The dynamic cycling of O-linked GlcNAc (O-GlcNAc) on and off Ser/Thr residues of intracellular proteins, termed O-GlcNAcylation, is mediated by the conserved enzymes O-GlcNAc transferase (OGT) and O-GlcNAcase. O-GlcNAc cycling is important in homeostatic and stress responses, and its perturbation sensitizes the heart to ischemic and other injuries. Despite considerable progress, many molecular pathways impacted by O-GlcNAcylation in the heart remain unclear. The mitogen-activated protein kinase (MAPK) pathway is a central signaling cascade that coordinates developmental, physiological, and pathological responses in the heart. The developmental or adaptive arm of MAPK signaling is primarily mediated by Erk kinases, while the pathophysiologic arm is mediated by p38 and Jnk kinases. Here, we examine whether O-GlcNAcylation affects MAPK signaling in cardiac myocytes, focusing on Erk1/2 and p38 in basal and hypertrophic conditions induced by phenylephrine. Using metabolic labeling of glycans coupled with alkyne-azide “click” chemistry, we found that Erk1/2 and p38 are O-GlcNAcylated. Supporting the regulation of p38 by O-GlcNAcylation, the OGT inhibitor, OSMI-1, triggers the phosphorylation of p38, an event that involves the NOX2–Ask1–MKK3/6 signaling axis and also the noncanonical activator Tab1. Additionally, OGT inhibition blocks the phenylephrine-induced phosphorylation of Erk1/2. Consistent with perturbed MAPK signaling, OSMI-1–treated cardiomyocytes have a blunted hypertrophic response to phenylephrine, decreased expression of cTnT (key component of the contractile apparatus), and increased expression of maladaptive natriuretic factors Anp and Bnp. Collectively, these studies highlight new roles for O-GlcNAcylation in maintaining a balanced activity of Erk1/2 and p38 MAPKs during hypertrophic growth responses in cardiomyocytes. Ogt and Oga genes, necessary for O-GlcNAc cycling, are essential for mammalian development and survival  utilizes UDP-GlcNAc as a substrate and covalently links the GlcNAc moiety to Ser/Thr residues of intracellular proteins , 2, 3. Tsurvival , 9, 10. survival , 12, hypsurvival , 15, 16,survival , 18, 19,survival , 21. Thesurvival , 24, 25.survival . Focusede CaMKII , the AMPe CaMKII , and thee CaMKII .The mitogen-activated protein kinases (MAPKs) are a conserved family of kinases forming an ordered signaling cascade (MAP3K/MAP2K/MAPK), activated by mechanical and neuroendocrine factors acting on G protein–coupled receptors , 31. Thee.g., on Erk1/2 and p38, is sparse.While Erk1/2 are thought to be associated with adaptive physiological responses, p38 signaling kinases are associated with inflammation, maladaptive remodeling, and reduced cardiac contractility , 47, 48.In the present work, we examined whether short-term changes in O-GlcNAcylation could affect basal and stimulated MAPK signaling in cardiac myocytes. Our data indicate that decreasing O-GlcNAcylation with the OGT inhibitor OSMI-1 induces basal p38 phosphorylation at the early phase, whereas it blocks Erk1/2 phosphorylation and causes growth impairment at a later phase. We found several MAPK signaling members that could be putative targets for O-GlcNAcylation, including Erk1/2 and p38, but also upstream  and downstream mediators (Hsp27 and Creb). Our data reveal the widespread and intricate relationship between O-GlcNAcylation and MAPK signaling in cardiac myocytes, paving the way for further investigations into the role of O-GlcNAcylation in MAPK-related pathophysiology, including cardiac hypertrophy and heart failure.A). To investigate the PE-induced hypertrophic signaling, we employed SCH772984 and SB202190, chemical inhibitors to kinases Erk1/2 and p38α/β, respectively (B). As shown in B and C, PE increased NRVM size , and this was reduced by the Erk1/2 inhibitor SCH772984 . The p38 inhibitor SB202190 did not significantly affect PE-induced growth, while the combination of the two strongly suppressed PE-induced NRVM growth (C). The total numbers of nuclei per random field imaged were not significantly different across groups (D).Neonatal rat ventricular myocytes (NRVMs) provide a cellular model to investigate the role of the MAPK signaling cascade in cardiac hypertrophy. Exposure to the alpha-1 adrenergic receptor agonist PE  increased the phosphorylation of Erk1/2, p38, and the downstream target Creb A. To invectively , 60, 61.ectively B. As shoM growth C. The tos groups D.Figure E and F). In fact, the two inhibitors appeared to have an additive effect in suppressing PE-induced Erk1/2 phosphorylation (F). In cells treated with the p38 inhibitor SB202190, we observed a paradoxical increase in p38 phosphorylation. This, however, can be explained by considering that the phosphorylation of p38 is regulated by a feedback circuit, where inhibition of p38 derepresses its upstream kinase TAK1, leading to compensatory phosphorylation of p38, despite inhibition of its activity  block p38 at the active site of the kinase without impacting its phosphorylation (E and G).SCH772984 was potent in reducing the PE-induced phosphorylation of Erk1/2 and, interestingly, this response was also recapitulated by SB202190 , E and Frylation F. In celactivity . Furtherrylation . These trylation , E and GH and J).A downstream target of p38, MK2, phosphorylates the small heat shock protein Hsp27. We confirmed the functional inhibition of p38 by examining the phosphorylation of Hsp27. As expected, PE-induced phosphorylation of Hsp27 was completely suppressed by SB202190 . In contcTnT and Anp as markers of hypertrophic signaling. SCH772984 or SB202190 alone prevented the PE-induced cTnT expression and were most potent when used in combination (K). On the other hand, SCH772984, but not SB202190, significantly reduced PE-induced Anp (L), consistent with a regulatory pathway linking Erk1/2 activation to GATA-mediated transcription of Anp . In a proof of concept experiment, we treated cells with 200 μM Ac4GalNAz (negative control) or Ac4GalNAlk for 24 h, and protein extracts were used in Alkyne-Azide click reactions with CalFluor 647 azide, a probe that fluoresces only after it reacts with an alkyne . Furthermore, there was no background fluorescent reactivity in the absence of the click catalyst . Next, we treated NRVMs with Ac4GalNAz or Ac4GalNAlk as above and proteins were extracted from metabolically labeled cells. Alkyne-azide click reactions were performed with biotin azide and biotinylated proteins were pulled-down using streptavidin-conjugated agarose beads. The different fractions from the pull-down were subjected to Western blot with streptavidin IR Dye 800 showing the efficient enrichment of biotinylated proteins with this approach .Next, we interrogated whether p38 and Erk1/2 were O-GlcNAcylated. To do that, we employed metabolic labeling in cells using the sugar analogs Ac4GalNAz or Ac4GalNAlk. These are biosynthetically converted into UDP-GalNAz or UDP-GalNAlk respectively, serving as substrates of OGT in intracellular protein O-GlcNAcylation , 66, 67 Fig. 2n alkyne . Using iD). The MAPK-downstream transcription factor Creb was also detected, consistent with previous reports showing that it is O-GlcNAcylated (D). Moreover, we found that the other MAPK of interest, p38, was pulled-down and so was its downstream target Hsp27 (E). Taken together, these findings indicate that several members in the MAPK signaling pathway are putative targets for O-GlcNAcylation in cardiac myocytes.We next analyzed the biotinylated samples to identify evidence for O-GlcNAcylation on MAPKs Erk1/2 and p38. Indeed, by Western blotting, we found that the pulled-down fraction contained Erk1/2 and also its upstream MAP2Ks, MEK1/2 D. The MAAcylated . AnotherAcylated D. Moreovet Hsp27 E. Taken A and B). Consistent with earlier reports, OGA protein was significantly upregulated in cells treated with TMG , whereas OGT was significantly upregulated in cells treated with OSMI-1 . PE treatment  did not significantly change the levels of O-GlcNAcylation, although longer treatment durations (3 and 6 h) induced modest but significant elevations in protein O-GlcNAcylation .Protein O-GlcNAcylation was manipulated in NRVMs with Thiamet G (TMG) and OSMI-1, inhibitors of OGA and OGT, respectively , 70. TreDolichos biflorus agglutinin did not identify changes in glycan abundances between control and TMG- or OSMI-1–treated cells (A–E). Given OSMI-1’s structural similarity with UDP-GlcNAc, we examined whether OSMI-1 might elicit a confounding impact on N-linked glycosylation. However, lectin blots for Concanavalin A (ConA) revealed that the abundances of N-linked glycoproteins were not impacted significantly by OSMI-1 (F and G). On the other hand, tunicamycin, which blocks the addition of GlcNAc onto dolichol phosphate, induced a significant reduction in the ConA signal (F and G).Furthermore, we examined whether TMG or OSMI-1 altered the abundance of GlcNAc-, GalNAc-Galactose–, and GalNAc-Mannose–bearing glycoproteins, whose biosynthesis might be affected by potential alterations in UDP-GlcNAc levels caused by either TMG or OSMI-1. Lectin blots with biotinylated wheat germ agglutinin, peanut agglutinin, and ed cells , A–E. Giy OSMI-1 , F and GA signal , F and Gversus untreated, p < 0.001, A and B). In contrast, TMG alone or in combination with PE did not impact p38 phosphorylation. Moreover, OSMI-1 had no effect on Erk1/2 phosphorylation, either at baseline or in PE-treated conditions (A and C). p38 phosphorylation gradually increased during an 18-h exposure to OSMI-1, mirrored by a progressive increase in Hsp27 phosphorylation (D). Hsp27 phosphorylation was increased in PE-stimulated conditions, although OSMI-1 alone caused higher Hsp27 phosphorylation (E and F). We next examined if OSMI-1 caused widespread changes in the phosphorylation of MAPK substrates. While we found that phosphorylation of MAPK/CDK substrates was increased by PE treatment, this was not the case in OSMI-1–treated cells (G and H). These data suggest that the induction of p38 phosphorylation by OSMI-1 is a selective event that impacts the MAPK signaling pathway at a level that is distinct from alpha-1 adrenergic activation and the broad induction of its downstream effectors . Finally, the p38 inhibitor SB202190 was sufficient to completely abolish the OSMI-1–induced phosphorylation of Hsp27 and it was also effective at decreasing the OSMI-1–induced phosphorylation of the transcription factor Creb (K). Collectively, these experiments indicate that OGT inhibition selectively activates p38 phosphorylation and its downstream signaling.Next, we examined the effects of TMG and OSMI-1 on MAPK phosphorylation with or without PE. We found that OSMI-1 induced a nearly 4-fold increase in p38 phosphorylation . Importantly, all three inhibitors induced significant increases in p38 phosphorylation , confirming that the effect was not an epiphenomenon of the particular chemical structure of OSMI-1.Following the finding that reducing protein O-GlcNAcylation with OSMI-1 triggers p38 phosphorylation, we tested if other OGT inhibitors induce p38 phosphorylation. OSMI-4 is structurally related to OSMI-1 and inhibits OGT by competitive docking into the UDP-GlcNAc binding pocket of OGT . In addiE). Given OSMI-1’s impact on potently increasing p38 phosphorylation, we examined whether OSMI-1 was directly affecting p38 O-GlcNAcylation. To that end, we metabolically labeled HEK293 cells with Ac4GalNAlk, concomitantly with exposure to vehicle, TMG, or OSMI-1 . Using streptavidin-mediated pull-down, we found efficient enrichment of biotinylated proteins in Ac4GalNAlk-treated cells but not in Ac4GalNAz negative control cells . Follow-up Western blots showed that p38 was present in pull-downs from vehicle- and TMG-treated cells, but not in OSMI-1–treated cells . These findings further confirm the previous observations that p38 is O-GlcNAcylated at baseline and demonstrate that this can be prevented by OGT inhibition with OSMI-1.As shown earlier, using metabolic labeling with Ac4GalNAlk, we found evidence that p38 is directly O-GlcNAcylated E. Given A). Therefore, we employed siRNA-mediated knockdown of MKK3 and MKK6, to test whether their depletion could impair the observed OSMI-1–induced p38 phosphorylation. Indeed, we found that transfecting NRVMs with siRNAs targeting MKK3 or MKK6 significantly decreased OSMI-1–induced phosphorylation of p38 .Next, we examined whether upstream kinases might be involved in the phosphorylation of p38. There are currently very few commercially available inhibitors for MKK3 and MKK6, which are the immediate upstream activators of canonical p38 phosphorylation . The Ask1-specific inhibitor GS-444217 was tested for its potency to abolish OSMI-1–induced p38 phosphorylation . However, we observed that this treatment did not significantly reduce OSMI-1–induced p38 phosphorylation or the phosphorylation of downstream Hsp27 (A–C). Consistently, when a range of GS-444217 concentrations was used , we did not observe reductions in OSMI-1–induced p38 phosphorylation, and when a higher concentration of GS-444217 was used (10 μM), it potentiated instead of inhibiting OSMI-1–induced p38 phosphorylation and downstream Hsp27 phosphorylation (D–F). Next, we used the Tak1-specific inhibitor Takinib, either alone or in combination with GS-444217, and examined whether this treatment could impair OSMI-1–induced p38 phosphorylation. However, OSMI-1–induced p38 phosphorylation was still observed in the presence of Takinib, and it was further potentiated by the presence of GS-444217 (G and H). Finally, we examined the effect of Mlk3 inhibitor URMC-099. Unexpectedly, this inhibitor was potent in inducing p38 alone (I and J), making it unsuitable for the study of the OSMI-1–induced p38 phosphorylation mechanism. It is unclear why GS-444217 or URMC-099 induces p38 phosphorylation. One potential explanation could be due to off-target effects on other kinases operating upstream of p38 phosphorylation. Another possibility could be that accumulation of the kinase  in its inactive state in the cell elicits secondary pathways leading to the paradoxical p38 phosphorylation and activation.MAP3Ks Ask1, Tak1, and Mlk3 are well-known inducers of MKK3/6 in canonical p38 activation and have been described as potential MKK3/6 activators in cardiac myocytes A. The Asam Hsp27 , A–C. Corylation , D–F. NeS-444217 , G and H38 alone , I and JD and E). A modest reduction in OSMI-1–induced p38 phosphorylation was also observed with Tak1-targeting siRNA, whereas targeting Mlk3 did not reach statistical significance (D and E). Importantly, the reduction in p38 phosphorylation by Ask1-targeting siRNA led to a significant reduction in downstream Hsp27 phosphorylation (F).Given these drawbacks with the MAP3K inhibitors, we resorted to siRNA-mediated knockdown of the kinases. Importantly, we found that transfecting NRVMs with an Ask1-targeting siRNA induced a significant reduction in OSMI-1–induced p38 phosphorylation , D and Eificance , D and Erylation F.Taken together, these findings with siRNA-mediated MAP3K targeting illustrate that OSMI-1–induced p38 phosphorylation is mediated by MKK3/6 MAP2Ks that are in turn activated predominantly by Ask1 with potentially lesser inputs from Tak1 MAP3K.Ncf1) a key regulatory subunit that upon activation translocates from the cytosol to the plasma membrane to associate with NOX2 and activate the production of superoxide. In agreement with a role in this pathway, we found that targeting p47phox significantly reduced the phosphorylation of p38 as well as the phosphorylation of downstream target Hsp27 in OSMI-1–treated cells (A–C). Furthermore, we used siRNA to target the core subunit of NOX2, gp91phox. Similarly, this approach reduced the phosphorylation of p38 and Hsp27 (D–F), further implicating NOX2 as a causal upstream mediator of OSMI-1–induced p38 phosphorylation through the canonical Ask1–MKK3/6 pathway.Ask1 can be activated due to the accumulation of reactive oxygen species (ROS) in the cytosol. One source of cytosolic ROS in cardiomyocytes is the family of NADPH oxidases (NOX), multicomponent enzymes on the plasma membrane that transfer electrons from NADPH to oxygen leading to superoxide production . To examed cells , A–C. Fund Hsp27 , D–F, fuA). These findings indicated that the noncanonical pathway of p38 activation  was potentially subject to regulation by OSMI-1. To address that, we first examined whether the Hsp90 inhibitor geldanamycin could induce noncanonical p38 signaling in NRVMs. Indeed, geldanamycin alone increased the phosphorylation of Tab1, and this effect was additive to the induction of Tab1 phosphorylation caused by OSMI-1 (C and D). Similar to Tab1, geldanamycin increased the phosphorylation of p38 and Hsp27 in a manner that was additive to the effect of OSMI-1 (E–G). These findings indicate that OSMI-1 is not an inhibitor of Hsp90 per se but rather operates parallel to Hsp90 inhibition to further increase p38 autophosphorylation.In addition to the canonical activation of p38 by MAP3Ks, p38 can undergo noncanonical activation by autophosphorylation. p38’s autophosphorylation in cardiac myocytes is kept in check by the inhibitory complex Hsp90/Cdc37, which sequesters p38 from interacting with its activating scaffold Tab1 , 75. AddFig. 8versus OSMI-1/Tab1 si, p < 0.05, J and K), implicating Tab1 as an additional mediator of p38 phosphorylation in OGT-inhibited NRVMs.To further investigate the role of the noncanonical autophosphorylation pathway in OSMI-1–induced p38 phosphorylation, we knocked down Tab1 in NRVMs with siRNA. This approach led to a significant reduction in total Tab1 protein levels by 50 to 55% in the absence or presence of OSMI-1 . NotablyA and B). In contrast to reducing the phosphorylation of Erk1/2, treatment with OSMI-1 for 24 h further increased the phosphorylation of pathologic p38 (A and C). Interestingly, the increase in relative p38 phosphorylation was in part due to decreased protein levels of total p38 (A), suggesting perhaps the recruitment of compensatory mechanisms to restrict further pathologic increase in p38 activity. However, the phosphorylation of the p38-downstream factor Hsp27 was also significantly elevated in OSMI-1–treated cells, consistent with increased p38 activity (A and D). On the other hand, the 24-h OSMI-1 treatment had no significant effect on basal Creb phosphorylation and also did not impact its PE-induced phosphorylation (E and F).Next, we examined the impact of prolonged OSMI-1 treatment on the PE-induced effects on Erk1/2 and p38 phosphorylation. PE-induced phosphorylation of prohypertrophic Erk1/2 was significantly blunted by the 24-h OSMI-1 treatment , A and Bogic p38 , A and Cotal p38 A, suggesactivity , A and Drylation , E and FE), we examined whether the same was true for the 24-h treatment. Indeed, OSMI-1 significantly increased the protein levels of OGT and reciprocally decreased the protein levels of OGA (A–C).Given the increase in protein abundance of OGT observed after a 6-h OSMI-1 treatment E, we exas of OGA , A–C.G and H). This is consistent with the perturbed Erk1/2 and p38 signaling noted above. Furthermore, assessment of total nuclei numbers showed that OSMI-1, combined with PE, was associated with lower numbers (I), indicating that in addition to its growth-arresting effect, OSMI-1 also impairs myocyte viability in PE-stimulated conditions. At the molecular level, OSMI-1 suppressed the PE-induced increase of prohypertrophic genes cTnT and RCan1 (J and K) but still allowed the induction of pathologic markers Anp and Bnp (L and M).Finally, we examined whether manipulating O-GlcNAcylation could impact PE-induced NRVM hypertrophy. Cells were pretreated with OSMI-1 or TMG for 6 h followed by concomitant treatment with PE . PE induced a 2-fold increase in cell size in vehicle- and TMG-treated cells but remarkably, OSMI-1 prevented this hypertrophic growth , G and H numbers I, indicand RCan1 , J and K and Bnp , L and MA close relationship between phosphorylation signaling cascades and O-GlcNAcylation has been previously recognized , 77. ThePreviously, it was found that cardiac myocytes exposed to high glucose underwent increased O-GlcNAcylation, associated with increased phosphorylation of Erk1/2 . A positi.e., reducing O-GlcNAcylation increases p38 phosphorylation), we found that the same pathway was involved .Concerning the other MAPK of interest, p38, we found that OSMI-1 was a potent inducer of its phosphorylation and activity, both at early and late time points (6 h and 24 h). Importantly, OSMI-1 induced the phosphorylation of p38 in basally treated cells and it also had an additive effect on p38 phosphorylation when cells were treated with PE. Thus, decreasing O-GlcNAcylation in cardiomyocytes correlates with increased p38 phosphorylation. This is in contrast with previous work in leukocytes and glomerular mesangial cells, where it was found that increasing O-GlcNAcylation  potentiates rather than decreasing the phosphorylation of p38 , 84. Thee.g., due to hyperglycemia) lead to ROS production by NOX2 in cardiomyocytes , indicating that OSMI-1 might potentially have off-target effects impacting cell viability  cells exposed to OSMI-1 for 24 h did not have lower nuclear counts.TMG was tested against human lysosomal β-hexosaminidase and five other glycoside hydrolases and was found to be highly selective for OGA with a Kar range . Consistin cells . Due to in cells . Similariability . In lighe.g., MEK1/2, Erk1/2, Tab1, p38, Hsp27, and others), it remains an open question whether O-GlcNAcylation on this handful of proteins or on other substrates constitutes the key driving mechanism behind the observed impairment of PE-induced growth of cardiomyocytes. Therefore, further investigation is warranted to identify the most relevant substrate proteins and sites. The work presented here paves the way for such target-focused studies that can ultimately provide new approaches for intervening in the MAPK pathway in cardiac pathophysiology.In summary, we have uncovered a functional relationship between O-GlcNAcylation and MAPK signaling in cardiomyocytes and have identified several potential entry points for regulation. While we provide evidence for O-GlcNAcylation on a number of proteins within the MAPK signaling pathway  were seeded for experiments on culture plates precoated with 0.1% bovine gelatin  and incubated at 37 °C, 5% CO2 for 24 h. Typical seeding densities were 1.0 × 106 cells per well in a 6-well plate for Western blot experiments or 1.25 × 105 cells per chamber in ibidi μ-Slide (Cat. No. 80426) for confocal microscopy. On the next day, the cells were washed twice with prewarmed PBS and the medium was switched to 0% FBS Dulbecco’s modified Eagle’s medium  supplemented with insulin transferrin selenium . Following 24 h of serum starvation, the cells were stimulated with the indicated agonists and/or inhibitors for the specified durations for each experiment . In experiments for gene knockdown, cells were transfected with a lipofectamine/siRNA mix at the time of seeding. Briefly, dicer-substrate short interfering RNA was complexed with Lipofectamine RNAiMax  at room temperature for 15 min according to manufacturer’s specifications and the transfection mix was added to the cell suspension at a ratio of 40 pmol siRNA/1 × 106 cells.NRVMs were isolated from P0-P1 rats of either sex using enzymatic dissociation, according to a standard methodology as previously described , 117. Foe.g., IR Dye 800w goat anti-rabbit, Cat. No. 926-32211, LI-COR) typically at a 5000:1 dilution in 5% BSA in 0.1% Tween20-TBS. For the detection of biotinylated targets, we used IRDye800w Streptavidin . Quantification of band intensities was performed with Image Studio Lite (LI-COR). Normalization for protein loading was performed based on intensities obtained by staining the membranes with REVERT 700 Total Protein Stain .Cells were washed with ice-cold PBS (3×) and lysed in RIPA buffer  supplemented with 2 μM TMG, 1 mM PMSF, and 1× protease and phosphatase inhibitors . Lysates were sonicated and protein concentration was quantified with the bicinchoninic acid assay . For Western blot, protein samples were combined with 50 mM DTT and 1× LDS sample buffer , and 10 μg total protein per lane was resolved by electrophoresis, typically run in NuPAGE Bis-Tris 4 to 12% gradient gels . Proteins were transferred onto nitrocellulose membranes . The membranes were blocked with 5% bovine serum albumin (BSA) in TBS for 1 h at room temperature and then incubated with primary antibodies, typically at a 1000:1 dilution in 5% BSA in 0.1% Tween20-TBS, overnight at 4 °C  onto nitrocellulose membranes . Following blocking with 5% BSA in TBS, membranes were incubated with 5000:1 diluted lectins  overnight at 4 °C. Following extensive washes with TBS-T, the bound biotinylated lectins were detected with IRDye800w Streptavidin. To control for the reactivity of streptavidin with endogenously biotinylated proteins, some experiments were done as negative controls where no lectin was included in the overnight step. These experiments yielded reactive bands at ∼75 and also at 125 and 250 kDa.For the detection of glycans with lectin blotting, we used lectin kit 1  that includes biotinylated conjugates of wheat germ agglutinin, ConA, peanut agglutinin, and 4.5H2O, and either 20 μM or 50 μM CalFluor 647 azide . As a negative control for the click reaction, a set of reactions was set up as above in the presence of 50 μM CalFluor 647 azide, omitting the catalyst Cu/THPTA. The ‘click’ reaction took place for 1 h at room temperature in the dark and was quenched with 5 mM EDTA. Then, samples were mixed with 1× LDS sample buffer and 50 mM DTT and were resolved on a 4 to 12% Bis-Tris gradient gel which was subsequently scanned on the LI-COR Odyssey system using the 700 channel (685 nm infrared laser).The procedure was developed based on protocols described previously , 66, 67.g for 1 min and discarding the flow-through. The protein suspension was then diluted 5-fold in 50 mM Tris HCl, pH 8.0 , added to the washed streptavidin resin bed, and allowed to bind for 2 h at room temperature with end-over-end rotation. Afterward, the suspension was spun to obtain the ‘unbound’ fraction, while the agarose beads were washed 5× with IP wash buffer . To elute biotinylated proteins from the beads, we used a solution containing 80% acetonitrile, 0.1% formic acid, and 0.2% TFA and heating at 65 °C for 5 min . Following ‘click’ reaction and quenching, the reaction mix was cleared by methanol/chloroform precipitation as described above and proteins resuspended in 1.0% SDS. A fraction of this suspension was taken as ‘input’. Next, high-capacity streptavidin-agarose slurry  corresponding to 100 μl settled resin was placed into spin columns  and washed twice in water and then twice in 50 mM Tris HCl, pH 8.0. Washes were done by spinning at 1500or 5 min . The rel6 cells/well after appropriate treatments as indicated in the main text . The treated cells were washed with ice-cold PBS (3×) and RNA was extracted with the RNeasy kit , including a proteinase K digestion step to remove abundant sarcomeric proteins. In-column DNaseI treatment  was used to digest away genomic DNA. For reverse transcription, we used a QuantiTect reverse transcription kit  and an input of 850 ng of total RNA. To control for potentially residual genomic DNA carry-over, reactions without reverse-transcriptase were set up in parallel. Real-time quantitative PCR was performed with the Power SYBR Green master mix  in the presence of 500 nM forward and reverse primers  and then fixed with prewarmed 4% paraformaldehyde for 15 min. The fixative was then washed off with PBS and cells were permeabilized with 0.1% Triton X-100 for 5 min at room temperature. Permeabilization was followed by PBS washes and finally blocking overnight at 4 °C with 1% BSA in 0.1% tween-20 PBS. To stain F-actin, we exposed cells to 5 units/ml phalloidin-Alexa 594  diluted in 1% BSA, 0.1% Tween-20, PBS for 1 h at room temperature. An additional 15-min staining step was done with 5000-fold diluted Hoechst 33342  to stain nuclei, and then cells were washed with PBS. Imaging was carried out with the FV3000RS confocal microscope (Olympus) using the 10× objective  and 405 nm and 561 nm lasers to excite Hoechst and Alexa 594, respectively. Laser power and gain were kept constant across imaging sessions and four fields of view were captured per chamber.https://cellprofiler.org/). The Cell Profiler pipeline extracts nuclei from the blue channel and uses those as primary objects. Next, using nuclei as primary objects, the pipeline identifies the cells from the red channel  using the ‘Propagation’ method. Next, the cytoplasm is identified by subtracting the nuclear area from the cell area. The main output parameters of the pipeline utilized for quantification of the F-actin signal were the ‘Integrated Cytoplasmic Intensity’ measured as the intensity of the phalloidin signal in the cytoplasmic area. The mean integrated cytoplasmic intensity across all cells within a given field of view was used as a single data point. Additionally, the ‘Nuclear Counts’ measured as the total number of nuclei in a given field of view were used as a secondary parameter of overall cell viability/representation in a given field of view to confirm that sampling was performed from areas with comparable cell density.For data analysis, .oir images were uploaded into a Fiji-run custom-made macro to convert composite images into individual.tiff single-color channels (blue and red). The images were then uploaded into Cell Profiler  for any potential outliers. If necessary, one or more outliers were identified using the ROUT test (Q = 1%). To identify statistically significant differences between two groups, we performed unpaired student’s t test. When comparing three or more groups, we used one-way or two-way ANOVA depending on the number of factors . To identify statistically significant differences, the data were assessed for normality of distribution. If the majority of groups within an experiment had normally distributed data, we used the Tukey post hoc test. If the distribution of the data in the majority of groups was not normal, we used the Dunnet post hoc test. In all cases, the α level for statistical significance was 0.05. p values less than 0.05, 0.01, 0.001, and 0.0001 are symbolized as ∗, ∗∗, ∗∗∗, and ∗∗∗∗ respectively.Data were handled and plotted using Microsoft Excel and GraphPad Prism. Values in the graphs are reported as means ± standard error. The numbers of biological replicates are shown inscribed in their respective bars. Data were examined for normality of distribution using the Shapiro-Wilk test and then visually inspected (kyriakos@jhu.edu.All data presented are included in the article and can be available upon request from Kyriakos N. Papanicolaou, Johns Hopkins University School of Medicine, The article contains The authors declare no competing interests.


Castration-resistant prostate cancer (CRPC) with sustained androgen receptor (AR) signaling remains a critical clinical challenge, despite androgen depletion therapy. The Jumonji C-containing histone lysine demethylase family 4 (KDM4) members, KDM4A‒KDM4C, serve as critical coactivators of AR to promote tumor growth in prostate cancer and are candidate therapeutic targets to overcome AR mutations/alterations-mediated resistance in CRPC.co-glycolic acid (PLGA), the US food and drug administration (FDA)-approved material as drug carriers, was synthesized and its antitumor activity alone or with enzalutamide was evaluated using in vivo C4-2B xenografts.In this study, using a structure-based approach, we identified a natural product, myricetin, able to block the demethylation of histone 3 lysine 9 trimethylation by KDM4 members and evaluated its effects on CRPC. A structure-based screening was employed to search for a natural product that inhibited KDM4B. Inhibition kinetics of myricetin was determined. The cytotoxic effect of myricetin on various prostate cancer cells was evaluated. The combined effect of myricetin with enzalutamide, a second-generation AR inhibitor toward C4-2B, a CRPC cell line, was assessed. To improve bioavailability, myricetin encapsulated by poly lactic-Myricetin was identified as a potent α-ketoglutarate-type inhibitor that blocks the demethylation activity by KDM4s and significantly reduced the proliferation of both androgen-dependent (LNCaP) and androgen-independent CRPC (CWR22Rv1 and C4-2B). A synergistic cytotoxic effect toward C4-2B was detected for the combination of myricetin and enzalutamide. PLGA-myricetin, enzalutamide, and the combined treatment showed significantly greater antitumor activity than that of the control group in the C4-2B xenograft model. Tumor growth was significantly lower for the combination treatment than for enzalutamide or myricetin treatment alone.These results suggest that myricetin is a pan-KDM4 inhibitor and exhibited potent cell cytotoxicity toward CRPC cells. Importantly, the combination of PLGA-encapsulated myricetin with enzalutamide is potentially effective for CRPC.The online version contains supplementary material available at 10.1186/s12929-022-00812-3. Prostate cancer (PCa) is now the sixth leading cause of cancer-related deaths in men and the first in the UK and second in the United States . AndrogeNε-methyl group from Nε-methyl-lysine residues and belong to dioxygenases with α-ketoglutarate (α-KG) and Fe(II) as cofactors  for 15 min on ice, followed by 10 min of sonication cycle  on ice. The level of total histone 3 and trimethylated histone H3K9 was analyzed by western blotting using anti-histone 3  and anti-H3K9me3 .3) were seeded in a 96-well plate 1 day before the treatment. Cell proliferation was measured by MTT colorimetric assay according to the manufacturer’s instruction .Cells  for three days. For combination evaluation, a twofold serial dilution of each drug and its combination points above and below its IC50 value was used in an array format. Combination index (CI) values were calculated by CompuSyn software. The values of the combination dose–response curve, which represents drug-effect-shift analysis, were normalized to IC50 values of each single molar concentration of drugs (IC50 eq), and the plots were generated by Graphpad Prism 7.Cells were treated with enzalutamide  and myricetin , or a combined enzalutamide-myricetin mixture. The ICThe single-step precipitation method was employed for the preparation of PLGA formulation containing myricetin. Briefly, myricetin and PLGA were dissolved in an oil solution prepared by 62.1 mg of PLGA , 30 mg of D-α-tocopherol polyethylene glycol 1000 succinate , 3.1 mg cholesterol (Sigma-Aldrich), 3.1 mg of 1,2-dioleoyl-sn-glycero-3-phosphocholine , 6.6 mg 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy-(polyethyleneglycol)-2000]  , and 0.73 mg of myricetin in 1845 μl of DMSO, and added 308 μl oil into the 2150 μl of water dropwise under gentle stirring. The mixture was stirred for 30 min at room temperature, followed by centrifugation at 16,220 rpm to collect the PLGA-myricetin formulation. The PLGA particles were re-suspended by adding 150 μl phosphate saline. The myricetin-loaded and empty formulations were centrifuged at 16,220 rpm for 30 min at 25 °C. The pellet was recovered by DMSO and analyzed by Multiskan SkyHigh Microplate Spectrophotometer (Thermo Fisher Scientific) at 380 nm. The absorbance of the myricetin-loaded formation was normalized by subtracting it with the absorbance of empty particles prepared under the same condition.6 cells) were suspended in a culture medium and mixed with Matrigel Matrix . The mixtures were implanted subcutaneously into six- to eight-week-old male Balb/c nu/nu mice. When the tumor becomes palpable (100 ± 30 mm3), PLGA-myricetin , enzalutamide , and the combined treatment were administered respectively. Tumor volumes were measured for up to 3 weeks. The tumor volume was calculated using the formula: length × width × height × 0.52. Statistical analysis was conducted as described below. The animal protocols for mice experiments were approved by National Tsing Hua University Institutional Animal Care and Use Committee  and carried out under the institutional guidelines with animal welfare standards.C4-2B cells , and enzymatic experiments were performed by student’s https://xenabrowser.net). As shown in Fig. KDM4A, KDM4B, and KDM4C were significantly higher in PCa tumors than in normal tissues. Additionally, KDM4A, KDM4B, and KDM4C were significantly higher in the metastatic tumor specimens than in primary tissues based on the Grasso prostate dataset in the Oncomine database (https://www.oncomine.org/resource/login.html)  , indicating a pan-KDM4 inhibitor.Enzymatic analysis of available compounds revealed that three flavonoid compounds  exhibited potent inhibitory effects, particularly myricetin and quercetin . Kinetic analysis in the presence of increasing concentrations of the H3K9me3 peptide revealed that myricetin blocked the demethylation activity of KDM4A, KDM4B, and KDM4C by a non-competitive mode , indicating that myricetin did not bind to the H3K9me3 peptide-binding region  since we were not able to obtain well diffracting myricetin-liganded KDM4 crystals. As shown in Additional file We further evaluated the inhibitory effects of analogues based on three separate parts of myricetin: (4-phenylpiperazine-1-yl) (phenyl) methanone, (E)-N'-(benzylidene) isonicotinohydrazide, and pyrocatechol . The proliferation of the CRPC cell line C4-2B was also blocked by myricetin (IC50 = 20.6 ± 0.9 μM). In contrast, myricetin had essentially no effect on the immortalized normal RWPE-1 line. A higher level of H3K9me3 was observed in C4-2B cells treated with myricetin than in untreated cells, while a comparable H3K36me3 signal was observed, suggesting that myricetin specifically blocked H3K9me3 demethylation in C4-2B cells and reduced cell proliferation , enzalutamide (p = 0.0002), or the combination of PLGA‒myricetin and enzalutamide (p = 0.0002), led to a significant impairment of tumor growth in xenografts at day 13  and in the enzalutamide treatment (p = 0.0002) at day 21 . A structural difference is noted in the pyrocatechol part of these compounds: myricetin with three -OH groups, quercetin with two, and kaempferol with one. Docking analysis of KDM4B⋅myricetin reveals higher interaction energy  displayed the strongest inhibitory effects, particularly myricetin and quercetin (IC. recently report that flavonoids including myricetin inhibit the enzymatic activity of cytochrome P450 enzymes (CYP1A1 and CYP1B1) [. show that myricetin exhibits the highest inhibitory effect using the 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced CWR22rv1 cells which constitutively expresses CYP1 enzymes. It is thus possible that myricetin also targets CYP1 enzymes to display an anti-carcinogenic effect, which adds a chemoprotective role of myricetin in prostate cancer prevention. Further investigation is required to clarify the biological targets of myricetin and its underlying mechanisms involved in anti-carcinogenic/anti-proliferative activity in prostate cancer.Myricetin, by its nutraceuticals value, has drawn much attention to understanding its molecular mechanisms for medicinal translation. We have shown that myricetin effectively inhibits the growth of prostate cancer lines LNCaP, C4-2B, and CWR22rv1 but not the immortalized normal RWPE-1. Importantly, we have shown that myricetin which targets KDM4A − C curbs tumor growth in C4-2B xenografts, suggesting an anti-cancer agent in prostate cancer. Chaudhary et al50 value toward C4-2B than that of free myricetin, indicating that this form might enhance cellular uptake or penetration [Despite the potential health benefits of myricetin including anti-oxidative, anti-inflammatory, anti-cancer, and antidiabetic activities , its pooetration . RemarkaIn summary, a natural compound, myricetin, was identified as a pan-KDM4 inhibitor able to block demethylation activity (H3K9me3 and H3K36me3). Myricetin exhibited potent cell cytotoxicity toward LNCaP as well as CRPC cells (C4-2B and CWR22Rv1) but not toward normal immortalized cells. Importantly, we provide clear evidence that myricetin encapsulated in PLGA alone or in combination with enzalutamide effectively curbs tumor growth in C4-2B xenografts. Our results highlight a potential intervention strategy for CRPC.Additional file 1: Fig. S1. Schematic diagram of KDM4B inhibitor discovery from a library of natural products. Fig. S2. Analysis of interaction residues in the KDM4B-compound models. (A) The docked KDM4B·myricetin complex. Myricetin, M, occupied the catalytic pocket of KDM4B. (B) 2D diagram of KDM4B·myricetin interaction. Myricetin has seven H bonds, two Pi-anion, one Pi-Pi stacked, one Pi-Pi T-shaped, and one interaction with KDM4B. Fig. S3. The Fig. S4. Superposition of KDM4A and KDM4B structures. (A) The KDM4A⋅63S0 crystal structure (orange) and the modeled KDM4B⋅myricetin structure (slate blue) are superimposed. (B) Superposition of active-site residues based on (A). The bound 63S0 and the nearby residues are shown as thin sticks. Myricetin and the surrounding residues are shown as thick sticks. The oxygen and nitrogen atoms are red and blue, respectively. Table S1. Inhibition effect of selected compounds from TCM database on KDM4B. Table S2. Inhibition effects of analogues based on a myricetin’s fragment, (4-phenylpiperazine-1-yl) (phenyl) methanone, on KDM4A. Table S3. Inhibition effects of analogues based on a myricetin’s fragment, (E)-N'-(benzylidene) isonicotinohydrazide, on KDM4A. Table S4. Inhibition effects of analogues based on a myricetin’s fragment, pyrocatechol, on KDM4A. Table S5. Crystallographic data and refinement statistics.


The paper investigates potentials and challenges during the interpretation of prehistoric settlement dynamics based on large archaeological datasets. Exemplarily, this is carried out using a database of 1365 Neolithic sites in the Weiße Elster river catchment in Central Germany located between the southernmost part of the Northern German Plain and the Central Uplands. The recorded sites are systematically pre-processed with regard to their chronology, functional interpretation and spatial delineation. The quality of the dataset is reviewed by analyzing site distributions with respect to field surveys and modern land use. The Random Forests machine learning algorithm is used to examine the impact of terrain covariates on the depth of sites and pottery preservation. Neolithic settlement dynamics are studied using Site Exploitation Territories, and site frequencies per century are used to compare the intensity of land use with adjacent landscapes. The results show that the main trends of the Neolithic settlement dynamics can be derived from the dataset. However, Random Forests analyses indicate poor pottery preservation in the Central Uplands and a superimposition of Neolithic sites in the southernmost part of the Northern German Plain. Throughout the Neolithic the margins between soils on loess and the Weiße Elster floodplain were continuously settled, whereas only Early and Late Neolithic land use also extended into the Central Uplands. These settlement patterns are reflected in the results of the Site Exploitation Territories analyses and explained with environmental economic factors. Similar with adjacent landscapes the Middle Neolithic site frequency is lower compared to earlier and later periods. Humans always depended on natural resources and environmental conditions and altered Holocene landscapes themselves by different activities such as settlement and infrastructure construction, fire ignition, tilling, herding or mining on various time scales –4. ThereIn principle, this research approach is still being pursued to the present day. However, the methods applied have been improved: (1) additional terrain covariates are used to investigate settlement patterns. Until today, the most frequently studied terrain covariates include elevation, relief position, slope, exposition, distance to rivers, precipitation, temperature and soil varieties , 29. OccUp to now, a large number of diachronic studies dealing with long-term human-environment interactions between the Neolithic and the Middle Ages has been carried out . HoweverThe present study was carried out in the frame of an interdisciplinary geoarchaeological research project that systematically investigates the interplay between the Holocene geomorphological floodplain and slope dynamics, variations of the prehistoric and medieval settlement dynamics and climate changes in large parts of the loess-covered catchment of the Weiße Elster river in Central Germany , 65. We assessing the current state of knowledge on the Neolithic chronology and culture sequence in the study area by reviewing the local history of (field) researchevaluating Early, Middle and Late Neolithic site distributions with regard to archaeological field surveys and modern land useanalysing the interrelation between several terrain covariates, and the depth of Neolithic sites as well as local conditions for the preservation of Neolithic pottery and stone tools, to assess possible influences of geomorphological terrain dynamics and weathering on the archaeological recordidentifying possible biases in the archaeological recordanalysing Early, Middle and Late Neolithic settlement dynamics by using site densities, SETs and site frequenciesdiscussing the results in the light of observations from earlier studies in Central Germany2. Geographically, it is divided into two distinctive landscapes: the northern part belonging to the southernmost part of the Northern German Plain (German: Norddeutsches Tiefland), and the southern part in the Central Uplands (German: Mittelgebirgsschwelle)   66–69].–69.2. GeTo study prehistoric and medieval settlement dynamics in the study area, an archaeological database was built up. We recorded ca. 3100 sites between 2017 and 2020 using archaeological publications and local area files (German: Ortsakten) from the Saxonian Archaeological Heritage Office, the State Office for Preservation of Monuments and Archaeology Saxony-Anhalt, and the Thuringian State Office for Heritage Management and Archaeology. The database covers the time span from the Palaeolithic until 1200 CE, including 1365 sites that can be used to analyse Early, Middle and Late Neolithic settlement dynamics . These wThe identification of changes in the prehistoric settlement pattern requires archaeological data with a high chronological resolution. Therefore, the dating of each site was registered on different levels: epoch, period, phase and sub-phase , 87. TheTo analyse the spatial distribution of the recorded sites, we distinguished between sites with an accurate and a symbolic location. The first category applies to sites that can be located in the field. Symbolic coordinates were assigned to sites that cannot exactly be located due to missing or poor geographic information. In these cases, either the presumed location or the church of the village in whose district the site was discovered was taken as the point of reference .Based on the information given in the local area files and literature, three types of sites were differentiated: (I) Settlements are indicated by pits, post-holes, grinding stones, loom weights, spindle whorls or pottery fragments. (II) Burial sites were defined by the presence of human remains. (III) Stray finds included single objects or assemblages with little or no context information. Accordingly, these finds may be either lost objects or material remains from settlements or burial sites that have not yet been identified as such , 100. ThDue to frequent construction measures and/or archaeological field work in a relatively small area, the residuals of a settlement or burial site may be discovered in multiple spots close to each other. If each of these archaeological observations were taken as hints to an independent site, the erroneous impression of a densely settled area might emerge, when in fact only the intensity of archaeological observations is reflected. Therefore, we aggregated sites that (I) were less than 250 meters apart from each other, (II) associated with the same or a compatible dating , and (III) interpreted in the same or a compatible manner . Since the actual spatial extent of prehistoric sites remains unknown until they have been excavated individually, it is important to acknowledge that this aggregation is ultimately based on an arbitrarily chosen standard. Nonetheless, without this measure analyses of site distribution patterns are prone to result in an overrating of geographical parameters , 102.2  archaeological filters, i.e. the type and intensity of archaeological field work, (II) geographical filters such as modern land use, erosion, colluviation or weathering and (III) culturally intrinsic filters, i.e. the actual understanding of land use strategies in prehistory. The aim of archaeological source criticism is therefore to approximate the degree of distortion caused by the first two filters, and thus to evaluate the overall quality of the archaeological database , 107–109The local state of archaeological research will be presented in the form of key data describing the basic composition of the Neolithic record according to our database. These include the number of recorded sites with respect to their dating, the interpretation of their individual function, and the accuracy of their location as well as their status of publication , 53–59.It is possible to distinguish between intentional and non-intentional modes of site discoveries: The first category includes archaeologically motivated activities such as excavations, field surveys or aerial photography. In the second category, activities without archaeological motivation are subsumed, including construction measures, the extraction of raw materials (mining) or activities related to agriculture and forestry . The disFurthermore, territories of individual collectors might affect site distribution patterns. Therefore, it is necessary to discuss the Neolithic site distribution with respect to areas that were investigated by field surveys or aerial photography , 32, 39.Local geomorphic terrain dynamics are known to affect archaeological site visibility and the preservation of material remains: Firstly, colluviation and alluviation can lead to the superimposition and thus the protection of prehistoric sites at the cost of their visibility , 115. ThTo discuss possible effects of terrain covariates  on the dInitially, a random subset of the original dataset was generated by using the bootstrap method (random sampling with replacement) for each individual tree. Thus, we did not use the entire dataset for learning, but the same number of samples. Usually, the original dataset is represented in the sample set with ~63% of unique and ~37% of redundant samples. Thus, the remaining samples not represented in the training set are called out-of-bag (OOB) samples and used for validation.A second-level random component was used to build the individual tree. Therefore, at each node the best binary split on the training data was performed, based on a random subset of predictors . The numIf used for a classification, the results from each decision tree were aggregated on the basis of a majority vote.1-measure [Formula 5].The algorithm provides a distinct set of accuracy measurements to evaluate the performance of the RF model . These aThe out-of-bag error rate is calculated as follows:The Kappa value measures the agreement between predictions and observed cases. This is achieved by comparing the overall accuracy to an expected random chance accuracy. Therefore, the Kappa score is particularly useful on classifications with imbalanced data –124. TheCohen’s Kappa is calculated using the following formula :κ=Pr(aWhere Pr(a) represents the actual observed agreement (true positives) and Pr(e) chance agreement.1 represents column 1 marginal, cm2 column 2 marginal, rm1 row 1 marginal, rm2 row 2 marginal and n the number of observations.Where cm1-measure based on the confusion matrix, which are important measures for information retrieval , i.e. the impact of each variable on the prediction error when it is not taken into account , 128. Th1-measures. The statistical analyses were carried out using the packages randomForest . Available from: https://github.com/taiyun/corrplot.Here, we used R package (TIF)Click here for additional data file.S1 DatasetThis table contains all Neolithic sites from our study area and can be used to reproduce our results with regard to the status of research.(TXT)Click here for additional data file.S2 DatasetThis table can be used to reproduce our Random Forests analyses.(TXT)Click here for additional data file.S3 DatasetThis table can be used to reproduce our SET analyses.(TXT)Click here for additional data file.S1 References(DOCX)Click here for additional data file.


Coenzyme Q  is an obligate component of the mitochondrial electron transport chain. COQ7 is a mitochondrial hydroxylase that is required for CoQ biosynthesis. COQ7 belongs to di-iron carboxylate enzymes, a rare type of enzyme that carries out a wide range of reactions. We found that manganese exposure of mouse cells leads to decreased COQ7 activity, but that pre-treatment with cobalt interferes with the inhibition by manganese. Our findings suggest that cobalt has greater affinity for the active site of COQ7 than both iron and manganese and that replacement of iron by cobalt at the active site preserves catalytic activity. COQ7 is the penultimate enzyme in the coenzyme Q (CoQ) biosynthetic pathway. CoQ, also known as ubiquinone, is an endogenously synthesized, highly hydrophobic, lipid-soluble molecule found in virtually all animal cells as well as in plants and microbes . It is a pivotal component of the mitochondrial respiratory chain, where it functions as an obligate electron carrier, enabling ATP generation from oxidative phosphorylation . Loss of sufficient CoQ biosynthesis is associated with diverse severe pathologies . The CoQ biosynthetic pathway is highly conserved from yeast to humans. The final steps are carried out in the mitochondria . A deficiency of COQ7 in animal cells results in accumulation of its substrate, demethoxyubiquinone (DMQ) . Structurally, COQ7 has been identified to be a carboxylate-bridged di-iron enzyme . Consistent with this, we previously reported that exposure to the metal-chelating agent clioquinol inhibits CLK-1/COQ7 activity due to iron chelation .2+ions results in CoQ loss and accumulation of DMQ, which indicates COQ7 inhibition . CoQ is composed of a benzoquinone ring and a lipophilic poly-isoprenoid tail whose chain length varies according to species. Mouse cells have mostly CoQ9(9 referring to the number of isoprene units in the tail) but also a small amount of CoQ10. We observed similar effects of Mn2+exposure on CoQ9and CoQ10. Of note, as we extracted DMQ9and CoQ9from the same cells, we simply used the DMQ9to CoQ9ratio as the best measure of COQ7 enzymatic activity. As shown inFig. 1B, Mn2+treatment results in a dose-dependent increase of the DMQ9/CoQ9ratio and 2-fold lower CoQ9levels relative to protein content, while no significant effect on cell viability was observed. In contrast, no other metals tested, including Cu2+, Mg2+, Co2+, and Zn2+ were found to cause accumulation of DMQ .In this present study, we report that exposure of RAW264.7 cells to Mn2+does not also affect the total level of CoQ by some other mechanism as well. The overall consequences on cell and organism function of CoQ deficiency induced by excess Mn2+exposure however remain to be determined in future studies. Divalent ions of Mn and of Fe are similar in chemistry, and both tend to coordinate with six ligands and form octahedral complexes (Cotruvo and Stubbe 2012). This makes it difficult for proteins to discriminate between Fe2+and Mn2+on the basis of structure alone (Cotruvo and Stubbe 2012). Therefore, we speculate that inhibition of COQ7 activity by Mn2+was due either to interference with Fe2+uptake and/or by binding to COQ7 instead of Fe2+. To test this, we first determined the effect on COQ7 activity of simultaneous exposure to Mn2+and Fe2+. We found that Fe2+co-treatment significantly prevents the inhibitory effect of Mn2+on COQ7 activity . To explore this further, we pretreated the cells for 3h with Fe2+ to increasethe intracellular availability of Fe; then, after removal of Fe2+from the media, the cells were treated with Mn2+for a total of 24h before CoQ extraction. Under these conditions, the cells should have plenty of Fe2+, yet this had no effect on the inhibitory effect of Mn2+on COQ7 . This result suggests the possibility of a preference for Mn2+over Fe2+for binding to the metal binding site of the enzyme.Our finding suggests that Mn toxicity acting on COQ7 could lead to CoQ deficiency in vivo. Note however that our experiments cannot conclude that Mn2+also prevents Mn2+from inhibiting COQ7, perhaps even more efficiently than Fe2+. As with Fe2+, this could be due to competition with Mn2+ for uptake. However, surprisingly, and in contrast to what we saw with Fe2+, pre-loading cells with Co2+prevented the inhibitory effect of Mn2+on COQ7 . All the other metals tested, however, including Cu2+, Mg2+, Ni2+and Zn2+were, like Fe2+,unable to block the effect of Mn2+. Co2+has similar chemical properties as those metals in the same group on the periodic table, including Mn2+and Fe2+. However, it occurs much less frequently in metalloproteins, likely due to its low abundance in nature (Okamoto and Eltis 2011). Mis-metalation with Co2+at protein binding sites has been shown previously (Okamoto and Eltis 2011). We postulate that the way Co2+prevents Mn2+from inhibiting COQ7 is that Co2+can physically and functionally replace the lower order metals (Fe2+and Mn2+) at the catalytic site of COQ7, at least under the condition of an excess of Co2+. More specifically, as Co2+lies towards the tighter binding end of the Irving–Williams series compared to Mn2+, the binding of Co2+to the di-iron cluster of COQ7 would render subsequent Mn2+treatment less effective at inhibiting COQ7, meaning that Mn2+is not be able to compete out Co2+due to its relatively lower affinity for binding. Treatment of human embryonic kidney cells (HEK-293) with Mn2+also led to reduced activity of COQ7, and Co2+pre-loading prevented the effect .In addition, we found that co-treatment with Co2+from the medium, addition of Fe2+in the medium immediately after Mn2+washout significantly accelerates the recovery of COQ7 activity . This is consistent with our hypothesis that inhibition of COQ7 activity by Mn2+is by substituting for Fe at the di-iron active site of the enzyme. Co2+addition shows the same effect , which is again consistent with the notion that Co2+is as functional as Fe2+at the active site of COQ7. In this model, after interruption of Mn2+ treatment, the presence of Co2+would allow Mn replacement by Co, leading to rapid restoration of COQ7 activity. As expected, the other tested metals, including Cu2+, Mg2+, and Zn2+showed no effect on the course of the recovery of the COQ7 activity after Mn2+exposure was terminated.Furthermore, we observed that, while COQ7 activity only slowly recovers after the removal of Mn2+is associated with a change of COQ7 expression level. The COQ7 level was found to be higher in Mn2+-treated cells and the change could not be prevented by co-treatment with Co2+, despite its ability to maintain COQ7 functionality . The effect of Mn2+is specific for COQ7 as the level of PDSS2 was not affected . PDSS2 is another COQ-biosynthetic enzyme, which catalyzes the assembly of the polyisoprenoid side chain. Co2+treatment at a relatively high dose (250 µM), also leads to elevation of COQ7 concentration. We speculate that the increase of COQ7 level after Mn2+or Co2+exposure could be due to increased COQ7 protein stability because of Mn or Co at the enzyme’s active center.Lastly, we used Western blot analysis to determine whether the inhibition of COQ7 activity by MnMetal ions play key structural and functional roles for nearly half of all known proteins. A better understanding of how metalation is controlled and how it modulates the functions of proteins is important. COQ7 presents a unique opportunity to look at in vivo activity with different metals because of the ease with which the reaction products can be monitored in vivo. The sensitivity of cellular COQ7 to metals appears to be greater in the RAW264.7 macrophage line than in HEK-293 cells, possibly because of a greater propensity of macrophages to take up and accumulate metals. Nevertheless, we believe that the present study is the first to describe metal swapping in a di-iron carboxylate enzyme, which are enzymes that are of commercial and ecological interest . It warrants further studies and research into the metalation and mismetalation of di-iron carboxylate proteins.Cell culture2. For single reagent treatments or co-treatment experiments, reagents were added once to the culture in 6 well plates and cells were harvested after 24h of incubation. In pre-treatment experiments, cells were exposed to different metal ions for 3h, washed with phosphate buffered saline (PBS) and then treated with MnCl2for 24 hours. To test the effects of different metal ions on recovery of COQ7 activity after MnCl2removal, MnCl2- supplemented medium was removed after 24h of incubation. Following washes in PBS, fresh media containing different metal salts were added and CoQ was extracted at 3h after the Mn2+washout. All chemicals were obtained from Sigma-Aldrich.RAW264.7 were cultured in high glucose DMEM (Dulbecco's modified Eagle's medium) supplemented with 10% fetal bovine serum and 1% antibiotic/antimycotic mix at 37°C in a humidified atmosphere of 95% air and 5% COExtraction and high-performance liquid chromatography (HPLC) determination of CoQ9peak was identified using pure CoQ9. The DMQ9peak was identified by comparing to quinone extraction fromCoq7knockout mouse embryonic fibroblasts (MEFs) .CoQ extraction and quantitation using HPLC were performed as described previously. Briefly, cell lysates were prepared in a radioimmunoprecipitation buffer  and CoQ were extracted with ethanol and hexane (v/v 2/5). An Agilent 1260 Infinity LC system equipped with a quaternary pump (G7111A) and a variable wavelength detector (G7114A) was used. Chromatography was carried out on a reverse-phase C18 column  with 70% methanol and 30% ethanol as the mobile phase at a flow rate of 0.3 mL/min. The detector was set at 275 nm. The CoQWestern blotProtein samples were prepared in RIPA buffer, and 50 µg of lysates were subjected to 12% SDS-PAGE and visualized by using antibodies against COQ7, PDSS2, SOD2, or Porin/VDAC. Detection was performed with ECL reagent and exposed to x-ray film.Statistical analysisp-value of < 0.05 was considered significant for all tests.Statistical analysis and graphical presentation were carried out using GraphPad Prism 9.2 software. A


Thus, this study aimed to perform 3.5-mm-diameter C2 pedicle screw placement on models for performance assessment of CTA-based preoperative screw preclusion.We enrolled 152 patients who underwent CTA of the cervical spine between April 2020 and December 2020. Transverse pediculoisthmic width (TPW), oblique pediculoisthmic width (OPW), minimum pediculoisthmic diameter (MPD), internal height, and isthmus height were measured preoperatively. Subsequently, 1:1 3D-printed bone models were created, and a 3.5-mm-diameter C2 pedicle screw was placed on the models. All 3D-printed models underwent postoperative CT multiplanar reconstruction to evaluate the screw trajectory for the performance assessment of CTA-based preoperative screw preclusion.The ROC curves of the MPD, TPW, OPW, Internal height and Isthmus height showed that the optimal cutoff values for each of the five groups were measured values of 4.78, 4.44, 4.37, 4.22 and 5.59 mm, respectively. The AUC, sensitivity, and specificity of MPD were 0.992, 95.1% and 100%, respectively. The MPD had higher metrics than the TPW , internal height , and isthmus height . We found no evidence of a difference between MPD and OPW in terms of the AUC and sensitivity .C2 pedicle screw placement on 3D-printed models is useful for performance assessment of CTA-based preoperative screw preclusion. MPD measurement with CTA multiplanar reconstruction showed the best performance for judging acceptable or unacceptable screws. However, the definition of HRVA could be modified by a 4.2 mm-internal height or by measuring only the isthmus height for judging the preclusion of C2 pedicle screw placement. The C2 pedicle screw is the preferred technique for posterior atlantoaxial internal fixation because of its good biomechanical properties and high fusion rate –3. The pRecently, 3D printing technology has a large spectrum of applications in upper cervical spinal surgery including the creation of patient-specific drill guide templates and 3D-printed vertebral body implants –17. FurtOne-hundred-fifty-two patients who underwent CTA examination of the cervical spine at our hospital between April 2020 and December 2020 were enrolled, including 87 male (57.2%) and 65 female (42.8%), with an average age of 59.36 ± 13.73 years (range 16–87 years) at the time of CT scanning.The DICOM data were entered into RadiAnt DICOM Viewer software . Preoperative evaluation was performed as follows: transverse PIC width (TPW) was measured on the orthogonal axial plane Fig. A; internDICOM data were imported into the Mimics software  to reconstruct and generate a 3D bone model of the C2 vertebrae in the STL format. 1:1 physical bone models were created using a 3D printer  and 3D printing materials . The BoneMatrix materials can accurately mimic bone density characteristics, biomechanical characteristics and behave like native bone when force is applied such as drilling, reaming, or sawing. A 3-mm-diameter electric drill was used to drill slowly along the axis of the pedicle. A spherical probe was used to determine the integrity of the screw trajectory. A screw tap was then used to create a screw hole, and a 3.5-mm-diameter C2 pedicle screw was placed in the hole, according to the screw trajectory planned preoperatively  and MedCalc for Windows . The areas under the receiver operating characteristic curves (AUCs) with 95% CIs were compared using the DeLong test for the MPD, TPW, OPW, internal height, and isthmus height. The performance metrics  of MPD, TPW, OPW, internal height, and isthmus height were evaluated and compared using McNemar’s test. The optimal cutoff value for each CTA method was set at the highest Youden index to discriminate between acceptable and unacceptable screws. P < 0.001). The mean isthmus height (5.48 ± 1.49 mm) was greater than both minimum PIC diameter  and internal height . No significant differences were found between the minimum PIC diameter, oblique PIC width, and internal height. A significant difference was noted between the left and right MPD, OPW, and isthmus height measurements (P < 0.05).The data for the radiographic measurements and breached screws in the models are summarized in Table P < 0.05). The magnitude of the breach was classified as I in 22 cases (56.42%), II in nine cases (23.08%), III in five cases (12.81%), and IV in three cases (7.69%).One hundred and fifty-two models underwent 3.5-mm-diameter C2 pedicle screw placement, with 39 total breaches (12.83%) and 27 breaches (69.23%) on the right side, which was higher than the 12 breaches (30.77%) on the left side , internal height , and isthmus height . The specificity of MPD was significantly higher than that of OPW , although we found no evidence of a difference in the AUC and sensitivity between MPD and OPW  , 95.1% (252 of 265 screws), 100% (39 of 39 screws), 100% (252 of 252 screws), and 75% (39 of 52 screws), respectively. MPD had higher metrics than TPW  breached the cortex of the C2 PICs, 31 of 39 screws (79.5%) were located < 50% of the screw diameter beyond the cortical edge, and 27 of 39 screws (69.23%) had cortical breaches on the right PICs, which matched the narrower measurements of the right MPD, OPW and isthmus height.We regarded whether there was cortical breach after screw placement as a binary classification, the performance assessment of MPD, TPW, OPW, internal height and isthmus height was done with the ROC curve. MPD achieved an AUC of 0.992 and a sensitivity of 95.1% in the judgment of acceptable and unacceptable C2 pedicle screws, which exceeded those of the TPW, internal height, and isthmus height. Previous studies have reported that preoperative measurements with multiplanar CT reconstruction can be used to accurately evaluate the suitability of C2 pedicle screw placement. Yuan et al.  first reControversy continues over the criteria for defining a narrow pedicle. Maki et al.  showed tThe high-riding vertebral artery was originally defined based on the feasibility of C1-C2 transarticular screw placement , 21. AccIn terms of limitations, screw insertion on 3D-printed models was performed by several senior surgeons, and different experiences may have affected the accuracy of the screw placement. When screw malposition results from technical problems, the models are reprinted and the screws are reinserted. Secondly, although the BoneMatrix materials are able to mimic bone density and replicate cortex thickness, the X-ray absorptivity between model cortex and model bone marrow is indistinguishable, and we considered it inessential for evaluating the screw trajectory and the screw diameter beyond the cortical edge. Thirdly, we did not consider the direction of screw insertion on CTA MPR images preoperatively and postoperatively. However, an anatomical study comparing the measurements of different CT methods was not the goal of this study. The first step of the current study was to verify the accuracy of the CTA MPR. Future research should focus on planning an ideal starting point and screw trajectory using CTA MPR, which is expected to be clinically applicable. Despite these limitations, to our knowledge, this study is the first to perform C2 pedicle screw placement on 3D-printed bone models to assess the performance of CTA-based preoperative screw preclusion.C2 pedicle screw placement on 3D-printed bone models is useful for performance assessment of CTA-based preoperative screw preclusion. MPD measurement with CTA multiplanar reconstruction showed the best performance for judging acceptable or unacceptable screws. However, the definition of HRVA could be modified by a 4.2 mm-internal height or by measuring only the isthmus height for judging the preclusion of C2 pedicle screw placement.


Increasing ocean surface waves owing to decreasing sea ice in the Sea of Okhotsk (SO) is a major concern. However, long-term trends of ocean surface waves in the SO have not yet been investigated. Therefore, the long-term trends of wave power ( Knowledge of long-term signals of ocean surface waves is important for efficient management of coastal communities4 and offshore operations5. Thus, as climate change impacts are becoming increasingly prominent, long-term changes in ocean surface waves have received increasing attention8.Ocean surface waves can both serve as a climate proxy9. Simulating Waves Nearshore (SWAN10) and WAVEWATCH III (WW311) are third-generation spectral wave models developed based on the radiative transfer equation for global and regional wave forecasts; additionally, wave–ice parameterization models have been implemented for sea ice-induced wave dissipation. Recent studies investigated the quantitative impacts of sea ice on wave fields using these model simulations with and without sea ice in the marginal ice zones, such as in the Barents Sea and the Gulf of Bothnia12, SO13, and Indian Ocean14, ranging from one storm event to seasonal variations. Additionally, Iwasaki15 reported that sea ice significantly reduced the climatological mean and inter-annual variability (standard deviation) of wave power  has reported that its maximum value has been decreasing at a rate of 3.5% per decade (https://www.data.jma.go.jp/gmd/kaiyou/english/seaice_okhotsk/series_okhotsk_e.html). Thus, there is a great concern that ocean surface waves will increase due to the decrease in sea ice in SO. However, to the best of our knowledge, previous studies have not yet focused on the SO and not evaluated the long-term trends of ocean surface waves, although several studies have been conducted in the Arctic Sea20.The Sea of Okhotsk (SO) is known as a seasonal sea ice region, where sea ice appears during winter (December–February), after which it gradually starts receding Fig. . In gene24. For example, coastal inundation, erosion, and flooding depend not only on the wave height but also on wave period25. Therefore, the present study focused on Therefore, the present study aimed to evaluate the long-term trends of wave fields in the SO for 40 years and reveal the quantitative contributions of the surface winds and sea ice to the long-term trends. Recently, rather than wave height, mentclass2pt{minimmentclass2pt{minim26 and a satellite-derived sea ice product from the National Oceanic and Atmospheric Administration Optimum Interpolation (NOAA OI) sea surface temperature version 2.128 (hereafter denoted as JRA55-CTL). In addition to using JRA55-CTL, other two-wave model simulations were performed from the WW3 using surface wind and sea ice products acquired from the European Centre of Medium-range Weather Forecast Reanalysis-5 (ERA5)29 (hereafter denoted as ERA5-CTL) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA2)30 of National Aeronautics and Space Administration (hereafter denoted as MERRA2-CTL). Additionally, as simulations removed the effect of the long inter-annual time scale of sea ice on wave fields, three simulations were performed (denoted as the Clim-ICE run) (bottom part of Table Three wave model simulations were performed as the control (CTL) run  was used in the present study. Additionally, a simple diffusive scattering model (denoted as IS1 in WW3) was selected. The frequency space was 0.035–1.1 Hz, which was logarithmically discretized into 30 increments, and the directional resolution was 10°. GEBCO2020 (https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2020) was used to provide the bottom topography and coastlines.The details of the WW3 setup were the same as mentioned in Iwasaki26, the ERA529, and the MERRA230) and one satellite-derived sea ice product (NOAA OI28) were used for forcing wave model experiments). As shown in Table 37 and Antarctic38. The three Clim-ICE runs were conducted using hourly climatological data  of the ice concentration. The climatology of the sea ice concentration determined using three Clim-ICE runs was derived for the entire computation period . However, the wave simulations of 40 years using the JRA55 surface wind product and NOAA OI sea ice product  represented the model results by Iwasaki15.Table Further, the wind and sea ice concentration data were linearly interpolated to the same spatial grid in the wave simulation of both domains. Additionally, in this study, the sea ice area indicated the total area of grid points for the sea ice concentration in the SO and not the sea ice extent. Moreover, monthly SLP data from the three reanalysis products  were used in this study, although they were not used for wave calculations.–3) is the density of seawater and –2) is the gravitational acceleration.https://www.mlit.go.jp/kowan/nowphas/index_eng.html), provided by the Ports and Harbors Bureau, Ministry of Land, Infrastructure, Transport, and Tourism. Supplementary Table 39, the simulated To validate the model results, two hourly data of mentclass2pt{minimTo obtain the simulated values at the buoy positions, the fields were linearly interpolated to the positions using the surrounding four grid values acquired from Domain 2. Statistical parameters of bias, root mean square error (RMSE), and correlation coefficient (Supplementary Information.


Cairina moschata) plasma hydrolysate (MDPH) were investigated. According to the general research protocol for bioactive peptides, the crude ACE–IPs of Muscovy duck plasma were separated and purified by ultrafiltration, gel chromatography and reversed-phase high-performance liquid chromatography (RP–HPLC). Then the components with the highest ACE inhibition potential were selected for identification. Finally, the inhibition mechanism was explored by molecular docking and in silico simulated digestion. A total of 121 peptides was detected, and five were screened for synthesis verification and molecular docking. The peptide VALSSLRP revealed high ACE inhibitory activity (91.67 ± 0.73%) because this peptide bound tightly to the S1′ pocket and formed 3 hydrogen bonds. Meaningfully, this work provides some new information about the generation of ACE–IPs derived from duck blood plasma.In this study, angiotensin-converting enzyme inhibitory peptides (ACE–IPs) derived from Muscovy duck ( Cairina moschata), introduced from abroad to mainland China, is a special type of waterfowl that belongs to different species from ordinary domestic ducks [Muscovy duck   demonstrACE inhibition is one of the fundamental approaches to treating hypertension and associated diseases through maintaining angiotensin I, angiotensin II or aldosterone level to balance the renin–angiotensin system (RAS) and kallikrein–kinin system (KKS) . HoweverTenebrio molitor), in yeast extracts and in spirulina (Arthrospira platensis) [It has been reported that ACE–IPs were isolated from beans, cereal, vegetables, meat, milk and eggs ,13,14,15atensis) ,17,18. BThis study aimed to isolate and identify novel ACE–IPs and explore the interactions between purified peptides and ACE. To achieve this objective, the plasma’s crude peptides were successively separated and purified by ultrafiltration, gel filtration chromatography and reversed-phase high-performance liquid chromatography (RP–HPLC). The samples were identified using Nano-LC–ESI–MS/MS to obtain the amino acid (AA) sequences of the peptides. After screening, the selected peptides were synthesized, and their ACE inhibitory activity was verified. Additionally, molecular docking and in silico simulated peptide digestion were employed to reveal the molecular interactions between single peptides and ACE. Considering the synthetic peptides’ activity, molecular docking and in silico simulated peptide digestion results, the ACE inhibitory mechanism of peptides was also analyzed. The production and utilization of ACE–IPs from Muscovy duck plasma (MDP) could reduce economic costs and environmental conservation.Cairina moschata) blood was obtained from Anqing Yongqiang Agricultural Science and Technology Co., Ltd. . Alkaline protease  was purchased from Yuanye Biotechnology . Bicinchoninic acid (BCA) protein assay kit  was employed for protein content determination. ACE from rabbit lung (0.1 U/mL) and hippuryl–L–histidyl–L–leucine (HHL) was purchased from Sigma-Aldrich . Acetonitrile, formic acid and triethylamine were HPLC grade, while other reagents were of analytical grade.Muscovy ducks (w/v). The collected blood was transported to the laboratory under 4°C. The whole blood was immediately centrifuged , which was separated into MDP (protein concentration was determined to be 40.11 ± 1.31 mg/mL) and blood cells.Muscovy duck blood was collected in sterilized glass bottles that contained sodium citrate solution as an anticoagulant . The supernatant (termed “MDPH”) was stored at 4°C for further analysis.MDP was adjusted to pH 10.5 with 1 M NaOH and pre-incubated  in a water bath, then hydrolyzed by alkaline protease (2500 U/g) at 65°C for 5.85 h. During the enzymatic hydrolysis, the solution pH was adjusted to 10.5 every 30 min. After the reaction, the pH was adjusted to 7 with 1 M HCl, and alkaline protease was inactivated for 10 min using a boiling water bath. Then the solution was rapidly cooled in an ice bath and centrifuged  and the test sample, respectively.The method of Shi et al. was employed to measure the ACE inhibition activity . ACE, HHMDPH was ultrafiltered by a 3 kDa molecular weight (MW) cut-off (MWCO) membrane . Then two MDPH fractions with UF-I (<3 kDa) and UF-II (>3 kDa) were collected. Each fraction was freeze-dried and stored at −20 °C. Then, these two fractions were tested for ACE inhibitory activity to select the one showing higher activity for further purification and analysis.The procedure of Li et al. was explored with minor changes . The fraThe procedure outlined by Cao et al., was employed with minor adjustments . The mosm/z) for HCD fragmentation. The test conditions were as follows: Spray voltage positive ion 2000 V, ion transfer tube temperature 275°C, S-Lens RF level 60, scan range 350–1800 m/z, orbitrap resolution 60 K, collision energy 32%, isolation window 1.6, analysis duration 60 min. MS test raw file (Raw File) with software Mascot 2.2 was used to search the corresponding database  to obtain the protein results.The AA sequences of the fraction exhibiting the strongest inhibition potential were identified by HPLC  coupled with the Q-Exactive mass spectrometer . The purified peptide fraction was applied to a liquid chromatographic column . Mobile phases were: (A) ultra-pure water containing 0.1% formic acid; (B) 84% acetonitrile containing 0.1% formic acid. Elution condition was: 0–50 min, B 4–50%; 50–54 min, B 50–100%; 54–60 min, B 100%. The loading volume was 5 μL, and the flow rate was 0.3 mL/min. The capillary temperature and ionization voltage were set to be 200°C and 2.2 kV, respectively. Afterwards, mass spectrometry (MS) was performed using a Q-Exactive mass spectrometer. The data was acquired using a data-dependent top10 method dynamically choosing the most abundant precursor ions from the survey scan (300–1800 http://crdd.osdd.net/raghava/ahtpin).The sequence of peptides identified in the MDPH was further screened for ACE–IPs through the electronic prediction of antihypertensive peptides on the AHTPin online website , with purities >98%.http://www.rcsb.org), and the 3D structures of the identified peptides were prepared by UCSF Chimera 1.16 . The AutoDock Tool was used to add hydrogens, remove water, transform the files from .pdb format to pdbqt format and determine the target’s docking box. Molecular docking between ACE–IPs and ACE (PDB ID: 1O86) was performed using AutoDock Vina. The best binding mode was chosen based on the lowest binding energy. Pymol software was used to analyze the interactions between ACE and the peptides.The ACE crystal structure (PDB ID: 1O86) was derived from RCSB Protein Data Bank  at 37°C for 2 h. After the reaction, enzyme was inactivated for 10 min using a boiling water bath and rapidly cooled in an ice bath. Then the solution was centrifuged and freeze-dried to measure ACE inhibitory activity.MDPH i et al.  with minhttps://web.expasy.org/peptide_cutter/). The hydrolysis of identified peptide sequence in MDP was predicted using the known enzymatic cleavage sites. For this study, pepsin, chymotrypsin and trypsin were chosen for the analysis. Eventually, the BIOPEP database was used to identify the ACE–IPs of the hydrolysis fragments derived from digestion.This was performed by ExPASy PeptideCutter , while the significant variation was identified by Duncan’s multiple range test at a 95% confidence level. All the data were presented as mean ±SD. Graphical illustrations were derived by Origin 9.0.p < 0.05) higher for UF-Ι when compared to UF-II. As expected, ultrafiltration of MDPH led to a decrease in ACE inhibitory activity. The reason may be that UF-II contained more impurities, causing the decrease of ACE inhibitory activity. This result was in agreement with previous research, which indicated that the hydrolysate of <3 kDa from soybean protein isolate exhibited significantly (p < 0.05) the highest inhibition of ACE when compared to other fractions, and the ACE inhibitory activity increased with decreasing MW [Ultrafiltration was used to separate, purify and enrich the components of solutions according to the MW of samples . MDP wasasing MW .p < 0.05) poorest ACE inhibitory activity. MW is one of the most important factors affecting the ACE inhibitory activity of peptides. It is reported that the peptides with strong ACE inhibitory activity had small MW. ACE–IPs usually include 2-12 AAs [Meretrix lusoria) with the smallest MW purified by G-25 gel filtration chromatography presented the highest ACE inhibitory activity [To further purification, gel filtration chromatography was used to separate UF-Ι using Sephadex G-25. Gel filtration chromatography was based on MWs to separate peptides or proteins. Chromatograms of UF-Ι are shown in 2-12 AAs . Those lactivity . TherefoRP–HPLC was one of the most common methods for peptide separation, according to the difference in hydrophobic interactions between fractions. Due to its high sensitivity, wide applicability and column efficiency, RP–HPLC was used to purify small molecular samples . ComponeTo further explore the ACE inhibition mechanism, B6 was selected for identification. The combination of liquid chromatography and MS (LC–MS) has been the most promising method for identifying the AA sequences of peptides because of its good reproducibility, high sensitivity and high efficiency. Electrospray ionization (ESI) is one mass spectrometer instrument with high sensitivity and low detection limit, coupled to LC–MS for identifying peptides ,25. In tp < 0.05) higher than the other four peptides, reaching 91.67 ± 0.73% at 1 mg/mL (IC50 = 0.039 mg/mL). Nonetheless, the inhibitory activity of the other four peptides was less than 50% at the same concentration. QFQPGFSSS and TTPSYVAFTDTER had significance (p < 0.05) in the ACE inhibitory activity of 40.91 ± 5.65% and 26.71 ± 1.91%, respectively. There was no variation (p > 0.05), and hardly any ACE inhibitory activity of STGEAFVQFASQUEIAEK and NPDDITNEEYGEFYK with AA sequence lengths of 17 and 15, which were 1.57 ± 1.15% and 2.81 ± 1.03%, respectively.The length of the peptides was 8–25, and the mass was between 600 and 2600 Da a. The maMW is essential in affecting the inhibitory activity of ACE–IPs. The ACE–IPs usually include 2-12 AA residues . Natesh Additionally, the hydrophobicity of peptides is another major factor that significantly influences ACE inhibitory activity. The C-terminal domain of ACE is the leading catalytic site of Ang I, which has three catalytic active sites. The hydrophobicity of these catalytic sites is evident, resulting in the formation of the hydrophobic environment of the ACE C-terminal domain . At the Molecular docking is performed to select the best structure of receptor–ligand by calculating the interaction force between receptor and ligand. This optimizes the spatial skeleton of peptides, such as AA side chain, ligand position and bonding angle, to achieve the lowest binding energy . In receThe above result showed that VALSSLRP had strong hydrophobicity and could combine with ACE to inhibit its activity. This study explored the interactions of ACE and peptides using AutoDock Vina 1.1.2, the software to dock the peptide with ACE (PDB ID: 1O86). The docking result was saved according to the conformation of the lowest binding energy, and the interaction mode was presented by Pymol software to analyze the ACE inhibition mechanism. Three-dimensional binding mode and intermolecular interaction of peptide VALSSLRP, QFQPGFSSS, TTPSYVAFTDTER, STGEAFVQFASQEIAEK and NPDDITNEEYGEFYK to ACE were shown in 2+ and three active pockets, S1, S2 and S1′, respectively, which are more effective than other interacting sites. Ala354, Glu384 and Tyr523 in S1; Gln281, His353, Lys511, His513 and Tyr520 in S2; and Glu162 in S1′ are key active sites related to inhibiting ACE activity [2+ can form tetrahedral ligands with ACE AA residues His383, His387 and Glu411, which play a vital role in the stability between receptor and ligand. The docking result showed that VALSSLRP could form three hydrogen bonds with ACE, among which the Arg7 in the peptide chain can form hydrogen bonds with Glu162 in S1′ active pocket, indicating that the peptide can closely bind with S1′ pocket. At the same time, Ala2 and Ser4 in the peptide can form hydrogen bonds with Ala356 and His383 in ACE protein, respectively, strengthening the force between ligand and receptor protein, which may have caused the peptide’s low binding energy. Although some AA residues are not in the ACE active pockets, many studies have shown that these AA residues can form hydrogen bonds with ACE–IPs, which is conducive to the exertion of ACE inhibitory activity. The peptides EKVNELSKD, LHLPLLQ and LQDKIHP, identified from fermented Rubing cheese, can form hydrogen bonds with Ala356 and Arg522, His383, Ser355 and His387 in ACE, respectively, which were not belonging to any active pocket [The interactions between ligand and receptor include hydrophobic, van der Waals’s force, hydrogen bond, π bond and electrostatic interaction. Among them, the hydrogen bond interaction may be the strongest . Hydrogee pocket . Zheng ee pocket  found thp < 0.05) after pepsin treatment for 2 h, which was 42.06 ± 1.79%. After trypsin treatment for 2 h, the ACE inhibitory activity had no significant difference from that before digestion, which was 67.53 ± 1.32% (p > 0.05). This may be because the MDPH contained the cleavage site of pepsin. After MDPH had been digested, the active sites with ACE inhibition were reduced, decreasing ACE inhibitory activity. And after trypsin digestion, further hydrolysis led to the exposure of active sites and the enhancement of activity.Compared with before digestion (ACE inhibitory activity was 65.46 ± 1.15%), the ACE inhibitory activity of MDPH decreased significantly  than that of >3 kDa. The component of <3 kDa was separated by gel filtration chromatography, and two components (UF-I and UF-II) were separated. The ACE inhibitory activity of component B was significantly (p < 0.05) higher than that of component A. The component B was separated by RP–HPLC, and eight components were obtained. The activity of component B6 was the highest, and 121 peptide sequences were obtained by Nano-LC–ESI–MS/MS. After screening by AHTPin online website, five peptide sequences were selected for synthesis verification and molecular docking. VALSSLRP, with high ACE inhibitory activity of 91.67 ± 0.73%, can form three hydrogen bonds with ACE and closely combine with ACE protein S1′ pocket in silico simulated peptide digestion. In summary, deriving ACE–IPs from Muscovy duck blood could increase its economic value and promote environmental sustainability. Further study using cell or animal models is necessary to evaluate the ACE inhibitory activity for deeper elucidation.This study for the first time isolated and identified the novel ACE–IPs from Muscovy duck blood and explored the interactions between purified peptides and ACE. It was concluded that the component activity of <3 kDa was markedly higher (