGACTCATATTCTCGGTGTCCTGCTATCACACAGAACAACTTTCTAACCTGA.ATGGCGGGAGGAATGAAAGTGGCGGTCTCGCCGGCAGTTGGTCCCGGGCCCTGGGGCTCGGGAGTCGGGGGCGGTGGGACAGTGCGGCTACTCTTGATCCTCTCCGGCTGCTTGGTCTACGGCACAGCTGAAACTGATGTAAATGTGGTCATGCTTCAGGAATCCCAAGTTTGTGAAAAGCGTGCCAGCCAACAATTCTGTTACACAAATGTGCTTATCCCAAAATGGCATGATATATGGACACGGATACAGATCCGAGTAAATAGTTCCAGATTGGTTCGAGTCACCCAGGTGGAGAATGAGGAGAAACTGAAGGAGCTAGAGCAGTTTAGTATCTGGAACTTTTTTTCCTCCTTTTTAAAAGAGAAATTGAATGACACCTATGTTAACGTGGGTCTATACAGCACAAAAACCTGCCTCAAAGTTGAGATTATAGAGAAGGACACCAAGTACAGTGTCATTGTGATCCGGAGATTTGATCCCAAACTCTTTCTTGTTTTCCTTCTTGGACTTATGCTATTTTTTTGTGGAGACTTGCTGAGCAGAAGTCAAATTTTCTACTACTCTACTGGGATGACTGTGGGAATTGTGGCCTCTCTGCTAATCATCATTTTTATACTATCTAAGTTTATGCCTAAGAAAAGTCCCATTTACGTCATCCTGGTGGGAGGCTGGTCTTTTTCTCTGTACCTCATTCAACTAGTTTTTAAAAATTTACAAGAGATCTGGAGGTGTTACTGGCAGTATCTTTTAAGTTATGTCCTCACAGTTGGATTCATGAGTTTTGCAGTATGTTACAAGTATGGGCCCTTGGAGAATGAACGAAGTATCAACCTGCTGACCTGGACCTTGCAGCTGATGGGCCTGTGTTTCATGTATTCTGGCATCCAGATACCACATATTGCCCTTGCCATTATCATCATTGCTCTTTGTACTAAGAACCTGGAACACGGATCCATGGTGAGCAAGGGCGAGGAGACCACAATGGGCGTAATCAAGCCCGACATGAAGATCAAGCTGAAGATGGAGGGCAACGTGAATGGCCACGCCTTCGTGATCGAGGGCGAGGGCGAGGGCAAGCCCTACGACGGCACCAACACCATCAACCTGGAGGTGAAGGAGGGAGCCCCCCTGCCCTTCTCCTACGACATTCTGACCACCGCGTTCGCCTACGGCAACAGGGCCTTCACCAAGTACCCCGACGACATCCCCAACTACTTCAAGCAGTCCTTCCCCGAGGGCTACTCTTGGGAGCGCACCATGACCTTCGAGGACAAGGGCATCGTGAAGGTGAAGTCCGACATCTCCATGGAGGAGGACTCCTTCATCTACGAGATACACCTCAAGGGCGAGAACTTCCCCCCCAACGGCCCCGTGATGCAGAAGAAGACCACCGGCTGGGACGCCTCCACCGAGAGGATGTACGTGCGCGACGGCGTGCTGAAGGGCGACGTCAAGCACAAGCTGCTGCTGGAGGGCGGCGGCCACCACCGCGTTGACTTCAAGACCATCTACAGGGCCAAGAAGGCGGTGAAGCTGCCCGACTATCACTTTGTGGACCACCGCATCGAGATCCTGAACCACGACAAGGACTACAACAAGGTGACCGTTTACGAGAGCGCCGTGGCCCGCAACTCCACCGACGGCATGGACGAGCTGTACAAGGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCAGGTGGTGCAGGGCCCGGTGGTGCAGGTCCAGGTGGTGCAGGTCCAGGTGGTGCAGGTCCAGGTGGTGCTATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGCTGATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGGGCTACGGCCTGCAGTGCTTCGCCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCACCGCCGACAAGCAGAAGAACGGCATCAAGGCCAACTTCAAGATCCGCCACAACATCGAGGACGGCGGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCTACCAGTCCAAGCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAGTGA.ATGGCGGGAGGAATGAAAGTGGCGGTCTCGCCGGCAGTTGGTCCCGGGCCCTGGGGCTCGGGAGTCGGGGGCGGTGGGACAGTGCGGCTACTCTTGATCCTCTCCGGCTGCTTGGTCTACGGCACAGCTGAAACTGATGTAAATGTGGTCATGCTTCAGGAATCCCAAGTTTGTGAAAAGCGTGCCAGCCAACAATTCTGTTACACAAATGTGCTTATCCCAAAATGGCATGATATATGGACACGGATACAGATCCGAGTAAATAGTTCCAGATTGGTTCGAGTCACCCAGGTGGAGAATGAGGAGAAACTGAAGGAGCTAGAGCAGTTTAGTATCTGGAACTTTTTTTCCTCCTTTTTAAAAGAGAAATTGAATGACACCTATGTTAACGTGGGTCTATACAGCACAAAAACCTGCCTCAAAGTTGAGATTATAGAGAAGGACACCAAGTACAGTGTCATTGTGATCCGGAGATTTGATCCCAAACTCTTTCTTGTTTTCCTTCTTGGACTTATGCTATTTTTTTGTGGAGACTTGCTGAGCAGAAGTCAAATTTTCTACTACTCTACTGGGATGACTGTGGGAATTGTGGCCTCTCTGCTAATCATCATTTTTATACTATCTAAGTTTATGCCTAAGAAAAGTCCCATTTACGTCATCCTGGTGGGAGGCTGGTCTTTTTCTCTGTACCTCATTCAACTAGTTTTTAAAAATTTACAAGAGATCTGGAGGTGTTACTGGCAGTATCTTTTAAGTTATGTCCTCACAGTTGGATTCATGAGTTTTGCAGTATGTTACAAGTATGGGCCCTTGGAGAATGAACGAAGTATCAACCTGCTGACCTGGACCTTGCAGCTGATGGGCCTGTGTTTCATGTATTCTGGCATCCAGATACCACATATTGCCCTTGCCATTATCATCATTGCTCTTTGTACTAAGAACCTGGAACACGGATCCATGGTGAGCAAGGGCGAGGAGACCACAATGGGCGTAATCAAGCCCGACATGAAGATCAAGCTGAAGATGGAGGGCAACGTGAATGGCCACGCCTTCGTGATCGAGGGCGAGGGCGAGGGCAAGCCCTACGACGGCACCAACACCATCAACCTGGAGGTGAAGGAGGGAGCCCCCCTGCCCTTCTCCTACGACATTCTGACCACCGCGTTCGCCTACGGCAACAGGGCCTTCACCAAGTACCCCGACGACATCCCCAACTACTTCAAGCAGTCCTTCCCCGAGGGCTACTCTTGGGAGCGCACCATGACCTTCGAGGACAAGGGCATCGTGAAGGTGAAGTCCGACATCTCCATGGAGGAGGACTCCTTCATCTACGAGATACACCTCAAGGGCGAGAACTTCCCCCCCAACGGCCCCGTGATGCAGAAGAAGACCACCGGCTGGGACGCCTCCACCGAGAGGATGTACGTGCGCGACGGCGTGCTGAAGGGCGACGTCAAGCACAAGCTGCTGCTGGAGGGCGGCGGCCACCACCGCGTTGACTTCAAGACCATCTACAGGGCCAAGAAGGCGGTGAAGCTGCCCGACTATCACTTTGTGGACCACCGCATCGAGATCCTGAACCACGACAAGGACTACAACAAGGTGACCGTTTACGAGAGCGCCGTGGCCCGCAACTCCACCGACGGCATGGACGAGCTGTACAAGGAATTCCCTATTCAGTGGCTGTACATCACCTGCAGAAAGGTGTGTAAGGGAGCAGAAAAGCCTGTTCCCCCTCGTCTCCTGACAGAAGAAGAATATCGGATACAAGGAGAGGTAGAAACCCGAAAGGCTTTAGAGGAGCTCCGAGAATTTTGTAACAGTCCAGACTGCTCTGCTTGGAAGACTGTTTCTCGAATCCAGTCTCCAAAAAGATTTGCTGACTTTGTGGAAGGCTCTTCCCACCTCACGCCAAATGAAGTTTCTGTCCATGAGCAGGAGTATGGATTAGGGAGCATTATTGCCCAGGATGAAATCTATGAGGAAGCATCCTCTGAGGAGGAGGACTCATATTCTCGGTGTCCTGCTATCACACAGAACAACTTTCTAACCTGA.10.1242/biolopen.059656_sup1Supplementary informationClick here for additional data file.


Previous studies have shown that aging promotes myocardial apoptosis. However, the detailed mechanisms remain unclear. Our recent studies revealed that aging not only activates apoptosis, but also activates some anti-apoptotic factors. By quantitative phosphoproteomics, here we demonstrated that aging increases cytochrome c (Cytc) phosphorylation at threonine 50 (T50), a post-translational modification with unknown functional impact. With point mutation and lentivirus transfection, cardiomyocytes were divided into four groups: empty vector group, WT (wild type), T50E (as a phosphomimic variant), and T50A (non-phosphorylatable). TUNEL staining and flow cytometry were used to determine the apoptosis ratio in different groups after hypoxic/reoxygenated (H/R) treatment. The results showed that T50-phosphorylated Cytc suppressed myocardial apoptosis induced by H/R. Furthermore, Western Blot and ELISA measurements revealed that Cytc T50 phosphorylation inhibited caspase-9 and caspase-3 activity without altering caspase-8, BCL-2, BCL-XL, and Bax expression. In our study, we demonstrated that aging increases phosphorylation Cytc at T50 and this aging-increasing phosphorylation site can suppress H/R-induced apoptosis. Acute myocardial infarction is one of the leading causes of death worldwide. Thanks to the rapid development of interventional technology, the number of people dying from acute myocardial infarction has decreased significantly. On the contrary, more and more people are suffering from post-ischemic heart failure . Heart fSubstantial evidence indicates that apoptosis is the leading cause of ischemia-induced heart failure . Our preMore interestingly, our previous work demonstrates that aging not only stimulates pro-apoptotic pathways in cardiomyocytes, but also activates some anti-apoptotic factors , 9, 10. In the present study, we performed proteomics on myocardial tissue obtained from young and old mice. Compared with the young group, the expression of 88 proteins was up-regulated, and 80 were down-regulated in old mice. At a post-translational modification level, phosphorylation levels were up-regulated at 445 sites and down-regulated at 1526 sites in the old group. Phosphorylation level changes were widespread and stable in each sample. More importantly, these changes occurred in many essential apoptotic regulatory proteins, including MAPK14, Akt1, mTORC2, and p21. Upon further analysis of these results, changes in Cytc phosphorylation at T50 caught our attention as Cytc phosphorylation is involved in mitochondrial function . SurprisWe found a new aging-increasing phosphorylation site by proteomics, which may be associated with aging-related myocardial apoptosis regulation.in vivo and is closely related to apoptosis [P-value<0.05 as a cut-off, the change in the amount of differential modification >1.3 was considered as significantly up-regulated and less than 1/1.3 as significantly down-regulated.  and increased apoptosis in T50A group when compared with WT group  is also closely related to apoptosis. Lee et al. isolated Cytc from cow heart and identified the phosphorylation site by immobilized metal affinity chromatography/nano-liquid chromatography/electrospray ionization mass spectrometry. Tyr-97 is the first phosphorylation site discovered on Cytc . The othIn conclusion, there are several innovations in this study. First, we observed that aging increases phosphorylation Cytc at T50. Second, this aging-increasing phosphorylation site can suppress H/R-induced apoptosis. In addition, this anti-apoptotic effect is mediated mainly through inhibition of downstream caspase-9 and caspase-3 activity. Based on our previous studies , T50-phoIn summary, our study proved a new phosphorylation site that is increased in the process of aging. More importantly, phosphorylation at this site can decrease the apoptosis ratio induced by H/R.Approved by the institutional ethics committee, this study was in compliance with the United States National Institutes of Health guidelines. Male C57 mice (aged eight weeks and 18 months) were anesthetized with sodium pentobarbital. Mouse hearts were harvested by exteriorizing the heart via a left thoracic incision.Heart tissue from young and old mouse were harvested and delivered to PTM-Biolab Co., Ltd.  for phosphoproteomic analysis. The quantitative study of phosphorylated proteomics used TMT labeling and phosphorylated enrichment techniques and high-resolution liquid chromatography-mass spectrometry.http://www.ebi.ac.uk/GOA/). Firstly, converting identified protein ID to UniProt ID and then mapping to GO IDs by protein ID. If some identified proteins were not annotated by UniProt GOA database, the InterProScan soft was used to annotate protein’s GO function based on the protein sequence alignment method. Then proteins were classified by Gene Ontology annotation based on three categories: biological process, cellular component, and molecular function.Gene Ontology (GO) annotation proteome was derived from the UniProt-GOA database .A special antibody was ordered from PTM-Biolab Co., Ltd. an antigen. Specific antibody response was examined with immunohistochemistry staining.Myocardial tissue and AC16 cells were homogenized in an ice-cold lysis buffer. After homogenization, the lysates were centrifuged. The supernatant was saved and separated by electrophoresis on SDS-PAGE and transferred onto polyvinylidene difluoride-plus membranes. After blocking buffer, the immunoblots were probed with anti-T50 phosphorylation of Cytc, anti-Cytc, anti-Cleaved Caspase-3, anti-Caspase-3, anti-Cleaved Caspase-9, anti-Caspase-9, anti-BCL-2, anti-BCL-XL, anti-Bax, and anti-GAPDH antibodies overnight at 4° C, followed by incubation with fluorescent-conjugated secondary antibodies at room temperature for 1 hour.in situ by the use of 3, 3 diaminobenzidine kit .Paraffin sections made from young and aged mouse hearts were used for immunohistochemistry assays to detect protein expression levels of T50 phosphorylation of Cytc proteins. In accordance with the manufacturer’s instruction, tissue sections stained immunohistochemically are determined separately by two pathologists using the indirect streptavidin peroxidase method. The primary antibodies against T50  and horseradish peroxidase-conjugated IgG were used in this study. Then, the proteins were visualized in vitro, we generated T50E for constant phosphorylation and T50A for non-phosphorylation. Three sequences of WT, T50E and T50A are as below. WT:ATGGGTGATGTTGAGAAAGGCAAGAAGATTTTTATTATGAAGTGTTCCCAGTGCCACACCGTTGAAAAGGGAGGCAAGCACAAGACTGGGCCAAATCTCCATGGTCTCTTTGGGCGGAAGACAGGTCAGGCCCCTGGATACTCTTACACAGCCGCCAATAAGAACAAAGGCATCATCTGGGGAGAGGATACACTGATGGAGTATTTGGAGAATCCCAAGAAGTACATCCCTGGAACAAAAATGATCTTTGTCGGCATTAAGAAGAAGGAAGAAAGGGCAGACTTAATAGCTTATCTCAAAAAAGCTACTAATGAGTAA. T50E:ATGGGTGATGTTGAGAAAGGCAAGAAGATTTTTATTATGAAGTGTTCCCAGTGCCACACCGTTGAAAAGGGAGGCAAGCACAAGACTGGGCCAAATCTCCATGGTCTCTTTGGGCGGAAGACAGGTCAGGCCCCTGGATACTCTTACGAGGCCGCCAATAAGAACAAAGGCATCATCTGGGGAGAGGATACACTGATGGAGTATTTGGAGAATCCCAAGAAGTACATCCCTGGAACAAAAATGATCTTTGTCGGCATTAAGAAGAAGGAAGAAAGGGCAGACTTAATAGCTTATCTCAAAAAAGCTACTAATGAGTAA. T5OA:ATGGGTGATGTTGAGAAAGGCAAGAAGATTTTTATTATGAAGTGTTCCCAGTGCCACACCGTTGAAAAGGGAGGCAAGCACAAGACTGGGCCAAATCTCCATGGTCTCTTTGGGCGGAAGACAGGTCAGGCCCCTGGATACTCTTACGCCGCCGCCAATAAGAACAAAGGCATCATCTGGGGAGAGGATACACTGATGGAGTATTTGGAGAATCCCAAGAAGTACATCCCTGGAACAAAAATGATCTTTGTCGGCATTAAGAAGAAGGAAGAAAGGGCAGACTTAATAGCTTATCTCAAAAAAGCTACTAATGAGTAA. With these sequences, construct lentivirus vectors, purchased from Shanghai Yibeirui Bio-pharmaceutical Technology Co., Ltd. .Three sequences of WT, T50E and T50A were synthesized after codon optimization, purchased from Sangon Biotech Co., Ltd. . To study the effect of T50 phosphorylation Human adult ventricular cardiomyocyte cell line AC16 was purchased from the American Type Culture Collection . Cells were cultured in Dulbecco’s modified Eagle’s medium  containing 10% fetal bovine serum  and 1% penicillin/streptomycin in a humidified incubator with 5% CO2 at 37° C. In this study, four types of lentiviruses were used to infect AC16 cells. Infection efficiency was monitored by fluorescence microscopy 48 hours post lentivirus infection.In vitro cardiomyocyte H/R modelSeventy-two hours after cardiomyocyte transfection, all groups were treated with hypoxia (95% N2 and 5% CO2) for 6 hours , followed by normoxia  for 2 hours.Cardiomyocytes were cultured directly upon coverslips. TUNEL staining was performed per the manufacturer’s instructions . Total nuclei were stained by DAPI . Apoptotic index  was determined via blinded manner.After incubation for two days, the harvested cells were used for apoptotic determination by two methods. First, the apoptotic rate of transfected cells was evaluated by using Annexin V APC/PI apoptosis detection kit (KeyGEN) by following the manufacturer’s instruction and then analyzed using FACScan. All tests are carried out in triplicate. Second, by following the manufacturer’s instructions, Caspase-9 and Caspase-3 levels were detected utilizing human Caspase-9 ELISA kit (BC3890), Caspase-3 ELISA kit (BC3830) and Caspase-8 ELISA kit (BC3880) from Beijing Solarbio Science and Technology Co., Ltd. .The data were analyzed with Prism 8.0 . All values in the text and figures are presented as mean ± SD. Statistical differences were determined by Student’s t-test for comparison between 2 groups and ANOVA followed by Bonferroni multiple comparison test for comparison among ≥3 groups. Probabilities of.05 or less were considered statistically significant.The data used to support the findings of this study are available from the corresponding author upon request.in vivo or clinical significance should be explored in the future.In this study, phosphoproteomics were performed only in myocardial tissue from young and old mice, which reduces the targeting of the experiment. The relationship between T50 phosphorylation and mitochondrial release or caspase 9 interaction will be explored in the next step. In addition, the functional impact of Cytc T50 phosphorylation


Early detection of tumor cells by identifying universal Tumor Associated Antigens (TAA) can drastically change our diagnostic, theranostic and therapeutic possibilities to cure cancer. Human Telomerase Reverse Transcriptase (hTERT), a hallmark of cancer, could act as an optimal TAA candidate. Here we report about the development of a monoclonal antibody against hTERT peptide (α-hTERT mAb) presented on the surface of cancer cells and its possible applications as a pan-cancer marker. Liquid biopsies, an innovative tool in precision oncology, comprising the noninvasive analysis of circulating tumor-derived material to counteract limitations associated with tissue biopsies. Within the tumor circulome, the US Food and Drug Administration already approved the use of circulating tumor cells (CTCs) as valid liquid biopsies. However, currently CTCs are being trapped using antibodies against specific cancer types, with anti EpCAM as the most common antibody, directed mainly against solid tumors. Moreover, the precision medicine approach is based on specific cancer type directed antibodies. Our novel mAb against the hTERT 16-mer peptide, corresponding to amino acids 611–626, is capable of detecting various types of cancer cells both in vitro and ex vivo from tumors of patients with either hematological or solid tumors. This antibody does not bind to normal lymphocytes cells. Cleavage of our antibody to F(ab’)2 fragments increased its binding specificity to the tested cancer cells. Future studies may point to the use of this antibody in the procedure of capturing CTCs. Therefore, their isolation should be very specific [We are in the midst of major advances in understanding cancer biology and in development of rationalized biological drugs against specific targets in cancer cells . Howeverspecific .The most common strategy for capturing CTCs is based on the expression of specific markers on their membrane. By using the EpCAM and CK markers, the commercial technology of CellSearch enables the isolation of epithelial derived tumor cells by magnetic based antibodies ,6,7. AltIn order to overcome these drawbacks, new isolation strategies were being developed based on differences in their properties such as size, adherence, density, electric charge of tumor versus normal cells ,15,16,17Currently, there is no validated universal marker that enables a successful identification of large variety of CTCs as an early diagnosis strategy. Here we describe our approach using telomerase peptide presented on the membrane of all cancer cells for the development of an anti-telomerase antibody-based tool for pan cancer cells CTC isolation. Telomerase is the hallmark of the cancer cell. It is highly expressed in almost all types of cancer cells and is repressed in most somatic cells . A panelThe specificity of the newly synthesized α-hTERT mAb (designed as α-hTERT) was tested by assessing its ability to bind specifically the 16-amino acid telomerase peptide of which it was designed against (not shown). The binding of the newly synthesized α-hTERT mAb to various cancer cell lines and fibroblasts (non-cancer cells) was tested using a flow cytometry. The following cells were assayed: Jurkat (T cell leukemia), MCF-7 (breast adenocarcinoma), U266 (multiple myeloma), MEL (melanoma), JeKo-1 (mantle cell lymphoma), A549 (lung carcinoma). Fibroblast cells served as a negative control. The ability of the antibody to bind these cells is presented in The binding of α-hTERT mAb to cancer cells isolated from the peripheral blood of patients with CLL (ex vivo) was tested as described in Materials and Methods. A total of 57 blood samples at different stages of the disease , including primary diagnosed and treated, were tested. The binding to 4 representative blood samples of CLL and 4 solid tumors samples of different origin are shown in The lack of α-hTERT mAb’s binding to normal lymphocytes is depicted in 5 ovarian tumors and 9 ascites samples from ovary cancer patients were analyzed. The binding of α-hTERT mAb to PBMCs of a patient with Merkel carcinoma is presented in Ex vivo binding of the α-hTERT mAb to cells obtained from three patients with breast cancer is shown in The In addition, we were able to detect the binding of the α-hTERT mAb to a small subset of cells from two patients with lung carcinomas as presented by dot plots on In order to optimize the binding specificity of the α-hTERT mAb to the TERT antigen, the F(ab’)2 fragment was cleaved off using ficin. The F(ab’)2 fragment was detected by gel electrophoresis (SDS-PAGE), and its binding to CLL cells was analyzed by flow cytometry. The results of the binding assays of the F(ab’)2 fragment obtained from two different preparations are demonstrated in SEQ ID #15’-DVVMTQTPLTLSVTIGQPASISCKSSQNLLYSDGKTYLNWLLQRPGQSPKRLIYLVSKVDSGVPDRFTGSGSGTDFTLKISRVEAEDLGVYFCWQGTHLPYTFGGGTKLEIK-3’SEQ ID #25’VQLQQSGAELVRPGASVTLSCKASGYIFTDYEKHWVKQTPVHGLEWIGAIDPESGSTVYNQRFKGKATLTADKSSGTAYMELRSLTSEDSAVYFCFLLRLFAYWGQGTLVTVSA -3’The amino acid sequences of the proteins comprising the light chain of the anti-hTERT mAb was assessed by Edman degradation, and the sequencing of the heavy chain was performed using an in-gel protein digestion followed by liquid chromatography-mass spectrophotometry (LC-MS) procedures, as described in Materials and Methods. The light chain  and heavSEQ ID #35’-GATGTTGTGATGACCCAGACTCCACTCACTTTGTCGGTGACCATTGGACAACCAGCCTCCATCTCTTGCAAGTCAAGTCAGAACCTCTTATATAGTGATGGAAAGACATATTTGAATTGGTTGTTACAGAGGCCAGGCCAGTCTCCAAAGCGCCTAATCTATCTGGTGTCTAAAGTGGACTCTGGAGTCCCTGACAGGTTCACTGGCAGTGGATCAGGGACAGATTTCACACTGAAAATCAGCAGAGTGGAGGCTGAGGATTTGGGAGTTTATTTTTGCTGGCAAGGTACTCATCTTCCGTACACGTTCGGAGGGGGGACCAAGCTGGAAATAAAACGGGCTGATGCTGCACCAACTGTATCCATCTTCCCACCATCCAGTGAGCAGTTAACATCTGGAGGTGCCTCAGTCGTGTGCTTCTTGAACAACTTCTACCCCAGAGACATCAATGTCAAGTGGAAGATTGATGGCAGTGAACGACAAAATGGTGTCCTGAACAGTTGGACTGATCAGGACAGCAAAGACAGCACCTACAGCATGAGCAGCACCCTCACATTGACCAAGGACGAGTATGAACGACATAACAGCTATACCTGTGAGGCCACTCACAAGACATCAACTTCACCCATCGTCAAGAGCTTCAACAGGAATGAGTGTTAA-3’SEQ ID #45’-caggtgCAGCTGCAGCAGTCTGGGGCTGAACTGGTGAGGCCTGGGGCTTCAGTGACGCTGTCCTGCAAGGCTTCGGGCTACATATTTACTGATTATGAAAAGCACTGGGTGAAGCAGACACCTGTGCATGGCCTGGAGTGGATTGGAGCTATTGATCCTGAAAGTGGTAGTACTGTCTACAATCAGAGATTCAAGGGCAAGGCCACACTGACTGCAGACAAATCTTCCGGCACAGCCTACATGGAACTCCGCAGCCTGACATCTGAGGATTCTGCCGTCTATTTCTGCTTTTTACTACGGCTATTTGCTTACTGGGGCCAAGGGACTCTGGTCACTGTCTCTGCAGCC-3’The nucleic acid sequencing was performed using the degenerate primer-based sequencing technique described in Materials and Methods. The light chain and heavy chain nucleic acids sequences of α-hTERT designated herein as SEQ ID #3 and SEQ ID #4, respectively, are:SEQ ID #65’-GATGTTGTGATGACCCAGACTCCACTCACTTTGTCGGTGACCATTGGACAACCAGCCTCCATCTCTTGCAAGTCAAGTCAGAACCTCTTATATAGTGATGGAAAGACATATTTGAATTGGTTGTTACAGAGGCCAGGCCAGTCTCCAAAGCGCCTAATCTATCTGGTGTCTAAAGTGGACTCTGGAGTCCCTGACAGGTTCACTGGCAGTGGATCAGGGACAGATTTCACACTGAAAATCAGCAGAGTGGAGGCTGAGGATTTGGGAGTTTATTTTTGCTGGCAAGGTACTCATCTTCCGTACACGTTCGGAGGGGGGACCAAGCTGGAAATAAAACGGGCTGATGCTGCACCAACTGTATCCATCTTCCCACCATCCAGTGAGCAGTTAACATCTGGAGGTGCCTCAGTCGTGTGCTTCTTGAACAACTTC-3’The variable region of the light (VL) chain comprises the nucleotide sequence of SEQ ID #6:Many signal transduction pathways which mediate malignant transformation have been deciphered. Accordingly, new efficient anti-cancer rationalized drugs were developed . HoweverRecently, a novel technology has been developed for this purpose which is based on liquid biopsies . Liquid The isolation of CTCs is evolving as a promising approach for genetic analysis and therapeutic decisions in cancer and as a possible tool for detection of tumors. The available methods for isolation of CTCs are specific for various types of cancer and therefore the hunting of these CTCs is done by binding to a specific antibody which serves as a cancer marker, which varies among the different malignant cells as well as lacks in others. Herein we describe a somewhat novel approach in which we use the peptide of telomerase, the hallmark of cancer cells, as a bait to trap all cancer cell types based on the definition of the hTERT as a pan-cancer marker . This deTelomerase containing cells present on their membrane telomerase driven peptides. Our strategy is based on one of them, termed GV1001, a 16 amino acid peptide (EARPALLTSRLRFIPK) which was used to construct an antibody for the identification of cancer cells expressing telomerase . Our conSince the α-hTERT antibody is present specifically on cancer cells membrane, it was shown to be successfully used as a cancer related antigen for developing anti-cancer vaccination in several studies to treat advanced pancreatic cancer, non-small cell lung cancer, melanoma, and other cancers ,30,31,32Next, we showed that CLL cells obtained from patients differing in their CLL cells concentrations, bound our antibody. When we labelled the cells firstly with our antibody and only then with αCD19 and αCD5 as CLL markers, we got similar results, thus emphasizing the capability of the antibody to recognize and bind CLL cells . More imOur study has several limitations. First, hTERT is also expressed in actively dividing normal somatic cells, including small intestine, colon, lymph nodes and also hematopoietic stem cells, although to a much lesser extent compared to malignant cells ,34. In t). The fact that the extent of the antibody binding differed in various cell lines may stem from the different expression of the enzyme in these cancer cells. We have subsequently sequenced our specific antibody and provide herein the full sequence of both heavy and light chains of it.Second, the specificity of our mAb was evaluated by a parallel assessment of the binding of our mAB to ex vivo samples obtained from oncological patients and from healthy volunteers. To calculate the specificity and sensitivity of our antibody, we currently collect more samples in order to analyze these parameters by a Receiver Operating Characteristic (ROC) curve in the coming future. Third, we did not report the efficacy of our antibody, since only in CLL cells and not in the cells originated from the solid tumors we could compare the extent of the hTERT antibody binding to the presence of CLL markers on the neoplastic cells. All in all, we have characterized an anti-telomerase specific mAb which was shown to be specific to telomerase positive cells, both in vitro and ex vivo . The facFinally, to improve the specificity of the α-hTERT mAb we have cleaved it to F(ab’)2 fragment. As expected, the cleaved F(ab’)2 fragment bound cancer cells specifically ex vivo , in lineThese results show that our anti-hTERT antibody-based system is able to identify and isolate circulating tumor cells of various origin.One major pitfall in our study might have been that the antibody would not identify all cancer cells expressing telomerase and therefore the number of isolated CTCs will not be maximal; This point already proven not to be a problem, as our antibody is highly sensitive and specific for cancer cells. However, the advantages in the development of this “CTC trapping” anti “personalized medicine” based approach are clear: it is an easy cheap and noninvasive one.Hopefully the approach that we developed will be thoroughly explored in future studies which may result in the development of a more beneficial method to trap CTCs for the benefit of patients with cancer.2. Jurkat and MEL cell media were supplemented with 10% FBS, U-266 was grown in the presence of 15% FBS and JeKo-1 with 20% FBS. Fibroblasts were propagated in DMEM with 20% FBS supplemented with L-glutamine and penicillin/streptomycin/nystatin.Cells were grown under standard growth conditions. The following cells were used: Jurkat (T cell leukemia), MCF-7 (breast adenocarcinoma), U266 (multiple myeloma), MEL (melanoma), JeKo-1 (mantle cell lymphoma), A549 (lung carcinoma). Human mononuclear cells (MNCs) from healthy volunteers and human fibroblasts served as a negative control. All cell lines were originated from the American Type Culture Collection (ATCC). U-266, Jurkat, JeKo-1 and MEL were cultured in RPMI-1640, A549 and MCF-7 in DMEM supplemented with 10–20% fetal bovine serum (FBS), 100 units/mL L-glutamine and 1% penicillin/streptomycin/nystatin  at 37 °C with 5% COPatients’ samples were obtained upon consent via protocol # 0038-12 approved by the IRB of Rabin Medical Center.Peripheral blood samples were collected and stored at room temperature and processed within 24 h of the collection. All participants signed an informed consent. Peripheral blood mononuclear cells (PBMCs) were isolated from the blood samples using standard protocol with Lymphoprep .The hTERT peptide GV1001, EARPALLTSRLRFIPK, was synthesized by Genemed Synthesis . Hybridomas were produced by ProteoGenix . The Screening of the various antibodies containing supernatants was performed by ELISA and Flow Cytometry. The antibodies and isotype controls for flow cytometry were purchased from BioXCell Company .F(ab’)2 fragments of α-hTERT mAb were prepared according to the protocol provided by Pierce mouse IgG1 Fab and F(ab’)2 preparation kit . The fragments were generated by using ficin in the presence of cysteine and analyzed by polyacrylamide gel electrophoresis (PAGE).The specificity of monoclonal antibodies (mAbs) to the hTERT peptide was assessed by sandwich ELISA, using a standard protocol, wherein the tested mAbs binds to the hTERT peptide and not to the carrier protein (KLH) immobilized to the assay plate. Unbound antibodies were removed by washing with PBS. Subsequently, the bounded antibodies were detected by horse radish peroxidase (HRP)-conjugated anti-mouse IgG antibody and a 3,3′,5,5′-tetramethylbenzidine (TMB) substrate. The optical density was measured at 450 nm by microplate reader .The binding of the α-hTERT antibodies to a panel of cancer cells from patients was assessed by flow cytometry analysis. Blood, tumor and ascites samples were first stained with primary α-hTERT peptide mAb or isotype control, then incubated with FITC or AF647-conjugated α-mouse secondary Ab  for the detection of hTERT positive cells.Chronic lymphocytic leukemia (CLL) cells were marked as CD19+/CD5+ ; ovarian cancer cells were detected by following the marker CCCR5. Antibodies were purchased from ENCO, Jackson, Sigma and Abcam, CA, GB).All cells were prepared for the flow cytometry analysis according to a routinely used procedure. Cell lines: floating cells collected from the growth media by centrifugation, adherent cells were trypsinized. Peripheral blood mononuclear cells (PBMCs) obtained from whole blood were isolated by Lymphoprep .The flow cytometry data was acquired using a BD FACS Calibur flow cytometer , acquisition and analysis were performed using Cell Quest Pro software .PBMCs were centrifuged, the pellet was resuspended in PBS +2% FBS and equally split into tubes. Cells were subsequently stained with α-hTERT or isotype mAb, washed with Phosphate Buffer Saline (PBS) and incubated with fluorescently labeled secondary anti-mouse Ab . Following washing cells were stained with tumor markers antibodies and re-suspended in PBS for acquisition on a flow cytometer. The same setup and compensation were used for all CLL samples. Cell debris and aggregates were excluded on forward scatter vs. side scatter. Data is presented by dot plots, histograms and graphs.The sequencing of the light chain of the antibody was performed using Edman degradation, in gel protein digestion and liquid chromatography-mass spectrophotometry (LC-MS) conducted by the Mass Spectrometry (MS) biological services unit of the Weizmann Institute of Science, Rehovot.The heavy chain of the purified antibody was cleaved by trypsinization prior to LC-MS analysis.The nucleotide sequence was obtained by using degenerated primers based on predicted sequences designed at the Tel Aviv University. The Kapa HiFi HotStart PCR  was used for the PCR reaction and the procedure was performed according to routinely used protocol. The nucleic acid sequencing was performed by the Instrumentation and Service Center at Tel Aviv University.Immunofluorescence analysis was conducted as described previously . Basical