Conductin/axin2 is a scaffold protein negatively regulating the pro-proliferative Wnt/β-catenin signaling pathway. Accumulation of scaffold proteins in condensates frequently increases their activity, but whether condensation contributes to Wnt pathway inhibition by conductin remains unclear. Here, we show that the Gαi2 subunit of trimeric G-proteins induces conductin condensation by targeting a polymerization-inhibiting aggregon in its RGS domain, thereby promoting conductin-mediated β-catenin degradation. Consistently, transient Gαi2 expression inhibited, whereas knockdown activated Wnt signaling via conductin. Colorectal cancers appear to evade Gαi2-induced Wnt pathway suppression by decreased Gαi2 expression and inactivating mutations, associated with shorter patient survival. Notably, the Gαi2-activating drug guanabenz inhibited Wnt signaling via conductin, consequently reducing colorectal cancer growth in vitro and in mouse models. In summary, we demonstrate Wnt pathway inhibition via Gαi2-triggered conductin condensation, suggesting a tumor suppressor function for Gαi2 in colorectal cancer, and pointing to the FDA-approved drug guanabenz for targeted cancer therapy. Wnt/β-catenin signalling is frequently hyperactivated in colorectal carcinoma (CRC). Here the authors show that Gαi2 inhibits this signalling pathway by promoting the condensation of conductin/axin2 and β-catenin degradation, and consequently suppresses CRC growth. Most prominently, aberrantly activated Wnt signaling drives carcinogenesis in more than 90% of colorectal cancers2. The cytoplasmic proteins axin and its homolog conductin/axin2 are negative regulators of the Wnt pathway acting as scaffold proteins of the β-catenin destruction complex to promote β-catenin degradation4. In contrast to axin, which is constitutively expressed, conductin expression is initiated by β-catenin6. Thus, conductin functions as a negative feedback regulator to limit pathway activity. As a β-catenin target gene, conductin is highly expressed in colorectal cancer, but despite upregulation fails to suppress Wnt signaling and to prevent cancer growth5. We recently showed that conductin´s activity is partially suppressed by a short aggregating protein sequence in its regulator of G-protein signaling (RGS) domain7. RGS–RGS aggregation mediated by this aggregon prevents polymerization of conductin via its DIX domain, which is essential for efficient β-catenin degradation8. The aggregon is not conserved in axin, which accordingly shows efficient DIX-mediated polymerization and β-catenin degradation. Inactivation of conductin RGS aggregation either by aggregon mutation or a synthetic aggregon-targeting peptide unleashes conductin´s full activity leading to enhanced polymerization and β-catenin degradation7.The Wnt/β-catenin signaling pathway tightly controls the expression levels of the mitogenic transcriptional co-factor β-catenin, and deregulation of the pathway is causally associated with diverse human pathologies10. Biomolecular condensates are membrane-less organelles forming via liquid-liquid phase separation of macromolecules12. They are thought to act as super scaffolds by concentrating or excluding effector proteins and may play pivotal roles in signal transduction15. Although conductin is rather diffusely distributed, its polymers obtained after blocking the aggregon form likewise puncta and can be considered as condensates as well. The RGS aggregon might therefore represent a signaling hub by which cellular interactors of the RGS domain regulate conductin condensation for physiological Wnt pathway control.Axin polymers, which are visible as spherical cytoplasmic structures and therefore called puncta, were recently characterized as biomolecular condensates19. Gα12 and Gα13 are able to release β-catenin from cell junctions that is otherwise not available for signaling, thereby enhancing Wnt pathway activity in cancer cells with impaired β-catenin degradation21. Indirectly, Gαs activates Wnt signaling by promoting protein kinase A (PKA) activity, which phosphorylates β-catenin to increase its stability and transcriptional activity25. In contrast to activation, Wnt pathway inhibition by G-proteins remains poorly studied. As Gα proteins were shown to interact with the axin RGS domain26, they are good candidates for targeting the conductin RGS aggregon.There is evidence that trimeric G-proteins modulate Wnt signaling activity. Direct interactions of Gα proteins or Gβγ-subunits with Wnt pathway components, such as Gαo-axin, Gαs-axin, Gα13-dishevelled, or Gβγ-dishevelled binding have been described to activate Wnt signalingHere, we show that the Gα protein i2 (Gαi2) interacts with the conductin RGS domain and interferes with aggregation by masking the aggregon, thereby promoting conductin condensation and β-catenin degradation. Consistently, we characterize Gαi2 in knockdown and transient expression experiments as potent negative Wnt pathway regulator acting via conductin. Our findings have implications for colorectal cancer development and treatment: human cancer data suggest silencing of a Gαi2 tumor suppressor function during colorectal carcinogenesis. Moreover, the Gαi2-activating drug guanabenz (GBZ) inhibits the growth of Wnt signaling-driven colorectal cancer in vitro and in vivo suggesting that aggregon targeting can be exploited for therapy.7. Out of four investigated Gα proteins of the Gαi/o family, markedly Gαi2 and to some degree Gαi3 induced condensation, while highly related Gαi1 and Gαo were much less efficient, independent of expression levels , an RGS binding Wnt pathway component, structural modeling suggests no interference of binding with RGS aggregation, as both sites are clearly distinct , suggesting a functional connection between G-protein binding and polymerization  function of the conductin RGS domain  initiates conductin polymerization  resulted in spontaneous condensation  completely rescued diffuse cellular distribution, the high molecular weight aggregation complexes in native gels, and penetration in fractions of high sucrose density  did not impair condensation 4− treatment, rendering WT conductin as active as the aggregation-dead, spontaneously polymerizing QV mutant  and β-catenin target genes in human colon tissue  was significantly reduced: more than 50% of the patients with GNAI2 alterations are estimated to die within about three years after diagnosis compared to 25% of the patients without  is a specific FDA-approved agonist of α2-adrenoceptors, which are G-protein-coupled receptors (GPCR) activating Gαi signalingtin Fig. , and thitin Fig. . Moreovetin Fig. . In additin Fig. , initiattin Fig. , and enhtin Fig. , which asis Fig. . Importasis Fig. . Moreove35. Moreover, GBZ strongly decreased colony formation of colorectal cancer cells (SW480 and DLD1), which exhibit high endogenous Wnt signaling, but not of U2OS cells with lower endogenous pathway activity  or conductin (7%) mutations but far below the loss of APC (77%), the major tumor suppressor of the Wnt pathway2. As inactivating GNAI2 mutations are associated with a severe decrease of the three-year survival rate, these genetic alterations might become of value as a prognostic marker. In the other 94% of the patients, activation of Gαi2 by the specific α2-adrenoceptor agonist GBZ holds potential for therapy. GBZ inhibited Wnt signaling in colorectal cancer cells, and knockdown/knockout experiments showed the dependency of GBZ-mediated Wnt signaling inhibition on Gαi2 and conductin, suggesting that GBZ activates Gαi2 to promote conductin-mediated β-catenin degradation. Attenuated inhibition of Wnt signaling after α2a-adrenoceptor knockdown suggests a specific function of GBZ as α2-adrenoceptor agonist rather than off-target effects. Supportively, other α2-adrenoceptor agonists  similarly inhibited the Wnt pathway. In line with inhibiting a pro-proliferative pathway, GBZ treatment reduced the proliferation of colorectal cancer cells in a conductin-dependent manner in vitro  requires 16.8 mg GBZ per day, which is within the daily GBZ doses used to treat hypertension ranging from 8 to 64 mg50. We, therefore, believe that the FDA-approved drug GBZ, which has been safely used over decades to treat hypertension, holds potential for targeted colorectal cancer therapy.Of note, oral administration of GBZ via the drinking water reduced tumor growth in a xenograft mouse model for colorectal cancer and in APC2 atmosphere, and passaged according to ATCC recommendations. Transfections were performed using polyethylenimine , Lipofectamine2000 , or Oligofectamine  according to manufacturer’s instructions. Wnt3a medium was prepared as originally described51. For AlF4− treatment, cells were incubated in Tyrode’s solution without (control) or supplemented with AlCl3, NaF, and MgCl252. Bt2cAMP (D0627), clonidine (C7897), guanabenz 48, and UK 14,304 (U104) were obtained from Sigma-Aldrich, and forskolin (ab120058) and H-89 (ab143787) from Abcam.DLD1, HEK293, HEK293T, U2OS, and SW480 cells were cultured in low glucose Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal calf serum (FCS) and antibiotics at 37 °C in a 10% COAXIN2/Conductin knockout cells have been described previously7.To generate U2OS cells stably expressing GFP-Conductin, cells were transiently transfected, re-seeded at low density, and cultured in the presence of 500 µg/ml G418 for more than one week. Cell colonies, which clonally grew due to genomic integration of the expression vector including a neomycin resistance cassette, were isolated and assessed for expression of GFP-Conductin by immunofluorescence and Western Blot. HEK293 cells stably expressing shRNAs against Gα proteins were obtained similarly using selection with puromycin (10 µg/ml), and the shRNA sequences are provided below. CRISPR/Cas9-generated SW480 35. Organoids grew embedded in matrigel (Corning) in Advanced DMEM/F-12 medium supplemented with GlutaMAX, B-27, N-2 (ThermoFisher), 10 mM HEPES, 1 mM N-acetylcysteine (Sigma-Aldrich), 0.1 µg/ml mEGF (ImmunoTools), 0.1 µg/ml mNoggin (PeproTech), 20% R-spondin1-conditioned medium and antibiotics at 37 °C in a 10% CO2 atmosphere, and were passaged weekly. R-spondin1 medium was prepared from 293 T cells stably expressing HA-mR-Spondin1-Fc53. For the organoid growth assay, the medium of freshly passaged organoids was supplemented with 0, 10, or 50 µM GBZ, and after 120 h small (0–1 buds), medium (2–4 buds), and large (>4 buds) organoids were counted. The assay was performed in technical octuplicates.Intestinal organoids were derived from crypt fragments, which were mechanically released from the small intestine of a six-week-old C57BL/6 mouse and enriched by size exclusion of villi fragmentsFor colony formation assays, 2500 cells were seeded per well of a six-well plate and grown in medium with 1% FCS in the presence of 0, 10, and 50 µM GBZ for 96 h. Then, cells were fixed in 3% PFA, stained with ethidium bromide (50 µg/ml in PBS), and visualized in a UV gel documentation system (Herolab), where images were acquired at constant settings . From these images, colony numbers and sizes were quantified in an automated fashion as light objects using the MetaMorph analysis software (Molecular Devices). For MTT assays, 2000 cells per well of a 96-well plate were seeded or re-seeded 24 h after siRNA transfection, when required, and grown in medium with 1% FCS and 0 or 50 µM GBZ for 72 or 96 h. In case of transient expression experiments, cells with similar GFP (control) or Gαi2-GFP expression were sorted based on the GFP fluorescence and seeded as 2500 cells per well of a 96-well plate , and grown in medium with 1% FCS for 72 or 96 h. Then, cells were incubated with 0.5 mg/ml MTT (Sigma-Aldrich) for 4 h at 37 °C to allow MTT cleavage in vital cells, and the cleaved MTT formazan was dissolved and measured in a Spectra MAX 190 with SoftMax Pro software (Molecular Devices), according to manufacturer’s instructions. The measured formazan absorbance is directly proportional to the number of living cells. This assay was performed in technical quadruplicates (knockout and knockdown experiments), or quintuplicates (transient expression experiments).Cell proliferation was investigated in carboxyfluorescein succinimidyl ester (CFSE) dilution experiments. Cells were pulse-labeled with CFSE and grown in the presence of 0, 10, and 50 µM GBZ. After 72 h, the remaining CFSE fluorescence (green) was measured by FACS  in 50,000 cells per sample. Every cell division reduces the CFSE fluorescence intensity by 50% since CFSE is equally distributed between both daughter cells. For instance, a twofold higher CFSE fluorescence intensity means one cell division less in 72 h, which allows calculating how strong GBZ treatment affected cell proliferation Fig. .33, RGS6xHis-Gαi254, 6xHis-RGS1055, Gαo-GFP, Gαo-GST-HA56, Flag-β-Catenin, Flag-Cdt, Flag-CdtM327, HA-Cdt, HA-Cdt 2-345, GFP, GFP-Cdt, GFP-Cdt QV (Q188P V189S), GFP-Cdt QVL (L99R Q188P V189S), GFP-Cdt 2-345, GFP-Cdt 2-345 QV (Q188P V189S) are published7. Flag-Cdt QVL (L99R Q188P V189S), GFP-RGSCdt , GFP-RGSCdt QVL (L99R Q188P V189S), Gαi1-GFP, Gαi2-GFP, Gαi3-GFP, Gαi1-GST-HA, Gαi2-GST-HA, Gαi3-GST-HA, Gαi2-HA, Gαq-GFP, Gαs-GFP, Gα12-GFP, GST-RGSCdt (Conductin aa 67-210), GST-Cdt 2-210, RGS6xHis-RGSCdt (Conductin aa 67–210) and T7 expression vectors for in vitro protein synthesis  were cloned by standard molecular biology methods, and point mutations were introduced via site-directed mutagenesis: GFP-Cdt 2–345 QVL (L99R Q188P V189S), GFP-Cdt 2-345L (L99R), Gαi2-GFP G184S, Gαi2-GFP K210N, Gαi2-GFP G293E, Gαi2-GFP T328S. All generated expression vectors were verified by sequencing. The siRNAs targeting human conductin/axin2 (5′-GAGAUGGCAUCAAGAAGCA-3′) and Gαi2  have been used previously58. For α2a-adrenoceptor knockdown, the ON-TARGETplus Human ADRA2A siRNA SMARTPool from Dahrmacon was used. The following shRNA sequences were used for the stable HEK293 cells, shGαi1 (5′-cccGTGGAAAGATAGTGGTGTAttcaagaga TACACCACTATCTTTCCACttttt-3′), shGαi2 , shGαi3 .Expression vectors encoding for R-FlincA59. Cells displaying more than five condensate-like structures were classified as “with condensates”. Only cells with a uniform conductin staining covering at least 50% of the plasma membrane were classified as “with membrane recruitment”. For experiments with smaller differences, quantification was performed in a blinded fashion. Numbers of condensates per cell and cell areas for subcategorization of cells with condensates were determined via image-based analysis using the Spot Detector tool of the Icy open-source bio-imaging software 60. Fluorescence intensities were quantified from images acquired at constant exposure times by the MetaMorph analysis software (Molecular Devices), when required.Cells, which were transfected and/or treated as indicated in the figures, were fixed in 3% PFA  with a Plan-NEOFLUAR 40×/1.30 NA oil objective and a xiD CCD camera (Ximea). Images were acquired at constant exposure times every 3 min with Micro-Manager 1.4 , and movies were rendered using Photoshop 19.1.5 (Adobe).2, 20 mM Tris-HCl pH 7.5, 2 mM EDTA, 1% Triton X-100, 0.5% SDS, 10% glycerin, protease inhibitor cocktail) for better solubilization of Gα proteins, or in hypotonic lysis buffer  when assessing β-catenin levels. Protein samples were separated via polyacrylamide gel electrophoresis under denaturing or native conditions (no SDS), when indicated, and transferred onto a nitrocellulose membrane, which was probed with indicated primary and respective horseradish peroxidase (HRP)-conjugated secondary antibodies (see below). Protein bands were detected via light emission upon HRP-catalyzed oxidation of luminol in a LAS-3000 with Image Reader software (FUJIFILM) or a PXi6 (Syngene) with GeneSys software, and band intensities were quantified with AIDA 2D densitometry or Fiji-ImageJ when required.Cells were lysed in Triton X-100 containing buffers , in an SDS containing buffer .Escherichia coli (Novagen) in their logarithmic growth phase at 17 °C overnight. Bacteria were lysed in TBS buffer  with 1 mM PMSF in a One-shot Cell disruptor (Constant Systems). Recombinant His-tagged and GST-tagged proteins were precipitated from precleared lysates  on Ni-NTA beads (Pierce) and glutathione beads , respectively. Precipitated proteins were washed on beads and eluted using 300 mM imidazole (His-tag) or 20 mM reduced glutathione (GST-tag). For His-Gαi2, washed beads were incubated in TBS buffer containing 0.1 mM DTT, 10% glycerol, 100 µM GDP, and 5 mM MgCl2 at 4 °C on a rotary shaker overnight prior to elution. Purified proteins were rebuffered in TBS by at least a 10,000-fold exchange using centrifugal concentration units (Amicon).Protein expression was induced with 1 mM IPTG in transformed Rosetta Gami (D3) For in vitro protein synthesis of Gαi2-GFP and the cancer mutations K210N, G293E, and T328S, the PURExpress In Vitro Synthesis Kit (NEB) was used, according to the manufacturer’s guidelines.Cdt were lysed , and the GST-tagged G-proteins were precipitated on glutathione beads . After washing, the amounts of precipitated GST-Gα proteins and co-precipitated GFP-RGSCdt were assessed and quantified by Western blotting.Cells co-transfected with GST-tagged Gα proteins and GFP-RGSCdt in PBS containing 1% Triton X-100, 10% glycerol, and 10 µM GTP, and the pulldown was performed as described above.For pull-down with in vitro synthesized Gαi2-GFP proteins, the Gα proteins were incubated with purified recombinant GST-RGS3 and 5 mM KF, or with 10 µM GTPγS for 1 h at RT. Then, 1 µM of preloaded His-Gαi2 was incubated with 1 µM of indicated GST proteins in TBS buffer with 0.1% bovine serum albumin for 1 h at RT, before adding glutathione beads  for a final incubation of 1 h on a shaker. Glutathione-interacting proteins were precipitated on the beads, washed, and eluted. The amounts of precipitated GST proteins and co-precipitated His-Gαi2 were determined on Ponceau-stained gels and via Western blotting, respectively.In case of purified recombinant proteins, His-Gαi2 was preloaded in TBS buffer (see protein purification) supplemented with 10 µM GDP alone or together with 30 µM AlCl2. The kinetics of in vitro Gαi2-GTP binding and hydrolysis were measured as changes in BODIPY-FL-GTP fluorescence intensity, which increases upon G-protein binding and decreases due to GTP hydrolysis, using the Infinity M200Pro multiwell reader with i-control software (Tecan). Based on these measurements, the rate constant of GTP hydrolysis (apparent khydr) was calculated61.For analysis of GAP activity, 10 µM of indicated protein was mixed with 1 µM His-Gαi2 in TBS buffer (see protein purification). After 10 min preincubation, the fluorescent GTP derivative BODIPY-FL-GTP was injected into the mixture to a final concentration of 1 µM together with 5 mM MgCl62. After centrifugation  in an Optima MAX Ultracentrifuge using an MLS-50 rotor (Beckman Coulter), 20 fractions of equal volume were collected, and every second fraction was analyzed by Western blotting (see above).Transfected HEK293T cells were lysed in a Triton X-100-based buffer (see Western blot), and the lysates were loaded on a linear sucrose density gradient, which had formed via equilibration between a 50% (w/v) and a 12.5% sucrose solution in a 13 × 51 mm centrifuge tube (Beckman Coulter) upon horizontal incubation for 3 h at RTTCF optimal]: β-catenin-dependent expression, FOP-Flash [TCF far-from-optimal]: β-catenin-independent expression, CRE reporter: PKA activity-dependent expression, or pGL3-Basic: constitutive expression) together with β-galactosidase, co-transfected and/or treated as indicated in the figures, and lysed in luciferase assay buffer . Luciferase and β-galactosidase activities were assessed via light emission upon luciferin decarboxylation in a Centro LB 960 Microplate Luminometer (Berthold Technologies) with MikroWin 2000 software and as the release of yellow ortho-nitrophenol upon ortho-nitrophenyl-β-galactoside hydrolysis using a Spectra MAX 190 with SoftMax Pro software (Molecular Devices), respectively. Luciferase activities were normalized to β-galactosidase activities to correct for transfection variances. For TOP/FOP assays, TOP values were subsequently normalized to FOP values to correct for β-catenin-independent, unspecific changes (Microsoft Excel). All assays were performed in technical duplicates.Cells were transfected with a luciferase expression vector  in technical triplicates. Expression of β-catenin target genes  is presented relative to the housekeeping gene GAPDH to correct for minor variances in cell numbers between the analyzed samples.To quantify mRNA expression of β-catenin target genes, whole cellular mRNA was extracted , transcribed into cDNA , and the relative abundance of transcripts from individual genes was determined by SYBR Green-based qPCR  with gene-specific primer pairs (nu/Crl) mice (Charles River) were injected with 5 × 106 SW480 cells subcutaneously at the right flank. The mice were housed in a UNIPROTECT airflow cabinet (ZOONLAB) at 23 °C, 60% humidity, and a light–dark cycle of 12 h each. Ten days post-injection, the mice were distributed in eight matching pairs according to tumor sizes, and one animal per pair was treated with 50 µM GBZ in the drinking water for 20 days. Tumor growth was monitored over time via repeated caliper measurement, and the tumor volume was calculated as length × width × ½ width. In the end, tumors were excised, weight and volumes were calculated as length × width × height.For the xenograft model, eighteen seven-week-old, female BALB/c-Nude , about six-week-old female mice heterozygous for the Min allele were assigned to either the untreated control group or the GBZ treatment group, which was treated orally with 50 µM GBZ in the drinking water. The mice were housed at 21 °C, 53% humidity, and a light–dark cycle of 12 h each. At about 15 weeks of age, the mice were sacrificed, the small intestines were excised and cut into six pieces of similar length. Pieces four and five  were opened longitudinally, images were acquired together with a size standard, and the sizes of the individual tumors were determined in mm2 from these images using the MetaMorph analysis software (Molecular Devices). Similarly, lengths and widths of the spleens were determined in cm from images, and spleen volumes were calculated as (½ width)2 × π × length.For the APCThe study protocol was approved according to the Tierschutzgesetz in Germany by the Regierung von Unterfranken (55.2.2–2532.2–923), and we have complied with all relevant ethical regulations. The permitted maximal tumor size (diameter > 1.5 cm) was not exceeded.63, and treated with 1 mM Bt2cAMP overnight, if indicated. Then, the red fluorescence intensity was measured by FACS  in 50,000 cells per sample.U2OS cells were transfected with R-FlincA alone or together with Gαi2 at 37 °C for 24 h, incubated at 32 °C for the next 24 h, as described for fluorescence cAMP sensors65. Structural alignments of RGS domains and presentation of the structures were performed with PyMOL .The model structure of the conductin RGS domain was calculated by SWISS-MODEL using the crystal structure of the highly similar axin RGS domain (PDB ID: 1DK8) as templateGNAI2 (Gαi2) in colorectal cancer we used the well-curated and carefully-collected data of the TCGA datasets COAD and READ2, which are provided by the National Cancer Institute (NCI). Analyses of copy number variations (CNV), cancer mutations, and survival were performed using the NCI Genomic Data Commons (GDC) Data Portal. For experimental follow-up, we chose all detected Gαi2 missense mutations that were rated “deleterious” by the Sorting Intolerant From Tolerant algorithm66. For the survival analysis, colon cancer patients from the COAD data set with GNAI2 missense mutations (5 cases) or copy number loss (21 cases) were combined  and compared to patient cases with available CNV data and simple somatic mutation data that did not show GNAI2 alterations  using the GDC Data Portal analysis tool (NCI). For analyzing the mRNA expression data provided with the COAD, READ, and The Genotype-Tissue Expression  datasets, GEPIA was used68.To analyze the expression and mutation of t-tests were performed in a non-paired /(1 − R2)), with n − 2 degrees of freedom , when required.To probe the datasets for statistical significance of differences two-tailed Student’s ed Figs.  or paireval Fig.  were tesFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Reporting Summary


A bio-epoxy surface adhesive for adherence of the metal component species to glass substrate with desirable adhesion strength, converted controlled removal upon request, and bio-based resource inclusion was developed. For the development of resin, three different lignin-based aromatic monophenols, guaiacol, cresol, and vanillin, were used in the chemical epoxidation reaction with epichlorohydrin. The forming transformation process was studied by viscoelasticity, in situ FTIR monitoring, and Raman. Unlike other hydroxyl phenyls, guaiacol showed successful epoxide production, and stability at room temperature. Optimization of epoxide synthesis was conducted by varying NaOH concentration or reaction time. The obtained product was characterized by nuclear magnetic resonance and viscosity measurements. For the production of adhesive, environmentally problematic bisphenol A (BPA) epoxy was partially substituted with the environmentally acceptable, optimized guaiacol-based epoxy at 20, 50, and 80 wt.%. Mechanics, rheological properties, and the possibility of adhered phase de-application were assessed on the bio-substitutes and compared to commercially available polyepoxides or polyurethanes. Considering our aim, the sample composed of 80 wt.% bio-based epoxy/20 wt.% BPA thermoset was demonstrated to be the most suitable among those analyzed, as it was characterized by low BPA, desired boundary area and recoverability using a 10 wt.% acetic acid solution under ultrasound. Among the more common adhesives, those based on thermosetting polymers known as epoxy resins are widely used, since they are able to provide excellent adhesion to various surfaces and are characterized by versatility, high chemical and thermal resistance, and acceptable mechanical properties ,2. NeverOne of the alternatives for BPA-based epoxies can be extracted from lignocellulosic biomass, which can be easily obtained from low-cost industrial residue  and biofuel. Due to its low impact on the environment, inexpensiveness, and abundance, it is also promising for use on an industrial scale. Lignin, in particular, shows good prospects due to its complex polymeric structure rich with a variety of multi-substituted phenolic molecules. These active sites open up possible reaction paths toward epoxidation . On the From the literature, it can be seen that the strength of adhesion of a lignin-based epoxy adhesive strongly depends on its chemical composition, synthesis procedure, and degree of substitution of the adhesive with bio-epoxy. In this manner, adhesives with specific properties can be developed by optimizing the aforementioned conditions. For example, lignin-based epoxy reinforcement materials were tested for their adhesion of an aluminum dolly to a stainless-steel substrate by Ferdosian et al. , where dIn our work, lignin derivate-based bio-epoxy resins were developed and used to adhere a PVC-based e-component to glass substrates. The compatibility of the adhesive with both the glass substrate and the e-component, as well as its contribution to reduce the harmful impact on the environment, was taken into account. The research was aimed at identifying a bio-epoxy substituted adhesive with specific properties, which were determined by the industrial project partner for the purpose of developing a recyclable glass e-bottle. The strength of the adhesion of the PVC component to the glass substrate was expected to be great enough to allow the handling of the glass substrate, but low enough for its removal in a controlled environment without damaging the glass. In this manner, the target pull-off strength from the glass was approximately 4 MPa, which would allow the removal of the e-component using an acetic acid solution with no visible impact on the substrate. In the first part of the research, three different monophenols were used for the synthesis of bio-based epoxy adhesives, namely, guaiacol, methyl guaiacol, and vanillin. All three monophenols were previously used for the development of bio-based epoxy resins ,14,15. TGuaiacol , methyl guaiacol (≥98%), vanillin (99% Reagent Plus), bisphenol A (97%) and diethylenetriamine  were purchased from Sigma-Aldrich . Epichlorohydrin (ECH), sodium hydroxide 2-propanol (Emparta ACS) and sodium chloride were obtained from MERCK . Glass samples were provided by Steklarna Hrastnik . Commercial adhesives Araldyte 2028-1 and CHS Epoxy 510 were provided by Henkel . All materials and chemicals were used as received.The chosen starting monophenols derived from lignin were guaiacol, vanillin, and methyl guaiacol; their structures are portrayed in The synthesis of bio-based epoxy molecules was performed according to the altered procedure published by Krol et al. . A 100 mCommercially available epoxy  was partly substituted by the prepared bio-epoxy samples at concentrations of 0, 20, 50, and 80 wt.%. Diethylenetriamine  was used as a hardener at a weight ratio of 90:10 (epoxy mixture to hardener). Stabilization after the addition of the hardener was achieved by the addition of 7.845 g of acetone per 1 g of DETA. Additionally, polyurethane adhesive Araldyte 2028-1 was used as a reference. The sample codes and composition of used adhesives are described in A glass substrate was provided by Steklarna Hrastnik . To ensure the repeatability of pull-off tests, the glass substrates were firstly polished with P60 sandpaper in order to remove irregularities, followed by polishing with sandpaper of decreasing roughness (P240–P400–P600–P1200–P2500) to achieve a smooth surface. Polishing was performed using a rotational polisher  at 400 rpm with the glass in alternating positions (four quadrants). The glass and sandpaper were rinsed with water following each change in polishing direction. Lastly, the substrates were polished using a 3 µm diamond suspension.The aluminum dolly measuring 14 mm in diameter was polished with sandpaper of decreasing roughness (P400–P600–P1200–P2500) using a rotational polisher (Buehler Metaserv 250) at 400 rpm until an even appearance was achieved. The dolly was then dipped in the prepared adhesive and fixated to the bottom of the glass substrates. Curing was performed overnight, at 70 °C in the case of the reference polyurethane adhesive or at 90 °C in the case of the epoxy adhesives.FTIR analysis was performed in situ, during the epoxidation reaction of the monomers, as well as on the final synthesized monomers; in all instances, the background was subtracted.−1 in the iC IR 4.3 software, and 50 scans were taken for each spectrum. The analysis started when the 2-propanol was poured into the reactor and ended when the reaction was completed.For the in situ monitoring, FTIR probe Dicomp AgX, 9.5 mm × 2 m Fiber Silver Halide (Mettler Toledo), was connected to a React IR 45 m  with an inbuilt LN2 NCT detector. The resolution of the FTIR detector was set to 8 cm−1, and 64 scans were collected per FTIR spectrum.For the final epoxy monomers, FTIR spectra were recorded on a Spectrum Two FTIR , equipped with a UATR Two detector . The resolution was set to 8 cmA WITec Alpha 300R confocal Raman microscope  was used for obtaining the Raman spectra of the epoxy monomers, as well as curing analysis, using a laser wavelength of 532 nm.1H-NMR spectra were recorded on a Bruker 600 MHz NMR spectrometer , using CDCl3 as the solvent and TMS as an internal standard (δTMS = 0.0 ppm).The rheology measurements of PU samples were performed on an Anton Paar MCR301  under non-isothermal dynamic oscillation profiles. The rheometer was equipped with a parallel geometry of 25 mm. The samples were heated by convection, a solvent trap was used to minimize solvent evaporation, and a temperature control hood was used to prevent heat dissipation. A constant flow of dry nitrogen was applied to eliminate any other chemical processes during heating, and a gap of 0.5 mm was used. Profiles were recorded at a temperature ramp of 2 °C/min, between room temperature and 200 °C, while identifying G’ and G’’. G’ (Pa) is the storage modulus and represents the elastic portion of the viscoelastic behavior. Higher G’ values are related to the majority of the sample being in a “solid” state (G’ > G’’). Its counterpart G’’ (Pa) is the loss modulus and represents the viscoelastic portion of the viscoelastic behavior. Higher G’’ values are related to the majority of the sample being in a “liquid” state (G’’ > G’). The intersect of both curves (G’ = G’’) shows the point at which the material behaves more similarly to a solid than a liquid , which indicates the curing point. Therefore, when G’ and G’’ are stable, curing can be regarded as completed. Absolute values give an indication of the rigidity obtained after curing. The influence of the bio-epoxy substitution on G’, G”, and the gel or curing point was examined.The pull-off test was applied to measure the adhesion force of the glued dollies, using a Positest ATM AT09817 . The maximum measurable forces were ~20 MPa.The sample was ultrasonicated in 10% acetic acid solution at 50 °C until complete removal of the dolly.An attempt was made to prepare the epoxy resins from the starting monophenols derived from lignin, namely guaiacol, vanillin, and methyl guaiacol, while BPA-based epoxy resin was synthesized for the purpose of comparison. To gain better insight into the reaction of the bio-epoxy resins and successful epoxidation, in situ FTIR was used to analyze the chemical changes during the reaction. The results are portrayed in −1 to ~1589 cm−1, whereas the second shift occurred for all three mentioned samples, from ~1507 cm−1 to ~1492 cm−1. After 90 min of reaction time, these peaks shifted back to their original values. These phenomena occurred as a consequence of pH changes. With an increase in the reaction time, samples E_GUA, E_M-GUA, and E_BPA showed an enhancement of the peak at approximately 1040 cm−1, indicating the presence of C–O bonds [−1 with increasing reaction time, which could indicate the formation of water as reaction side product [−1 (O–H bending) [−1 with an increase in reaction time, which can be attributed to the C–Cl bonds in ECH (850–550 cm−1) [−1 increased after the addition of NaOH and decreased after 90 min of reaction, implying that the water produced during epoxidation may have entered an unwanted side reaction. Furthermore, this sample showed a decrease in intensity of the peak at 1040 cm−1 with an increase in reaction time, which may indicate a reaction of the aldehyde group, possibly with alcohol.Right after the addition of the NaOH, peak shifts were observed in the case of samples E_GUA, E_VAN, and E_M-GUA (marked with red arrows). The first peak shift occurred only for sample E_GUA, from ~1604 cm–O bonds , confirm product , althougbending)  occurred50 cm−1) . On the The obtained products differed in viscosity, color, and quantity. Photographs of the obtained products are shown in The obtained products differed in color, where E_GUA and E_BPA appeared as clear liquids, while E_VAN was dark yellow. The latter also stood out among all samples regarding the quantity of the obtained product, since a lower amount was obtained in comparison to the other samples. Sample E_M-GUA resulted in a solid product at room temperature, which limited its use as an adhesive. The lower viscosity of the guaiacol-based sample indicates that shorter polymer chains were formed when guaiacol was used as a reactant, which could be explained by its simple structure relative to the other lignin-based monophenols.The success of the epoxidation was further studied using Raman spectroscopy. Due to crystallization at room temperature, sample E_M-GUA was not evaluated; thus, only the Raman spectra of E_GUA and E_VAN were studied. The spectra of monophenols GUA and VAN were also added to gain insight into the changes in chemical structure of the samples .−1 occurred, correlating to the characteristic vibrational peaks of the epoxy group. Moreover, after the reaction, two peaks appeared at 1139 cm−1 and 866 cm−1, which represent the asymmetric and symmetric stretching C–O–C vibrations of epoxy molecules [−1, an increase in the peak intensity could be seen in the case of E_GUA, representing the epoxy ring vibration, while an increase in the peak at 917 cm−1 represents the epoxy ring deformation [When comparing the spectra of E_GUA and pure GUA, an increase in intensity of the peaks around 3000 cmolecules . At 1259ormation . The Ram−1) [On the other hand, the reaction was not successful in the case of sample E_VAN, which confirmed the results obtained from the in situ FTIR analysis. Even though a low-intensity peak representing C–O stretching in an alkyl aryl ether (1254 cm−1)  appearedOn the basis of these results, E_GUA was the only bio-epoxy monomer which was stable at room temperature and underwent the epoxidation reaction. The proposed reaction scheme describing the epoxidation of guaiacol is presented in Since guaiacol was identified as the most appropriate reagent for the synthesis of bio-epoxy for the partial replacement of BPA, the influence of synthesis parameters on the sample properties was further studied. Accordingly, two new samples were prepared, namely, sample E_GUA_4h, where the time of the synthesis was increased from 1.5 h to 4 h, and sample E_GUA_2NaOH, where the concentration of NaOH used in the synthesis was doubled. A description of the synthesized samples E_GUA_2NaOH and E_GUA_4h is given in Compared to the E_GUA sample, samples E_GUA_4h and E_GUA_2NaOH resulted in a slightly smaller quantity of the finished product. Both samples, E_GUA_4h and E_GUA_2NaOH, appeared slightly yellow.The synthesized samples based on guaiacol were studied using FTIR, and their spectra were compared to that of pure guaiacol, in order to determine the influence of the synthesis procedure on the chemical properties of the epoxies .−1, corresponding to symmetric and asymmetric C–H stretching in aromatics (~3070 cm−1), asymmetric and symmetric –CH3 stretching , and asymmetric –CH2 stretching (~2930 cm−1) [−1 and 1407 cm−1 indicate C = C stretching in aromatics, whereas the bands in the region of 1454–1440 cm−1 can be attributed to the stretching and bending vibrations of aliphatic hydrocarbons (CH3 and CH2) [−1 and 1222 cm−1 correspond to C–O stretching in phenolic C–OH and O–CH3 groups [All of the observed samples showed absorption bands in the region of 3070–2840 cm30 cm−1) ,23. The and CH2) ,23,24,25−1 and 3416 cm−1 in the epoxy samples, representing the hydroxyl (O–H) groups of monophenols [−1 and 915 cm−1, representing epoxide ring vibrations [Regardless of the synthesis conditions, all of the guaiacol-based samples, E_GUA, E_GUA_4h, and E_GUA_2NaOH, underwent the epoxidation reaction. This was confirmed by the absence of absorption bands at ~3520 cmophenols ,26, whicbrations .1H-NMR analysis. For comparison, the 1H-NMR spectra were recorded for pure guaiacol, ECH, and guaiacol epoxies  were identified at 2.89 and 2.74 ppm, where the signal at 2.89 ppm of the product overlaid that in unreacted ECH. Additionally, 1H-NMR experiments provided insight into the ratio of mixture components in the final isolated products. The biggest difference among reaction isolates  was in the presence of unreacted ECH, whereby both E_GUA and E_GUA_2NaOH contained a significant amount of unreacted ECH, with the molar ratio between the epoxy product and unreacted ECH being approximately 2:1. On the other hand, only traces of ECH were found in the case of GUA_4h, supporting it as the sample of choice for further investigation.In view of the obtained results, sample E_GUA_4h was used for partial substitution of commercially available epoxy adhesive. The proposed chemical reaction of E_GUA_4h (guaiacol glycidyl ether) and E_BPA (bisphenol A diglycidyl ether) with hardener diethylenetriamine to form the partially substituted bio-epoxy adhesive is shown in Samples composed of BPA epoxy and E_GUA_4h in concentrations of 20, 50, and 80 wt.% were prepared, and the influence of the degree of substitution of the samples on the rheological properties  was examined. The gel point is a significant factor when determining the temperature and time of material hardening. The rheology tests were also performed on reference epoxy and polyurethane adhesives, and the results are presented in Among the studied samples, reference polyurethane adhesive (Ref_PU) possessed the lowest curing temperature and resulted in the hardest material after curing. In the case of the epoxy samples, the curing temperature increased with an increase in the concentration of E_GUA_4h in the epoxy mixture, which rose above 100 °C for the highest (80 wt.%) concentration of bio-epoxy. For the industrial partner, a curing temperature below 100 °C was desirable; thus, despite the gel point of sample 80_bioE being above this value, curing of this sample was possible below the given temperature with an elongation of the curing time.Next, an aluminum dolly was adhered to the glass substrate using the studied adhesives. On the basis of the rheology results, the Ref_PU adhesive was cured overnight at 70 °C, whereas the epoxy adhesives were cured overnight at 90 °C. The long curing time allowed the successful curing of samples 50_bioE and 80_bioE, despite the lower curing temperature. After the curing, the pull-off test was performed in order to determine the strength of adhesion of the dolly to the glass substrate. Results of the pull-off test are presented in All of the adhesives successfully adhered the dolly to the glass substrate, although the pull-off test portrayed varying pull-off tension values. As previously mentioned, the purpose of this research was not to develop the strongest adhesive, but to develop an adhesive with low BPA content, which could also provide reliable adhesion between glass and metal, as well as easy removal of the metal component under controlled conditions. Thus, the pass or fail threshold was set at a pull-off tension of ~4 MPa.The reference epoxy adhesive (sample Ref_E) showed the strongest adhesion among the samples and was, therefore, not suitable. Even though the polyurethane reference adhesive (sample Ref_PU) was proven to be the hardest material in the rheology analysis, its adhesion strength was lower than that of the epoxy adhesive, although it was still too strong to achieve the desired results. In the case of both reference adhesives, the glass substrate was damaged upon detachment of the aluminum dolly, which further proved their unsuitability. The pull-off test performed on the samples where partially substituted bio-epoxy was used showed that the adhesion strength of the adhesives could be easily adjusted by changing the degree of substitution. The reason for the reduced adhesion strength of the bio-substituted epoxy samples lies in the reduced crosslinking density of the epoxy adhesive due to the chemical structure of E_GUA_4h. In this manner, sample 20_bioE resulted in strong adhesion, which was comparable to that of the commercially available polyurethane adhesive. Furthermore, when 50 wt.% epoxy was substituted with guaiacol-based bio-epoxy, the pull-off tension decreased drastically and approached the desired value. Surprisingly, compared to sample 50_bioE, the adhesion strength slightly increased in the case of sample 80_bioE, where the bio-epoxy was the prevailing component of the epoxy adhesive. A possible explanation for this phenomenon is the air entrapped in the adhesive. In the case of samples with a lower degree of substitution, the curing time was shorter, whereas sample 80_bioE needed a longer curing time, which resulted in a lower content of entrapped air bubbles and, therefore, greater contact area between the glass and adhesive. Moreover, none of the substituted epoxy adhesives damaged the glass upon forceful removal; thus, detachment of the aluminum dolly was restricted to the adhesive, rather than the substrate.According to the results of the pull-off test, sample 80_bioE showed the most desirable properties, since the presence of BPA-based epoxy in the adhesive was drastically decreased and the aluminum dolly could be attached to the glass substrate with the desired adhesion strength. The increased concentration of bio-based epoxy in the adhesive, however, influenced a color change of the sample, which went from transparent to brown with the increase in bio-epoxy concentration, as shown in An important property of the developed adhesive was easy removal of the e-component from the glass substrate upon request. This property would allow the recyclability of the glass with the attached e-component. For this purpose, experimental removal of the aluminum dolly was performed on the glass substrate with the metal dolly attached using 80_bioE adhesive. The sample was ultrasonicated at 50 °C in a 10% acetic acid solution, which has good compatibility with PVC based materials as well. This method allowed the spontaneous detachment of the metal component from the glass substrate; thus, the developed adhesive successfully demonstrated all of the predetermined properties.The prepared bio-epoxy adhesive was used by our industrial partner, for the development of a recyclable e-bottle, presented in Three lignin-based monophenols, namely, guaiacol, vanillin, and methyl guaiacol, were used for the synthesis of bio-epoxy resins for partial BPA replacement inside an epoxy-based adhesive to achieve the desired adhesive characteristics. The epoxidation reaction was successful in the case of guaiacol and methyl guaiacol; however, the latter resulted in a crystalline product, which limited its use. The influence of the synthesis parameters of the guaiacol-based epoxy was further researched, whereby the synthesis time was increased from 1.5 h to 4 h, and the concentration of NaOH in the synthesis reaction was doubled. Following a comparison of the isolated mixtures, it was determined that a modified procedure with the reaction time increased to 4 h was most appropriate, regarding the purity of the desired product. Therefore, the guaiacol-based epoxy synthesized for 4 h was used for partial substitution of a BPA-based epoxy adhesive in concentrations of 20, 50, and 80 wt.%, and the properties of the adhesives were compared to those of commercially available BPA epoxy and polyurethane adhesives. An increase in the concentration of guaiacol epoxy resulted in an increased gel-point temperature, thus necessitating an increase in temperature or time for successful curing. The adhesion strength of the bio-substituted epoxy adhesive was found to be tunable by varying the degree of substitution of the BPA-based epoxy with the guaiacol-based epoxy. For the intended use of the adhesive, the sample containing 80 wt.% guaiacol epoxy and 20 wt.% BPA epoxy exhibited the most promising results, whereby stable adhesion of metal dolly was achieved along with the potential for its release under controlled conditions. Furthermore, the proposed formulation achieved a drastic decrease in BPA content, which poses an environmental and health hazard. Our bio-based product was successfully tested on an industrial gluing line as a proof of concept to achieve a sustainable e-Bottle product.


The Fe3O4-GOx possesses intrinsic glucose oxidase (GOx), catalase (CAT) and peroxidase (POD)-like activities, and can catalyze pH-switchable glucose-initiated GOx/POD and GOx/CAT cascade reaction in acidic and neutral environment, respectively. Specifically, the GOx/POD cascade reaction generating consecutive fluxes of toxic hydroxyl radical spatially targets the acidic biofilm (pH ~ 5.5), and eradicates biofilm to shorten the inflammatory phase and initiate normal wound healing processes. Furthermore, the GOx/CAT cascade reaction producing consecutive fluxes of oxygen spatially targets the neutral wound tissue, and accelerates the proliferation and remodeling phases of wound healing by addressing the issues of hyperglycemia, hypoxia, and excessive oxidative stress. The shortened inflammatory phase temporally coupled with accelerated proliferation and remodeling phases significantly speed up the normal orchestrated wound-healing cascades. Remarkably, this Fe3O4-GOx-instructed spatial–temporal remodeling of DU microenvironment enables complete re-epithelialization of biofilm-infected wound in diabetic mice within 15 days while minimizing toxicity to normal tissues, exerting great transformation potential in clinical DU management. The proposed PNCC concept offers a new perspective for complex pathological microenvironment remodeling, and may provide a powerful modality for the treatment of microenvironment-associated diseases.The management of diabetic ulcer (DU) to rescue stalled wound healing remains a paramount clinical challenge due to the spatially and temporally coupled pathological wound microenvironment that features hyperglycemia, biofilm infection, hypoxia and excessive oxidative stress. Here we present a pH-switchable nanozyme cascade catalysis (PNCC) strategy for spatial–temporal modulation of pathological wound microenvironment to rescue stalled healing in DU. The PNCC is demonstrated by employing the nanozyme of clinically approved iron oxide nanoparticles coated with a shell of glucose oxidase (FeThe online version contains supplementary material available at 10.1186/s12951-021-01215-6. Diabetes affecting 451 million people has become a global epidemic, and poses mounting public health concerns due to its clinical complications . DespiteBy individually targeting the issues of hyperglycemia, hypoxia, bacterial infection and excessive oxidative stress, sophisticated strategies for pathological wound microenvironment remodeling have been presented including glucose regulation , local oResearch into enzyme cascade catalysis may provide insight into potential strategies for the development of desired biocompatible DU microenvironment modulator. Enzymes are natural biomaterials capable of catalyzing various chemical reactions to mediate biological processes . In livi3O4-GOx) is composed of an iron oxide nanoparticle (Fe3O4 NPs) core and a glucose oxidase (GOx) shell. GOx is an oxidoreductase that can catalyze the oxidization of glucose to produce hydrogen peroxide (H2O2) [3O4 NPs approved by the United States Food and Drug Administration (FDA), exhibits intrinsic pH-dependent peroxidase (POD) and catalase (CAT)-like activities that can catalyze the decomposition of H2O2 into oxygen and hydroxyl radicals (·OH), respectively [3O4-GOx with closely co-localized GOx and Fe3O4 NPs. Specifically, the GOx/POD cascade reaction generating consecutive fluxes of toxic hydroxyl radical spatially targets the acidic biofilm (pH ~ 5.5), and eradicates biofilm to shorten the inflammatory phase and initiate the normal wound healing of proliferation and remodeling. Furthermore, the GOx/CAT cascade reaction producing consecutive fluxes of oxygen spatially targets the neutral wound tissue, and accelerates the proliferation and remodeling phases of wound healing by addressing the issues of hyperglycemia, hypoxia, and excessive oxidative stress. The shortened inflammatory phase temporally coupled with accelerated proliferation and remodeling phases significantly speed up the normal orchestrated wound-healing cascades, enabling efficient DU treatment.Herein, we introduce a pH-switchable nanozyme cascade catalysis (PNCC) strategy for spatial–temporal modulation of pathological wound microenvironment to rescue stalled healing in DU Scheme . The nane (H2O2) . Fe3O4 Nectively –37. Ther3O4 NPs was first synthesized based on a thermal decomposition method  (PAH) electrostatic coating and GOx covalent modification  was quantitatively determined by bicinchoninic acid (BCA) assay , revealing the high structural stability and excellent solubility of Fe3O4-GOx in an aqueous environment , indicating the depletion of glucose. Meanwhile, H2O2 was detected in the glucose solution after incubation with Fe3O4-GOx nanozyme, and its concentration was found to be gradually increased with the increase of Fe3O4-GOx nanozyme  analysis , and the production of O2 was evaluated. As shown in Fig. 2 was detected in the systems of Fe3O4-GOx/glucose-H2O2 and Fe3O4 NPs/glucose-H2O2 rather than the control systems of PBS/glucose-H2O2 and GOx/glucose-H2O2, indicating the remarkable CAT activity of Fe3O4-GOx and Fe3O4 NPs. Moreover, compared to the system of Fe3O4 NPs/glucose-H2O2, a relatively lower O2 concentration was found in the system of Fe3O4-GOx/glucose-H2O2 due to the O2 consumption during glucose oxidation, confirming the occurrence of coupled GOx/CAT cascade reaction in the system of Fe3O4-GOx/glucose-H2O2. This result indicates that the toxic H2O2 produced in glucose oxidation as well as oxidative stress progression of DU wound can be converted into beneficial O2 by Fe3O4-GOx, making synergistic tissue hypoxia and oxidative stress amelioration in DU become possible and forming a self-oxygen supply system to accelerate glucose oxidation. In addition, the production of O2 in the Fe3O4-GOx/glucose-H2O2 systems with different pH values was assessed. As shown in Fig. 3O4-GOx/glucose-H2O2 under neutral (pH 7.5) and slightly acidic (pH 6.5) conditions produced much higher level of O2 compared to that under acidic (pH 5.5) and alkaline (pH 8.5) conditions, showing a neutral environment-preferred GOx/CAT cascade reaction. This pH-dependent CAT activity of Fe3O4-GOx nanozyme is consistent with the Fe3O4 NPs previously reported [3O4-GOx. Additional file 3O4-GOx/glucose system. It was found that the gluconic acid generated basically did not affect the pH value of the Fe3O4-GOx/glucose system (pH only dropped by 0.32 ± 0.14 within 60 min) at the glucose working concentration (20 mM) we employed. Predictably, such a small pH change will not affect the CAT-like activity as well as the GOx/CAT cascade catalysis performance of Fe3O4-GOx/glucose system.To assess the CAT activity of Fereported . The oxi3O4-GOx and the occurrence of coupled GOx/POD cascade reaction in the system of Fe3O4-GOx/glucose were evaluated. As shown in Fig. 3O4-GOx nanozyme rapidly catalyzed the oxidation of 3,3′,5,5′-tetramethylbenzidine  in the presence of glucose, and produced a yellow-colored oxidation product with an absorbance maximum around 450 nm after adding sulfuric acid as the reaction termination reagent [3O4 NPs/glucose and GOx/glucose. Moreover, the Fe3O4-GOx/glucose system achieved a higher TMB oxidation efficiency in the conditions of pH 5.5 and pH 6.5 than that in pH 7.5 and pH 8.5, exhibiting an acidic environment-preferred GOx/POD cascade reaction  degradation and terephthalic acid (TA) fluorescent assays [3O4-GOx/glucose, indicating the formation of ·OH. Consistent with MB degradation result, fluorescent TAOH derived from the reaction of TA and ·OH was only detected in the system of Fe3O4-GOx/glucose, and showed an acidic environment-preferred generation trend  staining as well as biomacromolecules oxidative damage assay of bacteria were carried out. Figure Escherichia coli  and methicillin-resistant Staphylococcus aureus  in the different treatment groups. It was found that E. coli and MRSA in the treatment group of Fe3O4-GOx/glucose exhibited much greater DCF fluorescence than the control group (PBS/glucose), indicating that the intracellular ROS level was significantly increased in bacteria after nanozyme treatment. In addition, enhanced generation of malondialdehyde (MDA) and carbonylated proteins was observed in bacteria treated by Fe3O4-GOx/glucose compared to the control (PBS/glucose), demonstrating the occurrence of more serious intracellular oxidative damage of biomacromolecules (membrane lipid and protein) after nanozyme treatment  as expected, confirming the broad-spectrum antimicrobial activity of nanozyme.As an important ROS substance, ·OH produced by Fe3O4-GOx nanozyme, live/dead bacterial staining assay and scanning electronic microscopy (SEM)-based bacterial morphology study were performed. As shown in Fig. E. coli and MRSA showed clear and smooth bodies and was stained green by the nucleic acid dye of SYTO 9 in the treatment group of PBS/glucose, exhibiting a normal survival state of living bacteria. In sharp contrast, cellular deformation and surface collapse as well as propidium iodide dye  staining were obviously found for E. coli and MRSA in the treatment group of Fe3O4-GOx/glucose, suggesting the bacterial cell wall and membrane disruption-involved mechanism behind the broad-spectrum antimicrobial activity of Fe3O4-GOx nanozyme [3O4-GOx/glucose was greatly improved compared to the control (PBS/glucose), indicating the structure disruption of bacteria after nanozyme treatment. Bacterial biofilm, a bacterial community wrapped by self-produced extracellular polymeric substances, is the main form of bacterial infection in DU wound [3O4-GOx toward biofilm was also assessed. As shown in Additional file E. coli biofilm was effectively inhibited by the Fe3O4-GOx/glucose. In addition to biofilm formation inhibition, integrated MRSA and E. coli biofilm were almost eradicated in the treatment group of Fe3O4-GOx/glucose while the biofilms remained intact in the control group (PBS/glucose) . Furthermore, apart from outstanding anti-oxidant activity similar to the commonly used anti-oxidant hydrogel [3O4-GOx also possessed additional pH-switchable antimicrobial capability, which makes it more suitable for DU wound treatment.To conduct in vivo DU treatment, an MRSA biofilm-infected wound model of diabetic mouse was employed to mimic the clinical symptoms of hyperglycemia, hypoxia, excessive oxidative stress, and biofilm infection of DU. This mouse model was constructed by creating a full-thickness wound extending through the panniculus carnosus in the back of genetically modified diabetic mouse followed by MRSA inoculation to form biofilm in situ Fig. a. To evareatment , Fe3O4-Ghydrogel , the Fe33O4-GOx treatment, indicating the complete re-epithelialization of wound  and GOx , the Fe3O4-GOx nanozyme has predictable excellent biosafety in vitro and in vivo. By performing antiproliferation assay, HUVEC cells were found to possess high viability after incubation with Fe3O4-GOx nanozyme, Fe3O4 NPs, and GOx respectively for different times, confirming their wonderful biocompatibility in vitro , respectively, and the blood biochemical assay as well as organ histopathological analysis was carried out on the 7th day of injection. As summarized in Additional file 3O4-GOx to the metabolism of liver and kidney of mice. Moreover, no lesions and inflammation were found in the main organs of the mice treated by Fe3O4-GOx compared to the control from the histopathological staining images, exhibiting outstanding in vivo biocompatibility , and eradicates biofilm to shorten the inflammatory phase and initiate the normal healing course. Furthermore, the GOx/CAT cascade reaction producing consecutive fluxes of oxygen spatially targets the neutral wound tissue, and accelerates the proliferation and remodeling phases of wound healing by addressing the issues of hyperglycemia, hypoxia, and excessive oxidative stress. Notably, the shortened inflammatory phase temporally coupled with accelerated proliferation and remodeling phases significantly speed up the normal orchestrated wound-healing cascades, enables complete in vivo re-epithelialization of biofilm-infected diabetic wound within 15 days, demonstrating strong capability for in vivo DU management. More importantly, inheriting the excellent biocompatibility from the GOx and clinically approved Fe3O4 NPs, the Fe3O4-GOx nanozyme exerts great potential for clinical transformation. We believe that the proposed PNCC concept offers a new perspective for complex pathological microenvironment remodeling, and may provide a powerful modality for the treatment of pathological microenvironment-associated diseases.In summary, we report a PNCC strategy for spatial–temporal modulation of pathological wound microenvironment to rescue stalled healing in DU by employing Fe3·6H2O), sodium oleate, oleic acid, 1-octadecene, poly  , phosphate buffered saline (PBS), tetramethylammonium hydroxide, N-(3-(dimethylamino)propyl-N′-ethylcarbodiimide) hydrochloride (EDC), N-hydroxysulfosuccinimide sodium salt (NHS), 3--2,5-diphenyl-2 H-tetrazolium bromide (MTT) assay kit and 2ʹ,7ʹ-dichlorohydrofluorescein diacetate , glucose oxidase (GOx) were purchased from Sigma-Aldrich. 3,3′,5,5′-tetramethylbenzidine (TMB), 2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), o-phenylenediamine , methylene blue (MB), terephthalic acid (TA) and hydrogen peroxide (H2O2) assay kit were obtained from Beyotime Biotechnology. Bicinchoninic acid (BCA) protein assay kit, malondialdehyde (MDA) and protein carbonyl assay kit were obtained from Nanjing Jiancheng Institute of Biological Engineering. Live/dead bacteria viability kit was purchased from Thermo Fisher Scientific. All other chemicals were obtained from Adamas-beta and used without further purification. Deionized (DI) water  was used in all the experiments.Iron (III) chloride hexahydrate  were collected by centrifugation at 12,000 rpm for 15 min and dispersed in 1 mL of DI water for GOx modification. First, 4.1 mg EDC and 4.5 mg NHS were added to 1 mL of 10 mg/mL GOx to activate the carboxyl group. After 30 min, 1 mL of Fe3O4/PAH were added to the mixture, and stirred at 900 rpm for 24 h at room temperature. After the reaction was completed, the mixture was centrifuged at 12,000 rpm for 15 min, and the precipitate (Fe3O4-GOx nanozyme) was washed with DI water, and finally resuspended in DI water for further characterization and application.To prepare Feg method , 57. In 3O4-GOx nanozyme was determined by MTT assay using human umbilical vein endothelial cells (HUVEC). In brief, HUVEC cells were seeded into a 96-well plate  and cultured overnight. Then the cells were treated with Fe3O4-GOx nanozyme with different concentrations . After 24 h of culture, MTT reagent was added and the cell viability was evaluated with a microplate reader.The biocompatibility of Fe2O2) of Fe3O4-GOx nanozyme was evaluated by glucose depletion and H2O2 generation. For glucose depletion assay, Fe3O4-GOx nanozymes with different concentrations  were incubated with diabetic blood sample containing around 20 mM glucose for 5 min, and the blood glucose concentration was measured with a glucometer. For H2O2 generation assay, Fe3O4-GOx nanozymes with different concentrations  were incubated with 20 mM glucose solution for different times . Then the mixtures were incubated with H2O2 detecting reagent for 30 min, and the amount of H2O2 generated was determined by measuring the absorbance of the mixture at a wavelength of 560 nm.The GOx activity  was co-cultured with glucose solution (20 mM) for 5 min under different pH conditions , and oxygen probes  was used to detect the amount of oxygen generated in the mixture.The CAT activity of Fe3O4-GOx nanozyme and the occurrence of coupled GOx/POD cascade reaction was assessed by the oxidation of POD substrates of TMB, ABTS and OPD respectively, in the system of Fe3O4-GOx/glucose. Briefly, Fe3O4-GOx nanozymes (200 µg/mL of iron element) was co-cultured with glucose solution (20 mM) for 5 min, and POD substrate  was then added and incubated for 30 min under different pH conditions . The oxidation of TMB (using sulfuric acid as a stop reagent), ABTS, and OPD was evaluated by measuring the absorbance of the mixture at the wavelength of 450, 734, and 492 nm, respectively.The POD activity of Fe3O4-GOx nanozyme to generate ·OH after incubation with glucose. MB can be degraded by ·OH to generate MB-OH, while TA can react with ·OH to generate fluorescent TAOH. In typical experiments, Fe3O4-GOx nanozymes (200 µg/mL of iron element) was co-cultured with glucose solution (20 mM) for 5 min, then MB degradation and TA fluorescent assay were performed respectively. In MB degradation, the mixture of Fe3O4-GOx nanozyme and glucose was incubated with MB (10 µg/mL) for 24 h, and the absorbance of the mixture at the wavelength of 664 nm was recorded. In TA fluorescent assay, the mixture of Fe3O4-GOx nanozyme and glucose was incubated with TA (0.5 mM) for 12 h, and the fluorescence spectrum of the mixture was measured.MB degradation and TA fluorescent assay were employed to assess the ability of FeStaphylococcus aureus (MRSA) (ATCC 33591) and Escherichia coli (E. coli) (ATCC 8739) were used in our experiments. MRSA and E. coli were cultured in tryptic soy broth (TSB) medium and lysogeny broth (LB) medium, respectively, and harvested at the exponential growth phase before use. For antimicrobial experiments, 106 CFU of bacteria were incubated with the mixture of Fe3O4-GOx nanozyme (200 µg/mL of iron element) and glucose (20 mM) for 2 h in the condition of pH 6.5, and the antimicrobial performance was evaluated by the live/dead bacterial staining assay and SEM-based bacterial morphology investigation.Methicillin-resistant 3O4-GOx nanozyme treatment were mixed with the dye solution containing SYTO 9 and propidium iodide for 30 min in the dark, and then imaged with a confocal fluorescence microscope. Live bacteria were stained by SYTO 9 with green color, while dead bacteria were stained by propidium iodide with red color due to the damage of cell membrane and wall [Live/dead staining assay kit was used to evaluate the viability of bacteria. In brief, the bacteria before and after Feand wall .3O4-GOx nanozyme treatment were fixed with glutaraldehyde (2.5%) in the dark for 2 h, and dehydrated by ethanol solution with different concentrations  for 10 min. The dehydrated bacterial samples were dropped on silicon wafer, and imaged with FESEM after nitrogen drying and platinum coating.The morphology of bacteria was characterized by field-emission scanning electron microscopy (FESEM). In brief, the bacteria before and after Fe7 CFU of E. coli or MRSA) inoculation for 2 days before treatment to form biofilm in situ.Type II diabetic mice  were purchased from the Nanjing Si Ke Rui Biological Technology Co., Ltd., and allowed to acclimatize for 1 week in the laboratory. All animal experiments were carried out in compliance with the protocols approved by the Shandong University Laboratory Animal Center. To construct the mouse model of DU, the diabetic mouse was firs anesthetized, and an oval wound  was created on the back of mouse using disposable biopsy punch followed by bacteria  were added onto the infected diabetic wound, and pictures of the wound were taken every day to record their festered areas. After 15 days treatment, the mice were executed and the skin tissues at the wound sites were dissected. A variety of experimental methods including bacteria culture, hematoxylin and eosin (HE) staining, Masson’s trichrome staining, gram staining and CD31 immunohistochemistry staining, were performed to evaluate the actual therapeutic ability of Fe3O4-GOx nanozyme for in vivo DU treatment.The MRSA biofilm-infected wound of diabetic mice were divided into four treatment groups including FeAdditional file 1. Additional experimental methods and figures.


Pancreatic cancer is one of the principal causes of tumor-related death worldwide. CXC chemokines, a subfamily of functional chemotactic peptides, affect the initiation of tumor cells and clinical outcomes in several human malignant tumors. However, the specific biological functions and clinical significance of CXC chemokines in pancreatic cancer have not been clarified.Bioinformatics analysis tools and databases, including ONCOMINE, GEPIA2, the Human Protein Atlas, DAVID, GeneMANIA, cBioPortal, STRING, DGidb, MethSurv, TRRUST, SurvExpress, SurvivalMeth, and TIMER, were utilized to clarify the clinical significance and biological functions of CXC chemokine in pancreatic cancer.Except for CXCL11/12, the transcriptional levels of other CXC chemokines in PAAD tissues were significantly elevated, and the expression level of CXCL16 was the highest among these CXC chemokines. Our findings also suggested that all of the CXC chemokines were linked to tumor-immune dysfunction involving the abundance of immune cell infiltration, and the Cox proportional hazard model confirmed that dendritic and CXCL3/5/7/8/11/17 were significantly associated with the clinical outcome of PAAD patients. Furthermore, increasing expressions of CXCL5/9/10/11/17 were related to unfavorable overall survival (OS), and only CXCL17 was a prognostic factor for disease-free survival (DFS) in PAAD patients. The expression pattern and prognostic power of CXC chemokines were further validated in the independent GSE62452 dataset. For the prognostic value of single CpG of DNA methylation of CXC chemokines in patients with PAAD, we identified 3 CpGs of CXCL1, 2 CpGs of CXCL2, 2 CpGs of CXCL3, 3 CpGs of CXCL4, 10 CpGs of CXCL5, 1 CpG of CXCL6, 1 CpG of CXCL7, 3 CpGs of CXCL12, 3 CpGs of CXCL14, and 5 CpGs of CXCL17 that were significantly associated with prognosis in PAAD patients. Moreover, the prognostic value of CXC chemokine signature in PAAD was explored and tested in two independent cohort, and results indicated that the patients in the low-risk group had a better OS compared with the high-risk group. Survival analysis of the DNA methylation of CXC chemokine signature demonstrated that PAAD patients in the high-risk group had longer survival times.These findings reveal the novel insights into CXC chemokine expression and their biological functions in the pancreatic cancers, which might serve as accurate prognostic biomarkers and suitable immunotherapeutic targets for patients with pancreatic cancer. Pancreatic cancer is one of the top five lethal malignant tumors, with an extremely poor prognosis , 2. PancCXC chemokines (CXCL1 to 17), a subfamily of soluble cytokines with small and highly conserved cytokines, are mainly concentrated in the TME and play an essential role in the initiation and progression of various tumors by binding to G-protein-coupled receptors (GPCRs) , 11. To In this study, comprehensive bioinformatics was performed and analyzed for the CXC chemokines in patients with pancreatic cancer, and the biological characteristics, accurate prognostic biomarkers, and potential drug therapeutic targets were explored, helping further understand the molecular mechanism and management of pancreatic cancer.www.oncomine.org) and GEPIA2 (http://gepia2.cancer-pku.cn/#index) bioinformatics analysis tools. In ONCOMINE database, the CXC chemokines satisfying statistically significant expression criteria p-value < 0.05 were screened, which ranked within the top 10% with fold change (FC) > 2. In GEPIA2 database, the p-value < 0.01 and |logFC | ≥ 1 were the cutoff criteria. Besides, the relative transcriptional levels of CXC chemokines were obtained in the GEPIA2 database, and the most highly expressed CXC chemokine was identified. Finally, the protein expression of the CXC chemokines family between pancreatic cancer and normal tissues were explored using the Human Protein Atlas database .The differentially expressed CXC chemokines and its transcription levels in pancreatic cancer were identified through the ONCOMINE  (www.oncd GEPIA2  (http://https://david.ncifcrf.gov/summary.jsp) and Metascape (https://metascape.org/). False discovery rate (FDR) < 0.05 and gene counts ≥ 10 were significant criteria.The GO (Gene Ontology) function analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis for CXC chemokines were conducted using DAVID , 18 (verhttps://cistrome.shinyapps.io/timer/) is a bioinformatics analysis online tool to explore and analyze tumor-infiltrating immune cells. TRRUST (https://www.grnpedia.org/trrust/), an online analysis tool for the prediction of transcription factor targets, was utilized to understand the biological significance of CXC chemokines.TIMER database   database, STRING (https://string-db.org/) database, and GeneMANIA (http://www.genemania.org) database separately. cBioPortal was utilized to explore the information of genetic alteration, survival curves, and co-expression of CXC chemokines in PAAD. STRING, a significant online database for the comprehensive protein–protein interaction (PPI) analysis, was utilized to study how proteins interact with each other, and 0.4 was set as the minimum required interaction score. Finally, the interaction analyses of the CXC chemokines in PAAD were further explored by the GeneMANIA.To fully understand the molecular features and biological characteristics of CXC chemokines, CXCL1–17 (not including CXCL15) were uploaded to the cBioportal  , a web server for discovering drug–gene interactions or potentially available drug categories, was used to explore the potential druggable genes and drugs of CXC chemokines in PAAD patients, and interaction score > 1 was the significant criterion.To further verify the clinical significance of the CXC chemokines in PAAD patients, the expression levels at different pathological stages were all studied using the GEPIA2 database, an online bioinformatics analysis tool data from the TCGA database. DGidb  (https:/https://biit.cs.ut.ee/methsurv/). Furthermore, to obtain a more comprehensive expression level and prognostic value of the CXC chemokine signature in pancreatic cancer, in this study, the 16 candidate CXC chemokines were used to calculate the risk score for each patient based on SurvExpress database (http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp). The computational formula was as follows: n = 88) and high-risk groups (n = 88) based on the mean value of the risk score. Survival curve was explored according to the high- and low-risk groups of the CXC chemokines. Meanwhile, the prognostic value of the DNA methylation of CXC chemokine signature in patients with pancreatic cancer was also explored using SurvivalMeth .To assess whether CXC chemokines are accurate prognostic markers for predicting PAAD patient survival, we explored the prognostic value of a single CXC chemokine in survival of patients with PAAD based on GEPIA2, the statistically significant criteria considered on the basis Pr (>F) < 0.05. Meanwhile, the prognostic value of single DNA methylation CpG of CXC chemokines in PAAD patients was also explored using MethSurv   were explored using ONCOMINE. The expression levels in 16 CXC chemokines (not including CXCL15) between pancreatic cancer and normal pancreatic tissues were analyzed. Data from TCGA (the Cancer Genome Atlas database) revealed that the expression levels of CXCL2/3/5/6/7/8/9/10/13/14/16/17 were significantly higher in pancreatic cancer tissues than normal tissues . However, genetic alteration in CXC chemokines was associated with better disease-free survival (DFS) of PAAD patients (p = 0.014). Thirdly, the co-expression of CXC chemokines was explored by the Pearson’s correlation coefficient for PAAD samples , and the results showed a significantly positive correlation among the expression of CXCL1, CXCL2, CXCL3, CXCL9, CXCL10, and CXCL11 (p = 7.441e−3), CXCL9 , CXCL10 , and CXCL11 . BRCA2 gene mutation was significantly associated with CXCL5 , CXCL9 , CXCL10 , CXCL11 , CXCL13 , and CXCL 17 . Fourth, a comprehensive PPI information network of the CXC chemokines was constructed using STRING. As expected, the network consisted of 16 nodes and 111 edges with the enrichment p-value < 1.0e−16   functional enrichment of CXC chemokines was analyzed using DAVID 6.8. As shown in p < 0.05). In addition, the findings showed that CXCL4 and CXCL17 are negatively correlated with the infiltration of macrophage and dendritic cells , CXCL3 (p = 0.014), CXCL5 (p = 0.025), CXCL7 (p = 0.029), CXCL8 (p = 0.025), CXCL11 (p = 0.041), and CXCL17 (p = 0.011) were significantly associated with the clinical outcome of PAAD patients , CXCL3 (p = 0.0115), CXCL5 (p = 0.0008), and CXCL8 (p = 0.0145) are related to different clinicopathological stages in PAAD. In addition, of the 16 CXC chemokines, the expressions of 6 CXC chemokines show significant correlations with drug–gene interaction, namely, CXCL2, CXCL4, CXCL8, CXCL10, CXCL12, and CXCL13 , CXCL9 , CXCL10 , CXCL11 , and CXCL17  are connected with unfavorable OS of patients with PAAD  are an unfavorable prognostic factor of DFS in PAAD patients , CXCL9 (p = 7.32e−05), CXCL10 (p = 1.15e−06), CXCL11 (p = 6.95e−10), and CXCL17 (p = 1.77e−04) (p = 0.002), histological grade (p = 0.008), and survival status (p = 0.013). The effectiveness of prognostic prediction of the combined CXC chemokines in PAAD patients is explored by Kaplan–Meier survival analysis based on SurvExpress. As shown in p = 5.753e−07, HR = 3.17, 95% CI 2.02–4.99). The ROC curves for survival prediction by the CXC chemokines assessed the accuracy of the prognostic model  amino acid motif and play a crucial role in the regulation of tumor-related angiogenesis and in tumor proliferation and sustenance , can inhibit tumor angiogenesis , CXCL3 (p = 0.013), CXCL6 (p = 0.012), CXCL8 (p = 0.01), CXCL9 (p = 0.026), CXCL14 (p = 1.49E−5), and CXCL16 (p = 0.003) in PAAD. Buchholz et al. (p = 0.001) and CXCL9 (p = 6.21E−4) in pancreatic ductal adenocarcinoma significantly increased. The datasets of Badea et al. (p = 0.001), CXCL3 (p = 3.8E−7), CXCL5 (p = 3.37E−13), CXCL6 (p = 1.85E−6), CXCL8 (p = 9.92E−12), CXCL9 (p = 1.27E−5), CXCL13 (p = 0.01), CXCL14 (p = 1.51E−6), and CXCL16 (p = 6.64E−12) significantly rose in pancreatic ductal adenocarcinoma compared with normal samples. Similarly, moreover, the research of Pei et al. (In this study, the relationship between CXC chemokines and clinical significance in PAAD was explored using several bioinformatics analysis tools for a better understanding of the initiation and progression in PAAD. The findings showed that most CXC chemokines were upregulated in PAAD, which were correlated with clinical outcomes. These findings were in accordance with the results of expression levels reported by Iacobuzio-Donahue et al. , who fouz et al.  also prea et al.  also sugi et al. , Logsdoni et al. , Ishikawi et al. , Segara i et al. , and GrüL5 p = 3.E−13, CXCThere are several limitations of the study. Firstly, the study simply relied on bioinformatics analysis, and there were no biological experiments to verify the results. Secondly, molecular mechanism and functions of CXC chemokines were under-researched. Thirdly, there were no bioinformatics analysis platforms for exploring the efficiency of PAAD-related drug targets.In conclusion, our findings reveal the novel insights into CXC chemokine expression and their biological functions in pancreatic cancers, which provide a breakthrough for the exploration of prognosis biomarkers and immunotherapeutic targets for patients with pancreatic cancer. Further work is required to systemically understand the biological functions and clinical significance of CXC chemokines in PAAD.The original contributions presented in the study are included in the article/All authors contributed to the article and approved the submitted version. JH and ZC downloaded and analyzed the data. DW and KR designed the study. JH, ZC, and CD drafted the manuscript. DW and CD reviewed and revised the manuscript. SL and CD edited the figures and tables of the article. All authors made substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A marathon was recently run in less than 2 h by a man who ran the three fastest marathons ever recorded in a span of three years—Eliud Kipchoge—in the Tokyo Olympic games. Here, we demonstrate that the best marathons were run according to a pace distribution that is statistically not constant and with negative asymmetry. The concept of mirror race enables us to show that the sign of asymmetry is not due to sampling fluctuations. We show that marathon performance depends on pacing oscillations between extreme values, and that even the best marathons ever run differ and can be improved upon. The utilization of extreme values and oscillations allows for recovery and optimization of the complementary aerobic and anaerobic metabolisms. Our findings suggest new ways to approach the pacing for optimizing endurance performance. For centuries, the limits of physiology and athletic records have fascinated scientists. In 2019, the sub-two-hour marathon barrier was broken with a time of 1:59:40.2. In 1925, Nobel laureate Archibald Vivian (AV) Hill published “The Physiological Basis of Athletic Records” [Nowadays, one century later, advances in wearable sensor technology have enabled real-time measurement of physiological data during exercise . Future Hill’s and Kennelly’s approach to running the fastest marathon was based on the constant pace paradigm.In Hill’s reference to Kennelly’s 1906 paper, he stated that the ideal way to run a race was not necessarily to win, but to achieve a new athletic record for the distance, and to do it by running it at a constant speed. Fifty years later, the physicist Joseph Keller modelled the predominating view that a runner should maintain a constant pace to achieve the shortest time in his paper: “A Theory of Competitive Running“ . To deteIn the same way, Arthur Kennelly desired to measure all points of a race. Delving deeper into Kennelly’s paper: “It is to be noted that all these speeds are average speeds over the courses. There is no evidence among the records to show what the speed was at different points in the course. So far, as concerns anything appearing in the data, the speed of a runner, for example, which averages 7.17 ms per s over a 1 km course, might be 10 m per s in the first part and 5 in the last part, or vice versa. Evidence is lacking to show what the facts are, and they are of great importance to the science of athletics. The speed of a world’s record type of trained runner might be determined at any or all points of a course, either by securing a light recording chronograph on the back of his belt, with a thread paid out as he ran, or by pacing the runner with a light motor-car carrying an automatic speed“ , p. 328.Physiologic processes are inherently never constant. French physiologist Claude Bernard wrote, in his classic book , that: “In the same way, the observed paces in a marathon are far from constant at an average speed. The best marathons are run with an asymmetric distribution of paces .Nowadays, it is possible to test the hypothesis that the best marathon performance is run with a variable pace and to challenge the paradigm of a constant pace. Statistically, a race is run with an oscillating pace between extreme values that are inter-played by optimizing fuel, recovery, and avoiding VO2 and heart rate drift. Using mathematical statistics, we aim to show that an asymmetric distribution is optimal for marathon running, to delve deeper into the pace distribution, and to find the exact outline of the optimal pacing signature by analysing the official best world marathon performances by Dennis Kimoto (Berlin 2014) and Eliud Kipchoge . Furthermore, previous marathon studies reported, also run in actual conditions, that physiological oscillations were the subjacent of real pace variations, even when the marathon pace is planned to be constant .The Berlin marathons were official competitions and are official world records. While Monza 2017 and Vienna 2019 were exhibition marathons accomplished with rotating pacers, an electric pace vehicle, a laser beam projecting the ideal position on the road and ideal conditions. At Vienna, Kipchoge ran at a consistent average pace of 2:50 min per km (4:33.5 min per mile) and was 11 s ahead of schedule halfway through the marathon. He even accelerated in the final kilometre. Given that the best marathon performances, including the Olympics, were run by a single man (Eliud Kipchoge), this shows the possibility of adding a new dimension of running performance beyond the chronometer and proposing a qualitative way of optimizing the physiological capacity for running a marathon at 21 km/h. Our approach represents a powerful possibility for the marathon runner to validate the optimal aspects of his/her performance. However, before exploring the possibilities of providing feedback to a marathon runner, we must test the consistency of such a model in Eliud Kipchoge, who is purported to have a robust “performance template“. They have shown this to be primarily related to the increased confidence that the distance in question can be completed without unreasonable levels of exertion or injury [Our data consists of six marathon races: Berlin 2014, Monza 2017, Berlin 2018, Berlin 2019, Vienna 2019, and Tokyo 2021. They were performed by Dennis Kimetto, D.K. (Berlin 2014), Eliud Kipchoge, E.K. , and Kenenise Bekele, K.B. (Berlin 2019).http://run.hwinter.de, accessed on 1 September 2021).For a given marathon, our data were collected from ith km, all values being integers.The original dataset includes marathons that provide timing data for 1km race segments ; the requirement for 1 km segments is based on the need to track changes in pacing during different stages of the marathon. The accuracy of the data is, then, higher than those collected by strava from the individual GPS measurement which depend on GPS models . Thus, ith order statistic .average pace the real number We call official average pace obtained by dividing the official total time by the distance 42.195 km. In our data, this only occurs for Vienna 2019. The resulting difference may, as usual, come from the phenomenon of rounding error, with the result that a total of rounded numbers is not equal to the rounded version of the original total. Furthermore, in our case, a rather precise value of the sum of the paces (minus the time taken to run the last 195 m) is known as the total time. The difference may also, in the case of a marathon, be accounted for, at least partially, by a very fast or slow pace during the last 195 m, that are not included in our measurements.It is important to note that this value may differ slightly from what can be called the Since our aim is to provide computational methods for big data that have more available, we will therefore describe the stages of a statistical study starting after the sequence The standard deviation A coefficient of variation used extensively is Karl Pearson’s dimensionless coefficient of variation given byIn our study, a fundamental role will be played by the skewness coefficientrmula 3.8 in the RComputations from our data are summarized in The races are not listed chronologically, but according to the performance and the sign of V that the two performances, Vienna 2019 and Monza 2017, approached the uniform pace strategy. We will see, in fact, that these two performances and neither the other four marathon were run uniformly. This is discussed in the next paragraph.It might seem, in view of the small values of As in everyday language, uniformity of the pace will mean for us that the race is run at an approximately constant pace. Mathematically, the sequence Non-uniformity of the pace can be measured by the range of the observed pace distribution, that is, the difference between the greatest and smallest values observed, given byNon-uniformity of the pace may also be measured by the difference between the fastest and the slowest 10 km,Note that the well-known negative split is given by formulaThe ratiosIn view of this table, it is apparent that none of the six races manifests a range attributable to random fluctuations about an average target speed. In other words, none was run at an approximately constant pace, in spite of the fact that two of them, Monza 2017 and Vienna 2019 were planned to be run at a constant pace. The smallest ranges are achieved (as expected at Monza) at races designed to be run at a constant pace. As a matter of fact, 8 s, even for a non-elite runner, is a significant time-difference for a single kilometre, as well as 43 s for 10 km.The most spectacular variation occurred for Tokyo 2021’s Once the spread of the pace has been shown to be significant, it is legitimate to raise the issue of its shape, in particular, its symmetry or lack of symmetry.A first insight into the general shape of our distribution is provided by a diagrammatic representation of the data, see the bar charts of grouped data in A striking feature shared by the six races is that they belong to the class of moderately asymmetric bell-curves .To this end, we make use of Zar’s statistic denoted by the Ref. , or Equathe Ref. , restateFormulas leading to Zar’s statistic are as follows.z, computed from our In the case of a normal distribution for z takes values fairly, or even significantly different from zero. A positive skewness of the pace is associated with the failed performance of Monza 2017.For the six races, A negative skewness of the space is associated with the successful performances.Furthermore, when the skewness is negative, we observe the final times are better resulting in a higher  skewness see .The skewness may also be measured in the way the range Gedankenexperiment).Even though most distributions from the real world manifest asymmetry, we can show that the negative asymmetry observed on the six registered races cannot be explained by mathematical randomness, as shown by the following thought experiment 1≤i≤4and in vquations  we inferRemarkably, Berlin 2019 manifests a similar split of 15, 6, and 21 km . Hence, the mirror race would also be much more difficult to perform in this case.For Tokyo 2021, Berlin 2014, and Monza 2017, the average pace is distinct enough from an integer value, so that each kilometre can be labelled as fast/difficult (red) or slow/easy (green), see For Vienna 2019, a cursory look at the bar chart suggests that the distribution can be seen as the mixture   means that runners must choose the ideal pace, allowing them to achieve the optimal performance without famously “hitting the wall“ or slowing down from extreme fatigue 10 km or less before the finish line (see the Ref. ).We must underline that negative pacing can help runners to achieve better results . However, a great majority of runners just want to complete a marathon or enjoy running, without focusing too much on optimizing their pacing. Therefore, for them, even pacing is easier to follow and control. Indeed, for both elite and recreational runners, planning variable pacing is not practical. The pacing is mainly controlled by CNS and it is highly automated. Therefore, any change in pacing can be hard to achieve, resulting from runners either slowing down or speeding up too much, particularly if they are less experienced runners. Please elaborate some more, and possibly add this as a limitation of this study. In addition, speeding up and slowing down requires additional muscle force to be exerted, thus spending extra energy even if it allows for elevation of the average pace. We still have to check the difference in energy spent by continuous energy cost measurements between variable and even pace race, at least in a real half-marathon race.The non-arbitrary sign of asymmetry, which cannot be accounted for by sampling fluctuations in speed but by physiology, is explained by the “mirror race“. It is important to understand that a runner cannot recover lost time in the first part of a marathon (as in Monza) using their speed reserve . RecVariable pace running focuses the optimization of energy. These concepts were developed by AV Hill over 100 years ago with his papers on muscular exercise, lactic acid, the supply and utilization of oxygen, all helping to establish the concept of “an-aerobic“ energy production during exercise, with oxidative restoration in recovery (see the Ref. ); it is Furthermore, it has been shown that critical speed can be accurately estimated from a non-exhaustive self-pace run . Even ifIndependent of the race or the runner, the pace is never symmetric, implying that the central tendency and deviation do not entirely characterize the pacing signature. A negative asymmetry of pace distribution signifies a faster race and the higher the absolute value of the pacing asymmetry is, the better the performance is. Indeed, according to our hypothesis, our results show that for each of these high-level races, we could give the general shape of the data distribution. Independent of the marathons studied, we always obtained an asymmetric shape. Interestingly, we showed that the asymmetries for the two exhibition marathons, were different: negative for Vienna (the first 2-sub-hour marathon race (1 h 59 min 40 s and the Ref. ).Although a parabolic J-shaped pacing profile (in which the “fast-start“ is faster than the middle part of the race but is slower than the end spurt) is observed in many championship races , we showWe also showed how the difference in extreme values distributions allows a characterization of the optimal marathon as negatively asymmetric in the sense that the fast-km pace frequency decay occurs more progressively than the slower km pace. Analysing the “big data“ of marathons reveals that foremost, the individual experience of training and running a marathon offers personal gratification, whether or not they achieve their target of finishing less than 4 h 21 min 03 s or 4 h 48 min 45 s . Endurance running has never reached this level of popularity, especially among women. Marathon running is truly human by nature (see the Ref. ). In thiLet us show that the foregoing results are, obtained via calculus, in agreement with some already identified running strategies, especially faster start and finish that are observed even in the traditional (even-pacing) approach to record-setting results.q will represent the proportion of distance run fast during a marathon. For instance the runner runs Assume that The bar chart of the pace distribution s is denoted by Let us generalize this fundamental example. Let p to s, which is itself increasing with p, imply that p is concave. Up to a constant factor the speed function is given by v is an increasing convex function.The bar charts of the pace sequence considered up to now will be replaced by a continuous curve, the density function v and p in Equation Conversely, every increasing speed function Let us make these formulas explicit in the caseskewness  Chapter.Since These computations lead to rather unrealistic pace strategies. But keep in mind that our aim was only to produce some pace distribution with negative skewness.From the speed function in Equation (22), we can build two speed functions that will lead to the same density function The graphs of From a mathematical point of view, a running strategy can be outlined as follows: First, divide the race into sub-races, called “pace-cells“, and for each of these cells, select a speed function as The theory of oscillators seems well-suited for providing models for oscillating functions. Our analogy is not based on purely mathematical similarities, but is a consequence of our results. The interplay between extreme  values that we have unveiled, corresponds, for oscillators, to the well-known restoring forces. Indeed, Claude Bernard already pointed out that all biological systems are oscillators, in the sense that the value of a variable remaining constant at its mean value is never observed, that is to say biological systems are oscillators. Our statistical analysis illustrates this fact concerning the world’s best marathon runners.We proposed to focus on a time series generated by the oscillating pattern of the damped clock discussed in the Ref. . RemarkaHowever, to oscillate between extreme pace values, there is a need to have a high range of speed incompatible with monotonous training ,44. IndeExtreme value theory is used to predict the occurrence of rare events such as extreme floods, large insurance losses, stock market crashes, and human life expectancy. Indeed, some authors have already applied the extreme value theory to athletic events, using estimation methods involving moment method and maximum likelihood methods (MLE). Accurate estimations of future athletic records were accomplished using both methods. Hence, an interesting question is: under the present knowledge of training, materials , and anti-doping regulations, by how much further could athletes possibly exceed current world records in the near future? We used the extreme value theory dealing with the issues of extremes without considering them to be independent because we took into account (as stated by Hill), the “fatigue factor“.Running a marathon between extreme values, training must use the so-called “polarized training“ emphasizing low and high intensity, rather than medium intensity . A rhttps://www.sidmartinbio.org/how-many-runners-are-there/, (accessed on 1 September 2021). Using mathematics and physiology, we opened up new perspectives on how optimize endurance and power in a marathon. The pacing strategy of elite marathon runners is to start fast and then to recover by running just below their average pace for 2/3 of the racing distance, generating a negative asymmetry, that is, a median speed below the average. This “lazy“ race confirms AV Hills’ discoveries about the concept of “an-aerobic“ energy production during exercise, with oxidative restoration in recovery being a factor determining the variation of speed with distance.We analysed the three best real-world marathon performances ever ran to the hypothesis that Kipchoge optimizes his pacing in relation to his aerobic and anaerobic power and endurance. The conclusions from this could be a source of inspiration for the over 9 million marathon runners who crossed the finish line in 2018 (Esther Fleming, However, our aim was not to replace a coach’s advice, but only to show that the future of the best performances, as in world records, is perhaps to look to the variable pace shape.