Attachment between cells is crucial for almost all aspects of the life of cells. These inter-cell adhesions are mediated by the binding of transmembrane cadherin receptors of one cell to cadherins of a neighboring cell. Inside the cell, cadherin binds β-catenin, which interacts with α-catenin. The transitioning of cells between migration and adhesion is modulated by α-catenin, which links cell junctions and the plasma membrane to the actin cytoskeleton. At cell junctions, a single β-catenin/α-catenin heterodimer slips along filamentous actin in the direction of cytoskeletal tension which unfolds clustered heterodimers to form catch bonds with F-actin. Outside cell junctions, α-catenin dimerizes and links the plasma membrane to F-actin. Under cytoskeletal tension, α-catenin unfolds and forms an asymmetric catch bond with F-actin. To understand the mechanism of this important α-catenin function, we determined the 2.7 Å cryogenic electron microscopy (cryoEM) structures of filamentous actin alone and bound to human dimeric α-catenin. Our structures provide mechanistic insights into the role of the α-catenin interdomain interactions in directing α-catenin function and suggest a bivalent mechanism. Further, our cryoEM structure of human monomeric α-catenin provides mechanistic insights into α-catenin autoinhibition. Collectively, our structures capture the initial α-catenin interaction with F-actin before the sensing of force, which is a crucial event in cell adhesion and human disease. CryoEM structures capture the initial α-catenin interaction with F-actin before the sensing of force. Disruption of adherens junctions often leads to the development of cancer and vascular diseases4. Specifically, the disassembly of adherens junctions causes loss of cell polarity and contact inhibition that favor the epithelial-to-mesenchymal transition7. Such disruption of the epithelial polarity or cell-cell contacts often results in developmental defects12. Nevertheless, for cells to move and migrate, these specialized cell-cell junctions require dynamic regulation to allow continuous formation and severance of adhesive interactions between neighboring cells13.Multicellular organisms have well-defined and tightly regulated mechanisms for cell adhesion. Specifically, their endothelial, epithelial, and neuronal tissues require specialized cell-cell adhesion complexes, termed adherens junctions. These junctions are crucial for several normal functions, including embryonic morphogenesis, tissue integrity, homeostasis, and wound healing15. Cadherin is a transmembrane protein that dimerizes with cadherin of an adjacent cell and also binds β-catenin intracellularly. β-Catenin then binds the α-catenin to form the cadherin/β-catenin/α-catenin heterotrimer16. This complex mechanically couples neighboring cells to their respective underlying actin cytoskeleton17. The actin cytoskeleton is a key regulator, and its alterations control the morphogenetic movements of sheets of cells. Through its interactions with α-catenin, the cytoskeleton controls many cellular functions such as cell motility, cell shape, cell polarity, or the regulation of transcription.The cadherin-associated macromolecular complex is the master cell adhesion regulator that links neighboring cells19. This protein complex also senses and transmits mechanical tension between cells during collective cell migration13. Such mechanical forces are either external, for example, the shearing forces of flowing blood in vessels, or inside the cells whereby myosin motor proteins induce contraction of the actin cytoskeleton22. α-Catenin regulates the polymerization of the cytoskeleton that transitions cells between migration and adhesion. Thus, not surprisingly, α-catenin knockout mice display defective cell adhesion, migration, and proliferation23.In addition to providing cell-cell adhesion, binding of the cadherin/β-catenin/α-catenin heterotrimer to the actin cytoskeleton strengthens tissues mechanically27. However, only monomeric α-catenin binds β-catenin since the homo- and heterodimer binding sites overlap on the α-catenin amino-terminal domain. As a dimer, α-catenin resides in the cytosol and strongly induces filopodia, vital structures that initiate epithelial cell-cell contacts29 in contrast to undecorated F-actin, which is mostly limited to lamellipodia. In addition to regulating the organization of F-actin30, the α-catenin dimer also connects the plasma membrane to the actin cytoskeleton, thereby promoting cell adhesion and cell migration28. Mammalian α-catenin binds and bundles actin filaments30 and inhibits the seven-subunit actin-related protein2/actin-related protein 3 (Arp2/3)-mediated nucleation of F-actin assembly26. Depleting cytosolic α-catenin dimers increases the actin-dependent membrane dynamics and the cell-migration rate of Madin-Darby Canine Kidney epithelial cells31.α-Catenin is highly dynamic and functions either as a monomer or dimer. Both quaternary α-catenin states bind the actin cytoskeleton, although with different affinities that are increased by cytoskeletal tension32. Surprisingly, the carboxy-terminal α-catenin F-actin binding domains were not related by the two-fold axis that relates the remainder of the two α-catenin polypeptide chains. An apparently monomeric full-length murine α-catenin dimerized in the crystal and showed disorder for the amino-terminal and the carboxy-terminal domain and provided 6.5 Å insights for residues 85–262 and 290–631 (out of 906 residues) (PDB entry 4k1n)33. Another 4 Å murine dimeric truncated α-catenin (Δ1–81 and Δ884–906) crystal structure lacked interpretable electron density for the F-actin binding domain (PDB entry 6o3e)34. Therefore, any structural data on monomeric α-catenin is long overdue to fill this large knowledge gap. Here we report the 6.9 Å monomeric α-catenin cryogenic electron microscopy (cryoEM) structure. To the best of our knowledge, this is the first report of full-length α-catenin in its monomeric state that provides mechanistic insights into monomeric α-catenin function. Our data suggest that the linker preceding the α-catenin carboxy-terminal F-actin binding domain (FABD) forms an α-helix that restricts the flexibility of the FABD in monomeric α-catenin. Unexpectedly, monomeric α-catenin shows amino-terminal  flexibility, which has different functional consequences, and therefore provides structural clues about its autoinhibition in particular with respect to binding to F-actin.Binding studies have relied on α-catenin pull-downs with F-actin, which are difficult to interpret because of the tendency of α-catenin to precipitate and because α-catenin transitions between a monomer and a dimer. Structures of α-catenin have mainly used isolated α-catenin domains because of the flexibility of α-catenin and the monomer-dimer transition of α-catenin that hinder high-resolution structure determinations. We initially reported the human dimeric α-catenin Δ1-81 crystal structure (PDB entry 4igg) to 3.7 Å resolution30 and regulates cell migration and cell adhesion by simultaneously binding to F-actin and the plasma membrane28. Recently, the cryoEM structures of the isolated α-catenin FABD bound to F-actin provided insights into monomeric α-catenin36, as envisioned in a heterodimeric complex with β-catenin, at cell junctions. Here we report the much higher resolution (2.8 Å) cryoEM structure of human dimeric α-catenin bound to F-actin and the 2.7 Å cryoEM structure of F-actin. Surprisingly, the α-catenin FABDs are detached from the remainder of the molecule. Functionally important, the lack of interdomain interactions suggests that the amino-terminal α-catenin domains of dimeric α-catenin might bind to the plasma membrane independently of the free α-catenin FABD engaging in interactions with filamentous actin. Our data also suggest a bivalent mechanism. Collectively, our results advance our knowledge of how α-catenin functions allosterically to regulate specific signaling pathways depending on its oligomeric state.Further, α-catenin functions outside cell junctions where dimeric α-catenin modulates F-actin organizationTo gain molecular and mechanistic insights into the function of monomeric α-catenin, we solved the cryoEM structure of monomeric α-catenin to 6.9 Å resolution Fig. . Our sam33. Given the wealth of available crystal and AlphaFold37 structures, insights are obtained even at this modest resolution. For example, our monomeric α-catenin uncovered interactions of the middle domain with the FABD that have not been seen in any of the reported crystal structures. Specifically, the FABD interacts with the last 4-helix bundle of the middle domain through hydrophobic interactions. Charged residues surround the domain interface that might contribute towards the conformational flexibility of the full-length protein. In addition, the α-helix that we observe in the linker region that connects the middle domain to FABD (residues 636-650) is positioned centrally. This central location places this α-helix near the vinculin binding domain, the last 4-helix bundle of the middle domain, and the FABD. Therefore, this α-helix might play a role in the interdomain interactions that are severed depending on the functional state of α-catenin. Finally, the carboxy-terminal 43 residues were found to be disordered and could not be modeled.Surprisingly, in our structure, the two amino-terminal 4-helix bundle domains (residues 1–276) are disordered and not visible Fig.  which exvia a flexible linker (residues 262–277) that undergoes motions relative to the α-catenin middle domain38. In our previously reported dimeric crystal structure, the carboxy-terminal FABDs were oriented differently to engage in distinct inter-domain interactions with the first 4-helix bundle domain (residues 277–392) of the middle domain that binds to vinculin32  data that showed that the α-catenin amino-terminal domain connects 39. However, the finding that the amino-terminal domains are disordered and therefore not in contact with the α-catenin FABDs is functionally important, as it makes the FABDs readily available for binding to F-actin and thus explains its higher affinity. Our dimeric α-catenin cryoEM structure bound to F-actin and dimeric α-catenin crystal structure are also consistent with small-angle neutron scattering data that showed that the dimeric α-catenin is more extended compared to unbound dimeric α-catenin38.To gain molecular and mechanistic insights into the important functions of dimeric α-catenin outside the cell junction area, we determined the 2.8 Å cryoEM structure of the human Δ1–21 α-catenin dimer bound to F-actin Fig. ; Table 1Most α-catenin contacts with F-actin are formed by the second last two α-helices of α-catenin (residues 772–841) with two actin subunits. The regions corresponding to the fourth (residues 785-804) and fifth (residues 811–824) α-helices of the α-catenin FABD interact exclusively with the third subdomain (residues 145–180 and 270–337) of one actin subunit. The remaining α-catenin FABD interactions with F-actin occur predominantly with the longitudinal neighboring actin subunit. Such intermolecular interactions include a portion of fourth α-catenin FABD α-helix (residues 772–786) and regions of fifth α-helix (residues 834–841) with the actin DNAse I binding loop (D-loop). The most carboxy-terminal α-catenin region (residues 862–870) extends to bind the longitudinal neighboring actin subunit 32 with our human α-catenin bound to F-actin (this report) instead, therefore, provides new insights. In contrast to our F-actin-bound cryoEM structure  that connects the last two α-helices of the FABD. These are disordered in our F-actin-bound α-catenin cryoEM structure. Here, induced-fit causes α-catenin movements of ~19 Å of the carboxy-terminal half of the penultimate α-helix of the FABD 5-helix bundle  are similar to our dimeric α-catenin bound to F-actin structure and can be superimposed with root mean square deviations of 0.7 Å for 9745 atoms.  At our 2.8 Å resolution, we can fit the amino acids clearly into our obtained Coulomb potential map  α-catenin FABDs (residues 699–871) bound to (chicken or rabbit) F-actinmap Fig. , which sversus using other F-actin structures derived from a separate actin batch that could be used for such a comparison. Superposition of the actin subunits of α-catenin bound and unbound actin displays a root mean square deviation of 0.7 Å for 2888 atoms. The smallest and most flexible of the four actin domains harbors the so-called D-loop, for DNase I binding loop (residues 40–51). This loop changes its conformation upon adenosine triphosphate hydrolysis and mediates longitudinal actin-actin interactions42. Binding of α-catenin places its penultimate FABD α-helix against the D-loop, causing a shift of about 7 Å of the D-loop  that binds F-actin with 5 µM, with the cryoEM structures, was already noted becasue the four additional residues (869–872) are not observed in the cryoEM structures35. Indeed, the cryoEM maps for the truncated FABDs and our 2.8 Å map for our  residues 22-906 α-catenin sample allowed unambiguous tracing through residue 871. The carboxy terminus is also unstructured in the AlphaFold37 predicted structure and beyond residue 878 in our dimeric α-catenin crystal structure (PDB entry 4igg)32.Binding studies have relied on α-catenin co-sedimentation assays with F-actin, which are difficult to deconvolute because α-catenin tends to precipitate and because α-catenin transitions between a monomer and a dimer. Recent F-actin pull-down studies of monomeric α-catenin with amino-terminal deletions  and additional to Δ1–670, carboxy-terminal deletions  or a point mutation (W859A), showed affinities ranging from 0.4 µM (for Δ1–695) to 8.5 µM (for Δ1–664) with no binding detected for constructs that also had the carboxy terminus deleted (to just have residues 671–868 and 671–864) and over 35 µM affinity for the (residues 671–906) W859A mutant35. Notably, for our Δ1–21 α-catenin sample, no density is visible before residue 710. While the isolated α-catenin FABD structures bound to F-actin suggested a weakly bound amino-terminal FABD α-catenin α-helix, this α-helix is severed from the FABD helical bundle in our 2.7 Å cryoEM structure of Δ1–21 α-catenin bound to F-actin. Given that (i) our Δ1–21 α-catenin bound to F-actin has residues amino-terminal of 710 disordered, (ii) in our monomeric α-catenin structure residues 699-710 engage in extensive contacts by being sandwiched between residues 631-635 that connect the middle domain to the linker α-helix 636–650, and (iii) in the truncated F-actin bound FABD structures, the cryoEM maps for residues amino-terminal of 710 are weak and challenging to assign, it seems that the first FABD α-helix plays a role far beyond regulating the affinity for F-actin. Additionally, the α-helix (residues 636–650) in our monomeric α-catenin structure is a random coiled region in the AlphaFold predicted structure. This suggests that this α-helix might act as one of the tension sensors.The amino-terminal flexibility of the α-catenin FABD upon binding was documented by limited proteolysis that was consistent with the noted weaker density for residues 699-702 in the earlier murine cryoEM structure of the isolated α-catenin FABD bound to F-actin43. Our cryoEM structure of the strictly dimeric α-catenin bound to the filamentous actin and our cryoEM structure of full-length monomeric α-catenin on its own provide important molecular clues into the functions of α-catenin in regulating cell adhesion. The first finding with important insights into how α-catenin regulates the cytoskeleton is the observation that the α-catenin monomer has its amino-terminal domain isolated from its middle domain. This flexibility is consistent with size exclusion chromatography small-angle X-ray scattering data that showed that the flexible linker from the middle domain leaves the amino-terminal domain flexible39.Thus far, structural studies were limited to the α-catenin FABD or dimeric α-catenin, which leaves a void in our understanding of α-catenin monomeric specific functions. Our SEC-MALS data show, that deletion of the 81 amino-terminal α-catenin residues results in a purely dimeric protein that does not convert back into a monomer as was also shown for α-catenin Δ1–5632. Indeed, our monomeric α-catenin structure revealed the density corresponding to the linker that forms an α-helix that keeps the FABD close to the middle domain. Notably, the proximity of this α-helix to the first and to the last 4-helix bundle of the three middle α-catenin subdomains might restrict the FABD in the α-catenin monomer. We identified the first structural evidence of the linker region in our monomeric α-catenin structure. The central location of the linker α-helix and its position within the polypeptide chain, connecting the FABD to the middle domain, might be important in regulating the release of the FABD to bind to the actin cytoskeleton.Secondly, such lack of interdomain contacts is not the case for the carboxy-terminal domain, which is the FABD, of monomeric α-catenin, that engages in interactions with its middle domain that are not seen in our α-catenin dimer crystal structureThird, in the absence of tension, the FABDs in our dimeric α-catenin bind F-actin without any interdomain α-catenin interactions. Collectively, our cryoEM structures and previously published data suggested that two α-helices keep monomeric α-catenin from binding F-actin efficiently while these two α-helices seem to serve as a spring to sever the interdomain interactions of the FABD in dimeric α-catenin. Thus, our F-actin-bound dimeric and unbound monomeric α-catenin structures seem to explain the constitutively active state of dimeric and the inefficient binding of monomeric α-catenin.49. The nanomolar affinity of the vinculin FABD with the vinculin amino-terminal domain correlates with the large buried surface area of the vinculin FABD of about 1800 Å2. In comparison, the FABD in our monomeric α-catenin cryoEM structure buries only about one-third (631 Å2) of the vinculin solvent accessible surface area. Our monomeric vinculin and α-catenin structures thus explain why only vinculin is autoinhibited.Vinculin is structurally and functionally closely related to α-catenin. However, vinculin is monomeric and does not dimerize. In contrast to α-catenin, vinculin has its FABD buried and autoinhibited38. However, the isolated FABD seems to bundle F-actin as shown by total internal reflection fluorescence microscopy36 suggesting that α-catenin dimerization is not necessary for actin bundling. Instead, dimerization might be an additional mechanism to explain that dimeric, but not monomeric, α-catenin binds F-actin readily in the absence of force given the mono- versus bivalent binding. Indeed, binding one α-catenin FABD to F-actin brings the second and unbound FABD in proximity to the unoccupied site on F-actin by tethering  was subcloned into a modified pET28 vector with an amino-terminal octa-histidine tag followed by a PreScission protease cleavage site. The set of primers used in the cloning of these constructs is as follows:g for 15 min. Cell pellets were stored at −80 °C.Full-length human α-catenin was cloned into pGEX-6P-1 with an amino-terminal glutathione S-transferase (GST)-tag and a PreScission protease cleavage site by DNA Custom Cloning, USA. All α-catenin constructs were transformed into BL21(DE3) (Novagen) and expressed in 2 liters of Luria-Bertani media at 37 °C until the optical density at 600 nm reached 0.6–0.8. Induction was performed with 1 mM isopropyl β-D-1-thiogalatopyranoside for 20 h at 25 °C. Cells were pelleted by centrifugation at 5000 × Escherichia coli BL21(DE3) pellets containing amino-terminally His8-tagged α-catenin were thawed, resuspended in 500 mM NaCl, 20 mM Tris pH 8, and 1 mM β-mercaptoethanol. Cells were lysed by using a French Press. The lysate was clarified through centrifugation at 30,000 × g for 45 min and filtered. The supernatant was loaded onto two HisTrap HP 5 ml columns equilibrated in 500 mM NaCl, 20 mM Tris pH 8, and 1 mM β-mercaptoethanol, and connected in tandem to an AKTA FPLC at a flow rate of 1 ml/min. The column was then washed with a buffer containing 500 mM NaCl, 20 mM Tris pH 8, and 1 mM β-mercaptoethanol, followed by a second wash step with a buffer containing 500 mM NaCl, 20 mM Tris pH 8, 1 mM β-mercaptoethanol, and 100 mM imidazole. Elution was completed through a 200 ml gradient of buffer containing 500 mM NaCl, 20 mM Tris pH 8, 1 mM β-mercaptoethanol, and 500 mM imidazole. The elution peak was pulled and dialyzed overnight in 1 liter of a buffer containing 500 mM NaCl, 20 mM Tris pH 8, and 1 mM β-mercaptoethanol, after the addition of 200 μg of PreScission protease. The next day, the sample was loaded onto two HisTrap HP 5 ml columns equilibrated in 500 mM NaCl, 20 mM Tris pH 8, and 1 mM β-mercaptoethanol, which were connected in tandem at a 1 ml/min flow rate. The flowthrough containing cleaved α-catenin was collected, concentrated, and stored in 100 μl aliquots at −80 °C after adding 10% glycerol. For size exclusion chromatography, a 100 μl aliquot was thawed and loaded onto a Superdex S200 10/300 GL equilibrated in 150 mM NaCl, 20 mM HEPES pH 7.5, and 1 mM dithiothreitol. The protein eluted in two peaks, the first corresponding to dimeric α-catenin and the second to the monomeric α-catenin.To generate purified human α-catenin (residues 22–906), frozen Escherichia coli BL21(DE3) pellets containing GST-tagged α-catenin that we resuspended in lysis buffer , 2 mM β-mercaptoethanol and 1 mM phenylmethylsulfonyl fluoride). We lysed the cells by sonication, at 75% amplitude, for 3 min with 5 s and 10 s off cycles followed by centrifugation at 100,000 × g for 30 min at 4 °C. The clarified lysate was loaded onto a GST 16/10 Sepharose column that was equilibrated with 150 mM NaCl, 20 mM Tris pH 8, 0.5 mM EDTA, and 2 mM β-mercaptoethanol. We eluted the bound GST- α-catenin with 10 mM reduced glutathione. The peak fractions containing the GST-α-catenin were pooled, treated with 200 μg of PreScission protease, and dialyzed extensively against 2 liters of 150 mM NaCl, 20 mM Tris pH 8, 0.5 mM EDTA, and 1 mM β-mercaptoethanol. The dialyzed sample was then passed through a GST 16/10 Sepharose column, and the flowthrough fractions containing the GST-tag cleaved α-catenin were collected, concentrated, supplemented with 10% glycerol and flash frozen in liquid nitrogen as 500 μl aliquots until further processing. As a final step, the thawed aliquots were passed through a Superdex S200 10/300 GL column pre-equilibrated with 20 mM Tris pH 8, 150 mM NaCl, and 0.2 mM tris(2-carboxyethyl)phosphine (TCEP). The monomer and dimer peaks were pooled separately and loaded for a second time on Superdex S200 10/300 GL to get enriched monomer or dimer samples.For the purification of full-length α-catenin, we thawed frozen 53. Briefly, to determine the absolute molar mass, SEC-MALS analyses were carried out using an Agilent 1260 Infinity HPLC system consisting of a variable wavelength detector for ultraviolet absorption monitoring coupled in line with Dawn-Heleos II multi-angle light-scattering detector (Wyatt Technology) and OptiLab T-rex differential refractive index detector (Wyatt Technology). Samples were passed through an analytical Superdex S200 10/300 GL column pre-equilibrated with 20 mM Tris-HCl pH 8, 150 mM NaCl, and 0.2 mM TCEP at a flow rate of 0.5 ml/min. Bovine serum albumin  was used as a standard, and all the data acquisition and subsequent analyses were carried out using ASTRA software version 6.1.SEC-MALS was performed as described53. Briefly, rabbit skeletal muscle G-actin (50 μl at 1 mg/ml) was dialyzed against 500 ml of G-actin buffer  overnight at 4 °C to ensure complete depolymerization of actin. G-actin (at 0.6 mg/ml) was then polymerized with the addition of polymerization buffer  and left overnight to polymerize fully. F-actin prepared this way was stored at 4 °C and used for up to two weeks.Polymerization of F-actin was performed as describedFor dimeric human α-catenin (residues 22–906) bound to F-actin, grids were prepared by reconstituting the complex on the grid. C-flat 1.2/1.3 400 mesh grids were glow discharged for 60 s and placed into a Leica GP2 cryogenic plunger set to 65% humidity and 21 °C. First, 3.5 μl of 0.7 μM F-actin diluted in 150 NaCl, 20 mM Tris pH 7.5, and 1 mM dithiothreitol was placed on the carbon film side of the C-flat grid and left to incubate for 1 min before the successive addition and removal of 3.5 μl of 7.6 μM (0.74 mg/ml) F-actin. The addition and removal of α-catenin was repeated four times before blotting from the backside of the grid. Grids were immediately plunged frozen in liquid ethane and transferred into a JEOL cryoARM300 for screening and data collection.Vitrification of human monomeric α-catenin was carried out by applying 3 μl of 0.25 mg/ml α-catenin on a glow discharged 300 mesh Au-Flat holey grids (ProtoChips) with 1.3 μm holes at 95% humidity and 4 °C using a Leica GP2 plunge freezer. The blotting was carried out for 6 seconds, and the grids were immediately plunged frozen into liquid ethane. Frozen grids were then transferred into a JEOL cryoARM300 for screening and data collection.53. Briefly, movies were collected at a nominal magnification of 60,000 with parallel illumination with an Omega in-column energy filter tuned to a slit width of 20 eV. SerialEM54 (reference number to be corrected) was used to record electron micrographs using a K3 direct electron detector (Gatan) operated in correlated double sampling mode and a calibrated object pixel size of 0.72 Å with a corresponding flux of 7 electrons/pixel/second on the detector. Automatic alignment of zero loss peak is automatically carried out every 6 hours using the serialEM script.All datasets were collected on a JEOL cryoARM300 operated at 300 kV as described2per micrograph, fractionated into 40 frames to achieve a corresponding electron dose of 1.2 electrons/Å2per frame. For all other samples, the exposure time and dose rate were adjusted to provide a total dose of 60 electrons/Å2per micrograph, fractionated into 50 frames. High flashing of the cold field emission gun was set up through serialEM every 4 hours during data collection to maintain emission at nearly 10 µA. Astigmatism and coma-free alignments were performed with serialEM immediately before each data collection and repeated once a day for datasets lasting longer than one day.For full-length human monomeric α-catenin data collection, the exposure time and dose rate were adjusted to provide a total dose of 48 electrons/Åper stage shift was performed on the film area next to the central hole. Micrographs were collected with a targeted defocus of −0.6 to −2.6 μm, and gain-corrected movies were saved and transferred to a computing cluster for processing.For full-length human monomeric α-catenin, since Au-flat grids were used, astigmatism and coma-free alignments were performed using the carbon film of a C-flat grid that was briefly loaded before each data collection. For all datasets, micrographs were acquired from a pattern of 3 × 3 holes recording each image at the center of the hole. One focus step 55 was used for the entire processing workflow of all datasets. Raw movies were imported directly into cryoSPARC, and motion correction was performed with Patch motion correction56 and a contrast transfer function (CTF) estimation with a CTF patch estimation. Curation of micrographs was performed to eliminate (i) all micrographs that had a CTF fit worse than 3.5 Å for the F-actin-contained samples or 5 Å for full-length human α-catenin, and a total full-frame motion distance larger than 150 pixels, (ii) micrographs that were astigmatic, and (iii) micrographs where thick or crystalline ice was detected. For all F-actin-containing samples, the Filament Tracer function was used to pick particles with relaxed criteria to pick as many particles as possible and remove unsuitable particles during the early 2D classification steps.CryoSPARC55. The particles were then CTF refined twice with local CTF refinement57. A final round of helical non-uniform refinement58 was applied to lead to a 3D reconstruction of a map at 2.8 Å from 403,663 particles, with a helical twist of −166.7° and a helical rise of 27.5 Å (EMDB entry EMD-26772). From the same dataset, 2D classes with no evidence of decoration were separated after extensive 2D classification and used to reconstruct the unbound F-actin 3D map. Using the same processing steps as with the decorated particles, a map for unbound F-actin at 2.7 Å was achieved from 963,102 particles (EMDB entry EMD-26860), with a helical twist of −166.7°, and a helical rise of 27.4 Å.For α-catenin (residues 22–906) bound to F-actin, 25,442 micrographs were collected from four different grids to compensate for the partial or lack of decoration observed on many filaments. After micrograph curation, 7427 images were selected and processed further. Particles were extracted with a large box size of 1620 pixels (603 Å) to facilitate the separation of highly decorated from undecorated filaments through visual inspection. After extensive 2D classification, particles clustering in 2D classes with visibly high degrees of decoration were re-extracted with a smaller box size of 1080 pixels (402 Å), and 3D reconstruction was performed by using the helical refinement function of cryoSPARC37 was carried out.For full-length human monomeric α-catenin, 14,333 micrographs obtained after curation were used for processing. A blob picker with a particle diameter of 80 to 120 Å was used for initial particle picking, and after curation, about 6 million particles were extracted with a box size of 360 pixels (266 Å) and further binned to 180 pixels (corresponding to a pixel size of 1.44 Å/pixel). After several iterative rounds of 2D classification, the resultant particle stacks were used to reconstruct multiple ab initio classes to remove non-ideal particles. The most promising ab initio class was subjected to further curation through additional rounds of 2D classification. The resulting particle stack was subjected to homogeneous and local CTF refinement to improve the overall features of the map. Non-uniform refinement was subsequently carried out to improve the resolution (EMDB entry EMD-27717). A final round of masked non-uniform refinement with a mask prepared from a flexibly fitted α-catenin middle domain and FABD of an AlphaFold model36 was fitted into our 2.8 Å map using Chimera59 and initially refined with PHENIX61 using real-space refinement with simulated annealing63 to better accommodate our higher resolution map. The structure was then revised with COOT64. Despite using a low threshold and low-pass filter, α-catenin residues amino-terminal of amino acid 710 were not resolvable and not included. The density ends abruptly. There was also poor density for the short loop region connecting residues 804–811. Finally, there was no density for α-catenin residues carboxy-terminal of amino acid 871. For F-actin, residues amino-terminal of amino acid 5 and carboxy-terminal of amino acid 374 were not resolvable and not included. The remaining coordinates for both α-catenin and F-actin were visually inspected multiple times to improve complementarity through COOT64, and further refined through PHENIX61 by using rigid body real-space refinement, after which outliers were inspected and corrected. A total of six copies of F-actin and six copies of α-catenin were fitted into the map and the final structure was deposited with the Protein Data Bank (PDB entry 7utj).For determining the cryoEM structure of human α-catenin (residues 22-906) bound to F-actin, the 3.2 Å structure of the FABD of α-catenin bound to F-actin (PDB entry 6upv)59, after removal of the α-catenin coordinates. The structure was then initially refined with PHENIX61 by using real-space refinement with simulated annealing and then visually inspected multiple times to improve complementarity through COOT64, and further refined through PHENIX61 by using rigid body real-space refinement, after which outliers were inspected and corrected. The resulting structure was nearly identical to that of F-actin in our human α-catenin bound to the F-actin structure, except for a difference loop conformation (residues 45–50). The final F-actin structure at 2.7 Å was deposited with the PDB (entry 7uxf).For determining the cryoEM structure of unbound F-actin, the F-actin coordinates of our human α-catenin bound to F-actin (PDB entry 7utj) were fitted into the map by using Chimera37 was used to fit into our final cryoEM map using Chimera59. The model was then further subjected to real-space refinement in COOT64.For determining the cryoEM structure of the full-length human monomeric α-catenin (residues 1–906), the human α-catenin AlphaFold model65. Structural similarities were calculated, and variations reported as root mean square deviation were calculated using main-chain atoms with the “Super” command in Pymol.Model analyses were carried out in Pymol (v. 2.5.2) and Chimera (1.14)Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Demographic features, suggestive gynaecological symptoms, and immunohistochemical expression of endometrial β-catenin have a prognostic capacity for endometrial hyperplasia and carcinoma. This study assessed the interaction of all variables and developed risk stratification for endometrial hyperplasia and carcinoma.This cross-sectional study was conducted from January 2023 to July 2023 at two teaching hospitals in Makassar Indonesia. Patients (< 70 years old) with suggestive symptoms of endometrial hyperplasia or carcinoma or being referred with disease code N.85 who underwent curettage and/or surgery for pathology assessment except those receiving radiotherapy, or chemotherapy, presence of another carcinoma, coagulation disorder, and history of anti-inflammatory drug use and unreadable samples. Demographic, and clinical symptoms were collected from medical records. Immunohistochemistry staining using mouse-monoclonal antibodies determined the β-catenin expression  in endometrial tissues. Ordinal and Binary Logistic regression identified the potential predictors to be included in neural networks and decision tree models of histopathological grading according to the World Health Organization/WHO grading classification.Abdominal enlargement was associated with worse pathological grading (adjusted odds ratio/aOR 6.7 95% CI 1.8–24.8). Increasing age (aOR 1.1 95% CI 1.03–1.2) and uterus bleeding (aOR 5.3 95% CI 1.3–21.6) were associated with carcinoma but not with %β-catenin and H-Score. However, adjusted by vaginal bleeding and body mass index, lower %β-catenin (aOR 1.03 95% 1.01–1.05) was associated with non-atypical hyperplasia, as well as H-Score (aOR 1.01 95% CI 1.01–1.02). Neural networks and Decision tree risk stratification showed a sensitivity of 80-94.8% and a specificity of 40.6–60% in differentiating non-atypical from atypical and carcinoma. A cutoff of 55% β-catenin area and H-Score of 110, along with other predictors could distinguish non-atypical samples from atypical and carcinoma.Risk stratification based on demographics, clinical symptoms, and β-catenin possesses a good performance in differentiating non-atypical hyperplasia with later stages.The online version contains supplementary material available at 10.1186/s12905-023-02790-6. Endometrial hyperplasia is characterized by an increase of gland proportions to stroma in endometrial tissue, which in certain conditions may lead to endometrial cancer. Stratification of endometrial hyperplasia was based on the presence of atypical nuclear  where thAge, obesity, and certain chronic diseases such as diabetes and hypertension were associated with endometrial cancer and hyperplasia, particularly among Hispanic women  and ChinImmunohistochemistry staining of β-catenin in endometrial tissue also possesses a potential prognostic capacity. Along with E-Cadherin, β-catenin, an epithelial cell adhesion molecule, has a crucial role in the Wnt signal transduction pathway, affecting the epithelial integrity . Wnt-sigWe intended to assess interactions between associated factors of endometrial hyperplasia and carcinoma, the β-catenin expression in endometrial tissue, and the level of pathology in endometrial tissue. Moreover, we developed a novel risk-stratification system combining demography, clinical symptoms and β-catenin expression to classify the pathology level of endometrial hyperplasia and carcinoma among patients with gynaecological symptoms.This is a cross-sectional study conducted between January 2023 and June 2023 at two teaching hospitals in Makassar Indonesia. Any patients with high suspicion of endometrial hyperplasia and/or endometrioid endometrial carcinoma  referred for pathology assessment after curettage and/or surgical biopsy or hysterectomy were included, except for those above 70 years old, receiving radiotherapy, or chemotherapy, presence of another carcinoma, coagulation disorder, and history of anti-inflammatory drug use. Moreover, samples with the presence of dominant hemorrhagic or illegible for tissue processing were excluded, unless successful re-sampling had been performed.We obtained demographic data from medical records, including age at diagnosis, body mass index, number of parity, abortion and miscarriage, list of clinical symptoms and referral diagnosis by the gynaecologist. These data were collected during the initial admission of the patient at the hospital.We obtained the samples from either a curettage or a surgical procedure (hysterectomy). Block and section preparation was based on guideline  with somThis study modified a procedure from one study  as the cA β-Catenin mouse-monoclonal antibody (Cell Marque© The Netherlands) was given for 10 min followed by a PBS rinse twice, each for five minutes. This was then followed by HRP (Horseradish Peroxidase) (Cell Marque© The Netherlands) and rinsing with PBS twice, each for five minutes. Furthermore, the preparations were incubated with chromogen Diaminobenzidine (DAB) and washed with running water for 5 min, then immersed in hematoxylin solution for 5 min. The preparations were then washed again with running water. Subsequently, dehydration was carried out with graded alcohol  for 5 min each and then cleared with two Xylol solutions for 5 min each. The slides were dried and then covered with deck glass.Positive expression of β-catenin will appear brown on the nuclear, membrane and/or cytoplasm of tumour cells and the nuclear, membrane and/or cytoplasm of endometrial glands with hyperplasia. All slides that had been stained with the immunohistochemical method were assessed by two gynaecological pathologists independently.Expression of β-catenin protein binding in endometrial tissue with hyperplasia, both atypical and non-atypical could be seen as a brownish chromogen substance on the cell membrane and/or cytoplasm, which was observed with a light microscope at 10 high-power fields with 400x magnification. Intensity score ranged from 0 to 3, whereas percentage points ranged from 0 to 100%. The H-score was defined as the multiplication of percentage and intensity score. The classification of endometrial hyperplasia was following the World Health Organization (WHO) 2-level category with 70% diagnosis consistency . FurtherThe sample size was estimated with the effect size derived from the difference between the proportion of nuclear β-catenin expression between the premalignant endometrial hyperplasia and benign hyperplasia according to a systematic review . With a Age and body mass index were presented as continuous data and its mean ± standard deviation. Further classification of body mass index following the Asian classification. Episodes of parity, miscarriage or abortus were presented with median and interquartile ranges. No specific quantification and discretization were made for other variables.Descriptive statistics will elaborate on the characteristics of participants according to the pathology results. No missing data were imputed as all data included in this study should have complete responses. Normality tests along with the bivariate tests were conducted to assess the potential predictors. Differences between the two means were tested with independent t-test and Mann Whitney whereas three means were tested with Analysis of Variance/ ANOVA and Kruskal Wallis test. The Chi-square and Fischer Exact test concluded the association between categorical variables.Before the regression test, the selection of parameters was based on the p-value of the bivariate test of at least less than 0.2. Backward selection was also performed case by case. As the level of pathology result was in three levels, hence, ordinal regression would be applied, assuming that the assumptions are met. Re-classification of the results was made by merging two levels to create the binary response. In terms of differentiating carcinoma, a subset of atypical and non-atypical was merged into one subset, whereas atypical and carcinoma were merged into one variable to assess the discriminant ability in the benign stage . Adjusted odds ratio (aOR) presented by the exponential B value (expB) along with the 95% confidence interval (CI) of expB was presented.The Receiver Operating Characteristics (ROC) curve would assess the discriminant ability of β-catenin outcomes to carcinoma, and non-atypical classification without the presence of other predictors. The Area Under the Curve (AUC) and the cut-off point would be determined should the ROC curve not intersect and be located above the diagonal line.To accommodate other factors in predicting the carcinoma and non-atypical classification, we applied an Artificial Neural Network and Decision Tree model to build the classifiers. Details of syntax commands were attached as a The reliability issue in assessing the pathology features was handled by involving two pathologists and one gynaecologist for clinical consideration. We also acknowledge the time-to-assessment was different between the samples however, a time constraint was set that all the samples should be interpreted according to a standard diagnostic time (within 24 h after immunohistochemistry staining).A total of 167 participants were screened for clinical assessment. Following the eligibility criteria, nine samples from patients who fell outside the target age group were excluded, leaving 158 individuals for pathology assessment. As 35 participants were unable to undertake sampling procedures (curettage or surgery), and eight samples could not proceed for hematoxylin-eosin staining, this selection left 115 participants for immunohistochemistry screening (fifteen participants repeated the sampling procedures). Under the criteria for immunohistochemistry (IHC) screening, 25 samples were not eligible for IHC reading, thus the final samples for assessment were 90 samples as presented in Fig. 2. The most frequent symptom reported by individuals was abnormal uterus bleeding (63.3%) with the most referred clinical diagnosis as Endometrial Hyperplasia (31.1%). Specimens were mostly obtained through surgical incision and/or hysterectomy (62.2%). In terms of pathology classification, 32 patients (35.6%) presented with non-atypical endometrial hyperplasia, 27 with atypical form (30.0%), and 31 (34.4%) demonstrated carcinoma presentation. Table The mean age was 45.07 ± 9.07 (23–66 years old) with a median parity of 2 (Interquartile range 0–5) times. The majority of participants never experienced miscarriage or abortion (70%) and at normal body mass index (46.7%) although the mean BMI was 27.33 ± 5.22 kg/mrplasia 3.1%. Specp < 0.001). Interestingly, higher parity was observed in endometrial carcinoma although it was not significant (p = 0.101). Also, there were no significant differences in body mass index and abortion episodes (p > 0.05). Abdominal enlargement was seen more frequently in Endometrial Carcinoma (p = 0.001) but other symptom distributions were similar between the three classes. The majority of people referred with abnormal uterus bleeding were presented with non-atypical findings, and among 25 people referred with endometrial carcinoma, only one person had atypical findings. In terms of β-catenin expression, there was a significant difference between the three classes where β-catenin expression was higher in atypical cases, followed by carcinoma and non-atypical findings.Participants with endometrial carcinoma were significantly older compared to other types (p = 0.030). The β-Catenin intensity, percentage, area and H-score were not significantly different between carcinoma and non-carcinoma (Supplementary Table When considering the binary level of pathology results (Carcinoma versus Non-Carcinoma) and , different results could be seen. Aside from abdominal enlargement, menorrhagia  was significantly higher in non-carcinomatous patients . The selection of parameters was based on variables with a p-value of less than 0.2 in Supplementary Tables Model three and four in Table Considering these logistic models as a classifier, all models possess a good specificity of over 80% but not with sensitivity (less than 55%) to predict two different binary stagings. Further ROC  with a cut-off of % β-catenin score of 55  and later stages, as well as carcinoma, thus, increasing the precision and confidence in pathology assessment of endometrial hyperplasia.p > 0.05)  and ERβ (ESR2), estrogen induces the proliferation of stromal cells and endometrial epithelial cells, whereas progesterone performs a counteracting effect of estrogen . There ip > 0.05)  was achieved only with neural network model 3 and decision tree model 4. A study involving neural networks to predict a class of tuberculosis resistance demonstrates that neural networks outperform another classifier model when combining clinical symptoms, demography features and laboratory results, whereas decision trees possess higher sensitivity but very low specificity . In thisThis study applied a standardized preparation of samples from curettage and surgical procedure and the samples were processed according to a standard β-catenin immunohistochemistry staining. Moreover, robust statistical methods were implemented to derive the conclusions.Despite being justified by statistical estimation, the sample size should be increased to maximize the model building and model performance. Furthermore, prospective testing with new patients should be done to identify the consistency of diagnostic performance. In addition, the generalizability of the study is limited as this study was done in a single centre that supervised the two hospitals, although this centre accepts referred cases from the eastern part of Indonesia.p < 0.05). However, the distribution of the participants according to hormonal therapy was similar across pathology levels, thus this factor may not be a potential confounder.There is a question of whether hormonal therapy may affect the expression of β-catenin in endometrial tissue. Estradiol possesses a potential role in Wnt/β-catenin signalling by affecting the transcription of Wnt/β-catenin target genes  and enhaDiabetes and hypertension were associated with endometrial carcinoma. However, this study did not objectively assess diabetes and hypertension status, thus the expression of β-catenin among participants with the chronic disease could not be identified and the potential confounding issue was not explored.Lastly, there are some other potential predictors including Cyclooxigenase-2 (COX2) expression for endometrial carcinoma , but thiThe β-catenin expression, particularly percentage and H-Score is a good predictor to differentiate carcinoma, or even earlier stage when combined with clinical symptoms and demographic parameters. Furthermore, BMI was associated with β-catenin intensity and therefore, BMI reduction may play a role in reducing the risk of disease progression. This study also proposes a new cut-off for β-catenin percentage (55) that could discriminate non-atypical conditions from later stages although a prospective test is needed.Additional file 1. Supplementary file.Additional file 2: Supplementary Table 1. Characteristics of participants based on carcinoma and non-carcinoma findings. Supplementary Table 2. Characteristics of participants based on non-atypical and later stages findings. Supplementary Table 3. Generalized ordinal logistic regression of three pathology classes considering the h-score of β-catenin. Supplementary Table 4. Model structure of artificial neural network in predicting carcinoma using % of beta-catenin. Supplementary Table 5. Model structure of artificial neural network in predicting carcinoma using h-score of beta-catenin. Supplementary Table 6. Model structure of artificial neural network in predicting non-atypical finding using % of beta-catenin. Supplementary Table 7. Model structure of artificial neural network in predicting non-atypical finding using h-score of beta-catenin. Supplementary Table 8. Model structure of decision tree in predicting non-atypical finding using % beta-catenin. Supplementary Table 9. Model structure of decision tree in predicting non-atypical finding using h-score of beta-catenin. Supplementary Table 10. Summary of model performance. Supplementary Table 11. Additional analysis.Additional file 3: Supplementary Figure 1. Violin plots of β-catenin percentage and H-Score according to the three-level of histopathology. The median of %β-catenin and H-Score were higher in Atypical, followed by carcinoma and non-atypical hyperplasia. Supplementary Figure 2. ROC Curve of β-Catenin and H-Score in Differentiating Carcinoma, and Non-Atypical. Not that all ROC curve intersect the diagonal line, thus conclusion should not be made when considering beta-catenin as a single discriminant factor.


Endocrine therapies targeting estrogen signaling, such as tamoxifen, have significantly improved management of estrogen receptor alpha (ERα)‐positive breast cancers. However, their efficacy is limited by intrinsic and acquired resistance to treatment, and there is currently no predictive marker of response to these anti‐estrogens to guide treatment decision. Here, using two independent cohorts of breast cancer patients, we identified nuclear PRMT5 expression as an independent predictive marker of sensitivity to tamoxifen. Mechanistically, we discovered that tamoxifen stimulates ERα methylation by PRMT5, a key event for its binding to corepressors such as SMRT and HDAC1, participating in the inhibition of the transcriptional activity of ERα. Although PRMT5 is mainly localized in the cytoplasm of tumor cells, our analyses show that tamoxifen triggers its nuclear translocation in tamoxifen‐sensitive tumors but not in resistant ones. Hence, we unveil a biomarker of sensitivity to tamoxifen in ERα‐positive breast tumors that could be used to enhance the response of breast cancer patients to endocrine therapy, by fostering its nuclear expression. The analyses of PRMT5 expression in two large cohorts of luminal breast cancer samples and functional studies revealed a key role of PRMT5 expression in the nucleus of tumor cells in premenopausal women treated with tamoxifen. Tamoxifen is widely used as anti‐estrogen to treat ERα‐positive breast cancers in premenopausal women. However, 25% of patients relapse due to resistance to treatment, and there is currently no predictive biomarker for tamoxifen sensitivity. A better understanding of the mechanisms underlying resistance is essential to develop innovative strategies to improve clinical outcome for patients with endocrine‐resistant cancers.in vivo experiments revealed that tamoxifen triggers PRMT5 translocation to the nucleus of tumor cells where it methylates ERα. This event is a prerequisite for the recruitment of transcriptional corepressors to the promoters of target genes triggering repression of transcription and subsequent decrease in cell proliferation.Our study showed in two independent cohorts of breast tumors that a high nuclear expression of PRMT5 is associated with a prolonged survival of tamoxifen‐treated patients. We also identified nuclear PRMT5 as a key factor in the effects of tamoxifen. Importantly, These results may have potential clinical application at diagnosis as a high nuclear PRMT5 expression constitutes a predictive biomarker of tamoxifen sensitivity for premenopausal women. Future investigations may help to determine a way to target PRMT5 to the nucleus and thus improve tamoxifen sensitivity.et al, et al, et al, Breast cancer (BC) is the most common cancer among women worldwide , resulting from dysregulated gene expression and signaling. Among these PTMs, methylation of arginine residues performed by arginine methyltransferases (PRMTs) is emerging as an important player in cancer  were significantly associated with longer relapse‐free survival. Here, two independent cohorts of BC patients were used to analyze the predictive value of nuclear PRMT5 in luminal tumors. The identification of luminal BC was performed according to the flowchart . Of note, patients from the Validation cohort were more often treated with Aromatase inhibitor (68.8 vs. 55.2% in the Discovery cohort).H‐score cutoff for nuclear PRMT5 of 70, as previously published . In the Validation cohort, high nuclear PRMT5 expression was also associated with low‐grade tumors (P = 0.011), as well as with older age (P < 0.001) and higher body‐mass index or BMI (P = 0.015) at diagnosis.Using an P = 0.0014) but not for patients treated with AI (P = 0.43)  .In the Discovery cohort, we observed that high nuclear PRMT5 expression was statistically associated with prolonged disease‐free survival  and Validation cohort , giving a combined estimate of 0.55 .We thus speculated that high nuclear PRMT5 could be of prognostic value, and this was validated through adjusted analyses using Cox regression modeling Table . Indeed,P = 0.148; PRMT5 × menopausal status: P = 0.87; PRMT5 × stage: P = 0.99), the stratified Cox models highlighted better disease‐free survival for patients with high PRMT5 expression, at all stages, and particularly for stage III patients treated with Tam  using different antibody pairs. The images obtained revealed specific red dots predominantly in the nucleus of MCF7 cells, illustrating the interaction between ERα and PRMT5, which decreased upon treatment with Eo E2 Fig . The sigo E2 Fig . Intereso E2 Fig . To gaino E2 Fig . We alsoo E2 Fig .in vitro methylation experiments, to confirm the methylation of ERα by PRMT5. Purified GST‐ERα fragments containing the different structural domains  Fig . We founmRNA Fig .To evaluate whether this mechanism could be generalized, we performed a large‐scale analysis with a screening approach based on bimolecular fluorescence complementation (BIFC) using ERα as a bait. We used this system to assess changes in the network of ERα interactions induced by drugs. Here, we compared the ERα interactome in MCF7 cells treated or not with Tam in the presence or absence of G595. We analyzed ERα interactions induced by Tam treatment (comparison A) and we compared the interactors in the presence of Tam or Tam plus G595 (comparison B) Fig . We founIn vivo, we initially validated our SDMA‐based ERα methylation model by showing the lack of methylation in ERα‐negative tumor samples from a patient‐derived xenograft (PDX) model of BC and in a paraffin‐embedded section of fresh breast tumor treated with G595  , which targets ERα, is one of the preferred treatment options in the adjuvant setting for pre‐menopausal women with ERα‐positive BC. However, resistance to this treatment associated with disease relapse is a major clinical issue  (1 μM) (Sigma) or with the PRMT5 inhibitor GSK3326595 (abbreviated as G595) (Medchem express) (0.5 μM 72 h before Tam treatment).SMART‐pool siRNAs  were analyzed. The activities of the biological resource center (BRC) of the CLB (n° BB‐0033‐00050), namely biological material collection and storage, are regulated by the Ministry of Research (DC‐2008‐99 and AC‐2019‐3426). Samples were collected in the context of patient diagnosis, and parts that were not used for diagnosis were reassigned to research if patients were not opposed to it . The experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report. This study was approved by the ethics review board of the CLB (N° CMT2020‐16). The quality of the BRC is certified AFNOR NFS96900 (N° 2009/35884.2) and ISO 9001 (Certification N° 2013/56348.2).In our study, patients were divided in two cohorts, named Discovery and Validation cohort. The Discovery cohort gathered data from patients diagnosed between 2001 and 2003; the Validation cohort encompassed data from patients diagnosed between 2007 and 2008 , in the right flank of 8‐week‐old female NSG mice. The mice were also implanted subcutaneously with 90‐day slow release 17β‐estradiol (0.72 mg per pellet) hormone pellets (Innovative Research of America) into the left flank. After 4 weeks, tumors were randomized and included in the study when the average tumor volume reached 100–150 mm3. Mice were given 20 mg/kg Tam intraperitoneally 6 days/week. Tissues were processed and embedded in paraffin for histological assessment.Fixed tumors were a gift from Dr J. Carroll  and incubated for 48 h with or without 0.5 μM of G595. Slices were then fixed in 4% paraformaldehyde and paraffin embedded. Sections (4 μm) were then cut for standard histological analysis assessed by Hematoxylin phloxin saffron (HPS) staining and immunochemistry analysis using the PRMT5 antibody.All antibodies are listed in the Appendix Table 3 cells seeded onto a 96‐well plate were plated 5 h before incubation with different molecules (Tam or ethanol). Images were acquired using an IncuCyte ZOOM over 7 days, and cell proliferation was measured as the percentage of cell density observed over this time‐course. Results are represented as graphs indicating the rate of proliferation over time, extrapolated from at least three independent experiments, each performed in triplicate.4 × 10MCF‐7 cells were washed with PBS and fractionated into cytoplasmic and nuclear extracts using the “NE‐PER nuclear and cytoplasmic extraction kit” from Thermo Scientific following the manufacturer's guidelines.3VO4 and 1 mM β‐glycerophosphate). Protein extracts were incubated with primary antibodies overnight at 4°C on a shaker. Protein G or A‐Agarose beads were added, and the mix was incubated for 2 h at 4°C. The immunoprecipitated proteins were separated by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) and analyzed by Western blot, then visualized by electrochemiluminescence .Cells were lysed using RIPA buffer  supplemented with protease inhibitor tablets  and phosphatase inhibitors  was extracted and purified using TRI‐Reagent , prior to being reverse‐transcribed using using 1 μg total RNA as a template using Superscript III  protocol. Real time PCR was performed on a BioRad CFX Real‐Time PCR system using SYBR green supermic (BioRad). mRNA levels were normalized against the expression of 28S ribosomal mRNA as a reference. Primer sequences are listed in the Appendix Table Chromatin immunoprecipitation experiments were performed according to the manufacturer's protocol  with antibodies against ERα, SMRT and IgG. Results are expressed relative to the signal obtained with chromatin input. Primer sequences are indicated in the Appendix Table et al, 3H] methionine  for 90 min at 30°C. Methylation reactions were quenched by adding Laemmli sample buffer, heated at 95°C for 5 min, and separated by SDS–PAGE. Following electrophoresis, gels were soaked in Amplify reagent (Sigma) according to the manufacturer's instructions and visualized by autoradiography.The PRMT5/MEP50 complex (purchased from Sigma Aldrich) was incubated with the different domains of ERα fused to GST as already described  exposes protein/protein interactions et al, The hybridized fluorescent slides were viewed under a Nikon Eclipse Ni microscope. Images were acquired under identical conditions at ×40 magnification. Image acquisition was performed by imaging DAPI staining at a fixed Z Position while a Z stack of ±5 μm at 1 μm intervals was carried out. The final image was stacked to a single level before further quantification. On each sample, at least one hundred cells were counted. Analysis and quantification of these samples were performed without randomization/blinding using the Image J software (free access). PLA dots were quantified on 8‐bit images using the “Analyze Particles” command, while cells were counted using the cell counter plugin as previously described  for 2 h at room temperature. After washing, bound proteins were separated by SDS–PAGE and visualized by Western blot.An ERα‐expressing plasmid was transcribed and translated Formalin‐fixed paraffin embedded tumor tissues were used for analysis. The pathologist selected representative areas from breast invasive carcinomas. Triplicates from each tumor were inserted into TMA blocks which contained 40 tumors each. After deparaffinization and rehydration, tissue sections were boiled in 10 mM citrate buffer pH 8.0 at 95°C for 40 min. The slides were then incubated in 5% hydrogen peroxide in sterile water to block the activity of endogenous peroxidases. The slides were then incubated at 37°C for 1 h with the primary antibodies. The slides were subsequently incubated with a biotinylated secondary antibody bound to a streptavidin peroxidase conjugate . Bound antibodies were detected by adding the substrate 3,3‐diamino benzidine. Sections were counterstained with hematoxylin.H‐score.For scoring purposes, the intensity of staining in malignant cells was divided into four groups  and the percentage of stained cells was reported separately. Both intensity and percentage scores were then multiplied to obtain a single et al, MCF7 cells were purchased from the European Collection of Authenticated Cell Cultures (ECACC) and cultured in Dulbecco's modified Eagle's medium  supplemented with 10% fetal bovine serum (FBS), 1% (v/v) penicillin–streptomycin . These cells were transformed to express a bank composed of 1,812 pre‐selected open reading frames (ORF) Dataset  followin6 cells were seeded in T75 flasks in complete medium supplemented with 0.5 μM G595 or DMSO. Upon reaching 80% confluency, depending on the condition, the medium was replaced with medium supplemented with 0.5 μM GSK3326595 and/or 1 μM Tamoxifen and/or DMSO/ethanol. Subsequently MCF7‐PCA cells were transfected with pFN‐ ERα. Transfections were performed using JetPrime (polyplus) following the manufacturer's instructions.Three MCF7‐PCA cell vials were thawed and passaged separately for 5 days to recover. For each screening condition and replicate 3 × 10Cells containing a candidate interactor were sorted by flow cytometry using BD FACS Aria II.Genomic DNA extraction was performed for each harvested cell pool using PureLINK genomic DNA MiniKit (Invitrogen™). Genomic DNA samples were used for next generation sequencing  based on their own proprietary protocol.The sequencing results were normalized by count per million. To reduce background noise, a threshold was then set at 300 reads. This threshold corresponds to the value in cpm of the internal control HDAC1. For the final list of interactors, only ORF superior to the threshold in all replicates of the same condition were selected Dataset .H‐score equal to 70, considering low expression as PRMT5hscore ≤ 70 and high expression as PRMT5hscore > 70  was presented separately for the Discovery and Validation cohorts, as numbers and percentages for categorical variables or as mean, standard deviation, and range for continuous variables. Nuclear PRMT5 expression level was discretized using the previously published cutoff of The main outcome analyzed was disease‐free survival (DFS), defined as time elapsed from the date of diagnosis to the date of the first event or the date at last news, considering as an event either local‐ or distant‐disease progression, or death from any cause. Survival curves were drawn with the Kaplan Meier method and compared using the log‐rank test. Unadjusted and adjusted hazard ratios were estimated using Cox regression models, fitted separately for the Discovery and Validation cohorts, and combined using a fixed‐effects meta‐analysis model (Laird & Mosteller, All statistical analyses were carried out using the R software (R Core Team, This study includes no data deposited in external repositories.Coralie Poulard: Conceptualization; investigation; methodology; writing – review and editing. Thuy Ha Pham: Investigation; methodology. Youenn Drouet: Formal analysis; investigation; methodology; writing – review and editing. Julien Jacquemetton: Validation; methodology. Ausra Surmielova: Methodology. Benoite Mery: Methodology. Loay Kassem: Methodology. Jonathan Reboulet: Methodology. Christine Lasset: Methodology. Isabelle Treilleux: Investigation; methodology. Elisabetta Marangoni: Investigation; methodology. Olivier Trédan: Resources; supervision; funding acquisition; project administration; writing – review and editing. Muriel Le Romancer: Conceptualization; supervision; funding acquisition; 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.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 3Click here for additional data file.Source Data for Figure 4Click 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.Source Data for Figure 7Click here for additional data file.


We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algorithm for this class is presented that easily scales to thousands of observations and features. We prove minimax optimal convergence bounds for this class under a weak compatibility condition. In addition, we characterize the rate of convergence when this compatibility condition is not met. Finally, we also show that the optimal penalty parameters for structure and sparsity penalties in our framework are linked, allowing cross-validation to be conducted over only a single tuning parameter. We complement our theoretical results with empirical studies comparing some existing methods within this framework. The problem can be formulated as follows: we are given el (GAM) . It extesparsity, which states that, only a small (but unknown) subset of features is informative for the outcome. In this case, it is desirable to apply feature selection: to build a model for which only a small subset of While there are a number of proposals for estimating GAMs, a popular approach is to encode the estimation in the following convex optimization problem:β^,f^1,…A number of estimators have been proposed for fitting GAMs with sparsity. These estimators are generally solutions to a convex optimization problem. Though they differ in details, we show that most of these optimization problems can be written as: penalty  for featarameter . Howeverarameter  and/or oarameter . Generalarameter  as an alβ^,f^1,…We present a general framework for sparse GAMs with two major contributions, a general algorithm for computing  and a thhe lasso . Our mai problem  with p=1The highlight of this paper is the generality of the proposed framework: not only does it encompass many existing estimators for high-dimensional GAMs, but also estimators for low-dimensional fully nonparametric models and, parametric models in low or high dimensions. Brief examples of our framework’s generalizability include: establishing minimax convergence rates (under well-studied assumptions) where only sub-optimal rates existed ; recoverExtending GAMs , to GSAMsed GAMs  cannot bThe rest of the paper is organized as follows. In 2.In this section, we present our general framework for estimating sparse GAMs, discuss its salient features, and review some existing methods as special cases. Before presenting our framework, we introduce some notation. For any function GeneralizedSparseAdditiveModel (GSAM) encompasses estimators that can be obtained by solving the following problem:semi-norm on Our general framework for obtaining a roblem:β^,f^1,…As noted before, the tuning parameters for structure . We now present our result in Lemma 1 for a squared norm penalty followed by the extension to squared semi-norms in Corollary 2 . Consider an arbitrary, non-empty, index subsetIffor allthen for allmPstin , we definot both. In other words, for parametric regression we can simply add a sparsity-inducing penalty; for example, the elastic net . Our fast rates match the minimax rates under Gaussian data with a least squares loss A univariate function class, is said to have a logarithmic entropy bound if, for allandwe havefor some constantand parameterDefinition 6 A univariate function class, is said to have a polynomial entropy bound with smoothness if, for allandwe havefor some constantparameterThe concept of entropy is commonly used in the literature, particularly in nonparametric statistics and empirical processes, to quantify the size of function classes. The logarithmic entropy bound  holds fohe bound  commonlyTo simplify our presentation of bounds on the convergence rate, we use 4.2We now present our main results: upper bounds for the excess risk of GSAMs, specifically, bounds for Theorem 7 (Slow Rates for GSAM)Letbe as defined in The compatibility condition is said to hold for an index setwith compatibility constantif for alland all functionsof the formthat satisfyit holds thatfor some normDefinition 10 (Margin Condition)The margin condition holds if there is strictly convex functionsuch thatand for allwe havefor some normonhereis a neighborhood offor example, In typical cases, the margin condition holds withfor a positive constant c. We refer to this special case as the quadratic margin condition.theoretical compatibility condition (defined below) can be verified under suitable conditions. In The norm Definition 11 The theoretical compatibility condition is said to hold for an index setfor a compatibility constant , if for somealland all functions of the formthat satisfyit holds thatwhereis the population levelnorm.The theoretical compatibility condition holds trivially when we have independent covariates. In general, establishing verifying it depends on the smoothness penalty We now present our second theorem which establishes the bound Theorem 12 (Fast Rates for GSAM)Supposeandare convex functions and withandas defined in Theorem 7. Assume thatis sparse withwhere , and that the compatibility condition holds forFurther assume the quadratic margin condition holds with constant c, and that for a functionif and only ifThe constantis defined asandis such thatFurthermore, define the setasThen, on the setCorollary 13Letandbe as defined in Theorem 7 and assume the conditions of Theorem 12. Furthermore, for any function is such thatfor someand functionFurther assume that forare independent, uniformly sub-Gaussian:Finally supposeandThen, with probability at-leastwe have the following cases:Ifhas a logarithmic entropy bound, forwith constantsandIfhas a polynomial entropy bound with smoothness, then forwith constantsandconvex conjugate.We will discuss the significance of our theoretical results in the next subsection by specializing them to some well-studied special cases. Before discussing these specializations, we conclude this section by further generalizing Theorem 12. We will now assume a more general margin condition, for which we need to define the additional notion of a Definition 14 (Convex Conjugate)Letbe a strictly convex function onwithThe convex conjugate ofdenoted byis defined asFor the special case ofone hasTheorem 15 (Fast Rates)Assume the conditions of Theorem 12 and defineaswhereis the convex conjugate ofThen, on the setRemark 16 Note that our convergence rates include the termor constants which depend on it. For some choices ofthis can lead to poor finite sample performance. In such cases, prediction performance can be improved by solving insteadwhereis an additional tuning parameter. In theory, using two tuning parameters should lead to improved prediction, however in practice, tuning parameter selection over a discrete grid can become computationally cumbersome. A moderately-sized search grid might not yield a lower MSE and in fact, can lead to substantially higher MSE, particularly for large n. We illustrate this phenomenon via a small simulation study into the simulation study ofspecifically Scenarios 3 and 4, we additionally fit GSAMs by solving Our convergence rates are presented up to constants. To illustrate this fact, we consider the problemfor a constantthat does not depend on n or p. While The above results do not directly extend to some convex indicator penalties. For some convex indicator penalties, such as , we require a third type of entropy condition:Definition 19 The univariate function class, is said to have a polynomial entropy without smoothness bound if for allwe havefor some constantparameterand allOur results do not extend to convex indicator penalties because our proof relies on the fact that; function classes with polynomial entropy without smoothness do not usually have this property. We defer the extension to convex indicator structural penalties to future work.Remark 20 In Corollaries 8 and 13 we can replace the requirement of uniformly sub-Gaussian residuals by the weaker condition of uniformly sub-Exponential residuals. To be precise, we would requireHowever, sub-Exponential residuals would firstly require bounds for theentropy with bracketing. Theentropy with bracketing is a stronger notion thanentropy without bracketing . Secondly, we would also require uniform bounds for each univariate function, specifically, we needRemark 21 (Comparison of rates to existing work)Here, we highlight the key differences between our theoretical result and existing work. andestablish same rates of convergence as those in Corollaries 8 and 13. However, their work requires stronger assumptions. Both papers are restricted to the setting reproducing of kernel hilbert spaces (RKHS) and only allow an RKHS norm as the choice of smoothness penalty. They also assume known bounds on the additive functions or individual components. Additionally, assumes independence of covariates as opposed to a more general compatibility condition. The work ofextends the RKHS framework with results capturing the notion of weak sparsity; assuming bounds on the termforthey present rates (up to a constant) of the formSimilarly, present rates of the formBoth GLMs. As per a reviewer’s suggestion, in Firstly, the proposals of er rates  with M≍nslow rates under virtually no assumptions. As another example, we consider our own previous work =-package .SSP (SSP . Pst(f)=TF (TF . Pst(f)=In this section, to complement our theoretical results, we conduct a simulation study to study the finite sample performance of various GSAMs as a function of We simulate data for each of five simulation scenarios as follows: Given a sample size We fit each method over a sequence of 50 In In 6.6.1We use the methods of terature . As doneWe fit SSP, SpAM with he lasso . ApproxiThe box-plots of test error in the test set are shown in 6.2Lung: 54,675 gene expression measurements from 114 lung tissue samples from nonsmoking women with non-small cell lung carcinoma; data set consists of 56 tumor, and 58 normal tissue samples. Available on CuMiDa with accession number GSE19804.Prostate: 12,620 gene expression measurements from 124 prostate tissue samples; data set consists of 64 primary prostate tumor, and 60 normal tissue samples. Available on CuMiDa with accession number GSE6919_U95B.Breast: 35,980 gene expression measurements from 289 breast tissue samples; data set consists of 143 breast adenocarcinoma, and 143 normal tissue samples. Available on CuMiDa with accession number GSE70947.Oral cavity: 54,675 gene expression measurements from 103 mucosa cell samples; data set consists of 74 samples with oral cavity cancer, and 29 normal cell samples. Available on CuMiDa with accession number GSE42743.We now fit GSAMs for classification of gene expression data. We used the Curated Microarray Database (CuMiDa) : a reposOur goal is to correctly classify samples as either normal or cancer samples. We split the data as follows: 60% as training, 20% as validation and 20% as test data. On the training data we fit the lasso, SpAM with In 7.In this paper, we introduced a general framework for non-parametric high-dimensional sparse additive models. We show that many existing proposals, such as SpAM , SPLAM , SobolevWe established a proximal gradient descent algorithm which has a lasso-like per-iteration complexity for certain choices of the structural penalty. The computational framework presented in this paper, effectively reduce the problem of fitting high-dimensional GSAMs to fitting a univariate regression model with the relevant smoothness penalty. While algorithms and theoretical results for specific GSAMs, as well as some theoretical results for certain types of general GSAMs, exists, to the best of our knowledge, the general algorithm for GSAMs is a key novel contribution in this paper. Our theoretical analyses in https://github.com/asadharis/GSAM, implements the methods described in this paper.The R package GSAM, available on


Diabetes mellitus (DM) is considered to be a risk factor in carcinogenesis and progression, although the biological mechanisms are not well understood. Here we demonstrate that platelet-endothelial cell adhesion molecule 1 (PECAM-1) internalization drives β-catenin-mediated endothelial-mesenchymal transition (EndMT) to link DM to cancer.The tumor microenvironment (TME) was investigated for differences between colon cancer with and without DM by mRNA-microarray analysis. The effect of DM on colon cancer was determined in clinical patients and animal models. Furthermore, EndMT, PECAM-1 and Akt/GSK-3β/β-catenin signaling were analyzed under high glucose (HG) and human colon cancer cell (HCCC) supernatant (SN) or coculture conditions by western and immunofluorescence tests.DM promoted the progression and EndMT occurrence of colon cancer (CC). Regarding the mechanism, DM induced PECAM-1 defection from the cytomembrane, internalization and subsequent accumulation around the cell nucleus in endothelial cells, which promoted β-catenin entry into the nucleus, leading to EndMT occurrence in CC with DM. Additionally, Akt/GSK-3β signaling was enhanced to inhibit the degradation of β-catenin, which regulates the process of EndMT.PECAM-1 defects and/or internalization are key events for β-catenin-mediated EndMT, which is significantly boosted by enhanced Akt/GSK-3β signaling in the DM-associated TME. This contributes to the mechanism by which DM promotes the carcinogenesis and progression of CC.Video AbstractThe online version contains supplementary material available at 10.1186/s12964-023-01193-2. Increasing evidence suggests that cancer incidence appears to be increased in diabetes mellitus (DM) and even DM is considered to be a risk factor in carcinogenesis and progression , 2. HoweEndMT is an intricate cellular differentiation process in which endothelial cells detach from the cell monolayer and lose cell–cell contacts . During We previously found that the mesenchymal cell marker vimentin was strongly expressed, accompanied by a serious disappearance of PECAM-1, in the endothelium in human colon cancer with DM, comparing to that in CC without DM, which implies high-frequency EndMT events in CC with DM. Studies have reported that glucose concentrations can trigger the shift of endothelial cells to mesenchymal cells, which results in myofibroblast formation in diabetes-associated complications –12. TherIn this study, we determined that PECAM-1 defects and/or internalization and accumulation around the nucleus could promote β-catenin  translocation to the nucleus, which ultimately leads to EndMT in endothelial cells. During this process, the Akt/GSK-3β signaling pathway was pivotally activated by the DM-associated TME to enhance β-catenin nuclear translocation. Hence, the defect and/or internalization of PECAM-1 in the endothelium with enhanced Akt/GSK-3β signaling is a key mechanism for β-catenin-mediated EndMT in CC with DM.Human umbilical endothelial cells (HUVECs)  and human colon cancer cells (HCCCs) SW620, SW480, HCT116 and mouse colon cancer cells (MCCCs) MC38 (a gift from Xiaobo Li professor) were used in this study. HUVECs were cultivated with endothelial cell medium (ECM) containing high glucose , normal glucose  and 50% supernatant (SN) from HCCCs, respectively . HUVECs 6) was injected subcutaneously into the dorsal neck regions of 10 diabetic mice (DM group) and 6 normal mice (non-DM group), respectively [C57BL/6 male mice (20–22 g) were purchased from Cyagen Biosciences (Guangzhou). According to previous studies , 15, strectively . Two weeOne hundred and ninety-two CC patients, including 108 CCs with DM and 84 CCs, were obtained from Harbin Medical University cancer hospital during January 2015 to December 2021 in this study. Of them, thirty-six pairs of human primary CCs and matched noncancerous adjacent tissues (1.5 cm from the tumor-free margin) were used to protein and mRNA analysis experiments. Each sample was divided into two parts and stored in liquid nitrogen and 4% paraformaldehyde solution at 4 °C, respectively. Clinicopathologic features of recruited patients includes sex, age, DM duration, TNM stage, and survival time.Ten tissue specimens (5 CCs with DM and 5 CCs) were used for mRNA analysis by Arraystar Human LncRNA Arrays V5. Briefly, total RNA was extracted using TRIzol reagent  and quantified by a NanoDrop ND-1000. The RNA integrity was assessed using Agilent 2100 Bioanalyzer . Then RNA samples were amplified and transcribed into cDNA, which was subsequently labeled and hybridized to Agilent Array platform. After washing, array scanning was performed with an Agilent DNA Microarray Scanner (Agilent p/n G2505C) and the acquired array images were analyzed with Agilent Feature Extraction software (version 11.0.1.1). Data analysis was performed with using the GeneSpring GX v12.1 software (Agilent Technologies).2)-coated coverslips were fixed with 4% paraformaldehyde and performed immunofluorescence test of several markers of EndMT as described above. All images were taken by fluorescence microscopy  or/and confocal microscopy .Tumor tissues fixed with 4% paraformaldehyde were embedded by optimum cutting temperature (OCT) compound and cryosectioned into 6 μm thick sections. Referring to our previous methods , these sSpecific siRNA sequences table S to targeAccording to the manufacture’s protocol, the extracted whole proteins were determined using Bicinchoninic acid assay (BCA) . After separating by 10–15% SDS-PAGE gel, proteins were transferred to nitrocellulose filter or PVDF membrane, which followed by the blocking of 5% milk. Then the membrane was probed with primary antibodies  and Student’s t-test, one-way analysis of variance (ANOVA), χTo evaluate the effect of DM on CC, we analyzed the clinicopathologic features of CC patients and the xenograft volumes of mice with and without DM. Data showed that DM was positively linked to lymph node metastasis (stage N). CC patients with DM had worse survival rates than CC patients when the survival time was more than 70 months Fig. , but theGiven the role of the TME in tumor progression, we determined the biological diversity of the TME in clinical specimens with and without DM by human mRNA-microarray analysis. Gene Ontology (GO) analysis showed that intracellular vesicles with 17 upregulated genes were significantly enhanced in CC with DM compared to CC without DM  was elevated in cancerous tissues compared to that in noncancerous adjacent tissues in CC with DM; however, the opposite was observed in CC Fig. a, b. PhoIn addition to traditional complications such as cardiovascular disorders, cancer has been increasingly considered an emerging complication of DM in recent years . Cancer Our studies determined that the TME was remodeled in CC with DM and was dramatically involved in intracellular vesicle incidents. Bioinformatics analysis revealed that these vesicle-related genes were closely associated with EMT or EndMT. As expected, we confirmed a high-frequency occurrence of EndMT in CC with DM compared to that in CC, and the same results were confirmed in both animal models and cultured cells. These data suggest that DM induces TME reconstruction in CC with DM, which is involved in EndMT occurrence. In addition, many studies have shown that EndMT is a key mechanism in DM-associated fibrosis, such as diabetic kidney disease, cardiac fibrogenesis, and pulmonary fibrosis –21. TherTo elucidate the possible mechanism, we analyzed the potential interaction targets of PECAM-1 through Genemania databases and Co-IP experiments. Our data reveal that PECAM-1 interacts with β-catenin. Previous studies reported that PECAM-1 mediates β-catenin localization to the cell membrane , 25; howAs a well-known transcription factor, β-catenin is expressed in various cells and controls the transcription of many genes related to diverse biological processes. Of note, our study revealed a valuable expression profile of β-catenin in CC with and without DM. In detail, total β-catenin was significantly increased in CC with DM compared to that in CC, and it was obviously higher in cancerous tissues than in noncancerous adjacent tissues in CC with DM, but there was no difference in CC. Additionally, in vitro, HG and SNs increased the expression of total β-catenin, including nuclear β-catenin, in HUVECs. Knocking down β-catenin rescued the enhancements of EndMT-associated markers and alteration of cell shape induced by HG and SNs. Therefore, β-catenin is considered a vital modulator of EndMT in CC with DM. These results proposed that diabetes-induced PECAM-1 loss and/or internalization facilitated β-catenin accumulation in the cytoplasm and subsequently entered the nucleus, which ultimately promoted EndMT in the endothelium.β-catenin is a central member of the canonical Wnt/β-catenin signaling pathway which is widely studied in the context of diverse diseases, such as cancer, cardiovascular disease, and age-associated disorders , 28. As In summary, this study illustrates a novel mechanism by which PECAM-1 in the cytoplasm drives β-catenin-mediated EndMT via internalization, which is promoted by Akt/GSK-3β signaling in CC with DM Fig. . In brieAdditional file 1.Figure S1. Diabetes mellitus promotes the progression and TME remodeling in CC. Figure S2. HG and co-culture system induced the EndMT occurrence in vivo and in vitro. Figure S3. HG and co-culture system induced the EndMT occurrence in vitro. Figure S4. β-catenin is a crucial modulator during the process of EndMT in CC with DM. Figure S5. SNs induces the alteration of PECAM-1 and β-catenin in localization. Figure S6. Expressions of Akt, GSK-3β and Dvl were detected by western blotting. Table S1. Primary antibodies in this study. Table S2. Sequences of siRNA in this study. Table S3. Information of clinicopathologic features of CC patients with and without DM.