Tg) and the curing temperature. Beyond the onset points of the converged crosslinking conversion ratio of 3,3′-DDS and PES, their diffusion properties are obviously restricted with crosslinking conversion ratio. At low crosslinking conversion ratios (> 10%), the diffusion coefficients of triglycidyl p-aminophenol (TGAP) were 1.1 times higher than those of diglycidyl ether of bisphenol F (DGEBF) because of the lower molecular weight of TGAP. On the other hand, the diffusion coefficients of TGAP decreased when the crosslinking ratio was up to ~ 60% because, compared with DGEBF, it had more functional groups available to react with the curing agent. At higher crosslinking ratios, the diffusion coefficients of both resins converged to zero as a result of their highly crosslinked structures.To deepen understanding of diffusion-controlled crosslinking, molecular dynamics (MD) simulations are carried out by taking the diffusion image of 3,3′-diamino diphenyl sulfone  and polyethersulfone (PES) with epoxy resin varying temperatures from 393.15 to 473.15 K over crosslinking conversion of 0–85%. The diffusion of PES and 3,3′-DDS into the bulk increased with increasing the temperature as a result of enhanced mobility of the molecules when the difference between the glass-transition temperature ( In particular, epoxy resins have a high modulus, high specific strength, good heat resistance, and good electrical resistance7. So far, various kinds of epoxy resins with diverse functional groups as well as curing agents have been combined to satisfy industrial requirements. It has been recognized that the ultimate mechanical properties of epoxy systems are highly dependent on the state of cure or crosslink density, which takes root in the solution of epoxy resin and curing agents. On the other hand, highly crosslinked epoxy systems are prone to brittle failure when subjected to a strong external force. This is mainly arising from the fragile character of highly cured epoxy systems which behave as rigid yet brittle materials, exhibiting less resistance to crack initiation and growth. Therefore, various toughening agents with thermoplastic components such as polyethersulfone (PES)10, polysulfones12, polyimides13, and polyetherimides14 have been used in epoxy systems to improve the mechanical properties. General agreement was that the final properties of epoxy systems are determined by the dispersion of thermoplastic toughening agents within it. Therefore, it is important to understand the transport properties of thermoplastic toughening agents used in epoxy systems, not just the miscibility of epoxy systems. Moreover, the kinetic approach of an epoxy system is important not only for the final properties of an epoxy system but also for the performance of the composite system, which is semi-qualitatively measured by Cure Index16. Network formation and molecular structure of epoxy systems are determined by crosslinking reactions, which limit the diffusion of molecules17. Therefore, understanding the detailed kinetics of crosslinking is a key to manufacturing high-performance composites for special demands and applications, such as mechanically strong structures23.Epoxy resins are versatile thermosetting materials, which have attracted attention worldwide because of their applications in diverse industries such as coatings, adhesives, industrial tools, aerospace, electronic materials, and biomedical systems24. Nevertheless, a few have been known about the state of cure when gelation and vitrification phenomena are likely at high degrees of crosslinking conversion. As Jouyandeh et al. explained, the systems containing epoxy and curing agent undergo a chemically-controlled crosslinking at the early stage of the reaction where systems are almost liquid-like with low viscosity25. However, the thermal analysis fails to give useful information at later stages of the reaction where diffusion-controlled crosslinking is likely; even though that stage severely determines the ultimate properties of epoxy. In other words, understanding the diffusion properties when epoxy molecules are cured with curing agents is critical to achieving molecular-level control of the morphologies via the processing conditions. The molecular-level affinity of toughening agents with epoxy resins and curing agents also strongly influences the molecular diffusion behavior.Thermal analysis of curing has been systematically addressed by Jouyandeh and coworkers32. Other authors have recently investigated the mechanical properties of epoxy resin with curing agents37 and the phase separation of epoxy systems41 using computational materials science. Research into mechanical properties such as tensile and compressive stress–strain curves, Young’s modulus, shear modulus, glass-transition temperature (Tg), and the phase separation of toughening agents with epoxy resin and curing agents is critical to achieving detailed atomic-level understanding of their behaviors. Moreover, investigating the detailed diffusion properties of molecules in an epoxy system when the epoxy resins are cured with curing agents is important to optimize the processing conditions.In a series of work, we conducted a full-atomistic molecular dynamics (MD) simulation to investigate the transport properties of the components of an epoxy resin system. Computational materials science techniques have been efficiently used to investigate the structural morphologies and diffusion properties of polymers in organic systems42. Therefore, we constructed epoxy systems that contained two types of epoxy resin, a curing agent, and a toughening agent; then changed the temperature and conversion ratio on the basis of the MD simulation approach to elucidate their diffusion properties. The epoxy systems were composed of two resins to enable manipulation of their mechanical properties43 or their phase-separation behavior44. In particular, to elucidate the relationships between the diffusion coefficient and conversion ratio, we analyzed the diffusion properties of curing and toughening agents varying the temperature and the conversion ratio. In addition, we calculated the Tg of the epoxy system varying the conversion ratio to elucidate the relationship between the Tg and the conversion ratio and to predict the Tg as the conversion ratio was changed. Moreover, we calculated the change in the diffusion properties as a function of the number of epoxy groups available to promote the curing process of the epoxy system.Based on the above explanations, MD simulation can computationally image diffusion properties of components in the epoxy system and thermal properties as changing crosslinking conversion ratio and curing temperatures. Especially, the detailed diffusion properties of epoxy resin and curing agents with different sizes and molecular weights can calculate during the curing reactionp-aminophenol (TGAP), 3,3′-diamino diphenyl sulfone , and polyether sulfone (PES) composing the epoxy systems, respectively. The Polymer Consistent Force-Field (PCFF)45 was used to describe the DGEBF, TGAP, 3,3′-DDS, and PES because the PCFF has been used to successfully describe a resin and curing agent in epoxy systems36. The total energies  of molecular structures of the epoxy systems were calculated using Eq.  code46 developed by S. Plimpton at Sandia National Laboratory and the Forcite modules in the Materials Studio software package from BIOBIA 47. The electrostatic interactions were calculated using the particle–particle particle–mesh method48. The velocity Verlet algorithm49 was used for MD simulations with time steps of 1.0 fs for integrating equations of atomic motions.Figure sing Eq. :1\docume47. Detailed molecular compositions used in our simulations of DGEBF, TGAP, 3,3′-DDS, and PES are summarized in Table 50. The annealing procedure is described as follows: (a) The temperature of the initial structures of epoxy systems was gradually increased from 0 to 298.15 K over a period of 300 ps using NVT simulations. (b) The temperature was gradually increased from 298.15 to 600 K, accompanied by a gradual increase in the volume of the epoxy structures to 200% of their initial volume, over a period of 100 ps. (c) The temperature was maintained at 600 K over a period of 100 ps of the NVT simulations. (d) The temperature was then gradually decreased from 600 to 298.15 K, accompanied by a gradual decrease in the volume of the epoxy structures to their initial volume, over a period of 100 ps. (e) Steps (b)–(d) were repeated three times. (f) Epoxy systems were equilibrated over a period of 100 ps of NVT MD simulation at 298.15 K and 500 ps of isothermal–isobaric ensemble (NPT) simulation at 298.15 K and 1 atm. After completing the annealing procedure, we performed a crosslinking simulation to obtain cured epoxy structures with 0%, 10%, 30%, 50%, 70%, and 85% conversion at curing temperatures of 473.15, 453.15, 413.15, and 393.15 K on the basis of the schematic for the crosslinking reaction between resins and amines in Fig. Tg calculations. After the temperature was increased to 523.15 K, it was gradually decreased to 223.15 K over a period of 6 ns of NPT simulation to observe the change in density for the Tg calculation.The initial models of epoxy systems were constructed at a full-atomistic scale. The initial configuration of all components in the epoxy systems was established using the Monte Carlo method in Materials StudioFigure Figure r(t) and r(0) indicate the positions of the target molecules at time t and at t = 0, respectively. The diffusion coefficients were greater at low crosslinking conversion ratios than at high crosslinking conversion ratios because the monomers and oligomers were major components of the epoxy system. However, the diffusion coefficients decreased with increasing crosslinking conversion ratio because the monomers of the epoxy resin reacted with curing agents to form network structures.The diffusion coefficients of 3,3′-DDS and PES in the epoxy system with various temperatures and crosslinking conversion ratios are based on Figs. −3 Å2/ps per 1% of crosslinking conversion ratio, was 32%, 40%, 60%, and 69% at 393.15, 413.15, 453.15, and 473.15 K, respectively. We expect that higher temperatures of the epoxy system can expand the crosslinking conversion ratio range, thereby enabling control of the processing conditions of epoxy systems. Beyond the onset point of the converged crosslinking conversion ratio, the diffusion coefficients were close to zero and the molecules in the epoxy systems could not easily migrate to other sites. Thus, the liquid epoxy systems were transformed into an adhesive material with loss of fluidity and the distribution morphologies of the epoxy system were determined near the onset point of the diffusion coefficient.Figure 17. The onset points of PES were 24%, 26%, 41%, and 56% at 393.15, 413.15, 453.15, and 473.15 K, respectively. The onset points of PES have lower values than those of 3,3′-DDS at the same curing temperature because the PES molecules have a greater molecular weight than the 3,3′-DDS molecules. Moreover, the diffusion coefficient of 3,3’-DDS and PES were more affected by crosslinking conversion ratio at a lower temperature than at a higher temperature, and these results were well agreed with the experimental study17. In addition, understanding the relationship between the onset points of 3,3′-DDS and PES and the gelation point of the epoxy system is important for understanding the curing reaction and processing conditions of the epoxy system. The rate of the curing reaction decreased rapidly at low curing temperatures, likely because the diffusion properties of the molecules were limited before the crosslinking conversion ratio of the gelation points of our similar epoxy system reached approximately 50–60%52. By contrast, the curing reaction rate decreased more slowly at higher curing temperatures than at lower curing temperatures, likely because the values of the onset points of the diffusion coefficient of 3,3′-DDS are greater than the value of the gelation point. Therefore, the onset points of the diffusion of molecules should be compared with the difference between the Tg and the curing temperature according to the crosslinking conversion ratio because the difference between the Tg and the curing temperature strongly influences the diffusion of molecules.Figure Tg. Figure Tg of the epoxy system at various conversion ratios. The relationship between the Tg and the conversion ratio, as proposed by Venditti and Gillham53, is represented by Eq. . However, the diffusion coefficients of TGAP decreased more than those of DGEBF as the crosslinking conversion ratio was increased to ~ 60% because of the greater number of functional groups for crosslinking reactions with curing agents, which can lead to an increase in the molecular weight. The diffusion coefficients of DGEBF and TGAP thereafter converged to zero when the crosslinking conversion ratio was greater than 60%. Our study indicated that the relationships between diffusion property and curing temperature of curing agents and toughening agents at various temperatures and crosslinking conversion ratios to be analyzed at the molecular level. Moreover, the diffusion properties of the epoxy resins were measured according to the number of epoxy groups and molecular weight. Therefore, we expect that MD simulations will aid in optimizing the curing conditions of epoxy systems at the molecular level.We used full-atomistic MD simulations to investigate the diffusion kinetics of 3,3′-DDS and PES in an epoxy system as the temperature was varied from 393.15 to 413.15, 453.15, and 473.15 K and as the crosslinking conversion ratios were varied from 0 to 10%, 30%, 50%, 70%, and 85%. The MD simulations revealed that the diffusion properties of 3,3′-DDS and PES tend to increase with increasing temperature because of not only an increase in thermal energy but also an increase in the difference between their


CTNNB1 is known as the most frequently mutated proto-oncogene in liver cancer. We report that active β-catenin is essential in initiation and advancement of hepatocarcinogenesis. As a transcriptional activator of AKT2, β-catenin potentiates AKT2 phosphorylation of CAD, which in return stimulates de novo pyrimidine synthesis and liver cancer development. β-Catenin, AKT2, and pyrimidine synthesis inhibitors are promising therapeutics for the treatment of oncogenic β-catenin–associated cancer.β-Catenin encoding gene  CTNNB1, encoding β-catenin protein, is the most frequently altered proto-oncogene in hepatic neoplasms. In this study, we studied the significance and pathological mechanism of CTNNB1 gain-of-function mutations in hepatocarcinogenesis. Activated β-catenin not only triggered hepatic tumorigenesis but also exacerbated Tp53 deletion or hepatitis B virus infection–mediated liver cancer development in mouse models. Using untargeted metabolomic profiling, we identified boosted de novo pyrimidine synthesis as the major metabolic aberration in β-catenin mutant cell lines and livers. Oncogenic β-catenin transcriptionally stimulated AKT2, which then phosphorylated the rate-limiting de novo pyrimidine synthesis enzyme CAD  on S1406 and S1859 to potentiate nucleotide synthesis. Moreover, inhibition of β-catenin/AKT2-stimulated pyrimidine synthesis axis preferentially repressed β-catenin mutant cell proliferation and tumor formation. Therefore, β-catenin active mutations are oncogenic in various preclinical liver cancer models. Stimulation of β-catenin/AKT2/CAD signaling cascade on pyrimidine synthesis is an essential and druggable vulnerability for β-catenin mutant liver cancer. Liver cancer is the sixth most common cancer and the third leading cause of cancer death worldwide . It is tCTNNB1 (coding for β-catenin) (22% mutation rate) and TP53 (29% mutation rate) are the most frequently altered proto-oncogenes and tumor suppressor genes, respectively, detected mainly in HCC  (CTNNB1 mutations (CTNNB1 mutation is identified in more than 10% hepatocellular adenoma (HCA), which is a benign liver neoplasm with risk of malignant transformation  . Over haormation . Mutatioormation . Phospho cascade , 9. AlthCTNNB1 mutations in tumor lesions  signaling pathway in liver cancer development and its clinical significance in cancer treatment.To establish the causative relationship between aberrant β-catenin activation and liver cancer, we not only dissected the role of CTNNB1 mutation in cancer -Cre–mediated hepatic Ctnnb1 exon 3–deleted mice. However, these mice died within 20 to 30 d after birth with hepatomegaly . Since early mouse lethality due to high efficacy of β-catenin activation precluded us from using this model to study hepatocarcinogenesis, we inoculated 7-wk-old lox(ex3)/+β-catenin mice with Cre-adenoviruses (6 × 108 pfu per mice) via tail vein injection to generate mosaic Ctnnb1 exon 3 deletion in mouse livers (lox(ex3)/+β-catenin mice died within 50 to 100 d after Cre-adenovirus incubation  (lox(ex3)/+β-catenin mice also had a shorter lifespan albeit at a slower pace (lox(ex3)/+β-catenin mice but not wild-type (WT) mice developed prominent liver tumors with activated β-catenin (lox(ex3)/+β-catenin mice, three had benign neoplasms, eight had malignant ones, and the remaining eight mice had both types of tumors according to hematoxylin-eosin (H&E), reticular fiber, and CD34 stainings . Malignant lesions had strongly diffused staining of CD34 and reticulin widening, while benign lesions showed minimal/focal positivity of CD34 and relative normal reticulin staining. Positive Heppar1 and negative CK19 stainings indicated that the malignant tumors were HCC rather than ICC  . The remwer pace B. By 13 -catenin C–F. Amonthan ICC H. As it CTNNB1 and TP53 are the most commonly mutated proto-oncogenes and tumor suppressor genes in liver cancer. Guichard et al. reported that CTNNB1 and TP53 mutations were mutually exclusive, as only 2.4% (3/125) of HCC had co-occurrence of CTNNB1 and TP53 mutations, 11.5% (3/26) of TP53 mutant HCC had CTNNB1 mutations, and 7.3% (3/41) of CTNNB1 mutant HCC had TP53 mutation in that study  of CTNNB1 mutant HCC carried TP53 mutations  /+β-catenin mice to generate l/l; β-cateninlox(ex3)/+Tp53 mice. Cre-adenoviruses were then injected to mutate Ctnnb1 and/or delete Tp53 in liver accordingly /+Tp53 mice had shorter lifespans than l/lTp53 mice /+Tp53 mice but not l/lTp53 mice developed obvious liver tumors when they were 11.5 mo old (Ctnnb1 exon 3–deleted mouse embryonic fibroblasts (MEFs) in Tp53 null background from l/l; β-cateninlox(ex3)/+Tp53 mouse embryos with addition of Cre-adenoviruses in cell culture . Activated β-catenin and deleted TP53 MEFs exhibited stronger tumorigenic potential than TP53 null MEFs did in immunodeficient nude mice .at study . However reports , 18–20. able S2) , 18–20, ordingly A. Tp53l/l/l mice B. Tp53l/5 mo old C and D. 5 mo old E and F. CTNNB1 mutations in tumor lesions (HBV transgenic mice with lox(ex3)/+β-catenin mice to generate HBV mice and lox(ex3)/+HBV; β-catenin ones. These mice were then injected with Cre-adenoviruses to delete exon 3 of Ctnnb1 in hepatocytes of lox(ex3)/+HBV; β-catenin mice (lox(ex3)/+HBV; β-catenin mice had a shorter lifespan compared to HBV transgenic mice /+ mice G. HBV; βnic mice H and devnic mice I–L.lox(ex3)/+β-catenin mice appear to have shortened survival than that of HBV mice (P = 0.0645) or l/lTp53 mice (P = 0.0538) albeit without reaching statistical significance within the window of observation . Therefore, the relative potency of tumorigenic potential among β-catenin activation, transgenic HBV, and Tp53 deletion in mice is inconclusive. Combination of either transgenic HBV and β-catenin activation or Tp53 deletion and β-catenin activation was more potent than either transgenic HBV, β-catenin activation, or Tp53 deletion alone in malignant transformation . Pri-724, a β-catenin inhibitor, blunted the tumor growth in Cre-adenovirus–inoculated lox(ex3)/+HBV; β-catenin mice without changes in mouse body weight . Taken together, under various preclinical settings, oncogenic β-catenin promotes mouse hepatocarcinogenesis, which can be ameliorated by β-catenin inhibitor.Comparison of the survivals of various mouse models we generated revealed that lox(ex3)ERT2-Alb-Cre; β-catenin/+ mice . Untargeted metabolomic profiling was performed on both wildtype and Δ(ex3)/+β-catenin livers. Of 201 metabolites identified by liquid-chromatography (LC) mass spectrometry (MS) (Q-Exactive-plus), the abundances of 30 metabolites were different (P < 0.05) between wild-type and Δ(ex3)/+β-catenin livers . Comparison of the metabolites enriched in Δ(ex3)/+β-catenin mouse livers and MEFs identified three most significantly elevated metabolites: carbamoyl-asp, dihydroorotate, and orotate, which are starting molecules for de novo pyrimidine biosynthesis pathway .Liver is the major metabolic organ. Gain-of-function mutation of specific proto-oncogene often introduces metabolic vulnerabilities, which may be amenable to therapeutic intervention . To chec+ livers C. Becaus+ livers , 22, we 3–), and aspartate with ribose-phosphate to form heterocyclic nucleotides . To elucidate active β-catenin–mediated regulation of intermediates’ biosynthesis, we traced the metabolic flux in vivo by injecting WT and hepatic Δ(ex3)/+β-catenin mice with 15N-amide glutamine, which is incorporated into the pyrimidine ring. Incorporations of 15N into multiple intermediates of pyrimidine synthesis were enhanced in β-catenin mutant livers over their wild-type counterparts /+β-catenin MEFs , suggesting that β-catenin–regulated flux is not due to the change of glutamine availability. Furthermore, Pri-724 blocked pyrimidine synthesis in CTNNB1 exon 3–deleted human HB cells (HepG2) and mouse HCC cells (Hepa 1-6) as well as human HCC cells (M97h and Huh7) .De novo pyrimidine biosynthesis is a synthetic pathway that fuses nitrogen/carbon from glutamine, bicarbonate (HCOterparts D. Consis)/+ MEFs E and the)/+ MEFs F. NeitheSI Appendix, Fig. S5G), indicating that β-catenin might promote pyrimidine synthesis without GS involvement. Of note, the regulation of β-catenin on pyrimidine synthesis still existed after S6K depletion . Moreover, the relative flux of pyrimidine synthesis was largely reduced by Pri-724 even in the presence of excessive glutamine  to produce glutamine, which in turn activated mTOR in liver . S6K P7. Therefolutamine G. TherefSI Appendix, Fig. S7 A and B and β-catenin mutant livers as well as wild-type and β-catenin mutant MEFs to identify potential β-catenin–regulated phosphorylation sites of CAD. Among the critical phosphorylation sites for CAD enzymatic activity, S1859 and S1406 were highly phosphorylated in β-catenin mutant livers and MEFs . Furthermore, hyperphosphorylation of S1859 and S1406 was abolished by Pri-724 . Immunoblotting demonstrated β-catenin stimulation of S1859 and S1406 phosphorylation on CAD in livers, MEFs, HepG2, Hepa 1-6, M97h and Huh7 cells . Of note, Pri-724 abolished phosphorylation of S1859 and S1406 on CAD even in the absence of S6K . Taken into consideration of S6K phosphorylation of S1859 but not S1406 on CAD , and UMPS (uridine monophosphate synthetase), the critical enzymes in this pathway. However, the abundances of their transcripts and proteins were not altered by β-catenin . Consistent with a previous study . RNA-seq analysis was validated by the presence of β-catenin classical target genes, including Glul, Oat, and Axin2 among the differentially expressed genes . Because Akt2 presented in both the β-catenin up-regulated gene group (RNA-seq) and β-catenin–binding peak-enriched group (ChIP-seq) , AKT2 was identified as the sole β-catenin transcriptionally up-regulated kinase. Notably, the S1859 region of CAD is predicted to be recognized by basophilic kinases such as AKT . Moreover, AKT2 expression is positively corelated with CTNNB1 expression in human liver cancer samples in The Cancer Genome Atlas (TCGA) . In quest of the putative kinase(s), we performed ChIP sequencing (ChIP-seq) and RNA-seq of mouse livers C. β-Cateus study , β-catenh as AKT . Unbiaser region E, which on (Akt2 F. AKT2 eon (Akt2 G and I. s (TCGA) K. As allΔ(ex3)/+β-catenin MEFs with AKT2 kinase inhibitor, CCT128930 (CCT), for 6 h and then checked the status of CAD. We found that CCT reduced phosphorylation of S1859 and S1406 on CAD in a dose-dependent way . Because CAD is a substrate of S6K , we examined potential S6K involvement in AKT2 stimulation of CAD phosphorylation. However, the regulation of AKT2 on CAD phosphorylation still presented when S6K was depleted by siRNA . Taken together, AKT2 fosters CAD phosphorylation without involvement of S6K.To examine whether AKT2 phosphorylates CAD, we first treated dent way A. Consisdent way B. Interfby siRNA D and E. isrupted F. MoreovSI Appendix, Fig. S9 C–E). AKT2 coimmunoprecipitated with exogenous wild-type, but not S1859A- or S1406A-mutated CAD . AKT2 is thus a kinase for CAD.Next, we checked whether AKT2 directly phosphorylates CAD by biochemical assays. Co-IP assay revealed the interaction of endogenous CAD and AKT2 in various cell lines /+β-catenin MEFs , HepG2 , as well as in Huh7 . Furthermore, AKT2 promoted pyrimidine synthesis, even when S6K was depleted . To exclude the potential influence of moderate reduction of glutamine uptake on CCT-mediated inhibition of pyrimidine synthesis in some cell types , we provided CCT-treated cells with excessive glutamine (6 mM) and vehicle-treated cells with a standard dose of glutamine (2 mM). The relative flux of pyrimidine synthesis was largely reduced by CCT even in the presence of abundant glutamine . Taken together, AKT2 promotes de novo pyrimidine synthesis in various cell settings.Since AKT2 phosphorylates CAD, which is a critical enzyme in stimulation of pyrimidine synthesis, we checked the role of AKT2 in pyrimidine synthesis. Suppression of AKT2 with CCT or ), HepG2 B and Hep in Huh7 C and M97depleted D and E. Δ(ex3)/+β-catenin MEFs were more sensitive to proliferation inhibition with Brequinar (BRQ) (Δ(ex3)/+β-Catenin MEFs were more sensitive than wild-type MEFs to CCT treatment (Δ(ex3)/+β-catenin MEFs without affecting body weight in nude mice . With moderate influence on migration and invasion, β-catenin, AKT2, or pyrimidine synthesis inhibitor dramatically repressed proliferation and promoted apoptosis of HepG2, Huh7, and M97h cells . Moreover, BRQ blocked liver tumor initiation and advancement of lox(ex3)/+HBV; β-catenin mice injected with Cre-adenoviruses . Therefore, targeting AKT2-potentiated pyrimidine synthesis may be a promising therapeutic strategy for β-catenin mutant liver cancer.To explore therapeutic relevance of AKT2 stimulation of pyrimidine synthesis, we examined the efficacy of AKT2 or pyrimidine synthesis inhibitor in suppression of cell proliferation and tumorigenesis driven by oncogenic β-catenin. Compared to wild-type MEFs, ar (BRQ) A, a DHODar (BRQ) B. β-Catereatment C. In addoviruses G–I withoCTNNB1 is the most frequently mutated proto-oncogene in liver cancer   . Small vipelines , 29. Thecumented . Howeverebrafish . Therefodeletion . TherefoTP53 is the most mutated tumor suppressor in overall cancer. It is also the highest mutated gene (29%) in liver cancer   , 18–20, HBV transgenic mice grow liver tumors more than 12 mo after birth β-Catenin/+ mice were kindly provided by Haibin Wang, Xiamen University, Xiamen, Fujian, China (HBV transgenic mice (flox/floxTp53 mice (l/lTp53) (Alb-Cre mice (stock no. 003574) was from The Jackson Laboratory. ERT2-Alb-Cre mice were generated by Biocytogen. To delete exon 3 of Ctnnb1 in mouse liver 7 wk after birth, 6 × 108 pfu Cre-adenovirouses  were intravenously injected into lox(ex3)β-catenin/+ mice as previously reported (lox(ex3)HBV; β-catenin/+ mice were intraperitoneally injected with Pri-724 (5 mg/kg) every other week for 3 mo. The 7-mo-old lox(ex3)HBV; β-catenin/+ mice were intraperitoneally injected with BRQ  for 3 mo. ERT2-Alb-Cre mice were crossed with lox(ex3)β-catenin/+ mice to generate inducible lox(ex3)ERT2-Alb-Cre; β-catenin/+ mice. To delete hepatic exon 3 of Ctnnb1, these mice were injected intraperitoneally with 30 mg/kg tamoxifen for 6 consecutive days and killed by day 12 for analyses. The experimental protocols of all animal studies were approved by the Animal Center of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College (ACUC-A02-2014-003). Experimental procedures were compliant with the regulation of Beijing Administration Office of Laboratory Animals.n, China . HBV tranic mice  were purTp53l/l)  were purreported . The 5-mg for 15 min at 4 °C. The supernatant was then subjected to LC-MS metabolomic analysis.Livers were excised and immediately processed for metabolomics extraction. Equal amounts of liver tissue were lysed in 250 μL of extraction solution  per 10 mg of tissue in Precellysis vials following the manufacturer’s instruction. The suspension was immediately centrifuged at 16,000 × 5/well) and cultured in Dulbecco's modified eagle medium for 24 h. The cell number was counted in a parallel control dish. For intracellular metabolomic analysis, cells were quickly washed three times with Phosphate buffer saline and were then lysed in 1 mL precooled extraction solution per 1 × 106 cells. The cell lysates were vortexed for 5 min at 4 °C and immediately centrifuged at 16,000 × g for 15 min at 0 °C. The supernatants were collected and analyzed by LC-MS metabolomic analysis. Total protein contents of cell pallets were also determined.Cells were plated onto six-well plates  test. Statistical significance between two groups was subjected to Student’s Supplementary File


It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state‐of‐the‐art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual‐driven, to the most recent one, machine‐driven. Computational approaches to organocatalysis: This review is focused on the different computational approaches that have been applied within the organocatalysis field. A huge effort from the theoretical chemistry field has been made in order to prove that de novo design of effective catalysts for different types of reactions is a long‐term target, yet a palpable phenomenon. Catalysis has been a paramount research topic in Chemistry since being separated from ‘alchemy’, proving its value by accommodating many important procedures in this age. Traditionally, it has been largely accepted that transition metal organometallic compounds and enzymes have been the two main types of effective catalysts, in particular in the field of asymmetric catalysts. Consequently, small organic molecules have been rarely used by synthetic chemists as catalysts during the past century, even though the very first pure organocatalyst was reported in 1912. Bredig described a modestly enantioselective alkaloid‐catalysed cyanohydrin synthesis  to study the possible pathways that may arise from a catalyst of dual nature such as chiral phosphoric acid catalyst, Figure Traditionally, distereovergence was explained only by sterical effects from the catalyst, however, mechanistic studies revealed that non‐covalent interactions (NCIs) were usually more important at guiding the reaction than mere sterical hindrance.ee) of a catalyst; showing how it is governed by the delicate balance between NCIs and the sterical factors present in the different transition states (TSs)involved.[Nowadays, computational chemistry is much more advance and the general workflow has changed over the last five years, Figure , bottom,involved.17, 18in silico determination of the intermediates along the current synthetic routes has led to a more profound understanding of the geometrical factors and NCIs that, individually or in a cooperative fashion, favour the production of the compound of interest. Overall, computational studies have revealed that the organocatalysts’ stereoselectivity is mainly determined by the presence (or absence) of electronic, steric and dispersion interactions between the catalyst and the species involved in reactivity ,R and S enantiomer were automatically analysed. Due to the numerous catalysts assessed through this automated methodology, a new origin of stereoselectivity was found and paved the way for the potential computational design of novel catalysts. Similarly in 2016, Doney et al. found new trends in the enantioselectivity of asymmetric propagations by screening a size‐able number of catalysts using AARON; leading to the design of a new catalyst with an ee exceeding 99 %.With a similar aim, the QM‐based open‐source Automated Reaction Optimiser for New Catalysts (AARON),Many other automatized software and packages for catalyst design are being gradually integrated into the field of organocatalysts; specifically the following programmes mentioned have the potential to be applied to the growing field. In 2018, Globally Optimal Catalysts, GOCAT,Despite the aforementioned advances in the automated design of novel catalysts, the description of the catalytic activity and enantioselectivity in organocatalysis often still requires expensive mechanistic QM analyses or MM approaches. Recently, many efforts have been dedicated to relating molecular properties – or descriptors – such as bond lengths, angles, vibrational modes and charges, with observable reactivity outputs, so as to quantitatively determine activities and selectivities of novel organocatalysts. Identification of patterns within these large datasets for subsequent interpolation becomes an impossible task, and one must rely on machine learning (ML) methods.ee values by Reid et al., by construction of a training set of MM and DFT‐derived quantitative structure‐reactivity relationships (QSRR) of 367 nucleophile additions to imines catalysed by aromatic phosphoric acids.ee values of similar catalytic processes within 5 %. A similar methodology was used for the determination of the DFT‐calculated ee value of asymmetric propargylation catalysts, whereby the construction of a dataset containing structural parameters from the intermediates preceding and succeeding the enantiodetermining TS resulted in an outstanding R2 value of 0.97 for out‐of‐sample catalysts.ee values within 3 % of the experimental.This was applied to the determination of The development of machine learning methods for finding and characterising novel asymmetric organocatalysts is gaining popularity given the myriad of readily available multi‐purpose software, such as PyTorch, Tensorflow or Keras. Furthermore, given the broad range of impact of organocatalysis, most current state‐of‐the‐art organocatalytic strategies opt to rely on variations of known, deeply studied routes. It has been discussed in the literature that novel pathways should not be derived from statistical models based on current models, but use template‐based strategies which allow for a more wide exploration of the field of action without the bias of current literature.With increasing computational capacity and new techniques, over the last two decades computational methods have provided a more economic approach to exploring different chemical systems. These developments have brought the synthetic and computational chemists together, and demonstrate the power of this synergy in catalyst design.In this review, we have summarised all the different computational approaches that have been applied within the organocatalysis community. Starting with the traditional approach in which computational models are used to elucidate the origins of selectivity in organocatalysis. Evolving to the so‐called, computational rational design, in which predictive models are generated involving quantum‐mechanical calculations on complex chemical systems; confirming the power of automatized methodologies in catalyst design; and finally, describing the use of thousands of chemical descriptors to elucidate patterns in multidimensional data.As it has been clearly stated in this review, a huge effort from the theoretical chemistry field has been made in order to prove that de novo design of effective catalysts for different types of reactions is a long‐term target, yet a palpable phenomenon.The authors declare no conflict of interest.1Nika Melnyk accomplished her Bachelor of Science degree in chemical sciences with medicinal chemistry from the Technological University Dublin (TUD) where she carried out her thesis on the computational analysis of antimalarials containing an endoperoxide bond under the supervision of Dr. Sarah Rawe and Dr. Cristina Trujillo. Her thesis established an interest for computational chemistry and she is currently doing her PhD under the supervision of Dr. Cristina Trujillo focusing on the catalysis of organic reactions through non‐covalent interactions in Trinity College Dublin.Iñigo Iribarren carried out his chemistry studies at ‘‘Universidad Autónoma de Madrid’’ as well as his master studies in theoretical and computational chemistry. He spent three months at ‘‘Sorbonne Universite’’ in Paris under the supervision of Dr Contreras developing a module for the non‐covalent interaction index software. He carried out his master thesis at ‘‘Instituto de Química Médica ’’ under the supervision of Prof. Alkorta focused on studying charged dimers linked by hydrogen bonds. Currently, Iñigo is at Trinity College Dublin doing his PhD under the supervision of Dr Cristina Trujillo focused on weak interactions on catalysis.Eric Mates‐Torres performed his undergraduate studies in the “Universitat Autònoma de Barcelona”, specialising in the computational study of organometallic catalysts under the supervision of Prof. Agustí Lledós and Prof. Jean‐Didier Maréchal. After this, he carried out his PhD in the group of Prof. Max García‐Melchor in Trinity College Dublin, focusing on the development of cathode materials for sustainable energy applications. He is currently collaborating with the group led by Dr Cristina Trujillo in Trinity College Dublin.Cristina Trujillo obtained her PhD in Theoretical and Computational Chemistry in 2008 at Universidad Autónoma de Madrid (UAM). During 2008–2016, she held several postdoctoral positions in Spain, Prague and Ireland. She joined Trinity College Dublin in 2019 being awarded with a Science Foundation of Ireland‐Starting Investigator Research Grant. She will join the University of Manchester as a lecturer in Computational Chemistry after summer. She has expertise in fundamental topics within Computational Organic Chemistry. Cristina's research group investigates the application of computational techniques in the design of organocatalysts, as well as the prediction and control of catalytic processes.


Lysosomes are highly dynamic organelles involved in the breakdown and recycling of macromolecules, cell cycle, cell differentiation, and cell death, among many other functions in eukaryotic cells. Recently, lysosomes have been identified as cellular hubs for the modulation of intracellular signaling pathways, such as the Wnt/beta-catenin pathway. Here we analyzed morphological and functional characteristics of lysosomes in muscle and non-muscle cells during chick myogenesis, as well as their modulation by the Wnt/beta-catenin pathway. Our results show that (i) muscle and non-muscle cells show differences in lysosomal size and its distribution, (ii) lysosomes are found in spherical structures in myoblasts and fibroblasts and tubular structures in myotubes, (iii) lysosomes are found close to the plasma membrane in fibroblasts and close to the nucleus in myoblasts and myotubes, (iv) lysosomal distribution and size are dependent on the integrity of microtubules and microfilaments in myogenic cells, (v) alterations in lysosomal function, in the expression of LAMP2, and in Wnt/beta-catenin pathway affect the distribution and size of lysosomes in myogenic cells, (vi) the effects of the knockdown of LAMP2 on myogenesis can be rescued by the activation of the Wnt/beta-catenin pathway, and (vii) the chloroquine Lys05 is a potent inhibitor of both the Wnt/beta-catenin pathway and lysosomal function. Our data highlight the involvement of the Wnt/beta-catenin pathway in the regulation of the positioning, size, and function of lysosomes during chick myogenesis. Skeletal muscle fiber formation is a highly complex process that occurs during embryonic development and postnatal life and involves several regulatory networks . While tLysosomes are membrane-bound organelles that can vary considerably in size, shape, subcellular positioning, luminal pH, enzyme content, cargos, and function ,3. ThereDespite the recent increase in the understanding of the role of lysosomes in cell signaling, little is known about their role during skeletal muscle differentiation. Here, we studied morphological and functional features of lysosomes in muscle and non-muscle cells in chick myogenic cell cultures. Our results show the involvement of the canonical Wnt/beta-catenin pathway in the regulation of the positioning, size, and function of lysosomes in muscle cells.Rabbit polyclonal antibody against the major lysosomal membrane protein LAMP2 (#PA1-655) was from ThermoFisher . Mouse monoclonal antibodies against alpha-tubulin (clone DM1a) and rabbit polyclonal antibodies against beta-catenin (#C2206) were from Sigma-Aldrich . Mouse monoclonal anti-Lamin A/C (#4777) was from Cell Signaling Technology . NucSpot live cell nuclear stain was from Biotium Inc. . DNA-binding probe 4,6-Diamino-2-phenylindole dihydrochloride (DAPI), Hoechst nuclear stain (#33342), Alexa Fluor 488-goat anti-rabbit IgG antibodies, and Alexa Fluor 546-goat anti-mouse IgG antibodies were from Molecular Probes .5 cells/35 mm culture dishes in 2 mL of medium. Cells were cultured on 22 mm-Aclar plastic coverslips  previously coated with rat tail collagen. Cells were grown in a humidified 5% CO2 atmosphere at 37 °C. After the first 24 h, cultures were fed daily with fresh 8–1–0.5 cultured medium.The primary cultures of myogenic cells were prepared from breast muscles of 11-day-old chicken embryos as previously described . Chickenv/v) enriched in Wnt3a . For acidic cellular organelle labeling, cells were stained with 2 ng/mL acridine orange solution for 10 min at 37 °C and 5% CO2. The cytoskeleton interfering drugs Taxol (1 µM), Colcemid (0.05 µM), and Cytochalasin B (0.1 µM), all from Sigma-Aldrich, were used in 24 h cells.For lysosomal inhibition, 24 h chick myogenic cells were treated with the chloroquine Lys05  at a final concentration of 1 μM; or with 0.1% DMSO, both for 24 h. For the activation of the Wnt/beta-catenin pathway, 24-h cells were treated with 5 µM 6-bromoindirubin-30-oxime  or conditioned media  or enhanced green fluorescent protein (eGFP) were from Integrated DNA Technologies . The Gallus gallus sequences of siRNA that were selected to target LAMP2 or eGFP are:siRNA sequences specific for LAMP2 of Gallus gallus;eGFP from 5′ rArArGrCrUrGrArCrCrCrUrGrArArGrUrUrCrArUrCrUrGCA 3′ (strand);5′ rUrGrCrArGrArUrGrArArCrUrUrCrArGrGrGrUrCrArGrCrUrUrGrC 3′ (passenger);Gallus gallus (seq 1467-1492);LAMP2 from sense 5′ rGrArArUrUrGrGrArArArUrUrUrArCrUrUrGrArArGrCrUAC 3′;antisense 5′ rGrUrArGrCrUrUrCrArArGrUrArArArUrUrUrCrCrArArUrUrCrArU 3′.4 cells per well. Cells were treated with increased concentrations of Lys05  for 24 h. After treatment, cells were lysed with passive lysis buffer  and assayed for Firefly and Renilla luciferase activity using the dual-luciferase reporter assay (Promega #E1960). Luminescence was measured using the Modulus IITM Microplate Multimode Reader (Promega). Control media (DMEM F-12) were used as negative controls for Wnt/beta-catenin reporter stimulation by BIO or CHIR99021 in RKO B/R. All experiments were performed in triplicate and repeated at least three times.RKO-pBAR/Renilla (RKO B/R) or SW480-pBAR/Renilla (SW480 B/R) cells were seeded into 96-well plates with 2.0 × 10Chick myogenic cells were rinsed with PBS and fixed in absolute methanol at −20 °C for 5 min. Then, they were permeabilized and blocked in PBS with 0.1% saponin, 1% BSA, and 5% horse serum for 30 min. Cells were incubated overnight at 4 °C with primary antibodies . After incubation, cells were washed for 30 min in the permeabilization and blocking solutions and incubated for 1 h at 37 °C with Alexa Fluor-conjugated secondary antibodies . Nuclei were labeled with DAPI, Hoechst, or NucSpot. Cells were mounted in Prolong Gold (Molecular Probes) and examined with either an Axiovert 100 microscope  coupled to an Olympus DP71 high-resolution camera, a Leica TCS SPE laser scanning confocal microscope , or a DSU Spinning Disk confocal scanner mounted on an inverted fluorescent microscope . Control experiments with no primary antibodies showed only a faint background staining (data not shown). Image processing was performed using Fiji software, version 1.53q  and figuhttps://imagej.net/software/fiji/ (accessed on 25 August 2022). We measured the total area occupied by lysosomes in the three phenotypes of cultured cells  by analyzing images of Lysotracker , Hoechst (nuclear marker), and phase contrast microscopy (whole cell image). The limits of cells were accessed by phase contrast microscopy. The area occupied by lysosomes per cell was measured concerning the total cell area (area of lysosomes (%) ÷ total cell area (%)). DAPI and desmin double staining enables the identification of myoblasts through the presence of desmin and fibroblasts through differences in nuclear morphology and fluorescence intensity [The nuclear localization of beta-catenin, the diameter of myotubes, the diameter and area of lysosomes, as well as the distance between lysosomes and the nuclei, were quantified using Fiji software, ntensity . More spntensity . All dat2, 1 mM EGTA, 0.5 mM DTT, and 0.05% sodium azide) containing 0.1% NP-40 and protease inhibitor cocktail , vortexed for 5 s and incubated for 3 min on ice cold. Then, cells were transferred to a Dounce-type homogenizer and macerated 20 times. Subsequently, the cell extract was centrifuged at 13,400 rpm for 10 min, fractionating the nucleus (pellet) and cytoplasm (supernatant). The pellet was resuspended in hypotonic buffer solution, and then the pellet and the supernatant were centrifuged again at 13,400 rpm for 10 min, and the cycle was repeated three times. For immunoblot analysis, 100 μL of RIPA buffer , protease inhibitor cocktail, and DNAse were added to each sample.Nuclear and cytoplasmic fractions were prepared based on a previously described method . Culturew/v) polyvinylpyrrolidone (PVP)  in Tris-buffered saline with 0.01% Tween-20 (TBST) for 1 h, PVDF membranes were incubated overnight at 4 °C with primary antibodies against α-tubulin , beta-catenin , Lamin A/C  and desmin . The next day, membranes were incubated with horseradish peroxidase-conjugated secondary antibodies  and antibody-bound bands were visualized with the SuperSignal™ West Pico PLUS Chemiluminescent Substrate using a ChemiDoc™ XRS+ System with Image Lab™ Software, version 6.1 .The protein concentration in cytoplasmic and nuclear fractions was determined with the Bradford assay and denatured with a Laemmli buffer at 95 °C for 5 min. Samples were separated on 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto 0.45 μm polyvinylidene fluoride (PVDF) membranes . After the blockade of nonspecific protein signals in 2% , and they harbor three main cell phenotypes: myoblasts, myotubes, and fibroblasts [The lysosomal system has been implicated in several aspects of muscle structure and physiology, such as the control of myofiber homeostasis and muscle mass . Howeverroblasts ,13. The The collection of our results shows that (i) lysosomal size distribution and its distribution size are different when comparing muscle and versus non-muscle cells, (ii) lysosomes are found in spherical structures in myoblasts and fibroblasts and tubular structures in myotubes, (iii) lysosomes are found close to the plasma membrane in fibroblasts and close to the nucleus in myoblasts and myotubes, (iv) lysosomal distribution and size are dependent on the integrity of microtubules and microfilaments in myogenic cells, (v) alterations in lysosomal function, expression of LAMP2, and Wnt/beta-catenin signaling pathway affects the distribution and size of lysosomes in myogenic cells, (vi) the effects of the knockdown of LAMP2 on myogenesis can be rescued by the activation of the Wnt/beta-catenin pathway, and (vii) the chloroquine Lys05 is a potent inhibitor of both the Wnt/beta-catenin pathway and lysosomal function.Lysosomes are generally described as small spherical organelles, but they may form several microns-long tubules that interconnect as a network ,16,17. ILysosomal size is tightly controlled in cells and is intimately correlated with lysosomal function . LysosomLysosomes were found close to the plasma membrane in fibroblasts, whereas they were close to the nucleus in myoblasts and myotubes. It has been reported that the intracellular positioning of lysosomes correlates with their luminal pH and function . Since pThe intracellular positioning of lysosomes is dependent on their movement within the cell which has been associated with microtubules ,21. HereLAMP1 and LAMP2 are major lysosomal membrane proteins involved in lysosomal biogenesis and in the maintenance of the structural integrity of the lysosomal compartment . Our datImportantly, our results show the involvement of the Wnt/beta-catenin pathway in the regulation of the lysosomal morphology and function during chick myogenesis. Two activators of the Wnt/beta-catenin pathway, Wnt3a and BIO, were able to rescue the deleterious effects of the knockdown of LAMP2 in the formation of muscle fibers. Different mechanisms could be involved in the interplay between lysosomes and the Wnt pathway. It has been reported that GSK3 can be sequestered from the cytosol into lysosomes, which activates the canonical Wnt/beta-catenin pathway . FurtherIn conclusion, our data unveil new morphological and functional features of lysosomes during chick myogenesis and provide new insights into the role of lysosomes in the modulation of the Wnt/beta-catenin pathway in muscle.


PHMA length changes significantly impact the grafting density and solubility in organic solvents of as-prepared block copolymer functionalized MWNTs(bc@fMWNTs). Then, the bc@fMWNTs were introduced to epoxy, and the resulted epoxy/bc@fMWNT composites show better mechanical properties than neat epoxy and epoxy/p-MWNT composites. The results suggest that longer PHMA chains cause the two competitive and opposing effects on the dispersion state and soft interface. On the one hand, the longer PHMA chains on the surface of MWNTs would afford higher deformation for the matrix and enhanced mobility for MWNTs because of the soft and flexible nature of PHMA, enhancing the energy dissipation during strain. On the other hand, as the length of PHMA extends, the dispersion level of bc@fMWNTs in epoxy declines, which is harmful to the composite’s mechanical properties. Hence, epoxy/bc@fMWNTs composites with relatively short PHMA chains show the best tensile and fracture properties.The dispersion level of carbon nanotubes (CNTs) and interface design are two of the most crucial roles in developing the superior mechanical performance of polymer/CNT nanocomposites. In this work, a series of azide-terminated poly(glycidyl methacrylate)-block-poly(hexyl methacrylate) (PGMA- Epoxy is a good candidate in advanced technological fields as a thermoset resin because of its excellent mechanical and thermal performance . HoweverIt is well-known that the filler-matrix interfacial adhesion influences the composite strength. In contrast, the fracture toughness is more dependent on the interface structure between fillers and matrices . The fraIC by 38% and tensile strength by 22%, respectively. Furthermore, it was found that nanofillers with a soft interface and good adhesion with the epoxy host can effectively toughen epoxy while maintaining strength properties  , CuI , and DBU  were added. The solution was deoxygenated by bubbling with nitrogen for at least 30 min. The flask was immersed in an oil bath preheated to 70 °C for 24 h. The solid was separated from the mixture by centrifugation. The collected solid was redispersed in THF (50 mL) and separated by centrifugation three times. The solid mass (PGMA-b-PHMA @fMWNTs) was dried at 60 °C overnight in a vacuum oven.In a typical experiment, a 250 mL flask was charged with MWNTs-Alkyne (200 mg) and DMF (60 mL). After stirring and sonicating for 30 min at room temperature, azide-functionalized PHMA-fMWNTs was ultrasonicated with 20 mL of THF 1 h and the mixture was allowed to stand 24 h to settle any insoluble material. The precipitation was dried in an oven at 80 °C for 24 h and then weighed to determine the mass of solubilized MWNTs. Nine different concentrations of B1@fMWNTs in THF were obtained by diluting the supernatant from the above solution several times. The absorption spectrum of each of these solutions was then recorded, and the dependence of absorbance on concentration at 500 nm obeyed Beer’s law [The sample of B1@fMWNTs was dispersed in THF by ultra-sonication for 2 h at 40 °C and subsequently mixed with the epoxy resin by mechanical stirring and sonication for 30 min at 50 °C to obtain a black suspension. After completely removing the solvent by heating under a vacuum, the amount of epoxy resin and curing agent were added. The resin mixture was then placed in a planetary mixer and subjected to high-speed shear mixing and degassing steps. Then, the mixture was cast in preheated steel molds (60 °C) and cured at 80 °C for 120 min, 120 °C for 60 min, 140 °C for 60 min, and 160 °C for 120 min.The desired amount of bc@n) and Mw/Mn of the polymers were measured by gel permeation chromatography (GPC) equipped with Agilent 1100 GPC at room temperature, using THF as the eluent at a flow rate of 1 mL min−1. The UV–vis absorption spectra of bc@fMWNTs organic resolvent solutions, ranging from 350 to 750 nm, were measured with a UV–vis absorption spectrophotometer . Thermogravimetric analysis (TGA) of the fillers was conducted by TGA4000  at a heating rate of 20 °C/min under a nitrogen atmosphere. Both transmission optical microscopy  and transmission electron microscopy  were adopted to verify nanotubes’ dispersion in the matrix. An ultramicrotome  equipped with a diamond knife was used to cut the samples for TEM observation. Morphologies of the fracture surfaces of samples (after the tensile test) were sputtered with platinum and observed through scanning electronic microscopy (SEM) .The number-average molecular weight (M−1. The single edge notched bend (SENB) test was performed to measure the fracture resistance of epoxy/bc@fMWNT composites in mode I tension. The specimens were prepared according to the specifications, ASTM D5045-99, whose dimensions are 10 mm wide × 4 mm thick × 70 mm length. The SENB specimens were loaded in three-point bending at a crosshead speed of 10 mm/min and a span of 40 mm. For all mechanical tests, at least five samples of each blend were tested, from which the mean values and standard deviations were calculated. DMA properties were studied with a dynamic temperature ramp from 40 to 200 °C (ramping rate = 2 °C min−1) using a Q 800 analyzer , at 1 Hz with a constant strain of 20 mm by single cantilever mode. The tensile test of Epoxy/MWNT composites was conducted on a universal testing machine  at a tensile rate of 5 mm minAll polymerizations proceeded with reasonable control, as evidenced by the low product polydispersity index (PDI) . The GPC curve in n of the macro-CTAs (PHMA) rising from 24 to 120  of the epoxy nanocomposites were examined by dynamic mechanical analysis. fMWNTs showed an increased storage modulus (2405.6 MPa) compared to the neat epoxy (2220.8 MPa) in the glassy state at 40 °C, whereas adding 0.05 wt% pristine MWNTs to epoxy induced a slight increase in storage modulus (2329.3 MPa). This slight increase can be explained by the uniform dispersion and the strong interfacial bonding between B1@fMWNTs and the epoxy provided by the epoxy groups on the side chain of PGMA. It is reasonable to suppose that the load gets transferred from the epoxy matrix to the filler, resulting in the enhanced modulus [As mentioned before, the inter-layer PHMA segment in bc@modulus 29.3 MPa.  modulus . Tg. From fMWNTs have a different influence on the Tg values of the epoxy resin. For example, the Tg values for the composite with the poorly dispersed pristine MWNTs are slightly shifted to a higher temperature than the neat epoxy (from 153.9 °C to 154.3 °C). B1@fMWNTs further enhanced the Tg value to 156.2 °C. Then, the Tg values for the composite decrease slightly with a further increase in molecular weight of the PHMA segment . This phenomenon can be attributed to the following factors: (i) dispersion level of MWNTs in epoxy resin; (ii) tangled structure and large surface area of MWNTs; (iii) covalent bonding between the MWNTs and the epoxy resins; and (iv) molecular structure of grafted block copolymers and cross-linking degree of curved epoxy composites. These competing factors determine the change of Tg values. The tangled structures and large surface area likely inhibit epoxy chains’ mobility, effectively increasing the Tg value of materials. Additionally, the outer-layer reactive PGMA grafted on the MWNTs provides covalently interfacial bonding between MWNTs and the epoxy polymer chain, leading to the polymer’s confinement in the nanotubes’ vicinity. On the contrary, the poor dispersion of nanotubes would weaken the efficiency in restricting the mobility of the surrounding polymer [Tg value. Therefore, the increase in Tg value can be attributed to the better dispersion and improved interfacial adhesion between B1@fMWNTs and epoxy resin [The temperature at which the loss factor curve showed a maximum peak is often recorded as the  polymer . The flexy resin . Howeverxy resin . b-PHMA with different PHMA molecular lengths and the same PGMA molecular length were grafted on the MWNTs surface, enabling the carbon nanotubes to link with epoxy networks covalently. We attempted to study how the structural change of PHMA in bc@fMWNTs influences the mechanical properties of resulting composites. The tensile properties and fracture toughness for the neat epoxy and its composites with different fillers are shown in fMWNT composites exhibit higher tensile properties and fracture toughness than epoxy/p-MWNT composites. In this work, PGMA-fMWNTs, B2@fMWNTs, and B3@fMWNTs produce dramatically improved strength and toughness, along with a similar increase in stiffness. For the neat epoxy, the ultimate tensile strength is 82.0 MPa. At 0.050 wt% B1@fMWNTs, B2@fMWNTs, and B3@fMWNTs, the maximum tensile strengths of nanocomposites are 95.6 Mpa, 93.8 Mpa, and 93.1 Mpa, corresponding to the increases of 16.6%, 14.4%, and 13.5%, respectively. The tensile strength of bc@fMWNTs-filled epoxy composites slightly reduces as the molecular weight of PHMA reaches 8250 g mol−1 (B2@fMWNTs). The minor average tensile strength of cured epoxy composites is 76.1 Mpa, with B4@fMWNTs higher than the epoxy/p-MWNT composite (75.9 Mpa). The modulus of epoxy nanocomposites with 0.05 wt% bc@fMWNT show a lighter decrease as a function of molecular weights of PHMA increase from 4480 to 20,800 g mol−1 than the decrease in strength.Representative stress–strain curves for the neat epoxy and its nanocomposites are plotted in fMWNTs and epoxy, provided by the segment of PGMA, constrains the mobility of segments, and thus contributes to a load well transfer at the interface. On the other hand, due to the longer linear molecular length, the extending PHMA chains on the MWNTs can suffer from the worse dispersion state, confirmed by OM, and significantly higher deformation through the soft and flexible interphase. The worse dispersion and higher deformation are unfavorable for enhancing the load transfer in the host polymer. Hence, the tensile strength and modulus decrease as a function of the molecular weights of PHMA which increases from 4480 to 20,800 g mol−1. It is well-known that the modulus is dependent on the modulus and volume fraction of the composite constituents. Thus, there is a smaller decrease in a function of molecular weights of PHMA, in the case of modulus compared to strength. As illustrated in KIC and GIC) of the neat epoxy and its composites with 0.05 wt% different fillers are listed in KIC. The ICK of nanocomposites with p-MWNTs increased by ~10%, compared to the base value of neat epoxy. In comparison, the presence of bc@fMWNTs in nanocomposites results in a remarkable increase in fracture toughness. Especially, Epoxy/B1@fMWNT composite and Epoxy/B2@fMWNT composite show a sharp increase in KIC, followed by a trend in a slight decrease with longer PHMA chains. The composites with 0.05 wt% B2@fMWNTs exhibit maximum toughening effects, for which ICK and ICG increase by 46% and 92.6%, respectively. The abovementioned results show significant improvements in toughness, indicating the tunable and effective interphases obtained between the nanotubes and the matrix. At this moment, how the different PGMA-b-PHMA chains differ in influencing fracture toughness remains an open question. It is well-known that epoxide groups on the MWNTs surface provide good dispersion of bc@fMWNTs in the epoxy and chemical bonding between MWNTs and the matrix. Moreover, the PHMA chains with different chain lengths should create different soft interphase structures between the MWNTs and bulk epoxy, influencing the overall performance of nanocomposites [fMWNTs in the epoxy declines   is usedmposites . Hence, @fMWNTs) . This phfMWNT only required loading of 0.05 wt% to obtain a similar improvement in strength, KIC, or GIC, indicating our interfacial design works. To put the mechanical properties‘ results in the perspective of published studies of tensile strength and fracture toughness in epoxy/CNT nanocomposites, we find that the introduction of organic molecules in the functionalized CNTs improved either tensile strength or fracture toughness to a certain extent at 0.2–1 wt% loadings ,41. In tfMWNTs taken from tensile tests, were investigated by SEM to further research the fracture characteristics of the epoxy composites. The fracture surface of all samples shows dimple-like structures on the fracture surface. At the same time, the composites filled with bc@fMWNTs  show a sharp increase in fracture toughness, followed by a slight decrease with longer PHMA chains. The related mechanisms of the epoxy/bc@fMWNT composites are because longer PHMA chains cause the two competitive and opposing effects on the dispersion state and soft interface, which must be optimized to maximize the mechanical properties of composites.In this paper, we demonstrated the interfacial effects of grafted PGMA-


We propose that combination treatments involving chemotherapy plus β‐catenin inhibitors will enhance chemotherapy response and prevent disease relapse in T‐ALL patients.Understanding the molecular mechanisms that contribute to the appearance of chemotherapy resistant cell populations is necessary to improve cancer treatment. We have now investigated the role of β‐catenin/CTNNB1 in the evolution of T‐cell Acute Lymphoblastic Leukemia (T‐ALL) patients and its involvement in therapy resistance. We have identified a specific gene signature that is directly regulated by β‐catenin, TCF/LEF factors and ZBTB33/Kaiso in T‐ALL cell lines, which is highly and significantly represented in five out of six refractory patients from a cohort of 40 children with T‐ALL. By subsequent refinement of this gene signature, we found that a subset of β‐catenin target genes involved with RNA‐processing function are sufficient to segregate T‐ALL refractory patients in three independent cohorts. We demonstrate the implication of β‐catenin in RNA and protein synthesis in T‐ALL and provide  A β‐catenin‐dependent RNA processing program is identified in human T‐ALL cells that is informative for the identification of refractory T‐ALL patients at diagnosis. Inhibition of β‐catenin activity prevents T‐ALL cell recovery and leukemia survival after chemotherapy. T‐cell Acute Lymphoblastic Leukemia (T‐ALL) is a rare disease affecting children and adults. Combinations of high‐dose chemotherapeutic drugs have greatly improved the outcome of pediatric patients, but adults and the few children that relapse have still very dismal prognosis and targeted therapies are not yet available.in vitro and in vivo.Characterization of β‐catenin DNA‐binding sites and transcriptional targets in T‐ALL cell lines identified a β‐catenin‐dependent gene signature, involving multiple RNA processing elements, that is associated with initial induction failure in T‐ALL patients. Consistent with this observation, inhibition of β‐catenin sensitizes T‐ALL cells to chemotherapy treatments Our results indicate that β‐catenin positively regulates chemotherapy resistance, and suggest that specific β‐catenin‐dependent activities could be used for the stratification of patients with higher risk of induction failure. These same patients could benefit from combination treatments including β‐catenin inhibitors plus chemotherapy.et al, T‐cell Acute Lymphoblastic Leukemia (T‐ALL) is an aggressive hematological disease characterized by the outgrowth of cells of the T‐lymphoid lineage. T‐ALL is a rare disease with an average incidence of 0.6 in 100,000 per year. Children are mostly affected but combinations of chemotherapeutic (CT) drugs at high doses have considerably improved the outcome of these patients from around 50% relapse in 1996 to the current less than 20%. Adult patients have nowadays the worst prognosis  and relapse patients from both groups have very dismal prognosis  is needed for NOTCH1‐induced transformation of T‐ALL cells, but the mechanisms behind this requirement are not yet characterized. We here investigated the DNA‐binding activity of β‐catenin in two T‐ALL cell lines (RPMI8402 (pre‐T‐ALL) and Jurkat ) with high basal levels of nuclear active β‐catenin protein that is increased in response to GSK3β inhibition by 25 mM LiCl Fig . Chromatet al, et al, et al, Motif enrichment analysis of β‐catenin DNA‐binding regions uncovered ZBTB33/Kaiso as the most represented motif Fig . We alson = 3 per experimental condition) and found 4,929 differentially expressed genes (DEGs) between both conditions  or H3K27Ac and H3K4me3 compared with repressive H3K27me3 marks in different T‐ALL cell lines Fig , which set al, Since ZBTB33/Kaiso was also recruited to a subset of β‐catenin‐targets Fig  and has in vitro. We measured 5‐Ethynyl Uridine  incorporation to nascent RNA in β‐catenin‐depleted or inhibited RPMI8402 cells. We found that both sh‐β‐catenin and treatment with the β‐catenin inhibitors ICG‐001 or FH535 reduced the total RNA transcription compared with their controls , failed to achieve complete remission  or remained in complete continuous remission  and accumulation of induction‐failure (refractory cases) within the PA group  at diagnosis who either did not respond to the treatment  and overall survival  and OS (P = 0.32) among groups  followed by the ETP + ABD phenotype . In the poor prognosis P1 cluster of patients, the G1 module was primarily upregulated and included most of the genes involved in RNA processing functions. In contrast, G2 genes (enriched for mitotic and DNA repair functions) were downregulated and no function was significantly associate to the G3 module Fig .n = 3,268, P < 0.05, see Dataset P = 0.0055). These 42 genes were also sufficient to cluster refractory patients in a new unsupervised analysis, as expected  and DFS  with refractory samples (one in each cohort) comprised in the 25% of patients with the highest expression of G1  and the only “refractory” patient was found in the Q4 quartile, with highest expression of G1  with 253 primary samples corresponding to children and young adults  and found that cells with reduced levels of β‐catenin were 15 times more sensitive to the treatment than control cells  alone or with ICG‐001 for 2 days and after the drug washout we let the surviving cells recover in the presence or absence of the β‐catenin inhibitor Fig . In thesin vivo, we transplanted 5 × 105 RPMI8402 T‐ALL cells into sublethally irradiated NSG mice and were treated with a chemotherapy cocktail  starting 3 days later. The treated animals were divided in two groups receiving DMSO vehicle  or ICG‐001 β‐catenin inhibitor , respectively , however relapse and refractory patients still have a dismal outcome, and adult patient DFS is very poor compared with children (Ribera P = 0.03), being all the refractory cases inside these T‐ALL subtypes  showed a significant coincidence between β‐catenin‐ and Runx1‐ or Notch1‐bound regions . Contribution of these factors to β‐catenin‐dependent transcription will be further investigated.We have also examined whether other factors important for T‐ALL are able to collaborate with β‐catenin in the chromatin. Computational comparison of the β‐catenin target regions with regions bound by T‐ALL factors , Jurkat (DSMZ ACC. 282), CCRF‐CEM (ATCC CCL‐119), DND‐41 (DSMZ ACC. 525) and HEK 293 T (ATCC 3216) human cell lines were cultured in standard conditions and regularly tested for Mycoplasma contamination. Negative contamination status was confirmed by PCR before each experiment.−1) and dexamethasone  were kindly provided by Hospital del Mar. L‐Asparaginase was obtained from MedChemExpress (HY‐P1923).LiCl  was used at 25 mM. FH‐535  and ICG‐001  stocks were diluted in dimethyl sulfoxide  and used at the indicated concentrations. Vincristine  and retro‐transcribed using Transcriptor First Strand cDNA Synthesis Kit (Roche). cDNA is used for qPCR analysis. Primers used are listed in Appendix Table Q > 30) and length (length > 20 bp) with TrimGalore. Filtered sequences were aligned against the reference genome (hg38) with Bowtie2. High‐quality alignments were fed to HTSeq  using Illumina Nextseq500 platform. Raw paired‐end 150‐bp sequences were filtered by quality . Chromatin is concentrated using Vivaspin 20 columns (Sartorius) and pre‐cleared by adding 1% BSA, 12.5 μg μl−1 of salmon sperm DNA, 2.5–10 μg of irrelevant IgG  and protein G/A‐Sepharose. After pre‐clearing, chromatin fragments were incubated overnight with irrelevant IgG or the indicated antibodies and precipitated with protein G/A‐Sepharose. Crosslink was reversed overnight at 65°C and protein was degraded using 460 μg μl−1 of Proteinase K (Roche) for 2 h at 55°C. Chromatin was purified using the MiniElute PCR Purification Kit (Qiagen) and used either for sequencing or for quantitative PCR (qPCR). Fold enrichment was calculated relative to irrelevant IgG. Primers used are listed in Appendix Table Cells were crosslinked in 0.2 mM di‐succynimidyl glutarate  for 10 min (Estaras Q > 30) and length (length > 20 bp) with TrimGalore. Filtered sequences were aligned against the reference genome (hg38) with Bowtie2  normalizing to an input sequence from the same cell lines. Peaks were annotated with ChIPseeker .Chromatin was sequenced in the Genomics facility from Centre for Genomic Regulation (CRG) using Illumina HiSeq platform. Raw single‐end 50‐bp sequences were filtered by quality .g for 3 h.Recombinant lentiviruses were produced in the HEK293T cell line (ATCC Ref. CRL‐3216). Plasmids for lentiviral production  were introduced together with the desired lentiviral plasmid using polyethyleneimine. Supernatant was filtered with 45 μM filters and ultracentrifuged at 75,000 et al, sgRNAs were designed using Benchling  or with Puromycin 1.25 μg ml−1 (Sigma).shβCat  for 10 min at 4°C, centrifuged at 800 g and analyzed by electrophoresis. Antibodies used are listed in Appendix Table Cells were lysed with 10 mM HEPES, 1.5 mM MgClClick‐iT Plus OPP Alexa Fluor 488 Protein Synthesis Assay Kit or Click‐iT RNA Alexa Fluor 488 Imaging Kit (Invitrogen) were used for protein and RNA synthesis determination, respectively, following the manufacturer's instructions. Cells were analyzed with the LSRII Cytometer (BD Biosciences). Data analysis was performed using FlowJo.−1, 1 h in the dark). Cells were analyzed with the Fortessa Cytometer (BD Biosciences). Data analysis was performed using FlowJo.Cell cycle was determined by flow cytometry using Ki67‐APC and DAPI intracellular stainings. Unsynchronized RPMI8402 cells were collected upon β‐Catenin knockdown or after 24 and 48 h‐treatment with the β‐Catenin inhibitors ICG‐001 (10 μM) or FH535 (30 μM). Fixed and permeabilized cells  were stained with Ki67‐AF647  and DAPI (5 μg mlhttps://ocg.cancer.gov/programs/target) repositories. Informed consent was obtained from all subjects and experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report. The transcriptomes of primary samples from GSE14618 obtained by microarrays were analyzed with the affy and limma R package. Data from COG studies 9404 (50 patients) and 8704 (42 patients) were processed independently. Only 40 out of the 50 primary samples from COG study 9404 had available survival clinical data and were further used as a discovery cohort for the identification of predictive signatures. The discovery cohort included 21 complete remission, 13 relapses and six refractory patients and were classified into the phenotypes ETP (Early T‐cell precursor), ABD , LEF1 negative, TAL1, and TLX1. The corresponding clinical data was provided by Alejandro Gutiérrez . COG 8707 contained 42 patients with no survival data and was comprised by 24 remission, 17 relapsed and one refractory patient.The transcriptomes of T‐ALL patients were from GEO , EGA (EGAS00001000536) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative, phs000218. . Additional 27 RNASeq samples published by Atak et al  were dowl et al,  to asseset al, Patients were clustered using a non‐supervised hierarchical model based on the expression of different groups of β‐catenin targets. All non‐supervised hierarchical clusterings included were obtained using the pheatmap R package with default parameters (euclidean distance and complete clustering method). In each case, the number of patient and gene clusters were decided based on visual inspection of different expression patterns among clusters. In 3A, we produced a minimal predictive β‐catenin signature and we adjusted the number of patient clusters to include the majority of refractory cases within the same cluster. Survival curves were performed with the survminer R package. Hazard ratios were estimated with the peperr R package. Functional enrichment analysis was performed with enrichR  scores. Normalized gene expression data for the GSE14618 (COG9404 and COG8707 studies), EGA and TARGET cohorts were submitted to the GenePattern platform  were incubated with the indicated drugs for 48 h and cell viability was monitored by the MTT assay . MTT absorbance of the cells incubated in the vehicle (untreated) was set to 100% viability to calculate the dose–response curves. We used GraphPad Prism 8 to generate the logistic fitting curve by non‐linear regression model and to calculate IC50 for each experiment. Treatments were performed in triplicate and repeated three times.We followed the strategy depicted in Fig Animal work was conducted under pathogen‐free conditions and adhered to the guidelines from Generalitat de Catalunya and the ethics committee at Parc de Recerca Biomèdica de Barcelona (approved protocol number 10655 according to the European Union regulations).5 RPMI8402 cells. Animals were randomized into two treatment groups: animals receiving DMSO vehicle and chemotherapy , and animals receiving ICG‐001 and chemotherapy . Two more animals were left untreated as controls to monitor the disease. Three days after transplantation, animals were treated with an induction‐like chemotherapy combination , D , L ; Oshima et al, −1 day−1) or DMSO vehicle were administered daily (except days of chemotherapy administration) for 2 weeks and every 2 days for 1 week. Stock solutions of drugs were diluted in sterile saline (chemotherapy) or in 40% PEG300, 5% Tween‐80, 55% saline (ICG‐011 and DMSO) and administered intraperitoneally (ip). See Fig NSG mice  were sublethally irradiated (2 Gy) and retro‐orbitally transplanted with 5 × 10Antibodies used for flow cytometry analysis are listed in Appendix Table ImageJ version 1.53t was used to visualize and quantify cellular colonies. Acquired images were transform into 8‐bit greyscale format. After uniform setting of intensity threshold, the colony area is automatically calculated. Four different 10× fields were measured for each animal. Manual counting of the number of cellular colonies was also performed.No statistical methods were used to determine the sample size. The experiments were not randomized and the investigator were not blinded.in vitro drugs assays, statistical significance among groups was determined by Student's t‐test  or Mann–Whitney U test  for two‐group comparison or one‐way ANOVA with Tukey's correction.GraphPad Prism 8 software and R software environment were used for statistical analysis. Statistical parameters and significance are indicated in the figures and legends. For qPCR experiments and Violeta García‐Hernández: Conceptualization; data curation; formal analysis; supervision; validation; investigation; methodology; writing – original draft; writing – review and editing. David Arambilet: Formal analysis; investigation. Yolanda Guillén: Conceptualization; data curation; software; formal analysis; writing – original draft. Teresa Lobo‐Jarne: Data curation; software; formal analysis. María Maqueda: Resources; data curation; software; formal analysis; writing – review and editing. Christos Gekas: Investigation; methodology. Jessica González: Investigation. Arnau Iglesias: Investigation. Nerea Vega‐García: Resources; investigation. Inés Sentís: Resources; formal analysis. Juan L Trincado: Resources; formal analysis. Ian Márquez‐López: Investigation. Holger Heyn: Resources; supervision. Mireia Camós: Resources; supervision. Lluis Espinosa: Conceptualization; supervision; methodology; writing – original draft; writing – review and editing. Anna Bigas: Conceptualization; supervision; funding acquisition; visualization; methodology; writing – original draft; project administration; writing – review and editing.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Source Data for Expanded ViewClick here for additional data file.PDF+Click here for additional data file.Source Data for Figure 1Click here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 5Click here for additional data file.Source Data for Figure 6Click here for additional data file.