TET1 and E-cadherin, which are critical for epithelial-mesenchymal transition (EMT). This complex also affects the conversion of 5mC to 5hmC. This study demonstrates that the Snail/PRMT5/NuRD(MTA1) complex promotes the invasion and metastasis of cervical cancer in vitro and in vivo. This study also shows that PRMT5 expression is upregulated in cervical cancer and various human cancers, and the PRMT5 inhibitor EPZ015666 suppresses EMT and the invasion potential of cervical cancer cells by disinhibiting the expression of TET1 and increasing 5hmC, suggesting that PRMT5 is a potential target for cancer therapy.The biological function of PRMT5 remains poorly understood in cervical cancer metastasis. Here, we report that PRMT5 physically associates with the transcription factor Snail and the NuRD(MTA1) complex to form a transcriptional-repressive complex that catalyzes the symmetrical histone dimethylation and deacetylation. This study shows that the Snail/PRMT5/NuRD(MTA1) complex targets genes, such as  Protein arginine methyltransferase 5 (PRMT5) is a type II protein arginine methyltransferase (PRMT) that has been reported to catalyze the symmetrical dimethylation of arginine . PRMT5 iThe transcription factor Snail belongs to the C2H2 superfamily and is an important regulator of cell migration in embryonic development and cancer metastasis by activating the EMT program through direct inhibition of E-cadherin transcription . Snail cThe Mi-2/nucleosomal remodeling and deacetylase (NuRD) complex is one of the ATP-dependent chromatin remodeling protein complexes and is highly conserved among eukaryotes , 27. TheTET1, E-cadherin, and others. We demonstrated that PRMT5 promotes the invasion and tumorigenesis of cervical cancer and its expression is markedly upregulated in various human cancers. Our data suggest that the PRMT5 inhibitor EPZ015666 can lead to an induction of TET1 expression and 5hmC, and inhibit the invasive potential of cervical cancer cells, making PRMT5 a potential target for cancer diagnosis and treatment.In this study, we found that PRMT5 coordinates with Snail and the NuRD(MTA1) complex to achieve transcriptional silencing of target genes including The following antibodies were used: anti-PRMT5 ab109451), anti-H3 (ab32107), anti-MTA1 (ab71153), anti-MTA2 (ab50209), anti-H4R3me2s (ab5823), anti-H4R3me2a (ab129231), anti-H3R8me2s (ab130740), anti-H3R8me2a (ab127163), anti-5-hmc (ab106918), and anti-5-mc (ab10805) ; anti-WDR77/MEP50 (A301-561A) ; anti-HDAC1 (H3284), anti-HDAC2 (H3159), anti-RbAp46/48 (R3779), anti-b-actin (A1978), anti-Fibronectin (F3648), anti-Vimentin (V6630), anti-GAPDH (G8795), anti-FLAG (F3165), and anti-HA (H6908) ; anti-Mi-2 (SC11378), anti-MBD3 (SC271521), anti-OGT (SC32921); anti-DDB1 (SC25367), and normal IgG R/M ; anti-MTA3 (IM1012) ; anti-Snail (13099-1-AP) ; anti-E-cadherin (610181), anti-N-cadherin (610920), anti-α-Catenin (610193), and anti-γ-Catenin (610253) ; anti-TET1 (91171) . Protein A/G Sepharose CL-4B beads were from Amersham Biosciences , and protease inhibitor mixture cocktail was from Roche Applied Science . The short hairpin RNAs (shRNAs) were obtained from GenePharma Co., Ltd.  and all short interfering RNAs were from Sigma–Aldrich. The targeted sequences are listed in Supplementary File 09451, an2 at 37 °C. All the cell lines were authenticated by STR profiling and tested for mycoplasma contamination. Transfections were carried out using Lipofectamine 2000 or Lipofectamine® RNAiMAX Reagent  according to the manufacturer’s instructions. Each experiment was performed in triplicate and repeated at least three times. For RNAi experiments, at least two independent shRNA sequences were tested for each gene and that showing the highest efficiency was used. Stable cell lines expressing Snail, PRMT5, or MTA1 were generated by transfection of pCMV-Tag2B-Flag-Snail, PRMT5, or MTA1 and screened for expression in single colonies in the presence of 1 mg/mL G418. Recombinant lentiviruses expressing shPRMT5, shSnail, and shMTA1 were constructed by Shanghai GenePharma . Concentrated viruses were used to infect 5 × 105 cells in a 60-mm dish with 8 μg/mL polybrene in the medium. Infected cells were then sorted for target expression.The cell lines HeLa and SiHa used were obtained from the American Type Culture Collection  and maintained in Dulbecco’s modified Eagle’s medium . The Ca Ski cell line was obtained from ATCC and maintained in RPMI-1640 medium . All cell culture media were supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 U/mL streptomycin . Cells were maintained in a humidified incubator equilibrated with 5% CO8 cells and applied to an equilibrated HA column with a 1 mL bed volume to allow protein complexes to bind the column resin. After binding, the column was washed with cold BC500 buffer containing 50 mM Tris, 2 mM EDTA, 500 mM KCl, 10% glycerol, and protease inhibitors. HA peptide  was applied to the column to elute the HA protein complex as described by the manufacturer. Fractions of the bed volume were collected and resolved on sodium dodecyl sulfate (SDS)-polyacrylamide gels, silver stained, and subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) sequencing and data analysis.HeLa cells stably expressing HA-PRMT5 were generated by transfecting the cells with HA-tagged PRMT5 followed by selection in medium containing 1 mg/mL of G418. Anti-HA immunoaffinity columns were prepared using anti-HA affinity gel (Sigma–Aldrich) following the manufacturer’s instructions. Cell lysate was obtained from ~5 × 10HeLa nuclear extracts were prepared and dialyzed against buffer D  . Approximately 6 mg of nuclear protein was concentrated to 1 mL using a Millipore Ultrafree centrifugal filter apparatus , and then applied to an 850 × 20 mm Superose 6 size exclusion column (Amersham Biosciences) that had been equilibrated with buffer D containing 1 mM dithiothreitol and calibrated with protein standards . The column was eluted at a flow rate of 0.5 mL/min and fractions were collected.For immunoprecipitation assays, the cells were washed with cold phosphate-buffered saline (PBS) and lysed with cold cell lysis buffer for 30 min at 4 °C. Next, 500 μg of cellular extract was incubated with the appropriate specific antibodies or normal rabbit/mouse immunoglobin G (IgG) on a rotator at 4 °C overnight with constant rotation, followed by the addition of protein A/G Sepharose beads and incubation for 2 h at 4 °C. The beads were washed five times with cell lysis buffer . The immune complexes were subjected to SDS-polyacrylamide gel electrophoresis (PAGE) followed by immunoblotting with secondary antibodies. Immunodetection was performed using enhanced chemiluminescence  according to the manufacturer’s instructions.Escherichia coli BL21 cells, and crude bacterial lysates were prepared by sonication in cold PBS in the presence of a protease inhibitor mixture. In vitro transcription and translation experiments were performed with rabbit reticulocyte lysate . In GST pull-down assays, approximately 10 μg of the appropriate GST fusion protein was mixed with 5–8 μL of the in vitro transcribed/translated products and incubated in binding buffer . The binding reaction was then added to 30 μL of glutathione-Sepharose 4B beads  and mixed at 4 °C for 2 h. The beads were washed five times with binding buffer, resuspended in 30 μL of 2× SDS-PAGE loading buffer, and resolved on 12% gels. Protein bands were detected with specific antibodies by western blotting.GST fusion constructs were expressed in 7 cells were crosslinked with 1% formaldehyde, sonicated, precleared, and incubated with 5–10 μg of antibody in each reaction. Complexes were washed with low- and high-salt buffers, and DNA was extracted and precipitated. For Re-ChIP assays, immune complexes were eluted from the beads with 20 mM dithiothreitol. Eluents were then diluted by 30-fold with ChIP dilution buffer and subjected to a second immunoprecipitation reaction. The final elution step was performed using 1% SDS solution in Tris-EDTA buffer, pH 8.0. DNA template enrichment was analyzed by conventional polymerase chain reaction (PCR) using primers specific for each target gene promoter. The bands were then inverted to black by Photoshop to make it easier to recognize. The primers sequences are shown in Supplementary File ChIPs and Re-ChIPs were performed in HeLa cells as previously described . Briefly7 cells were used for each ChIP-seq assay. The chromatin DNA precipitated by polyclonal antibodies against PRMT5, MTA1 or Snail. The DNA was purified with the Qiagen PCR purification kit. In-depth whole genome DNA sequencing was performed by the CapitalBio Corporation, Beijing. The raw sequencing image data were examined by the Illumina analysis pipeline, aligned to the unmasked human reference genome  using ELAND (Illumina), and further analyzed by MACS . Enriched binding peaks were generated after filtering through the input data. De novo motif screening was performed on sequences ±125 bp from the centers of binding peaks using the CEAS and MEME systems. Pathway analysis was conducted based on Kyoto Encyclopedia of Genes and Genomes (KEGG).HeLa cells were maintained in DMEM supplemented with 10% fetal bovine serum. Approximately 5 × 10-ΔΔCt) with GAPDH used as an internal control. Primers used in quantitative RT-PCR are listed in Supplementary File Total RNA was isolated from the samples using Trizol reagent (Invitrogen) according to the manufacturer’s instructions. Any potential DNA contamination was removed by RNase-free DNase treatment (Promega). cDNA was prepared using the Transcriptor First Strand cDNA Synthesis Kit (Roche). Relative expression quantification was performed using the ABI PRISM 7500 Fast sequence detection system , which measures real-time SYBR Green fluorescence (Roche). Briefly, cDNA was mixed with 1 μL forward and reverse primers (5 μM of each), 8 μL RNase-free water, and 10 μL 2× PCR SYBR Green Mix buffer in a 20-μL reaction. Next, 40 cycles of PCR were conducted at 95 °C for 15 s, and 60 °C for 1 min within each cycle. Expression was quantified by the comparative Ct method , and incubated with appropriate primary antibodies overnight followed by staining with TRITC-conjugated secondary antibodies for 1 h at room temperature. The cells were washed four times, and the nuclei were stained with 4, 6-diamidino-2-phenylindole (DAPI) at a final concentration of 0.1 μg/mL. Images were visualized and recorded with an Olympus FV1000 confocal microscope .4 cells in 0.5 mL serum-free media were placed in the upper chamber of the transwell. The chamber was then transferred to a well containing 500 μL of media containing 10% fetal bovine serum. The HeLa cells were incubated for 18 h and Ca Ski cells for 24 h at 37 °C. Cells in the upper well were removed by wiping the top of the membrane with cotton swabs. The membranes were then stained, and the remaining cells were counted. Three high-powered fields were counted for each membrane.Transwell chamber filters  were coated with Matrigel. After infection with lentivirus, the cells were resuspended in serum-free media and 2.5 × 106 cells for shRNA group and 2.5 × 106 cells for overexpression group) or injected into the lateral tail vein (2 × 106 cells for shRNA group and 1 × 106 cells for overexpression group) of 6-week-old female SCID mice following randomization. For bioluminescence imaging, the mice were injected abdominally with 200 mg/kg of D-luciferin in PBS. At 15 min after injection, the mice were anesthetized, and bioluminescence was imaged with a charge-coupled device camera . Bioluminescence images were obtained with a 15-cm field of view, binning (resolution) factor of eight, 1/f stop, open filter, and imaging time of 30 s to 2 min. Bioluminescence from the relative optical intensity was defined manually. Photon flux was normalized to the background, which was defined from the relative optical intensity drawn over a mouse not injected with luciferin. No blinding was done.HeLa cells that had been transfected to stably express firefly luciferase  were infected with lentiviruses carrying shRNAs or overexpression constructs. These cells were subcutaneously implanted  at 4 °C overnight and then embedded in paraffin, sectioned at 8 μm onto Superfrost-Plus Slides, processed by standard protocols using DAB staining, and monitored microscopically.t-test. SPSS V.17.0 software  was used for statistical analysis. Data are representative of at least three independent experiments. Tumor datasets were downloaded from http://www.ncbi.nlm.nih.gov/geo and GSE numbers are shown in the Figures. Data for Kaplan–Meier survival analysis were from http://kmplot.com/analysis.Results are reported as the mean ± SD unless otherwise noted. Comparisons were performed using two-tailed unpaired To investigate the role of PRMT5 in cervical cancer progression, we collected 186 cervical cancer samples and adjacent normal tissues and performed tissue microarrays with IHC staining to examine PRMT5 expression. PRMT5 was strikingly upregulated in cervical cancer and its expression was positively correlated with histological tumor grade Fig. .Fig. 1PRNext, analysis of three published clinical datasets  revealed that PRMT5 is highly expressed in cervical cancer cells than in adjacent normal tissues Fig. . Kaplan–OCT4, KLF4, SOX2, NANOG, and ID1) were detected in spheroid cells overexpressing PRMT5, while PRMT5 knockdown decreased them  or injected into the lateral tail vein (n = 8) of 6-week-old female SCID mice. Tumor metastasis was measured after 6 weeks. In subcutaneously implanted groups, knockdown of PRMT5 resulted in a significant reduction in tumor growth  was stably expressed in HeLa cells. Cellular extracts were subjected to affinity purification using an anti-HA affinity gel. Mass spectrometric analysis revealed that PRMT5 co-purified with MTA1, MTA2, GATAD2B, HDAC1, and RbAp46/48, which are components of the NuRD complex, as well as with MEP50, OGT, DDB1, and Snail Fig. . The detTo confirm the physical association of PRMT5 with the NuRD complex and Snail, co-immunoprecipitation (co-IP) experiments were performed with HeLa cells. Immunoprecipitation (IP) with antibodies against PRMT5 and MEP50 followed by immunoblotting (IB) with antibodies against the components of NuRD complex and Snail demonstrated that the PRMT5-MEP50 complex co-immunoprecipitated with them. Reciprocally, IP with NuRD subunits and Snail antibodies and IB using antibodies against PRMT5 or MEP50 confirmed these interactions Fig. . To furtBased on the co-purification of Snail and the NuRD complex in the PRMT5 interactome and the functions of these components in cancer metastasis, co-IP experiments were performed using HeLa cells. The results demonstrated that all tested proteins, except for MTA3, efficiently co-immunoprecipitated with Snail. The results were also confirmed in Ca Ski and SiHa cells Fig. . These rTo verify the formation of a Snail/PRMT5/NuRD complex, HeLa nuclear proteins were fractionated by FPLC using a high-salt extraction and size exclusion approach. We found that PRMT5 eluted with an apparent molecular mass much greater than that of the monomeric protein. Western blotting revealed a major peak at ~500–1500 kDa for PRMT5-MEP50, the NuRD complex proteins and Snail Fig. . SignifiTo further support the physical association between Snail, PRMT5, and the NuRD(MTA1) complex and gain insight into the molecular details, GST pull-down experiments were performed by incubation of GST-fused PRMT5 (GST-PRMT5) with in vitro transcribed/translated Snail and individual components of the NuRD complex. These experiments revealed that PRMT5 interacted directly with Snail, MTA1, MTA2, and MTA3 Fig. . ReciproAdditionally, GST pull-down assays with the GST-fused N-terminal fragment (PRMT5-N), enzymatic domain (PRMT5-SAMD), or C-terminal fragment (PRMT5-C) of PRMT5 Fig.  and in vTaken together, these results not only provide strong support for a physical association between Snail, PRMT5, and NuRD(MTA1), but also reveal the molecular details involved in the formation of the Snail/PRMT5/NuRD(MTA1) complex.p-value cutoff of 10−3, we identified 4915 Snail-specific, 4049 PRMT5-specific, and 4330 MTA1-specific binding promoters  complex, we analyzed the genome-wide transcriptional targets by chromatin IP-based deep sequencing ChIP-seq). In these experiments, ChIP was conducted using HeLa cells with antibodies against Snail, PRMT5, and MTA1. Following ChIP, the DNA was amplified, labeled, and then sequenced (GEO accession number: GSE130194). Using a HiSeq2000 and a ers Fig. . Data wehIP-seq. ers Fig.  and binders Fig. , supportE-cadherin, α-Catenin, WDR94, SMYD3, FOXK2, MME, PTK2, PTPRJ, SKP1, CTNND2, ST5, TET1, and RBBP6, validating the ChIP-seq results  analysis in HeLa cells using specific antibodies against Snail, PRMT5, MTA1, MEP50, Mi-2, H4R3me2s, H3R8me2s, or IgG showed that the promoters of these genes were strongly enriched, including Snail, PRMT5, and MTA1 mRNA along with a scrambled shRNA control. The shRNAs significantly reduced the expression of their target genes without causing detectable changes in non-targeted genes. Additionally, knockdown of Snail, PRMT5, or MTA1 led to increased expression of TET1, FOXK2, E-cadherin, and α-Catenin at both the transcriptional and protein levels  complex. We demonstrated that Snail, PRMT5, and MTA1 co-occupied the promoters of et genes . SolubleTA1 Fig. . These rTo identify the functional association between Snail, PRMT5, and MTA1 on target promoters, HeLa cells with Snail, PRMT5, or MTA1 stably depleted were used. qChIP experiments with antibodies against these proteins indicated that depletion of one of the proteins resulted in marked reduction in the recruitment of corresponding proteins at the target promoters. Depletion of Snail also dramatically reduced the recruitment of PRMT5, MTA1, and Mi-2. Subsequent qChIP experiments showed that the levels of H4R3me2s and H3R8me2s were significantly decreased at target promoters upon depletion of Snail, PRMT5, or MTA1, while the level of H3Ac was markedly increased and the level of H3 did not change Fig. . These rAs described above, our genome-wide analysis indicated that the Snail/PRMT5/NuRD(MTA1) complex play important roles in cell migration and tumor cell invasion. Based on the roles of PRMT5, Snail, and MTA1 in EMT and cancer progression , 39, we n = 5). After 7 days, we treated the mice with EPZ01566  or vehicle (0.5% MC) for two 10-day periods. We found that treatment of EPZ015666 resulted in a significant reduction in tumor growth compared to control  is an orally bioavailable small-molecule inhibitor of PRMT5 enzymatic activity in the nanomolar range . We nextrol Fig. . Then werol Fig. .Fig. 7PRhttps://www.oncomine.com/) revealed high PRMT5 expression in many cancer types compared to adjacent normal tissues  complex to perform transcriptional silencing of a cohort of target genes, such as ent Fig. .Snail has been reported to interact with several corepressor complexes , 41, 42.FOXK2, which are known to be critically involved in EMT .–58.56–58S) cells . We founOver the last decades, cancer stem cells (CSCs) have emerged as a “bad seed” in the pathogenesis of cancer and are a subpopulation of cancer cells that can initiate, maintain, and regenerate within the tumor bulk , 61. IncEPZ015666 is an orally bioavailable small-molecule inhibitor of PRMT5. Interestingly, a study of mantle cell lymphoma (MCL) showed that EPZ015666 leads to tumor cell death . The latIn summary, we identified the new transcriptional silencing complex Snail/PRMT5/NuRD(MTA1). We demonstrated that PRMT5 promotes the invasion and tumorigenesis of cervical cancer in vitro and in vivo. Our data indicate that PRMT5 promotes tumorigenesis, progression, and metastasis and suggest that PRMT5 is a potential therapeutic target for cancer treatment.Supplementary File 1Supplementary File 2


Endothelial hyperpermeability is the initial event in the development of diabetic microvascular complications, and advanced glycation end products (AGEs) are suggested to cause much of the endothelial hyperpermeability associated with diabetes mellitus, but the molecular mechanism remains to be characterized. β‐catenin reportedly plays dual functions in maintaining normal endothelial permeability by serving both as an adhesive component and a signal transduction component. Here, we found that AGEs induced the phosphorylation of β‐catenin at residues Y654 and Y142 and the endothelial hyperpermeability was reversed when the two residues were blocked. In mechanism, phosphorylation of Y654 was blocked by Src inactivation, whereas phosphorylation of Y142 was reduced by a focal adhesion kinase inhibitor. β‐catenin Y654 phosphorylation induced by AGEs facilitated the dissociation of vascular endothelial (VE)‐cadherin/β‐catenin and the impairment of adherens junctions (AJs), whereas β‐catenin Y142 phosphorylation favoured the dissociation of β‐catenin and α‐catenin. Further investigation revealed that β‐catenin Y142 phosphorylation was required for AGEs‐mediated β‐catenin nuclear translocation, and this nuclear‐located β‐catenin subsequently activated the TCF/LEF pathway. This pathway promotes the transcription of the Wnt target, ADAM10 , which mediates VE‐cadherin shedding and leads to further impairment of AJs. In summary, our study showed the role of β‐catenin Y654 and Y142 phosphorylation in AGEs‐mediated endothelial hyperpermeability through VE‐cadherin/β‐catenin/α‐catenin dissociation and up‐regulation of ADAM10, thereby advancing our understanding of the underlying mechanisms of AGEs‐induced microvascular hyperpermeability. Disruption of AJs has been suggested to be the leading cause of endothelial hyperpermeability in response to pathological stimuli such as thrombin, TNF‐α and lipopolysaccharides.β‐catenin acts as a communication bridge between the intracellular segment of cadherin and the cytoskeleton.et al demonstrated that β‐catenin that dissociates in the cytoplasm is transferred into the nucleus with the help of B‐cell CLL/lymphoma 9 (BCL9), where it combines with T‐cell factor/lymphoid enhancer factor (TCF/LEF).In addition to its adhesive function, β‐catenin also serves as a signal transduction component. Clevers Src family kinases (SFKs) are the family of non‐receptor tyrosine kinase. As signalling enzymes, they consist of nine structurally related proteins, Src, Blk, Fyn, Yes, Lyn, Lck, Hck, Fgr and Yrk. Src is one of the most widely studied members in SFKs.Based on these previous observations, we suggested that the phosphorylation of β‐catenin at Y654 and Y142 affects the regulation of AJs and F‐actin remodelling, respectively, whereas the latter further activates the Wnt/β‐catenin pathway and promotes ADAM10 transcription. Thus, in this study, we investigated the role of β‐catenin phosphorylation in the pathology of AGEs‐induced endothelial hyperpermeability.22.1TM. 48 hours after transfection, the cells were treated with or without AGEs. Then, total protein was prepared and subjected to Western blot. The sequences targeted to Src were TGTTCGGAGGCTTCAACTCCT, and ADAM10 was CAGCAGAGAGAUAUAUUAATT whereas Control siRNA were AATTCTCCGAACGTGTCACGT, which were synthesized by GenePharma .siRNA transfection was carried out according to protocol provided by GenePharma . Briefly, HUVECs were cultured in 6‐well plates with 50%~ 70% confluences and then transfected with 20 nmol/L siRNA through siRNA‐MateTM kit. Briefly, cells were cultured to 90%‐100% confluences and then transfected with DNA complexes for 48‐72 hours, followed by treatments with or without AGEs. Total proteins were extracted and were detected to verify the transduction efficiency of siRNA or plasmid vectors, with the results shown in Figure The transfection of plasmids was performed according to LipofectamineThe cells lysates were collected and subjected to co‐immunoprecipitation and immunofluorescence to detect the AJs colocation, Western blot to detect ADAM10 protein level and VE‐cadherin shedding, Gene reporter assays to detect TCF/LEF promoter activity.2.2AGEs‐modified bovine serum albumin (AGEs‐BSA) was prepared based on the protocol of Schmidt AM et al2.3® III RT SuperMix  according to the manufacturer's protocol. Real‐time PCR was performed using SYBR® qPCR Master Mix with specific primers. The RNA concentration was determined by using a BioDrop spectrophotometer . Complementary DNA was reverse transcribed with HiScript2.4Cells were lysed using Lysis buffer containing phosphatase and protease inhibitors. After extraction, total proteins were first subjected to 10% SDS‐PAGE separation and then transferred to polyvinylidene difluoride membranes and subsequently blocked with 5% BSA dissolved in 1% TBST, incubated with specific primary antibodies of 1:1000 dilution overnight at 4°C. After three times washing, the membranes were incubated with respective secondary antibodies at room temperature for 1 hour, and signal was detected by chemiluminescence. Finally, analysis of band density was carried out by Tiangen imaging station and quantified with ImageJ.2.5TM Protein A/G Mix Magnetic Beads at room temperature for 30 min. Using magnetic stand to capture the beads, then remove the supernatant sample and disengage the magnet and gently wash the beads with binding/wash buffer. After the last wash, disengage the magnet and add 1×loading buffer to denature the elution.HUVEC lysate was harvested using 200 μL Cell Lysis Buffer for IP  with protease and phosphatase inhibitors, and then immunoprecipitation was performed according to protocol provided by Millipore. In brief, 200 μL total proteins were firstly incubated with 2 μL β‐catenin antibody overnight at 4°C and subsequently mixed with 40 μL Pure Proteome2.62  was used to measure monolayer permeability. To test transendothelial flux of FITC‐Dextran, cells were seeded on the upper chamber and incubated with FITC‐labelled dextran (1 mg/mL) for 45 min, then immediately collected 200 μL supernatant in the upper and bottom chamber, respectively, and determined the concentration of FITC‐dextran using HTS 7000 microplate reader. The permeability coefficient of dextran was calculated as follows: Pd = [A]/t × 1/A × V/[L], with [A] indicates the concentration of bottom chamber, [L] represents the upper ones, t represents time in seconds, A refers to the membrane area (in cm2), and V indicates the volume of the bottom chamber.Measurement of transendothelial electrical resistance (TER) and FITC‐Dextran transendothelial flux was performed to evaluate endothelial permeability. As us published previously,2.7HUVECs were seeded on the microporous petri dish and cultured to 80%~ 90% confluences following with 8h exposure of 100 μg/mL AGEs after β‐catenin Y142F plasmid transduction. After washed briefly with phosphate‐buffered saline (PBS), cells were firstly fixed with 4% formaldehyde for 10 minutes and then permeabilized in 0.5% Triton X‐100 for 10 minutes at room temperature. Afterwards, cells were blocked with 5% BSA and subjected to incubation of primary antibody overnight at 4°C. Next, the samples were incubated with appropriate Alexa‐Fluor‐coupled secondary antibodies. Finally, DAPI (1:1000) was administrated to label the nucleus, and Zeiss LSM780 laser confocal scanning microscope  was used to detect the immunofluorescent signals.2.8I = 1 − (iI − oI)/iI. ΔI indicates changes in light intensity. Ii refers to the light intensity inside the vessel, and oI means the light intensity outside the vessel. The excitation wave used to detect ﬂuorescence was 488 nm, whereas the emission wave was 525 nm.Wild‐type C57BL/6 mice (aged 6‐8 weeks) were acquired from the laboratory centre of Southern Medical University. The protocol was approved by the Animal Care Committee of the Southern Medical University of China and was in accordance with the National Institutes of Health guidelines for ethical animal treatment. According to the protocol of Moore et al,2.9C57 mice were intravenously injected with 200 μL 0.5%(w/v) Evans blue(EB) in saline through its lateral tail vein. After 2 hours, the mice were performed thoracotomy under pentobarbital sodium (50 mg/kg) anaesthesia and the heart perfused with saline to remove blood. Lungs subsequently were harvested, photographed, weighed and each placed in 2 mL tubes with 1 mL formamide and the tubes transferred to a 50°C water bath for 48h. Extravasation of EB into the interstitial tissue was quantified at 620nm wave length. Calculation formula: EB amount per gram of lung tissue (μg/g) = EB concentration (μg/mL) *1(mL)/ lung weight (g).2.10TM kit. Luminescence was measured by using SpectraMax M5 Multifunctional enzyme marker  24 hours after transfection. Experiments were performed in triplicate and repeated twice in independent conditions. Luminescence was normalized to Renilla to control for transfection efficiency and expressed as fold increase compared with that of control cells.Dual‐Luciferase Assay  was applied to quantify Wnt/β‐catenin signalling through TCF/LEF promoter activity in HUVECs treated with AGEs. Cells were transiently co‐transfected with TCF/LEF promoter luciferase and Renilla plasmids, at a ratio of 10:1 according to Lipofectamine2.11P <.05.All data were analysed by SPSS16.0 software and presented with mean ± SD with more than three independent experiments. One‐way ANOVA was used in statistical comparisons and LSD post hoc analysis was used to compare data among multiple groups with significant level set at 33.1Firstly, our results showed that AGEs time‐dependently induced endothelial monolayer hyperpermeability, as indicated by decreased TER Figure  and the 3.2P <.05) already observed after 10 minutes; phosphorylation then reached a peak at 60 minutes and finally returned to normal levels after 120 minutes  or a β‐catenin Y654 phospho‐mimic (Y654E). In contrast with the previous experiment with AGEs‐treated HUVECs that showed VE‐cadherin/β‐catenin dissociation and unchanged levels of either protein, cells pre‐transfected with the Y654F plasmid before AGEs exposure for 1 hour presented ameliorated dissociation of VE‐cadherin/β‐catenin Figure . Similar3.4For further exploration of the effect of β‐catenin Y142 phosphorylation, β‐catenin Y142 phospho‐deficient (Y142F) and β‐catenin Y142 phospho‐mimic (Y142E) vectors were constructed. HUVECs were pre‐transfected separately with the overexpression vectors followed by 100 μg/mL AGEs stimulation for 8 hours. The immunofluorescence results showed that the Y142F vector ameliorated the disruption of β‐catenin/α‐catenin caused by AGEs, whereas the Y142E plasmid simulated the effect of AGEs Figure . As prev3.5P <.01) occurring at 24 hours and a return to normal levels at 48 hours  in the membrane and that of the soluble VE‐cadherin ectodomain in the cellular supernatant were measured by western blotting and ELISA, respectively. As VE‐cadherin CTF is a substrate for subsequent γ‐secretase cleavage, the HUVECs used in our assays were treated in the presence of the γ‐secretase inhibitor, DAPT (10 μmol/L).3.7To further confirm the protective role of Y654 phospho‐deficient β‐catenin in microvascular permeability, an overexpression adenovirus vector (Ad‐Y654F) was constructed and administered to C57 mice followed by the intraperitoneal injection of AGEs (10 mg/kg) for seven consecutive days. The transfection efficiency of indicated adenovirus in mesenteric vessels and lung was verified and shown in Figure Moreover, the two tyrosine residues phosphorylation level of β‐catenin Y654 and Y142 site in mesenteric vein are both increased compared with the control groupFigure . At the 4Consistent with previous reports, the present study showed that AGEs are involved in endothelial hyperpermeability. However, the precise mechanism remained to be uncovered. It has been widely accepted that the precise regulation of endothelial permeability depends on the balance between intercellular adhesion forces and intracellular contraction forces.et al using a conditional knock‐in mouse model expressing a phospho‐mimic Y654 β‐catenin mutant.Based on our observations of the key role of AGEs in the dissociation of VE‐cadherin/β‐catenin and the impairment of AJs, which is consistent with the work by Otero et al,Here, focusing on AJ association and considering that the β‐catenin Y654 and Y142 residues phosphorylation reduces β‐catenin/VE‐cadherin and β‐catenin/α‐catenin binding, respectively, whereas other residues including Y86 and Y333 do not reported such effect, we chose these two tyrosine residues for this research study. Firstly, we showed that the enhanced β‐catenin Y654 phosphorylation in HUVECs induced by AGEs treatment led to AJ disruption and subsequent increases in endothelial permeability. A β‐catenin Y654 phospho‐deficient mutant plasmid (Y654F) abrogated this effect of AGEs on the AJs. Conversely, a β‐catenin Y654 phospho‐mimic plasmid (Y654E) triggered the dissociation of VE‐cadherin/β‐catenin in HUVECs. These data suggested that β‐catenin Y654 phosphorylation is a prerequisite for the AGEs‐evoked collapse of the AJs. Consistent with this, we found that Y654F prevented AGEs‐evoked endothelial monolayer hyperpermeability and rescued mesenteric microvascular hyperpermeability. These data point towards the key role of β‐catenin Y654 phosphorylation in AGEs‐induced endothelial dysfunction.However, the molecular mechanism underlying AGEs‐induced AJ collapse still needed to be elucidated. As an important member of the largest non‐receptor tyrosine kinase family, Src is known to be activated by AGEs and to participate in endothelial hyperpermeability.Next, we shifted our focus to Y142 of β‐catenin, another phosphorylated residue. In the AJs, α‐catenin tends to form a complex with β‐catenin rather than with itself.FAK is a focal adhesion‐associated protein kinase and has been widely investigated in terms of its modulation of endothelial permeability. FAK directly phosphorylates β‐catenin at Y142 and mediates VEGF‐induced hyperpermeability.It is widely accepted that β‐catenin is not only a structural component of cadherin‐based adherens junctions but that it also acts as a key nuclear effector of canonical Wnt signalling in the nucleus.The integrity of adherens junctions can also be affected by several proteases via the cleavage of the extracellular or intracellular domains of cadherins.In summary, in the current study, we focused on the effect of β‐catenin phosphorylation at Y654 and Y142 on AGEs‐induced endothelial hyperpermeability. We demonstrated the following: (i) Y654 phosphorylation as triggered by Src is responsible for the destabilization of endothelial AJs; (ii) Y142 phosphorylation as stimulated by FAK activation leads to the reorganization of F‐actin, endothelial contraction, β‐catenin nuclear translocation, and up‐regulated transcription of the Wnt target, ADAM10, which cleaves VE‐cadherin. These cellular processes act in concert to contribute towards endothelial barrier dysfunction in response to AGEs Figure . OverallNo conflicts of interest, financial or otherwise, are declared by all authors.Jie Weng: Investigation ; Methodology ; Project administration ; Visualization ; Writing‐original draft (supporting). Zhenfeng Chen: Investigation ; Methodology ; Project administration ; Visualization ; Writing‐original draft (supporting). Jieyu Li: Investigation ; Methodology ; Project administration ; Visualization . Qi He: Methodology (supporting); Validation (supporting); Visualization (supporting); Writing‐review & editing (supporting). DeShu Chen: Writing‐original draft (lead). Lin Yang: Conceptualization (supporting); Formal analysis (supporting); Investigation (supporting). Haiying Su: Project administration (supporting). Junlin Huang: Project administration (supporting). Shengxiang Yu: Project administration (supporting). Qiaobing Huang: Conceptualization (supporting); Supervision (supporting). Qiulin Xu: Conceptualization (lead); Supervision (lead). Xiaohua Guo: Conceptualization (lead); Funding acquisition (lead); Supervision (lead); Validation (lead); Writing‐review & editing (lead).The using mice were approved by the Animal Care Committee of the Southern Medical University of China and in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.


Outside school hours care (OSHC) is accessed by millions of children internationally. Recently, physical activity and screen time guidelines in OSHC were developed. This study described the current physical activity and screen time scheduling in Australian OSHC, obtained sector feedback on the guidelines and compared current- with best-practice.n = 3551 Australian OSHC directors. Participants reported scheduling for physical activity and screen time opportunities in before- and after-school care. Feedback was sought on the new guidelines, including barriers and enablers for implementation. Scheduling data were used to evaluate whether services were currently meeting the new guidelines; that is if time allocated matched with time recommended.A cross-sectional online survey was administered to p = 0.002) and socioeconomic  differences. Most participants (54–81%) agreed that the guideline’s recommended physical activity and screen time durations were appropriate, however, only 40% of participants’ OSHC services’ programs actually met the guidelines.Five hundred and sixty-six directors participated (response rate 16%). Physical activity and screen time practices varied widely , with state (Physical activity and screen time scheduling in OSHC is highly variable. Despite support for the guidelines, current scheduling practice in the majority of OSHC services surveyed do not meet best practice guidelines.The online version contains supplementary material available at 10.1186/s12889-022-13135-7. Engaging in adequate amounts of physical activity (PA) is associated with improved cardiovascular and bone health, academic performance, and quality of life , 2. FurtThe outside school hours period (before and after school and the weekends) provides an opportunity for the accumulation of PA and sedentary behaviours , 6. ManyGuidelines are evidence-based recommendations to encourage best practice . A recenGuideline implementation can be bolstered when current practice, and how this meets best-practice guidelines, is understood (i.e. defining the evidence-practice gap); when practitioners have had the opportunity to comment or feedback on the guidelines (improving credibility); and understanding the barriers and enablers to implementation . AccordiA cross-sectional online survey was conducted from June to August 2020. Ethical approval was provided by the University’s institution ethics board (no. 202898).A 150-item purpose-designed survey instrument was developed (supplementary file Location (state), average daily attendance, and the care sessions provided . Socioeconomic status was determined from postcode, based upon Australian Bureau of Statistics’ Socio-Economic Indexes for Areas (SEIFA) . It tookr = 0.41) and ST (r = 0.73) [Activities offered and typical duration of the activities were captured using a items developed in a previous OSHC study  Particip = 0.73) .The OSHC-sector PA and ST guidelines  and enablers  to guideline uptake were ranked (using based on factors identified from the Delphi study ). Open-eAll OSHC directors in any metropolitan, rural, or remote location in both public and private settings were eligible. A list of email addresses for all Australian OSHC service was obtained from the Australian Children’s Education and Care Quality Authority (ACECQA) website. The survey was open for 4 weeks, with weekly email invitation reminders. In addition, the survey was distributed to members of National Outside School Hours Services Alliance (NOSHSA)—the peak Australian OSHC body, and it was posted on OSHC specific Facebook® groups . A $100 random prize draw was offered for survey participation.All responses were downloaded into SPSS . DemograTo calculate the total amount of time scheduled for PA and ST in each OSHC service during before and/or after-school care sessions, the total number of 15-min timeslots offering any PA or recreational ST, respectively, were summed.ANOVA was used to determine whether the amount of time scheduled for PA varied by SES and state , whilst Kruskal–Wallis was used to examine whether ST practices varied by SES or state (because data were skewed data). In each case, two models were run: one for before-school and one for after-school care.p < 0.05.The PA and ST duration data were then used to calculate whether each OSHC service met each component of the guidelines, i.e., before-school PA scheduled opportunity ≥ 45 min; before-school ST scheduled opportunity ≤ 30 min; after-school PA scheduled opportunity ≥ 90 min; after-school ST scheduled opportunity ≤ 60 min. From this, the percentage of OSHC services meeting all four guideline components was calculated. ANOVA with Tukey Post Hoc testing was used to determine whether the number of guidelines components met varied according to state and SES tertile. For all statistical analyses, significance was set at Free text responses to open-ended questions were compiled and categorised into naturally emerging themes by the first author (RV) and cross-checked by the senior author (CM).n = 3518. A total of 566 OSHC directors (representing 566 discrete OSHC services) participated (response rate of 16%).Three thousand five hundred fifty one survey invitations were sent to unique email addresses listed for before and/or after-school care providers on the ACECQA database. After the first email, one major OSHC provider asked for all its services to be removed from the survey distribution list, leaving All Australian states and territories were represented, with most respondents from New South Wales (29%), South Australia (23%) and Queensland 19%). Results are summarised in Table 9%. ResulX2 = 102.6, p < 0.0001), with services based in South Australia more likely to respond compared to other states. However, they did not differ based on socioeconomic status  = 0.149, p = 0.93).Responders and non-responders differed based on state (n = 60) of services did not schedule any PA during before-school care. A small number  did not schedule PA in after-school care. Amongst services reporting any scheduled time for PA, it ranged from 30 to 210 min . The amount of time scheduled for PA (Table p = 0.002), but not SES . The most common types of PA offered during before- and after-school care were outdoor free play .The time scheduled for PA ranged widely in both before- and after-school, from 0 to 150 min  and 0 to 210 min  respectively  and SES . The most common screen-based activity offered in before-school and after-school was TV/DVD viewing. Full post hoc results for PA and ST scheduling are shown in supplementary file The time scheduled for screen-based activities ranged widely, from 0 to 135 min in before-school care mean 25, SD 39), and 0 to 195 min  in after-school care   of participants’ free text responses affirmed the importance and need for guidelines. The next most common feedback (92%) restated the notion that the ST guidelines should be stricter. For example, participants suggested that instead of offering ST daily, it should only be offered only for wet weather, Friday afternoons, or special occasions.Participants ranked barriers from most important to least important: (1) lack of indoor space for active play, (2) children's attitudes, (3) behaviour management, (4) family attitudes, (5) staffing beliefs and (6) workplace culture. The enablers were ranked (from most important to least important): (1) staff understanding, (2) adequate and appropriate staff training, (3) OSHC family education and understanding of the PA and ST guidelines, and (4) workplace policy.n = 13 provided elaborating comments, fell into two types: either than they were already achieving the guideline (and thus did not need to change to meet guidelines) or did not want to change their programming.Overall, more than three-quarters (77%) of OSHC directors reported feeling confident (scores 4 and 5) of their ability to adjust their OSHC services programming to meet the PA time recommendations in the guidelines. Of the directors who were not confident to adjust programming (scores 1–3), only n = 11) suggested they were either already meeting the ST guidelines, or that they were not interested in changing their ST practices.Similarly, 78% of OSHC directors reported they felt confident (scores 4 and 5 respectively) to adjust their OSHC programming to meet the ST guidelines. Again, of the directors who reported not feeling confident, their free-text comments (p = 0.002) and SES  for services meeting before-school care ST guidelines. There were also differences by state, but not SES for meeting after-school care ST guidelines  with full post-hoc tests provided in supplementary file There were no statistically significant difference by state or by SES for meeting the PA guidelines in before or after-school care. However, there were significant differences by state . OSHC services in Tasmania (100%) and Australian Capital Territory (100%) had highest rates of meeting all components of the guidelines, while South Australia (22%) and Western Australia (28%) had lowest rates of meeting the guidelines. There were statistically significant differences between low SES and high SES services for meeting all four guidelines . Full post-hoc results are provided in supplementary file Compliance with each guideline component, and all four guideline components in combination, for Australia overall and by state is shown in Fig. In general, OSHC directors were supportive of the need for PA and ST guidelines in OSHC and the durations recommended in the draft guidelines. At present, the amount of time scheduled for PA and ST varies widely between OSHC services, with state-level and socioeconomic differences. Approximately two in five OSHC services surveyed met the new OSHC sector guidelines.The most scheduled form of PA in OSHC throughout Australia was outdoor free play. This finding is consistent with the Curriculum Framework for School Age Care in Australia, which has a focus on the agency of children to engage in free play. The same finding was identified in earlier research conducted in South Australian OSHC services , and is Screen time practices in OSHC varied widely. Whilst 30–60% of OSHC services don’t offer recreational ST daily, some OSHC services offer ST for the entirety of their morning and afternoon care sessions, amounting to over five hours per day. The most common form of ST offered during OSHC was TV/DVD viewing – a finding consistent with Arundell’s 2016 systematic review, which similarly reported that TV viewing as the most common screen-based activity in after-school care . There wOSHC directors rated the leading barriers as lack of indoor space for active play, children’s attitudes, and behaviour management. This differed slightly from our recent Delphi study which suggested that educator beliefs and workplace culture would be the main barriers . This diMeeting the guideline criteria differed significantly by state and SES which has important implications for future implementation. State differences in practice may reflect state-level efforts that have been made in the OSHC sector. For example, Queensland and New South Wales have had state-level programs promoting PA in OSHC– PANOSH in Queensland , and “EaTo our knowledge, this is the first national-level study of PA and ST practices in OSHC in any country. Importantly, it provided end-users the opportunity to comment on the new national OSHC-sector guidelines, which will help ensure feasibility and bolster the guidelines’ acceptance in the future. By examining how current practice meets new OSHC-sector guidelines, the study provides insight into the current evidence-practice gap. A further strength was representation from all states and territories.Study limitations include the use of self-reported data, which is susceptible to social desirability bias . In addiThis study revealed there is a sizeable evidence-practice gap . The study also suggested that the new OSHC sector guidelines are well received by the sector, both in terms of the need and the specified durations of PA and ST recommended per care session. Concerted efforts will be needed to disseminate the guidelines to increase awareness of their existence, and familiarity with the content .A range of strategies will be needed to achieve this. In particular, findings highlighted state level and SES difference, a need for OSHC sector staff training on how to use these guidelines, including children’s play behaviours, activities to encourage PA and reduce competing interests  , 29.In conclusion, this is the first national-level study examining daily scheduling for PA and ST in Australian OSHC. PA and ST guidelines were welcomed and considered feasible by the sector. More OSHC services are meeting the PA recommendations than ST recommendations, and only around 40% are meeting best-practice guidelines. Concerted efforts are now needed to work with the sector to implement the PA and ST guidelines into practice.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.


CTNNB1 (β-catenin)-mutated subtype exhibits distinctive characteristics of immunosuppressive tumor microenvironment. For clarifying the molecular mechanisms, we first established human and mouse HCC cells with exon 3 skipping of β-catenin, which promoted nuclear translocation and activated the Wnt/β-catenin signaling pathway, by using newly developed multiplex CRISPR/Cas9-based genome engineering system. Gene set enrichment analysis indicated downregulation of immune-associated gene sets in the HCC cells with activated β-catenin signaling. Comparative analysis of gene expression profiles between HCC cells harboring wild-type and exon 3 skipping β-catenin elucidated that the expression levels of four cytokines were commonly decreased in human and mouse β-catenin-mutated HCC cells. Public exome and transcriptome data of 373 human HCC samples showed significant downregulation of two candidate cytokine genes, CCL20 and CXCL2, in HCC tumors with β-catenin hotspot mutations. T cell killing assays and immunohistochemical analysis of grafted tumor tissues demonstrated that the mouse Ctnnb1Δex3 HCC cells evaded immunosurveillance. Taken together, this study discovered that cytokine controlled by β-catenin signaling activation could contribute to immune evasion, and provided novel insights into cancer immunotherapy for the β-catenin-mutated HCC subtype.Comprehensive analysis of clinical samples has recently identified molecular and immunological classification of hepatocellular carcinoma (HCC), and the  Although anti-angiogenic agents and immune checkpoint blockers have currently emerged for HCC treatment2, it remains a leading cause of cancer-related death in the world3. To improve patient prognosis, categorization of tumor samples into subtypes and customization of cancer therapy for each subtype are essential in HCC, similarly to other types of cancer4. Several laboratories have proposed molecular classification of HCC on the basis of gene expression profiles in the past two decades, and a two-group model (proliferation and non-proliferation) is now widely accepted6. In our latest paper, recent advances in next generation sequencing technology elucidate that the non-proliferation group is further divided into two distinct subtypes, namely CTNNB1-mutated and metabolic disease-associated subtypes7.Hepatocellular carcinoma (HCC) is a complex disease with various risk factors, that is, chronic infection with hepatitis B virus and hepatitis C virus, alcohol abuse, metabolic disease including obesity and diabetes, and dietary toxins such as aflatoxins and aristolochic acidCTNNB1, encoding β-catenin, are most frequently identified in HCC, and accumulated in exon 3 (amino acid position 5–80) corresponding to the serine/threonine (Ser/Thr) phosphorylation site for GSK3β which normally promotes ubiquitination and degradation of β-catenin. Gain-of-function mutations in exon 3 or exon 3 skipping events contribute to stabilization, translocation from cytoplasm to nucleus, and then activation of the Wnt/β-catenin signaling pathway9. For examining this biological process, two genetically engineered mouse models have been developed; one is a transgenic mouse model with ectopic expression of mutated and stabilized β-catenin in which Ser33, Ser37, Thr41 and Ser45 are substituted by alanine residues10 or N-terminal deletion11; the other is a Cre/loxP-based mouse model harboring a mutant Ctnnb1 allele with loxP sequences in intron 2 and intron 3 for intrinsically skipping of exon 312.Somatic mutations of . have recently addressed that the Wnt/β-catenin signaling pathway is activated, particularly by CTNNB1 mutation, in non-T cell-inflamed tumors across cancer types including HCC13. In melanoma, β-catenin signaling upregulates IL-10 secretion, which impairs the capacity of dendritic cells (DCs) to cross-prime CD8+ cytotoxic T cells14, or downregulates CCL4 expression, resulting in DC defective recruitment and T cell exclusion15. However, although we and others have reported that CTNNB1-mutated HCC shows immune suppression16, the molecular mechanism is not fully clarified in HCC17.A series of studies has linked tumor-intrinsic Wnt/β-catenin signaling not only to oncogenesis and stemness, but also to cancer immune surveillance. T cell-inflamed phenotype, characterized by CD8+ T cell infiltration, is closely correlated with the efficacy of immune checkpoint blockade, whereas non-T cell-inflamed tumors rarely benefit. Luke et alIn this study, we established a novel model of intrinsically active β-catenin signaling by CRISPR/Cas9-mediated exon skipping in human and mouse HCC cells, and investigated how tumor β-catenin signaling evades the immune system in HCC.18. We first designed single guide RNAs (sgRNAs) targeting intron 2 (sgRNA-in2) and intron 3 (sgRNA-in3) of human CTNNB1 gene by using the GPP sgRNA Designer web tool, and constructed U6-driven sgRNA-in2 and H1-driven sgRNA-in3 expression plasmids, respectively. After confirming the mutation efficiency of the two sgRNAs, we next generated a lentivirus vector for simultaneously expressing them . As expected, CTNNB1Δex3 alleles were amplified by PCR of genomic DNA in the genetically engineered HuH7 cells (HuH7-CTNNB1Δex3), and β-cateninΔA5–A80 proteins were detected in the pools of the HuH7-CTNNB1Δex3 cells , originally provided from Feng Zhang’s laboratoryΔex3 and 3H3-Ctnnb1Δex3 cell pools, and western blotting analysis by using an antibody targeting amino acids 29–49 of β-catenin showed heterozygous  and homozygous  deletion of exon 3, encoding amino acids 5–80  as shown in Supplementary Table LGR5, RNF43, ZNRF3, AXIN2 and TCF7, implying positive and negative feedback loops. We examined the mRNA expression levels of them, and significant upregulation of LGR5, RNF43 and AXIN2 indicated activation of the β-catenin signaling in the HuH7-CTNNB1Δex3 and 3H3-Ctnnb1Δex3 subclones , which was consistent with the results of quantitative RT-PCR analysis. Gene set enrichment analysis (GSEA) of the HuH7-CTNNB1Δex3 and 3H3-Ctnnb1Δex3 cells revealed the close relationship between activation of the β-catenin signaling and downregulation of immune-associated gene sets  and CXCL2 .Among 114 cytokines registered in the CYTOKINE ACTIVITY gene set M14581), 16 and 8 cytokines were remarkably downregulated in human and mouse ely Fig. a,b, and 81, 16 anΔex3 and control cells (3H3-Ctrl) as illustrated in Fig. Δex3 cells was not changed  during double-strand break repair. We then mimicked activation of the β-catenin signaling by skipping exon 3, that is, joining the ends of intron 2 and intron 3 simultaneously cleaved by the CRISPR/Cas9 system. For this purpose, we established the novel multiplex CRISPR/Cas9-mediated genome engineering system of lentiCas9-Blast and improved lentiGuide-Puro plasmids, although Kabadi et al. have already produced a lentivirus vector containing a Cas9 transcription cassette and multiple sgRNA transcription cassettes20. This is because a Golden Gate cloning method is complicated compared with a conventional cloning method, and because two-vector system is superior to all-in-one vector system in functional viral titer18. Thus, our vector system enabled exon skipping with ease and efficiency, and could expand to other models, such as EGFRΔex19 and ERBB2Δex16 for activation of the EGFR signaling pathway, and POLD1Δex10 and POLEΔex9 for attenuation of exonuclease activity.Although previous studies have examined the relationship between the Wnt/β-catenin signaling pathway and immune surveillance, they are artificial due to overexpression of mutated β-catenin, such as β-catenin. have revealed that CTNNB1-mutated HCC is more accumulated in the ICI-resistant group than in the ICI-sensitive group21, which is consistent with the important finding that CTNNB1 mutation is enriched in non-T cell-inflamed tumors insusceptible to ICI therapy13. Since the mutation rate of CTNNB1 gene is relatively higher in HCC than in other types of cancer  including anti-PD-1 and anti-PD-L1 antibodies have provided a revolutionary approach to cancer therapy, and clinical trials of ICIs for various types of cancer are now ongoing and successful. In HCC, two anti-PD-1 antibodies nivolumab and pembrolizumab prolonged patient survival in phase II trials, however both monotherapies failed in phase III trials unfortunately. Harding et al17, CCL20 was commonly downregulated in HCC with β-catenin signaling activation, although there was no difference of CCL5 expression between human and mouse HCC cells with and without exon 3 skipping of β-catenin in this study. CCL20, alternatively named liver and activation-regulated chemokine (LARC), was originally discovered in the liver and strongly expressed in mononuclear cells near necrosis in the chronically inflamed liver and HCC. CCR6 is the selective receptor for CCL20, and the CCR6-CCL20 axis contributes to the recruitment of immature DC to the antigen entry site and the arrest of T lymphocyte on the endothelium in the early phase of immune response22. The chemokine receptor CXCR2 and its ligands CXCL1, CXCL2, CXCL3, CXCL5 and CXCL8 play critical roles in the chemoattraction of neutrophils towards tumor tissues. Similarly to tumor-associated macrophages, tumor-associated neutrophils can be polarized into either an antitumoral (N1) or a protumoral (N2) phenotype; the N1 phenotype is induced by TGF-β blockade, and expresses immunoactivating cytokines and chemokines for killing cancer cells23. Thus, the β-catenin signaling pathway might suppress immune response through decrease of cytokine levels.By comparing the present and previous studies. have previously reported that the Wnt/β-catenin signaling induces expression of a transcription repressor ATF3, and that ATF3 inhibits CCL4 expression in melanoma cells15, but the expression levels of transcription factors including ATF3 were not altered in human and mouse HCC cells with β-catenin signaling activation. The other is that β-catenin might change the epigenetic status of cytokine genes. Large-scale methylome data of HCC provided from the Cancer Genome Atlas Research Network clarified that downstream genes of the Wnt/β-catenin signaling pathway, such as LGR5, RNF43 and AXIN2, were demethylated at the promoter regions in CTNNB1-mutated HCC samples .19. We examined mutations of Ctnnb1, Trp53, Braf and Hras, which are frequently observed in human HCC and carcinogen-induced mouse HCC, and only detected the HrasQ61L mutation in the 3H3 cells. They were cultured in RPMI-1640 and DMEM  medium containing 10% fetal bovine serum (FBS), and 1% penicillin, streptomycin and amphotericin B (Wako), maintained in a humidified incubator at 37 °C in 5% CO2, and harvested with 0.05% trypsin-0.03% EDTA (Wako).Human HCC cell line HuH7 was purchased from the American Type Culture Collection . Mouse cell line 3H3 was derived from HCC tumor grown in a C57BL/6J MC4R-KO mouse fed with high fat dietKpnI site in front of the U6 promoter and replacing the HindIII site behind the sgRNA scaffold with an EcoRI site, named as LG-U6. The LG-H1 plasmid was also produced by replacing the U6 promoter with the H1 promoter in the LG-U6 plasmid. The LG-U6 and LG-H1 plasmid for expressing sgRNAs targeting intron 2 and intron 3 of β-catenin (sgRNA-in2 and sgRNA-in3) were constructed following the manufacture’s manual  and simultaneously expressing sgRNA-in2 and sgRNA-in3, and then treated with 10 μg/mL blasticidin and 10 μg/mL puromycin, respectively. The subclones with β-catenin alleles lacking exon 3 were isolated by limiting dilution.To generate the backbone plasmid for the CRISPR/Cas9 system, the lentiGuide-Puro (Addgene #52963) was modified by inserting a Cell pellets were suspended in TNE Buffer  with 1% proteinase K  at 55 °C overnight. Genomic DNA was obtained from cells by phenol–chloroform extraction. The primer sets and amplification conditions for PCR are listed in Supplementary Table Total RNA was extracted from cells by using RNeasy Plus Mini Kit . Contaminating DNA was removed by digestion with RNase-Free DNase Set (QIAGEN).For single-stranded complementary DNA synthesis, 1 μg of total RNA was reverse-transcribed by SuperScript III Reverse Transcriptase . Quantitative RT-PCR analysis was performed by using TB Green Premix Ex Taq II (TaKaRa Bio) with StepOne real-time PCR system (Thermo Fisher Scientific) according to the manufacturer’s instructions, and the ΔΔCt method was used for quantification. GAPDH was used as an internal control. The primer sets and amplification conditions for PCR are listed in Supplementary Table Sequencing libraries were prepared from total RNA with the TruSeq Standard mRNA Library Kit , and RNA sequencing was run on an Illumina NovaSeq 6000. Sequence reads were aligned to the human and mouse reference genome (GRCh38 and GRCm38) by STAR (2.7.0d), and transcript quantification was performed by RSEM (1.3.1). Differentially expressed genes were determined by using DESeq2 (1.14.1).After whole cell lysates were collected by using ice-cold RIPA buffer (Thermo Fisher Scientific), 30 μg of protein from each sample was subjected to electrophoresis through 10% sodium dodecyl sulfate–polyacrylamide gels and transferred onto Immobilon polyvinyldifluoride membranes . The membrane was blocked with 5% skimmed milk or bovine serum albumin for an hour at room temperature, adequately cut, and then incubated overnight at 4 °C with primary antibodies as follows;β-catenin , GAPDH  and lamin B1 , all of which were purchased from Cell Signaling Technology ; β-catenin  from Millipore-Sigma ; glutamine synthetase  from Abcam . Secondary antibodies were added, and signals were detected by using Clarity Western ECL Substrate  with LAS-3000 . The original image data of blots were shown in Supplementary Figure Cytoplasmic and nuclear proteins were separately extracted by NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific) according to the manufacturer’s instructions, and then Western blotting analysis was performed. GAPDH and lamin B1 were used for detecting cytoplasmic and nuclear fractionated protein, respectively.5 and 3 × 104 cells per well in 24-well plates, respectively, and transiently transfected with 400 ng TOPFlash or FOPFlash plasmids and 100 ng pNL1.1.TK plasmid  as an internal control by using PEI MAX . Two days after transfection, the relative light unit (RLU) was measured by using Nano-Glo Dual Luciferase Reporter Assay System (Promega) with FLUOstar OPTIMA-6 microplate reader  following the manufacturer’s instructions. Luciferase activity was calculated as the ratio of firefly RLU (TOPFlash or FOPFlash) to NanoLuc RLU (pNL1.1.TK).The M50 Super 8× TOPFlash plasmid (#12456) and the M51 Super 8× FOPFlash plasmid (#12457) from Addgene . The HuH7 and 3H3 cells were seeded at a density of 1 × 103 cells per well in 96-well plates, and incubated overnight before each assay. The number of cell lines was estimated by using WST-8 in accordance with the manufacturer’s instructions. Briefly, 1 h after 100 μL of Cell Counting Kit-8 solution  were added to each well, the absorbance was measured on a microplate reader  at 450 nm.Cells were seeded at a density of 5 × 10CTNNB1 and SASI_Mm01_00161710 for mouse Ctnnb1) were purchased from Merck KGaA , and transfected into cells by using Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific).Control siRNA duplex  and β-catenin-targeting siRNA duplex , 100 U/mL murine IL-2  and 10 ng/mL murine IL-7 (Peprotech).27. Briefly, eight-week-old male C57BL/6J mice were euthanized, and bone marrow was flushed out from femur and tibia by using a 1 mL syringe and a 27G needle. Bone marrow-derived monocytes (BMDMs) were washed, and then cultured in DC differentiation medium as follows; RPMI-1640, 10% FBS, 1% penicillin–streptomycin-amphotericin B, 20 ng/mL murine GM-CSF (Peprotech) and 5 ng/mL murine IL-4 (Peprotech). Six days after preculture, differentiated bone marrow-derived dendritic cells (BMDCs) were further cultured in conditioned medium collected from the 3H3-Ctrl cells or 3H3-CTNNB1Δex3 cells for 24 h to stimulate with cancer antigens.Isolation of mouse bone marrow and differentiation of DCs was performed as previously described3 cells per well in a 24-well tissue culture plate  or ultra-low attachment plate (for sphere culture) with primed immune cells for 48 h. Advanced DMEM/F12 (Thermo Fisher Scientific) with 0.5% B-27 supplement (Thermo Fisher Scientific), 20 ng/mL human EGF (Peprotech) and 1 μg/mL human FGF-basic (Peprotech) was used for sphere formation. To evaluate cytotoxic activity of immune cells in two-dimensional culture, cell viability was estimated by using CellTiter-Glo 2.0 reagent (Promega) with FLUOstar OPTIMA-6 microplate reader (BMG Labtech) according to the manufacturer’s instructions. For sphere culture, cancer cell area was measured by using ImageJ software.A day after T lymphocytes were co-cultured with BMDCs for priming, cells were plated at 5 × 106 cells were subcutaneously injected into C57BL6/J mice. Ten days after transplantation, mice bearing tumors were sacrificed, and the tumors were resected.After suspended in 100 μL Matrigel , 1 × 10Tissues were fixed overnight in 4% paraformaldehyde, embedded in paraffin, and sectioned (4 μm thick). Sections were immersed in sodium citrate (pH 6.0) buffer for antigen retrieval, and subsequently incubated with primary antibodies against CD8 , glutamine synthetase  and Ki-67  at 4 °C overnight. They were probed with anti-mouse or anti-rabbit IgG antibody labelled with peroxidase Histofine Simple Stain MAX-PO , and visualized with diaminobenzidine. Nuclei were stained with hematoxylin.Ccl20 and Cxcl2 were amplified by using PrimeSTAR MAX DNA Polymerase (TaKaRa Bio), and cloned into the XhoI and NotI sites of the CSII-EF-MCS-IRES-Hygro plasmid28. Cells were infected with the lentiviral vectors, and then treated with 500 μg/mL hygromycin.Entire coding sequence of mouse CTNNB1 hotspot mutations.Gene set enrichment analysis was performed with the MSigDB gene sets. Public genome, methylome and transcriptome data of 373 HCC samples were provided from the Cancer Genome Atlas Research Network, and downloaded from the cBioPortal site. Genome data divided them into 77 and 296 tumors with and without Supplementary Information.


People miss a high proportion of targets that only appear rarely. This low prevalence (LP) effect has implications for applied search tasks such as the clinical reading of mammograms. Computer aided detection (CAD) has been used to help radiologists search mammograms by highlighting areas likely to contain a cancer. Previous research has found a benefit in search when CAD cues were correct but a cost to search when CAD cues were incorrect. The current research investigated whether there is an optimal way to present CAD to ensure low error rates when CAD is both correct and incorrect. Experiment 1 compared an automatic condition, where CAD appeared simultaneously with the display to an interactive condition, where participants could choose to use CAD. Experiment 2 compared the automatic condition to a confirm condition, where participants searched the display first before being shown the CAD cues. The results showed that miss errors were reduced overall in the confirm condition, with no cost to false alarms. Furthermore, having CAD be interactive, resulted in a low uptake where it was only used in 34% of trials. The results showed that the presentation mode of CAD can affect decision-making in LP search. Visual search is an important part of our everyday life, whether it is searching for a mobile phone in a living room, a child in a playground, or car in a car park. Some visual search tasks have significant implications for our health and safety. For example, a baggage screener searching through x-rays for a prohibited item or radiologists searching through mammograms for a cancer. These latter searches are made all the more difficult given that the targets only appear rarely  uses computer algorithms to identify areas of interest within a mammogram and mark them for radiologists to inspect, with the aim to help readers better detect a cancer  has been observed at LP . However, if two observers read the same display independently (e.g. in separate rooms) then target detection was improved, as the response bias shift, typically observed at LP, was reduced. Although double reading leads to improved LP search and was previously deemed to be a cost-effective procedure to run in the UK, this practice may not be sustainable in the future as the population of women that need to be screened increases  in the USA, with the aim to improve work-flow and reduce demands on radiologists and trained readers . Here, different reading conditions can be scientifically manipulated and investigated, without the same ethical concerns needed in real-life reading where patient care is at stake. However, these studies are limited by the time-constraints of radiologists and trained readers, so that (1) studies may be under-powered due to the low availability of readers . Experiment 2 compared CAD presentation in conditions where CAD was presented automatically with the display to when CAD was always presented after initial reading of the display (confirm condition). It was predicted that having people search the display initially before CAD would lead to fewer false alarms and miss errors when the CAD cue was incorrect compared to when CAD appeared automatically. This is because participants’ judgements would not be affected by the presence of a salient CAD cue in initial reading. However, after people had viewed CAD then the proportion of targets that were found would be equivalent when the CAD cue accurately predicted the target location.In summary, there is no clear consensus, of the optimal way to present CAD when the target has a low prevalence. We know that presenting CAD concurrently with the search display, leads to an over-reliance on the CAD cues  would be identical to the confirm condition (Experiment 2) if people made the choice to use the CAD cue. That is, CAD would only be effective in the interactive condition if there was a behavioural preference to use this for the majority of trials. As CAD has been proposed to act as double reader, in place of a human observer  took part in Experiment 1. In all experiments, participants were recruited from the University of Warwick participant pool, had no prior training in reading mammograms and were paid for their time. All participants had normal or corrected-to-normal vision. Ethical approval for all studies was granted by the Humanities and Social Sciences Research Ethics Committee at the University of Warwick. Participant numbers were determined in advance based on previous research  of the Digital Database for Screening Mammography (DDSM) database . For the target absent trials, 675 trials (75%) were presented without any CAD cues (correct CAD). The other 225 trials (25%) of target absent trials contained a CAD cue placed on a random area of the mammogram  and confirm responses (in the interactive condition) were recorded. If participants chose not to see the CAD cue the experiment moved to the next trial.Alongside the confirm response in the interactive condition, in both the automatic and the interactive conditions participants had the option of correcting their responses. If the participants recognized that they had made an error, they were able to correct it on the following trial, by pressing the ‘Escape’ key during any time of the next trial . They were then shown 12 different mammograms, one after the other, each containing a cancer. Participants were asked to point to the cancer, while the experimenter was in the same room (the experimenter would provide feedback if needed). Once participants completed this cancer identification task and both the participant and experimenter were confident that the participant could identify a mass, they then proceeded to take a practice block before each experimental block. During this practice block the experimenter again ensured that participants were able to recognise the cancer, when present. If any of the participants had difficulties identifying the cancer they were shown more examples and could repeat the practice condition until both the participant and experimenter were confident that they were able to identify the cancer. However, all the participants responded correctly in the first practice session and none were asked to repeat it. RTs, self-corrections and error rates were recorded. Within each condition breaks occurred automatically every 200 trials, after which participants continued with the experiment when they were ready. Given the length of each condition, the automatic and interactive conditions took place over two different sessions, each lasting approximately 2 h. The presentation order of conditions was counterbalanced across participants.10  of 1 to 3 provides anecdotal evidence for the alternative, a BF10 of 3 to 10 provides substantial evidence for the alternative, a BF10 of 10 to 30 provides strong evidence for the alternative, a BF10 of 30 to 100 provides very strong evidence for the alternative and a BF10 of greater than 100 provides decisive evidence for the alternative. The inverse of these numbers (BF01) provide evidence in support the null hypothesis  the analyses were conducted using the self-corrected data  versus absent , t(18) = 1.11 p = 0.28, d = 0.26, with anecdotal evidence in support of the null, BF10 = 0.41.Participants chose to check CAD on 34.3% of all trials in the interactive condition. A F = 5.13, p = 0.036, ηp2 = 0.22 in which there were fewer miss errors in the automatic than the interactive condition. There was also a significant main effect of CAD, F = 65.04, p < 0.001, ηp2 = 0.78, in which there were fewer miss errors in the correct CAD, followed by incorrect CAD and then the no CAD conditions. There was a significant condition × CAD interaction, F = 6.33, p = 0.004 ηp2 = 0.26. Planned t-tests showed that with correct CAD there were fewer miss errors in the automatic compared to the interactive CAD condition, t(18) = 4.02, p < 0.001, d = 0.92, with very strong evidence in support of the alternative BF10 = 44.32. When CAD was incorrect there was no difference in miss errors between the automatic condition and the interactive CAD condition, t(18) = 1.72, p = 0.10, d = 0.40, with anecdotal evidence in support of the null, BF10 = 0.82. For no CAD trials there was also no difference in miss errors between the automatic and interactive condition, t(18) = 1.81, p = 0.09, d = 0.41, with anecdotal evidence in support of the null, BF10 = 0.93.Looking at Fig. F = 15.88, p < 0.001, ηp2 = 0.47, in which there were fewer false alarms in the interactive compared to the automatic condition. There was also a significant main effect of CAD, F = 26.46, p < 0.001, ηp2 = 0.60, in which more false alarms were made in the incorrect CAD condition compared to the no CAD condition. The condition × CAD interaction was not significant, F = 1.29, p = 0.27 ηp2 = 0.07. As the interaction was not significant the data were not analysed further.Looking at Fig. d′ (a change in sensitivity) and c (a change in criterion) across presentation conditions.d′ and c values.Signal detection theory was used to calculate how CAD affected d′ there was little overall difference in d′ between the automatic and interactive conditions. A 2 × 3 within-participants ANOVA on d′ with factor of condition (automatic vs interactive CAD) and CAD  showed there to be no main effect of condition, F = 5.39e−4, p = 0.98, ηp2 = 2.99e−5. There was a significant main effect of CAD, F = 41.10, p < 0.001, ηp2 = 0.70, in which d′ was greatest in the correct CAD, followed by the no CAD and then the incorrect CAD conditions. There was a significant condition × CAD interaction, F = 6.06, p = 0.005 ηp2 = 0.25. Planned t-tests showed that with correct CAD there was no difference in d′ between the automatic and the interactive CAD condition,t(18) = 1.85, p = 0.08, d = 0.42, with anecdotal evidence in support of the null, BF10 = 0.98. Neither was there a difference in d′ between automatic and interactive conditions when there was no CAD, t(18) = 0.41, p = 0.69, d = 0.09, with substantial evidence in support of the null, BF10 = 0.26, or when CAD was incorrect, t(18) = 1.56, p = 0.14, d = 0.36, with anecdotal evidence in support of the null, BF10 = 0.67.Figure AD, F2, 3 = 41.10,c with factor of condition (automatic vs interactive CAD) and CAD  showed there to be a main effect of condition, F = 4.32, p = 0.05, ηp2 = 0.19, in which c was greater in the automatic condition compared to the interactive condition, and a main effect of CAD, F = 70.46, p < 0.001, ηp2 = 0.80, in which c was greatest in the no CAD condition followed by the incorrect CAD and then correct CAD conditions. The condition × CAD interaction was also significant, F = 4.88, p = 0.01 ηp2 = 0.21. Planned t-tests showed that there was no difference in c between the automatic and interactive conditions when CAD was correct, t(18) = 0.73, p = 0.48, d = 0.17, with substantial evidence in support of the null, BF10 = 0.30, or when there was no CAD, t(18) = 1.62, p = 0.12, d = 0.37, with anecdotal evidence in support of the null, BF10 = 0.72. However, c was greater in the automatic than the interactive condition when CAD was incorrect, t(18) = 3.65, p = 0.002, d = 0.84, with strong evidence in support of the alternative BF10 = 22.0.Figure As mentioned above, participants only chose to check CAD in the interactive condition on 34% of trials. To examine, how participants responded in the interactive condition when they chose to check CAD, error rates from these trials were compared to those of the automatic condition  = 0.36, p = 0.56, ηp2 = 0.02. There was a significant main effect of CAD, F = 52.54, p < 0.001, ηp2 = 0.77, in which there were fewer miss errors in the correct CAD, followed by incorrect CAD and then the no CAD conditions. There was no significant condition × CAD interaction, F = 1.88, p = 0.17, ηp2 = 0.11. As the interaction was not significant the data were not analysed further.Figure F = 1.24, p = 0.28, ηp2 = 0.07. There was a significant main effect of CAD, F = 45.42, p < 0.001, ηp2 = 0.73, in which more false alarms were made in the incorrect CAD condition compared to the no CAD condition. The condition × CAD interaction was significant, F = 5.03, p = 0.04 ηp2 = 0.23. Planned t-tests showed that when CAD was incorrect, a greater number of false alarms were made in the interactive checked-CAD condition than the automatic condition, t(17) = 2.60, p = 0.02, d = 0.61, with substantial evidence in support of the alternative, BF10 = 3.15. There was no difference in false alarms between the automatic and interactive checked-CAD condition, when no CAD cue was presented, t(18) = 0.14, p = 0.89, d = 0.03, with substantial evidence in support of the null, BF10 = 0.24.Figure Experiment 1 compared whether presenting CAD alongside the mammogram (in the automatic condition) led to better search at low prevalence than when participants could choose to interact with CAD. The results showed that overall, people made fewer miss errors in the automatic condition compared to the interactive condition . However, they also made more false alarm errors in the automatic condition compared to the interactive . The results are mixed in terms of outcomes. In terms of cancer detection, the automatic condition showed superior performance. In terms of minimising false alarms, the interactive condition was the better presentation mode.Overall, the data from both conditions replicate the over-reliance pattern observed in previous work  conditions. In a clinical setting, an increase in false alarms would manifest as an increase in the number of women that are falsely recalled for further tests. This has serious financial and psychological implications for the women involved Aro, . Having d′) with an increase in sensitivity to detect a target when the CAD cue was correct in comparison to the no CAD and incorrect CAD condition. Although there was a trend for sensitivity to be lower in the interactive condition for correct CAD trials, , there was no overall difference in sensitivity when d′ was compared across automatic or interactive conditions. When examining criteria, CAD had an effect on response criteria with a shift to a more liberal response criteria in the correct CAD condition. Interestingly, there was an overall shift in response criteria between presentation modes with the interactive condition showing a more liberal response bias than the automatic. This shift in response bias was likely to be driven by the incorrect CAD condition. Interestingly, there was no clear effect of this response bias on the miss errors or false alarms when the interactive CAD trials were examined, as a whole. However, looking at the error rates in Fig. Unsurprisingly, CAD had an effect on sensitivity  took part in Experiment 2. All participants had normal or corrected-to-normal vision. Participants were recruited separately from Experiment 1, however, they were not excluded from participation had they already taken part in Experiment 1.9Twenty participants . To rectify this, and as most people responded within this time period, we removed all trials where participants took longer to respond than 10 s from analysis. Trials where participants responded faster than 200 ms were also removed from analysis. In total, these outliers led to the removal of 1.1% of all data. Error rates and mean correct reaction times for all conditions are presented in Figs. F = 11.43, p = 0.003, ηp2 = 0.38, in which overall there were fewer miss errors in the confirm condition than the automatic condition. There was also a main effect of CAD, F = 52.42, p < 0.001, ηp2 = 0.73, in which there were fewer miss errors in the correct CAD, followed by incorrect CAD and then the no CAD conditions. There was a significant condition × CAD interaction, F = 22.15, p < 0.001 ηp2 = 0.54. Planned t-tests showed that participants made fewer miss errors in the automatic than the confirm CAD condition when the CAD cue was correct, t(19) = 3.19, p = 0.005, d = 0.71 with substantial evidence in support of the alternative BF10 = 9.39. However, when the CAD cue was incorrect participants missed more targets in the automatic condition than in the confirm CAD condition, t(19) = 4.90, p < 0.001, d = 1.10, with decisive evidence in support of the alternative, BF10 = 277.75. There was no difference in miss errors across conditions when the target was present with no CAD cue, t(19) = 1.55, p = 0.14, d = 0.35, with anecdotal evidence in support of the null, BF10 = 0.65.Looking at Fig. F = 0.53, p = 0.48, ηp2 = 0.03. However, there was a significant main effect of CAD, F = 16.52, p < 0.001, ηp2 = 0.47, in which more false alarms were made in the incorrect CAD compared to the no CAD condition. There was also a significant condition × CAD interaction, F = 6.19, p = 0.02 ηp2 = 0.25. Planned t-tests showed there was no significant difference in false alarms between the automatic and confirm CAD conditions when the incorrect CAD cue was shown, t(19) = 0.48, p = 0.64, d = 0.11, with substantial evidence in support of the null, BF10 = 0.26. Neither was there a significant difference in false alarms between conditions when no CAD cue was present, t(19) = 1.83, p = 0.08, d = 0.41, with anecdotal evidence in support of the null, BF10 = 0.93.Looking at Fig. d′ (a change in sensitivity) and c (a change in criterion) across presentation conditions. Figure d′ and c values.Signal Detection Theory was used to calculate how CAD affected d′ there was little overall difference in d′ between the automatic and confirm conditions. A 2 × 3 within-participants ANOVA on d′ with factor of condition (automatic vs confirm CAD) and CAD  showed there to be no main effect of condition, F = 0.04, p = 0.85, ηp2 = 0.002. There was a significant main effect of CAD, F = 39.73, p < 0.001, ηp2 = 0.68, in which d′ was greatest in the correct CAD, followed by the no CAD and then the incorrect CAD conditions. There was a significant condition × CAD interaction, F = 7.14, p = 0.002 ηp2 = 0.27. Planned t-tests showed that with correct CAD there was no difference in d′ between the automatic and the confirm CAD condition, t(19) = 1.82, p = 0.09, d = 0.41, with anecdotal evidence in support of the null BF10 = 0.92. Neither was there a difference in d′ between automatic and confirm conditions when there was no CAD, t(19) = 0.42, p = 0.68, d = 0.09, with substantial evidence in support of the null, BF10 = 0.25, or when CAD was incorrect, t(19) = 1.81, p = 0.09, d = 0.40, with anecdotal evidence in support of the null, BF10 = 0.91.Figure c with factor of condition (automatic vs confirm CAD) and CAD  showed there to be a main effect of condition, F = 4.35, p = 0.05, ηp2 = 0.19, in which c was greater in the automatic condition compared to the confirm condition and a main effect of CAD, F = 74.20, p < 0.001, ηp2 = 0.80, in which c was greatest in the no CAD condition followed by the incorrect CAD and then correct CAD conditions. The condition × CAD interaction was also significant, F = 5.76, p = 0.007 ηp2 = 0.23. Planned t-tests showed that there was no difference in c between the automatic and confirm conditions when CAD was correct, t(19) = 0.92, p = 0.37, d = 0.21, with substantial evidence in support of the null, BF10 = 0.34, or when there was no CAD, t(19) = 1.76, p = 0.10, d = 0.39, with anecdotal evidence in support of the null, BF10 = 0.85. However, c was greater in the automatic than the confirm condition when CAD was incorrect, t(19) = 3.82, p = 0.001, d = 0.85, with very strong evidence in support of the alternative BF10 = 31.93.Figure Given that we are interested in how presentation modes of CAD affect miss errors and false alarm rates, two separate ANOVAs were run to compare responses across the interactive condition of Experiment 1, with the confirm condition of Experiment 2.F = 5.25, p = 0.03, ηp2 = 0.12, in which overall there were fewer miss errors in the confirm condition than the interactive condition. There was also a main effect of CAD, F = 66.49, p < 0.001, ηp2 = 0.64, in which there were fewer miss errors in the correct CAD, followed by incorrect CAD and then the no CAD conditions. There was no significant condition × CAD interaction, F = 0.69, p = 0.51 ηp2 = 0.02. As the interaction was not significant the data were not analysed further.There were fewer miss errors overall in the confirm compared to the interactive condition. There was also an effect of CAD. A 2 × 3 ANOVA on miss errors with a between-participant factor of condition (interactive vs confirm CAD) and within-participant factor of CAD  showed there to be a significant main effect of condition, F = 3.12, p = 0.09, ηp2 = 0.08. However, there was a significant main effect of CAD, F = 18.64, p < 0.001, ηp2 = 0.34, in which more false alarms were made in the incorrect CAD compared to the no CAD condition. There was no significant condition × CAD interaction, F = 0.01, p = 0.93 ηp2 = 2.06e−4. As the interaction was not significant the data were not analysed further.Although there was an effect of CAD on false alarms there was no effect of presentation mode. A 2 × 2 ANOVA on false alarms with a between-participant factor of condition (interactive vs confirm CAD) and CAD (incorrect CAD vs no CAD) showed there to be no main effect of condition, Experiment 2 compared an automatic condition, to a confirm condition. The results showed that the confirm presentation mode showed better performance than the automatic presentation mode, in terms of fewer miss errors and no cost to false alarms. A between-experiment comparison further showed that there was an overall benefit of the confirm mode in comparison to the interactive mode in terms of fewer miss errors  and no cost in terms of false alarms.Overall, the results from the automatic condition again indicate an over-reliance on the CAD cue, replicating data from Experiment 1 and Kunar et al. , 2017b. all trials in both conditions. There was no difference in miss errors on trials when the CAD cue was not presented. However, on trials when the CAD cue highlighted an incorrect area, fewer miss errors were observed in the confirm compared to the automatic condition. There are two reasons why this might be. First, it could be that the presence of the CAD cue led participants to re-search the display more thoroughly when they were asked to confirm. To investigate this RTs were compared for participants initial response (without CAD) in the confirm condition to their second response, after they had been shown CAD. However, the results showed that RTs were significantly shorter after the second response when CAD had been presented,  = 7.40, p < 0.01, d = 1.65, with decisive evidence for the alternative, BF10 = 32,359.48). This suggests that participants were not taking the time to search the display thoroughly when they were being asked to confirm their response. Instead, it is more likely that participants believed the CAD algorithm had detected an anomaly that they have missed. In this case, they may be more likely to trust in the CAD cue versus their own judgement, following the over-reliance hypothesis.Unlike Experiment 1, there was no difference in false alarms between conditions. That is, neither the automatic of confirm presentation mode showed a benefit or a cost over the other in terms of false alarms. Examining miss errors, across all trials, fewer targets were missed in the confirm condition compared to the automatic condition (21% vs 29%). However, this difference in miss errors varied depending on the accuracy of the CAD cue. When CAD was accurate, more targets were missed in the confirm condition than the automatic condition. This is surprising, given that participants saw the CAD cue on d′) with an increase in sensitivity to detect a target when the CAD cue was correct in comparison to the no CAD and incorrect CAD condition. However, there was no overall difference in sensitivity when d′ was compared across automatic and confirm conditions . When examining criteria, CAD had an effect on response criteria with a shift to a more liberal response in the correct CAD condition than in the incorrect and no CAD conditions. Participants also showed an overall difference in response bias between presentation conditions, where criteria in the confirm condition showed a more liberal response bias than in the automatic condition. Please note, that the SDT results are puzzling in relation to the overall error data. In terms of SDT, lower miss errors in the confirm condition would be thought to occur with a change in response bias and/or an increase in sensitivity. Although there was a change in response bias, which may have explained why participants missed fewer targets, this should also have resulted in an increase in false alarms. Furthermore, the d′ data showed no overall change in sensitivity. One reason for these differences may be that the decrease in confirm miss errors looks to be largely driven by the incorrect CAD cues. This may explain the results, as d′ in these trials showed a hint of change in which d′ was marginally greater in the confirm than in the automatic conditions. Further research would be needed to confirm this.Examining the SDT analyses, similar to Experiment 1, CAD had an effect on sensitivity . One potential reason may be that participants found it easier to over-ride the salient CAD cue if there was a delay to its onset with the original mammogram display. There is evidence to suggest that top-down attentional guidance mechanisms can increase with display time . People were better able to detect a cancer, from a non-cancer, when it was prompted by CAD. There was also an effect of CAD on response criteria so that when CAD was correct participants required less evidence to respond to a target’s presence. This change in response bias mitigates the typical conservative shift observed in LP conditions  responding to a ‘non-target’ area which they had falsely identified as a target. Future work should therefore consider using a localisation response, whereby participants clicked on the location of a target. This would help remove any ambiguity as to whether participants were responding correctly to the target or incorrectly to a non-target area.Of final note, the results in this paper have implications for practice and policy in terms of CAD use and training of mammogram readers. Having CAD be presented in confirm mode should be the default recommendation for all clinical settings. However, it is also important to train mammogram readers as to how to best engage with this technology. For example, to mitigate errors due to confirmation bias and over-reliance effects, readers could be required to complete a training programme to educate them on cognitive and psychological factors that we now know influence medical search. This could include training on cognitive biases, decision-making, perception, prevalence and inattentional blindness (see Drew et al.,