Protein phosphorylation, critical for cellular regulatory mechanisms, is implicated in various diseases. However, it remains unknown whether heterogeneity in phosphorylation of key structural proteins alters tissue integrity and organ function. Here, osteopontin phosphorylation level declined in hypo- and hyper- phosphatemia mouse models exhibiting skeletal deformities. Phosphorylation increased cohesion between osteopontin polymers, and adhesion of osteopontin to hydroxyapatite, enhancing energy dissipation. Fracture toughness, a measure of bone’s mechanical competence, increased with ex-vivo phosphorylation of wildtype mouse bones and declined with ex-vivo dephosphorylation. In osteopontin-deficient mice, global matrix phosphorylation level was not associated with toughness. Our findings suggest that phosphorylated osteopontin promotes fracture toughness in a dose-dependent manner through increased interfacial bond formation. In the absence of osteopontin, phosphorylation increases electrostatic repulsion, and likely protein alignment and interfilament distance leading to decreased fracture resistance. These mechanisms may be of importance in other connective tissues, and the key to unraveling cell–matrix interactions in diseases. In recent years, the role of extracellular matrix (ECM) proteins and their post-translational modifications (PTMs) in modulating cell activity, cell-matrix interactions, and biomineralization processes has sparked tremendous interest in different connective tissue biological systems. In particular, it has been postulated that different levels of phosphorylation of matrix proteins play a critical role in coordinating calcification processes in normally  and pathhypophosphatemia (hyperphosphatemia (asia (-/-Fgf23 mice) are hyperphosphatemic, but like the Hyp mice also show accumulation of OPN (−/−Alpl mice) displays mineralization deficiencies characterized by rickets/osteomalacia as well as elevated levels of inorganic pyrophosphate (PPi). The −/−Alpl mice also show increased levels of phosphorylated OPN compared to wild type (WT) mice  mice . InteresWT) mice . As suchThe degree of OPN phosphorylation has significant effects on its structure and physiological function . For exaIn this study, we investigate the role of OPN phosphorylation on bone fracture. To execute this, we first demonstrate that in mouse models of impaired phosphate regulation and increased skeletal fragility, the level of OPN phosphorylation declines. Next, we captured the effects of phosphorylated OPN on bone fracture toughness (resistance to crack propagation and fracture) by developing methods to enzymatically phosphorylate and dephosphorylate WT and OPN-deficient mouse bones ex vivo, then measure the resultant change in their mechanical competence. In an effort to gain a better understanding of the various factors that contribute to the mechanical function of phosphorylated OPN in bone matrix, we then conducted atomic force microscopy-force spectroscopy (AFM-FS) experiments demonstrating the effect of pH, ion charges, and phosphorylation levels on the energy dissipation properties of the OPN network using simplified synthetic and physiologically relevant surfaces. Based on these results, we propose that for appropriate mechanical function of bone, the phosphorylation status of OPN promotes fracture toughness up to a beneficial point. Phosphorylation or dephosphorylation alters the interaction between charged groups on OPN, and between OPN and bone mineral leading to increased or decreased energy dissipation.-/-Fgf23 mice. Total protein was quantified using a colorimetric detection system. From each sample, 2 µg of protein extract was loaded onto a 4–20% gradient SDS-PAGE gel. Since the vast majority of OPN phosphorylation occurs at serine residues, we performed immunoblotting for phosphoserine in the mineral-binding protein extracts. In the bone matrix of both Hyp and -/-Fgf23 mice, we found that mineral-bound OPN increased    phosphorylation (WT-p=0.007) . We obsep=0.006) . Althougp=0.006)  was not p=0.006) , indicatWT-phosphorylated vs. WT-nonphosphorylated control, p=0.009). In contrast, toughness declined in Opn KO mice following ex-vivo phosphorylation  indicating that phosphorylation of other bone matrix proteins in the absence of OPN did not improve the fracture resistance of bone  .We observed higher fracture toughness with phosphorylation of WT bones ( of bone . Ex-vivop=0.012)  while an2+ concentration, the detachment energy increased more significantly as compared to H2O and Na+ environments. At pH 6.0 however, the protein and HA bear opposite charges. The slightly acidic pH lead to a moderate dissolution of HA over time, and therefore, it is expected that Ca2+ ions are present in solution from the beginning of the experiment. Further addition of Ca2+ ions decreased energy dissipation attributable to the reduction of sacrificial bond formation  studies using an in-vitro experimental system to demonstrate that the phosphorylation status of OPN can affect bone toughness by altering energy dissipation. At pH 8.5, both OPN and hydroxyapatite (HA) surfaces are negatively charged. Under high Caormation   has been recently implicated in disease models of phatemia , hyperphphatemia , and/or ia   . These s2+, Na+) . The higd status . In the ubstrate . Ca2+-met energy , which it energy . Thus, vt energy . Similart energy  whereas,t energy . These r2+ ions strong cohesion and excessive crosslinking of the OPN meshwork reduces the stretching ability of the meshwork, leading to shorter pulls, increased repulsion of all positive sites, which likely increased distance in the meshwork, and diminished detachment energy. We postulate that the increase in global phosphorylation of other bone matrix proteins in the absence of OPN ( e.g. other SIBLING matrix proteins) may also potentially result in increased protein alignment and larger interfilament distance between mineralized collagen fibrils, to a detrimental degree that decreases matrix interaction, energy dissipation, and consequently fracture resistance in osteopontin-deficient mice  increases interfacial adhesion and toughness  were collected and bone proteins extracted as described previously  was added and samples were gently rotated at 4°C for 1 hr, followed by the addition of 50 µL of Protein A/G PLUS-Agarose beads  and gentle rotation at 4°C for 1 hr. Samples were spun at 2000 × g for 1 min, and supernatants were removed. Beads were then washed in cold 100 mM sodium acetate buffer, pH 5.5 three times, and immunoprecipitated proteins were eluted in 2 × Laemmli protein loading buffer. Samples were resolved on a 4–20% gradient SDS-PAGE gel, transferred onto PVDF membranes and immunodetected using anti-mouse osteopontin  and anti-phosphoserine  antibodies. Two technical replicates were performed for Previously frozen femora were dissected from twenty-eight six-month-old male Opn KO (n = 14) and C57BL/6NCrl wild-type  mice. The sample size reflects the number of independent biological replicates and were based on results from previous pilot studies and publications from the laboratory . Bones wOne limb of each animal (left or right) was randomly selected for phosphorylation and the contralateral limb served as the non-phosphorylated control [Opn KO (n = 7) and WT (n = 7)]. Phosphorylation was conducted by incubating the samples for 48 hr at 30°C with casein kinase-II (CK2) and the reaction buffer . Adenosine triphosphate (ATP) (2 mM) was added to the buffer as the phosphoryl donor for CK2. The incubating solution also contained protease and phosphatase inhibitors , and antibiotics [ampicillin (100 μg/μL) and kanamycin (50 μg/μL)]. ATP, CK2, and antibiotics were also added second time to the reaction at the 20 hr of incubation. The non-phosphorylated samples (i.e. controls) were placed in a similar solution without added enzymes for the same time period and temperature.In a different set of animals, one limb was randomly selected for de-phosphorylation and the contralateral limb served as the nondephosphorylated control [Opn KO (n = 7) and WT (n = 7)]. De-phosphorylation was conducted by incubating the samples for 48 hr at 37°C with calf intestinal alkaline phosphatase (CIP) and the CIP reaction buffer . In pilot and published studies  we did nc at maximum load for each sample (All femora were scanned using micro-computed tomography (μCT) at 70 kVp, 114 mA, 200 ms integration time and at high resolution 10.5 µm voxel size  for measuring bone geometry. Following in-vitro phosphorylation and dephosphorylation treatment, samples were loaded in three-point bending until failure at a loading rate of 0.001 mm/s (Elf Enduratec 3200). The resulting load displacement curve was used to calculate a single-valued fracture toughness Kh sample . ToughneAfter mechanical testing, all bones were defatted, lyophilized (freeze-dried), and weighed (approximately 20–40 mg). Samples were then placed in eppendorf tubes with 600 μL of extraction buffer consisting of 0.05 M EDTA, 4 M guanidine chloride, and 30 mM Tris-HCl. The bones were subsequently homogenized, centrifuged, and the supernatant collected . The supernatant from each sample was placed into a micro-dialysis vial and underwent simultaneous protein isolation and demineralization over two days at 4°C, pH 7.4, against several changes of 1 x PBS and 5 mM EDTA.The amount of protein in the samples was quantified using the Coomassie Plus (Bradford) Assay. The measurement of phosphorylated proteins was done using the pIMAGO-biotin Phosphoprotein Detection assay kit  . Because phosphorylation modifies the organic matrix including OPN, we compared the change in fracture toughness caused by phosphorylation of the organic matrix with  and without osteopontin  by independent samples t-test. The same analysis was done for dephosphorylated samples and nondephosphorylated controls conducted on separate animals. All analyses were conducted using IBM SPSS 21 and two-tailed significance threshold set at 0.05 for both paired and independent samples t-test.The mean and standard deviation were calculated for total protein phosphorylation amount and fracture toughness. Paired samples t-test was used to compare differences between the groups  unless otherwise stated.3PO4 (0.3 M) and 250 mL aqueous solution of CaCl2·2H2O (0.5 M) to 500 mL ultrapure boiling water. To avoid temperature fluctuation, both reactants were added at a rate of approximately 2.5 mL per minute under continuous stirring. Prior and during the addition of the reactants, nitrogen gas was bubbled through the solution in order to remove the dissolved CO2. At all times, the pH was kept between 9.0 and 10.0 by the addition of concentrated NH4OH solution. Upon the completion of the addition, the solution was kept under stirring for 24 hr at 80°C before cooling to room temperature. To retrieve the HA crystals, the suspension was filtered through a 0.22 μm membrane filters . Finally, the crystals were dried and ‘matured’ at 150°C for 24 hr and stored in a desiccator. The end product was characterized by means of X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy equipped with Energy-dispersive X-ray analyser . XRD analysis was performed using a standard powder diffractometer (Siemens D8) with Ni-filtered CuKa1 radiation (λ = 0.154059 nm) and the acquired diffraction spectra were matched against JCPDS reference data using the EVA XRD software. The FTIR spectra were acquired using an Excalibur spectrophotometer  at a resolution of 2 cm−1 using the KBr pellet method.Synthetic HA was produced for the functionalization of the AFM cantilever in order to simulate the mineralized fiber – NCP – mineralized fiber interaction. The preparation of the synthetic HA crystals was performed by the simultaneous addition of 250 mL aqueous solution of H2HPO4) solution at liquid (mL)/powder (g) ratio of 0.32, homogenized by a spatula for 1 min in agate mortar and then spread carefully on Silastic M RTV Silicone Rubber moulds. The specimens were kept in 100% humidity for 12 hr and then placed in 60 mL of Ringer’s solution at 37°C for 7 days to harden. During the maturation period, the a-TCP is transformed into calcium-deficient HA following the hydrolysis of the a-TCP according to the reaction 3Ca3(PO4)2 + H2O → Ca9(HPO4)(PO4)5OH (HA surfaces were prepared through the ‘maturation’ of CaP cements in Ringer solution as described previously . In brie(PO4)5OH .In bovine milk, OPN is subjected to proteolytic processing by proteinases such as thrombin , plasmin2, 110 mM NaCl, 10 mM HEPES), and ultra pure water (H2O). Each solution was divided into separate vials and ph adjusted for each experiment using either HCl or concentrated NaOH solution.The buffers used were the same as previous studies . More spThe lyophilized OPN was dissolved in ultrapure water (concentration 2 μg/µL) and absorption of OPN film on the model surfaces (HA or mica) was accomplished using the ‘drying droplet’ method. During this process, a small drop (4 μL) of OPN solution was deposited onto a freshly cleaned and dried HA or mica surface which was previously glued on the bottom of the fluid cell using 5 minute-setting epoxy. The droplet was then left to dry inside the AFM hood forming a thin protein film on the model surface, and then rehydrated with the appropriate solution.One aggregate of synthetic HA crystals was glued to a tipless monolithic silicon AFM probe  using epoxy glue . For this, a few micrograms of the synthetic HA crystals were added in 5 mL of ethanol and stirred vigorously to produce a dispersion. At this stage, 500 μL of this dispersion were deposited onto a glass slide and left to dry. A droplet of epoxy was placed by the side of the dry crystals and the glass slide was placed into the AFM. The AFM probe was then engaged carefully onto the epoxy, pulled back, and engaged again on the aggregate of choice. After two minutes in contact, the probe was withdrawn and left in the AFM for an additional 30 min to ensure complete setting of the epoxy. An example of the end result is presented in i.e. the glass slide). A line was then fitted on the loading part of each force curve and the slope of the fitted line was used as the InvOLS. The mean InvOLS value of all ten curves was then used as the InvOLS of the cantilever. In the case of the HA-functionalized cantilevers the spring constant was reassessed using the thermal method post-functionalization and the resulting value was used for the analysis. Force spectroscopy measurements of phosphorylated/dephosphorylated OPN on mica surfaces were conducted using Olympus BL-RC150VB-C1 Bio-levers ; spring constant 6 pN/nm (0.006 N/m), while stiffer (c. 0.18 N/m) cantilevers were used for the HA experiments. Maximum force from force spectroscopy experiments are reported in Force spectroscopy measurements of the adhesive properties of the OPN film under various ionic environments were conducted by means of an atomic force microscope  using an open fluid cell setup. Following All force curves were exported in ASCII (plain text files) and processed in Matlab. Each force curve was split into its approaching and retraction parts . Energy  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Increasing attention in the bone field is focused on understanding the nuances of the composition of extracellular matrices and how post translational modifications of constituent proteins alters cellular interactions, tissue integrity and function. In an elegant series of experiments, the authors show that the phosphorylation status of osteopontin can be modulated, altering its energy dissipation properties thereby promoting fracture toughness, but that there is a ceiling to this effect. The mechanisms by which phosphorylation alters facture toughness are explored in detail and these results may be informative for understanding the properties of the extracellular matrix in other connective tissues.Decision letter after peer review:eLife. Your article has been reviewed Clifford Rosen as the Senior Editor, a Reviewing Editor, and three reviewers. The following individual involved in review of your submission has agreed to reveal their identity: Virginia Ferguson (Reviewer #1).Thank you for submitting your article "The Role of Extracellular Matrix Phosphorylation on Energy Dissipation in Bone" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data and or revision, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:There is a great deal of interest in the field in determining how the non-collagenous aspects of the bone matrix contribute to the overall resistance of bone tissue to fracture. In this manuscript, the authors seek to investigate the importance of phosphorylation status of osteopontin to bone toughness and how this non-collagenous extracellular matrix protein impacts fracture toughness. Of particular interest in this paper is how the contribution of osteopontin phosphorylation to whole bone toughness is explored at the nano-scale, protein-mineral interactions level. They determine that the phosphorylation of OPN increases electrostatic interactions and cation-mediated crosslinks, allowing for increased energy dissipation under loading. Overall, this was considered to be a well written and interesting paper comprised of well thought out experiments.During the review many requests for additional information and clarification were made by the reviewers but few experiments. The reviewers wondered if alternate mechanisms could be competing with OPN towards affecting fracture toughness. In other instances, it was wondered if alternate explanations might not explain these data. These concerns are summarized below.Essential revisions:-/- mouse model , and what sources of error may exist and be generated from specific methods .1) The limitations of this study were glossed over in terms of the limits of the OPN2) Could there be additional interactions that were potentially overlooked where phosphorylation status changes the way that OPN interacts with other matrix molecules in bone? What about the pH effects on other proteins, GAGs, etc. in bone – could these have complicated or contributed to the results that were observed in this manuscript in any meaningful way? The sole focus of this manuscript on OPN is beneficial, but it also excludes many other concomitant effects of phosphorylation/dephosphorylation and pH that could compound the experimental outcomes that were observed. The authors are asked to briefly justify (and consider including in their manuscript) a justification of why these effects on other matrix molecules were not the dominant outcome – in other words that OPN is the sole or predominant contributor to the endpoint measurements of interest .-/- group within the figure legend, and Figure 4B is missing "C" in the "Change in.…" title. To provide readers a transparent interpretation of the results, consider overlaying dot plots on the bar charts. That is, show the actual data points. This will also convey the sample size more clearly.3) Figure 4, Figure 5 include bars for statistical comparisons (marked with *) that are sometimes not located in a manner to compare two groups. It is thus difficult to see which groups are intended to be marked in a comparison. Figure 5 also seems to have lost the -/- for OPN4) Data from control mice in Figure 4 and Figure 5 don't seem to match. What could this mean when the differences in means are comparable to those observed in some of your experimental comparisons? Does this cast doubt on the overall results of fracture toughness testing? How can we believe the results you present if the WT groups are not producing repeatable results?5) Why was 48 hours chosen for phosphorylation and dephosphorylation? Is the (de)phosphorylation achieved with this protocol physiologically-relevant?6) The position and mass of the HA cluster on the cantilever would be expected to affect the spring constant of the cantilever. How was this accounted for? It is only mentioned that the cantilevers were calibrated before functionalization.7) For AFM testing, how was the effect of different shaped crystal clusters on electrostatic interaction forces accounted for? Numbers of replicates/group for AFM testing and some other assays were unclear. This is essentially a contact mechanics question, since the surface area of contact could be quite different for different clusters. More detail about the normalization procedure could help here . How was the full contact threshold force decided? For experiments with changing buffers, was the order of buffer change randomized? Otherwise, buffer effects may be confounded with washing away part of the OPN or the crystal.8) Did mineralization or collagen crosslinking change with phosphorylation or dephosphorylation treatments of femurs? The authors state that "loss of OPN resulted in reduced fracture toughness without accompanied changes in calcium variability", but it is not clear if it is assumed that mineralization did not change with (de)phosphorylation processes.9) Statistics: In the case of a significant interaction, which the authors have found, simple effects can be tested, but family-wise error should be considered and accounted for to correct for the greater likelihood of Type I error. It’s not correct to t-test first and then use ANOVA to look for an interaction. It is better to fit your data to a two-factor ANOVA, and in the case of an interaction, perform post-hoc simple effects testing that is corrected for these multiple comparisons. Further, details ensuring model fit  would be appreciated. It is suggested to present data as a mean instead of an average. "Average" can be defined as mean, median, mode… etc.10) The sentence “Treatment affected fracture toughness of WT bone…” is confusing. There is a significant interaction, and that p-value should be reported, but instead, p-values are reported for phosphorylation and dephosphorylation.-/- mice, the mice were very young (6 weeks old). The mice were much older (6 months) for fracture toughness testing. Phosphorylation changes with aging; how much less phosphorylation would these mouse models have at 6-mo of age, and how would this relate to the WT mice?11) For quantification of phosphorylation in Hyp and FGF2312) The fracture toughness of bone originates from nanoscale energy dissipation at the protein-mineral interface. While this toughening mechanism is important, it is not the only important toughening mechanism in bone. Toughening mechanisms span length-scales and involve mineral, collagen, and their interactions. A slight modification to language to contextualize this toughening mechanism as an important one of several would be helpful.13) The authors' finding that there is a “sweet spot” of phosphorylation was considered to be very interesting. However, this concept of sweet spot confused the reviewers in the manner that it was presented. The authors did not provide a fracture toughness vs. phosphorylation relationship. That is, by continuing the increase in phosphorylation of WT bone, does the improvement in fracture toughness stop and decline? With the loss of OPN, phosphorylation had a negative effect. This was interpreted to mean that phosphorylation levels returned to normal, but really, the finding implicates the phosphorylation of OPN as being required to improve toughness. It is not clear what subset of phosphorylation values within the studied range of phosphorylation here is relevant in disease. The “sweet spot” mechanism likely needs to be removed or qualified. Either way, its description is rather difficult to follow.14) The in vitro (de)phosphorylation had some variability. Did amount of (de)phosphorylation correlate (within groups) with fracture toughness?15) The authors postulate that TG2 and phosphorylation may separately act as toughening mechanism. This is supported by FGF23 and Hyp mice having increased TG2, which should toughen bone, but also phosphorylation. But what if TG2 also has a nonlinear relationship with bone mechanics? The reviewers were concerned that there was not enough evidence in the paper to say that TG2 and phosphorylation are independent, but felt this is a very interesting discussion point. Absolutes should be tempered here.16) A reviewer asked if one could over-phosphorylate the bovine OPN to potentially observe the quadratic relationship with energy dissipation for confirmation of the relationship observed for whole-bone testing? Some discussion on this point would be appreciated.eLife. Your revised article has been evaluated by Clifford Rosen (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "The Role of Extracellular Matrix Phosphorylation on Energy Dissipation in Bone" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before formal acceptance, as outlined below. Please note that these requested clarifications were agreed as necessary by all reviewers during the post review correspondence.Reviewer #1:This manuscript is well written and is on a topic that is compelling and important to the field. The authors have given sufficient attention to the concerns expressed by the reviewers, including myself. I feel satisfied that the responses were diligent and thorough and the manuscript is improved as a result.Upon revisiting Figure 8A, I agree that a quadratic fit is not necessarily the best choice and that an exponential function should be considered. Concerningly, if the very most highly phosphorylated point is excluded, the data look very much like a plateau. This would challenge the “sweet spot” idea as applied to just WT bones. As reviewer 3 notes it “seems like the study showed that phosphorylation promotes fracture toughness (to a point) if OPN is present”. I agree with this interpretation based on the presented data, which is consistent with more of a asymptote than a sweet spot in Figure 8A.Reviewer 3 asks “why doesn't the increase in fiber network modulus and strength continue to increase in phosphorylation”? I read this differently, with the interpretation that modulus and strength could indeed increase with phosphorylation but to a detrimental degree that decreases strain. Clarification is required as the reader will likely be confused on this point as well.It would be helpful to show all data (not just four means) for Figure 8B and to indicate which data correspond with each group. This would alleviate confusion surrounding this figure.The authors should clarify in the Abstract that the phosphorylation and dephosphorylation experiments were conducted ex vivo.Reviewer 3 comments that the abstract only mentions one mechanism of the three in the discussion. My bigger issue is that mechanisms 1 and 3 are co-dependent as presented . These are stated for the AFM experiments but not for the fracture toughness and phosphorylation experiments.Reviewer #2:The revised manuscript is considerably improved. The detailed Response to reviewers is appreciated, as well as the updates to figures and text. The revised Figure 8 is particularly appreciated, notwithstanding the comments made by other reviewers.I agree that their findings are exiting and that the explanations need to be improved so that the readers do not need to make inferences that may be off track.Reviewer #3:The study is intriguing because it shows how phosphorylation promotes fracture toughness of bone if osteopontin is present. However, this reviewer still struggles with the authors' interpretation of the mechanisms by which phosphorylation does this (they propose 3). A number of questions come up after reading the Abstract and Discussion. I like the concept that modulus and strength increases with phosphorylation but at a cost of decreasing strain after some threshold. The authors just need to do a better job describing each mechanism and how they are inter-related so that readers don't have to interpret their interpretation.How did the authors decide on fitting a quadratic equation to fracture toughness vs. phosphorylation levels of WT bone? To this reviewer, it looks like fracture toughness plateaued after 1.5 in Figure 8A. What about fitting an exponential function, exp(1/phos)?Discussion: "However, further increase in the degree of most favorable interaction (i.e. larger than the "sweet spot" level) can cause a rapid decrease in ultimate strain, and thus, energy dissipation." What is the physical mechanism that supports this assertion? In other words, why doesn't the increase fiber network modulus and strength continue to increase with an increase in phosphorylation?-/- bone, this mechanism is gone. Is the relationship between fracture toughness of OPN-/- bone and phosphorylation levels (from the 2 enzyme treatments) non-linear? Sorry, Figure 8B is not too convincing with only 4 data points.Another thing that is challenging to the interpretation of the results is that fact OPN bridges mineral crystals, presumably more bridges with more phosphorylation. Correct? This is the first mechanism described. In the OPN Essential revisions:-/- mouse model , and what sources of error may exist and be generated from specific methods .1) The limitations of this study were glossed over in terms of the limits of the OPN The limitations of the study are now reported in the Discussion.2) Could there be additional interactions that were potentially overlooked where phosphorylation status changes the way that OPN interacts with other matrix molecules in bone? What about the pH effects on other proteins, GAGs, etc. in bone – could these have complicated or contributed to the results that were observed in this manuscript in any meaningful way? The sole focus of this manuscript on OPN is beneficial, but it also excludes many other concomitant effects of phosphorylation/dephosphorylation and pH that could compound the experimental outcomes that were observed. The authors are asked to briefly justify (and consider including in their manuscript) a justification of why these effects on other matrix molecules were not the dominant outcome – in other words that OPN is the sole or predominant contributor to the endpoint measurements of interest .-/-Opn groups. By using -/-Opn mice we can determine the contribution of all other matrix proteins in the absence of OPN. Likewise, by comparing the change in fracture toughness caused by phosphorylation or dephosphorylation of the organic matrix with osteopontin  and without osteopontin , we can isolate the contribution from OPN alone.Our experiments were conducted at pH 7.4 under physiological conditions, and any instability in pH would affect proteins in both WT and lamina limitans , and throughout bulk bone, so alterations in vivo may affect many processes where phosphorylation is important. These would seem to be predominantly involved in mineral-binding and possibly modifying TG2 crosslinking sites, and in cell signaling that may affect mechanosensation and bone remodeling. Dephosphorylation of OPN by the enzymatic activity of TRAP also affects osteoclast integrin binding in vitro, and thus altered bone remodeling over time affecting bone's mechanical properties. In addition to its interaction with mineral, the phosphorylation status of OPN is likely to influence its interaction with collagen and osteocalcin. Reflecting this, new text has been added to the Discussion section.OPN in bone resides at its surfaces  in the thin structure known as the -/- group within the figure legend, and Figure 4B is missing "C" in the "Change in.…" title. To provide readers a transparent interpretation of the results, consider overlaying dot plots on the bar charts. That is, show the actual data points. This will also convey the sample size more clearly.3) Figure 4, Figure 5 include bars for statistical comparisons (marked with *) that are sometimes not located in a manner to compare two groups. It is thus difficult to see which groups are intended to be marked in a comparison. Figure 5 also seems to have lost the -/- for OPN As suggested, these figures have been corrected. Thank you.4) Data from control mice in Figure 4 and Figure 5 don't seem to match. What could this mean when the differences in means are comparable to those observed in some of your experimental comparisons? Does this cast doubt on the overall results of fracture toughness testing? How can we believe the results you present if the WT groups are not producing repeatable results?Opn-/- mice have lower fracture toughness compared to WT mice . This study was not powered to detect differences between controls of different genotypes, but rather the experimental design was to examine differences between paired groups i.e. WT-dephosphorylated vs WT-controls. The data in Figure 4 and Figure 5 are from a different set of control bones and fracture toughness values vary across bones from the same batch of mice due to inherent differences between animals, and due to variations in any mechanical testing method including fracture toughness testing. In this case, we have minimized variations between animals by conducting pairwise comparison . This has been noted in the Discussion section.There is inherent variability in fracture toughness testing despite the fact that the maximum load method for measuring fracture toughness of mice bone used in this study has the least variability compared to other methods . We and others have shown with a larger sample size that 5) Why was 48 hours chosen for phosphorylation and dephosphorylation? Is the (de)phosphorylation achieved with this protocol physiologically-relevant?Our in vitro studies were performed using enzymes which require physiological conditions for the reaction to be successful. The physiological reaction time for a given enzyme, both in vivo and in vitro, is its lifetime. All enzymes stop to function (i.e. “die”) due to, for example, enzyme unfolding, damage to the active center, adsorption on the tube wall, etc. In pilot and published studies  we did not observe an increase in either phosphorylation or dephosphorylation of bone samples after 48 hours. This has been noted in methods section in vitro Phosphorylation and Dephosphorylation.6) The position and mass of the HA cluster on the cantilever would be expected to affect the spring constant of the cantilever. How was this accounted for? It is only mentioned that the cantilevers were calibrated before functionalization.Thank you for raising this point. The text has been modified in subsection “Force spectroscopy experiments” to include the requested information. In the case of the HA-functionalized cantilevers, the calibration of the cantilever’s spring constant, done prior to functionalization, was used to calibrate the Inverse Optical Lever Sensitivity (InvOLS), which required force curves to be acquired on a nominally infinitely stiff surface.  was determined by acquiring ten (10) force curves on a nominally infinitely stiff surface (i.e. the glass slide). A line was then fitted on the loading part of each force curve and the slope of the fitted line was used as the InvOLS. The mean InvOLS value of all ten curves was then used as the InvOLS of the cantilever.) The reason we avoided calibrating the InvOLS post-functionalization was to reduce the chance of mechanical damage to the functionalized end of the cantilever, which could affect both the adhesive (epoxy) and the integrity of the HA aggregate.Following InvOLS calibration and cantilever functionalization, the spring constant was re-assessed using the thermal noise method and the new value was used for the analysis. However, we agree that the presence of a foreign body on the cantilever can influence its behavior. To ensure comparable results, care was taken so that the selected HA aggregate was always of similar shape and size. This was further confirmed post-calibration by assessing the amount of cantilever deflection. It is worth noting that small variations in spring constant calibration would mainly affect the reading of maximum force and, in this study, we are presenting and discussing the relative energy dissipation change between the OPN groups, which is primarily affected by the pulling Length.7) For AFM testing, how was the effect of different shaped crystal clusters on electrostatic interaction forces accounted for? Numbers of replicates/group for AFM testing and some other assays were unclear. This is essentially a contact mechanics question, since the surface area of contact could be quite different for different clusters. More detail about the normalization procedure could help here . How was the full contact threshold force decided? For experiments with changing buffers, was the order of buffer change randomized? Otherwise, buffer effects may be confounded with washing away part of the OPN or the crystal.2O (for OPN on HA). For each condition, we collected 50 < n < 60 force curves for the OPN on HA experiments and 80 < n < 100 for the OPN on mica experiment. Full contact was ensured by pressing the cantilever against the coated surface to the point where the force curve slope approached infinity, condition which was achieved for contact forces > 5 nN. The order of the buffer change was not randomized. It followed the order shown in Figure 6 .Our experiments were conducted in relative terms; thus, correcting for variations of crystal cluster shape was not applicable. We normalized our measurements to the dissipation levels in EDTA (for OPN on mica) and to dissipation levels in H8) Did mineralization or collagen crosslinking change with phosphorylation or dephosphorylation treatments of femurs? The authors state that "loss of OPN resulted in reduced fracture toughness without accompanied changes in calcium variability", but it is not clear if it is assumed that mineralization did not change with (de)phosphorylation processes.The lack of changes in calcium variability was reported in Thurner et al., 2010. Crosslinking between collagen remains unchanged. Dephosphorylation reverses the inhibitory effect of OPN on HA formation in vitro and thus mineralization may be affected. In this study, the experiments were conducted under physiological conditions with buffer solutions containing magnesium chloride, calcium, and EDTA to prevent any alterations in mineral. Thus, our conditions do not induce crystallization. This has been noted in the Discussion section.9) Statistics: In the case of a significant interaction, which the authors have found, simple effects can be tested, but family-wise error should be considered and accounted for to correct for the greater likelihood of Type I error. It’s not correct to t-test first and then use ANOVA to look for an interaction. It is better to fit your data to a two-factor ANOVA, and in the case of an interaction, perform post-hoc simple effects testing that is corrected for these multiple comparisons. Further, details ensuring model fit  would be appreciated. It is suggested to present data as a mean instead of an average. "Average" can be defined as mean, median, mode… etc.-/-Opn and WT mouse bone, and the goal was to test whether modification of OPN by phosphorylation  affect the outcome. Thus, given that in vivo phosphorylation and dephosphorylation of WT and -/-Opn mice may cause changes other than in the level of phosphorylated OPN, we designed our experimental method where WT and -/-Opn mouse bones were paired and altered in vitro under carefully controlled conditions to modify a selected aspect. Pairwise comparisons where both limbs are from the same animal are valid . Both Kolmogorov-Smirnov and Shapiro-Wilk Tests of Normality revealed that phosphorylation levels and fracture toughness were normally distributed (p>0.05). Next, to determine whether phosphorylation of osteopontin contributes to fracture toughness, we compared δ-WT (the change between WT-phosphorylated and WT-non-phosphorylated control), and δ--/-Opn (the change between -/-Opn phosphorylated vs -/-Opn non-phosphorylated controls). By comparing δ-WT and δ--/-Opn the difference detected would be attributed to the presence of osteopontin since all other proteins that were phosphorylated would be subtracted as background. Two-way ANOVA tests are not designed to evaluate the difference between WT control and WT phosphorylated and -/-Opn control and -/-Opn phosphorylated but between the mean values of these four groups and do not allow us to test whether phosphorylation of OPN causes a difference.Kindly note that, as mentioned above and in the manuscript , this study is motivated from results showing differences between 10) The sentence “Treatment affected fracture toughness of WT bone…” is confusing. There is a significant interaction, and that p-value should be reported, but instead, p-values are reported for phosphorylation and dephosphorylation.Please see the response to the above comment where we include the rationale of not performing two-way ANOVA tests. To avoid any confusion; this sentence has been removed.-/- mice, the mice were very young (6 weeks old). The mice were much older (6 months) for fracture toughness testing. Phosphorylation changes with aging; how much less phosphorylation would these mouse models have at 6-mo of age, and how would this relate to the WT mice?11) For quantification of phosphorylation in Hyp and FGF23Hyp and Fgf23-/- mice, and in our view, this makes an important initial contribution that we chose to mention here. Quantification of the amount of phosphorylation in these models, and at different ages, will be done in future studies as an additional full-scale project. Fracture toughness testing on the 6-week old mice used here is very difficult to perform, and less reliable, but older mice will be incorporated into our future planned work on these mutant mice.This is a very interesting question. Our assay for the quantification of global protein phosphorylation was recently developed  and to date no study has quantified the amount of phosphorylation in normal and diseased bone during development. We have previously reported that total phosphorylation of bone matrix proteins including OPN declines with age . Despite our looking at only one mouse age so far, our results here are the first to show that global phosphorylation, as well as OPN phosphorylation, declines in osteomalacic 12) The fracture toughness of bone originates from nanoscale energy dissipation at the protein-mineral interface. While this toughening mechanism is important, it is not the only important toughening mechanism in bone. Toughening mechanisms span length-scales and involve mineral, collagen, and their interactions. A slight modification to language to contextualize this toughening mechanism as an important one of several would be helpful.The statement has been clarified to “Fracture resistance of bone emerges from various mechanisms that exists at multiple length scales across bone hierarchy, which involves in part growth and packing of mineral foci into larger crossfibrillar aggregates such that the extracellular matrix becomes highly mineralized[…]Both the OPN-crosslinked protein networks and the OC-OPN complex presumably work together to control deformation and separation of mineralized collagen fibrils” please see Discussion section.13) The authors' finding that there is a “sweet spot” of phosphorylation was considered to be very interesting. However, this concept of sweet spot confused the reviewers in the manner that it was presented. The authors did not provide a fracture toughness vs. phosphorylation relationship. That is, by continuing the increase in phosphorylation of WT bone, does the improvement in fracture toughness stop and decline? With the loss of OPN, phosphorylation had a negative effect. This was interpreted to mean that phosphorylation levels returned to normal, but really, the finding implicates the phosphorylation of OPN as being required to improve toughness. It is not clear what subset of phosphorylation values within the studied range of phosphorylation here is relevant in disease. The “sweet spot” mechanism likely needs to be removed or qualified. Either way, its description is rather difficult to follow.2 .Thank you for the suggestion. We have revised Figure 8 and provided a fracture toughness vs phosphorylation graph using the values from the study. By continuing the increase in phosphorylation of WT bone, fracture toughness does improve and then declines. This relationship is modeled by following equation y = 2.086+ 2.574x- 0.6415x14) The in vitro (de)phosphorylation had some variability. Did amount of (de)phosphorylation correlate (within groups) with fracture toughness?Due to the variability, the amount of dephosphorylation did not correlate with fracture toughness. In addition, the correlation coefficient will detect a linear relationship, but our data shows that global phosphorylation levels in WT bone follows a non-linear relationship as shown in comment #12.15) The authors postulate that TG2 and phosphorylation may separately act as toughening mechanism. This is supported by FGF23 and Hyp mice having increased TG2, which should toughen bone, but also phosphorylation. But what if TG2 also has a nonlinear relationship with bone mechanics? The reviewers were concerned that there was not enough evidence in the paper to say that TG2 and phosphorylation are independent, but felt this is a very interesting discussion point. Absolutes should be tempered here.We agree that there is not enough evidence and have tempered down the independence of TG2 and phosphorylation. Please see the Discussion section.16) A reviewer asked if one could over-phosphorylate the bovine OPN to potentially observe the quadratic relationship with energy dissipation for confirmation of the relationship observed for whole-bone testing? Some discussion on this point would be appreciated.2+ ions. As shown in the AFM experiments, situations that lead to high Ca2+ ion concentration result in shielding of the phosphorylated sites, charge reversal and repulsion. While the lack of experiments on OPN with varying phosphorylation-levels may be seen as a limitation, we nonetheless provide data points for the most extreme cases, and, different to the physiological system, control the concentration of Ca2+ ions. Taken together, our experiments do reproduce the effects seen in the animal models. We are planning to do further studies with variably and reproducibly phosphorylated OPN, as has recently been achieved in co-author Sorensen's lab.Thank you for this suggestion. We have added relevant discussion on this topic (please see the Discussion section). In particular, OPN is phosphorylated by the FAM20C kinase. The primary motif for phosphorylation is S/T-X-E/S(P)/D. Bovine milk OPN contains approximately 28 phosphorylation sites and all but a few residues in this motif are phosphorylated. If more sites should be phosphorylated, a kinase that phosphorylates serines/threonines in another motif should be used. However, in this context, we do not think that this approach would be successful as the non-phosphorylated serines in bovine OPN are not located in recognition sequences of any specific kinase. Therefore, milk OPN is already close to the over-phosphorylated OPN animal model. In hindsight, it may be possible to perform measurements on partially de-phosphorylated OPN and, in this sense, assess the whole curve shown. However, we note that several assays would be necessary to characterize the modified protein. Importantly, the consequence for mechanical properties of OPN networks is also influenced by the abundance and concentration of CaReviewer #1:This manuscript is well written and is on a topic that is compelling and important to the field. The authors have given sufficient attention to the concerns expressed by the reviewers, including myself. I feel satisfied that the responses were diligent and thorough and the manuscript is improved as a result.Upon revisiting Figure 8A, I agree that a quadratic fit is not necessarily the best choice and that an exponential function should be considered. Concerningly, if the very most highly phosphorylated point is excluded, the data look very much like a plateau. This would challenge the “sweet spot” idea as applied to just WT bones. As reviewer 3 notes it 'seems like the study showed that phosphorylation promotes fracture toughness (to a point) if OPN is present. I agree with this interpretation based on the presented data, which is consistent with more of a asymptote than a sweet spot in Figure 8A.Thank you for raising this point. Figure 8A has been revised with an exponential function which is also significant (p<0.001). Consistent with suggestion from reviewer #3, our data shows that fracture toughness increases with phosphorylation only when OPN is present. Figure 8B has been added to reinforce this point.Reviewer 3 asks “why doesn't the increase in fiber network modulus and strength continue to increase in phosphorylation”? I read this differently, with the interpretation that modulus and strength could indeed increase with phosphorylation but to a detrimental degree that decreases strain. Clarification is required as the reader will likely be confused on this point as well.Based on the revised Figure 8A, these previous statements have been removed and we provide the following information to explain the increase of fracture toughness with phosphorylation for wildtype samples (Discussion section):2+ ions”“We observed a non-linear dose response relationship between the level of global matrix phosphorylation and bone fracture toughness in wildtype mice . Phosphorylation explained ~36% of the variance in fracture toughness and this relationship was not observed in the absence of OPN . Taken together, this data supports the previously mentioned mechanism involving increased interaction energy and sacrificial bone formation between OPN and HA as well as between OPN polymers. The AFM-FS studies show that adhesion is not only dependent on the charge of OPN and HA under a certain environment but also the availability of free CaIt would be helpful to show all data (not just four means) for Figure 8B and to indicate which data correspond with each group. This would alleviate confusion surrounding this figure.As described above in response #1 the previous Figure 8A and the related sweet spot hypothesis have been removed. Thank you for the suggestion, all the data points are now included in Figures 8A and 8B.The authors should clarify in the Abstract that the phosphorylation and dephosphorylation experiments were conducted ex vivo.ex-vivo phosphorylation of wildtype mouse bones and declined with ex-vivo dephosphorylation. In osteopontin-deficient mice, global matrix phosphorylation level was not associated with toughness.”This has been clarified. We now state that “Fracture toughness, a measure of bone’s mechanical competence, increased with Reviewer 3 comments that the abstract only mentions one mechanism of the three in the discussion. My bigger issue is that mechanisms 1 and 3 are co-dependent as presented . These are stated for the AFM experiments but not for the fracture toughness and phosphorylation experiments.Statistical clarification for the phosphorylation and fracture toughness experiments are now included in subsection “Data analysis for global phosphorylation”. We now state “All analyses were conducted using IBM SPSS 21 and two-tailed significance threshold set at 0.05 for both paired and independent samples t-test.”Reviewer #2:The revised manuscript is considerably improved. The detailed Response to reviewers is appreciated, as well as the updates to figures and text. The revised Figure 8 is particularly appreciated, notwithstanding the comments made by other reviewers.I agree that their findings are exiting and that the explanations need to be improved so that the readers do not need to make inferences that may be off track.Thank you for the feedback. Figure 8 has been revised and the Discussion has been reorganized to limit inferences from the reader.Reviewer #3:The study is intriguing because it shows how phosphorylation promotes fracture toughness of bone if osteopontin is present. However, this reviewer still struggles with the authors' interpretation of the mechanisms by which phosphorylation does this (they propose 3). A number of questions come up after reading the Abstract and Discussion. I like the concept that modulus and strength increases with phosphorylation but at a cost of decreasing strain after some threshold. The authors just need to do a better job describing each mechanism and how they are inter-related so that readers don't have to interpret their interpretation.How did the authors decide on fitting a quadratic equation to fracture toughness vs. phosphorylation levels of WT bone? To this reviewer, it looks like fracture toughness plateaued after 1.5 in Figure 8A. What about fitting an exponential function, exp(1/phos)?Thank you for the suggestion. Kindly note that as mentioned above, an exponential function rather than a quadratic function is now used to fit the relationship between phosphorylation levels and fracture toughness of WT bone subjected to ex vivo phosphorylation/dephosphorylation .Discussion: "However, further increase in the degree of most favorable interaction (i.e. larger than the "sweet spot" level) can cause a rapid decrease in ultimate strain, and thus, energy dissipation." What is the physical mechanism that supports this assertion? In other words, why doesn't the increase fiber network modulus and strength continue to increase with an increase in phosphorylation?It is possible that fiber realignment and altering the net effective charge on the protein network can have detrimental effects on bulk tissue strain and thus fracture toughness. However, in light of the updated Figures 8A and 8B, these previous statements have been removed.-/- bone, this mechanism is gone. Is the relationship between fracture toughness of OPN-/- bone and phosphorylation levels (from the 2 enzyme treatments) non-linear? Sorry, Figure 8B is not too convincing with only 4 data points.Another thing that is challenging to the interpretation of the results is that fact OPN bridges mineral crystals, presumably more bridges with more phosphorylation. Correct? This is the first mechanism described. In the OPNThe interpretation of the result that OPN bridges mineral crystals and more bridges are formed with more phosphorylation is correct. This mechanism explains the continuous relationship between increased phosphorylation levels and increased fracture toughness with the presence of osteopontin as shown in Figure 8A. However, in the absence of OPN there seems to be an alternative mechanism where the phosphorylation and dephosphorylation of bone matrix increases and decreases toughness in a binary/step-wise fashion as seen from the results in Figure 4 and Figure 5. In particular, we postulate that the increase in global phosphorylation of other bone matrix proteins in the absence of OPN  may potentially result in increased protein alignment and larger interfilament distance between mineralized collagen fibrils to a detrimental degree that decreases matrix interaction, energy dissipation, and consequently fracture resistance. Taken together, these results show that phosphorylation affects bone toughness through two mechanisms. First involving increased bridge formation between OPN and mineral crystals (adhesion) and between OPN molecules (cohesion) with increasing phosphorylation, and the second involving other SIBLING proteins where increased phosphorylation results in increased protein alignment and larger interfilament distance leading to decreased matrix interaction and fracture resistance. Recent work has also shown that the characteristic fibrillar structure of collagen is lost and the collagen network is disorganized when Osteopontin is absent  which is likely to affect mechanical function of OPN KO mice. Please refer to the Discussion section.


K-means method in R software. Specific immunoglobulin E antibodies against food (egg white and milk) and inhalant allergens (D. pteronyssinus and D. farinae) were measured at 0.5, 1, 1.5, 2, 3 and 4 years of age. By using K-means clustering, the dynamic changes in serum IgE levels was significantly stratified into 3 clusters . A persistent total IgE levels higher than 100 kU/L appeared to be associated with higher prevalence of sensitization to food but not mite. However, a persistent IgE levels higher than 200 kU/L was not only remarkably related to increased prevalence of mite sensitization, but also risk of eczema at age 1 and allergic rhinitis and asthma at age 2, 3 and 4. In conclusion, a persistent total serum IgE level ≥ 200 kU/L since infancy is strongly associated with the presence of food and mite sensitization, as well as the development of eczema in infants, and rhinitis and asthma later in early childhood.There are few studies addressing the longitudinal analysis of serum IgE levels and its impact to the development of atopic diseases in early childhood. We investigated 170 children who regularly followed up at our clinic for 4 years in a birth cohort study with at least 3 time-points of serum samples. The pattern of total serum IgE levels from 6 months to 4 years of age was clustered using  IgE, produced by plasma cell, can recognize an allergen specifically and mediate an immune response. The immune system becomes sensitized, such that subsequent encounters with the same allergen lead to release of varies chemokines and cytokines, which results in causing symptoms of atopic diseases, for instance, local inflammation in eczema, mucous hypersecretion in rhinitis and bronchospasm in asthma3. An increase in total serum IgE levels in infancy is associated with food sensitization, while elevated total serum IgE levels during early childhood correlate strongly with mite sensitization4. In addition, an additive effect on total serum IgE production is perceived when there is combined allergen sensitization4. However, the dynamic changes of total serum IgE levels relevant to allergen sensitization have not been well determined.Several studies have presented the relationships between total serum IgE and allergen sensitization in children of different ages6. Clinically, food sensitization appears to be associated with eczema, whereas mite sensitization is strongly related to rhinitis and asthma8. However, total serum IgE level is considered as a high sensitivity predictor of atopic diseases9. Elevated total serum IgE indicates high possibility of the presence of atopic diseases in children with allergy-like symptoms. Nevertheless, the longitudinal trends of total serum IgE levels and their association with allergen sensitization and atopic diseases during early childhood are still lacking.The association between allergen sensitization and atopic diseases have been well demonstratedThe major aim of this study was to determine the total serum IgE levels from 6 months to 4 years of age in children from a Taiwan birth cohort study. The dynamic changes of total serum IgE levels were analyzed and their relevance to the presence of allergen sensitizations and risk for atopic diseases was also examined.10. At 4 years of age, atopic diseases including eczema, rhinitis, and asthma were physician-diagnosed in 20, 77, and 31 of these 170 children, respectively. The demographic characteristics of enrolled subjects, total serum IgE levels, and the diagnosis of atopic disease from 6 months to age 4 are summarized in Supplementary File In the birth cohort, initially 258 children were recruited, out of which, 182 (70.5%) children completed a 4-year follow-up at the clinic. A total of 170 children with serum samples obtained at least 3 time-points during the follow-up period were enrolled into this study. There were no significant differences in the baseline characteristics among these 170, 198, and all the 258 children studiedK-means clustering was performed for serum IgE levels from 6 months to 4 years of age using R software in 170 children. The dynamic changes in serum IgE levels during early childhood were stratified into three clusters notably  at the age of 4 years.Figure Total serum IgE level is commonly elevated in patients with allergic diseases. It has been suggested to be utilized in predicting the development of allergic disorders. However, the dynamic changes and values of total serum IgE levels relevant to the development of sensitization to allergens and risk of atopic diseases remains unclear, especially in children. This study provides the respective values of total serum IgE levels that have significant association with sensitization to various allergens and atopic diseases during different stages of childhood.11. The secondary lymphoid organs are not completely mature in infants, reducing the likelihood of IgE class switching12. In this study, the serum IgE levels in 6 month-old infant are generally low (< 100 kU/L) which is in agreement with previous studies that showed rarity of IgE-producing cells at 6 months of age13. Therefore, the serum IgE level of infant below the age of 6 months may not be useful in predicting the development of any diseases.IgE is secreted by class-switched B cell. The class-switch recombination of a B cell requires an antigen-dependent receptor-ligand binding interaction with an activated Th2 cell. This process usually takes place within secondary lymphoid tissues. However, the somatic hypermutation and class-switch recombination of B cells are rare in infants below the age of 6 months15. Food sensitization in infant should be noticed early due to its essential association with eczema, a common allergic skin disease with disrupted skin barrier17. Eczema could be exacerbated by food allergens by initiating the immune response19. To prevent eczema flare ups or from getting worse, early diagnosis and further avoidance of food allergens are relevant to infant. In our study, children with serum total IgE level > 100 kU/L are demonstrated to have food sensitization. A significantly higher prevalence of eczema was also found in children with serum total IgE level ≥ 200 kU/L at the age of 1 in our study. Therefore, total serum IgE level > 100 kU/L in infants may indicate they have food sensitization and a respectively higher risk of eczema. In addition, when infant has skin symptoms and with serum total IgE level ≥ 200 kU/L, physicians should consider the diagnosis of eczema, followed by early treatment and prevention.The production of IgE in infant older than 6 months starts to rise after exposing to food allergens4. Furthermore, children with high house dust mite-specific IgE level is at the highest risk of rhinitis and asthma20, which is in consonance with our study that an increased serum total IgE level (≥ 200 kU/L) strongly correlated with mite-specific IgE levels appeared to significantly increase risk of rhinitis and asthma after age 2. Thus, in clinical practice, young children with serum total IgE level ≥ 200 kU/L might have a high possibility of the presence of mite sensitization and rhinitis and asthma should be considered in such instances with allergic symptoms.The increase of serum total IgE level of children after the age of 2 is more related to mite sensitizationLimitations of this birth cohort include the relatively small enrolled population of 170 children, and thus limited statistical power to detect the association for subanalyses. However, the strength of this study is manifested by its long-term, longitudinal follow-up and regular measurement of total serum and allergen-specific IgE levels which established the dynamic relationships of IgE levels with allergen sensitizations and atopic disease development over time during early childhood.In conclusion, serum total IgE level could be predictive of allergen sensitization and atopic diseases in early childhood. In infancy, serum total IgE level > 100 kU/L may reveal the presence of food sensitization, while serum total IgE level ≥ 200 kU/L may indicate a high risk of eczema. Young children with persistent serum total IgE level ≥ 200 kU/L appear to be associated with high prevalence of mite sensitization and be at risk for allergic rhinitis and asthma. Thus, for children with serum total IgE level ≥ 200 kU/L from infancy to early childhood, there is a high chance of developing eczema in infants and rhinitis and asthma later in life, providing early diagnosis and treatment for childhood atopic diseases. However, further studies with larger sample sizes are required to validate our findings.4. The detailed of information regarding demographic data, child’s sex, family history of atopy, exposure to passive smoking, household income, and history of breastfeeding was collected and analyzed. Atopic diseases were evaluated and diagnosed by the same pediatric pulmonologist at the clinic. Diagnosis of atopic diseases including eczema, allergic rhinitis, and asthma was described in our previous study21. This study was approved by the Ethics Committee of Chang Gung Memorial Hospital (No. 103-6236A3). All experiments in this study were performed in accordance with the relevant guidelines and regulations, and written informed consent was obtained from the parents or guardians of all the study subjects.We enrolled children who completed a 4-year follow-up in a birth cohort study launched in Taiwan. Detailed descriptions regarding subject recruitment of this birth cohort study were reported previously10, total serum IgE level was measured using ImmunoCAP  and IgE sensitization was defined as IgE levels > 100 kU/L. For clustering, total serum IgE levels at different ages were imported into R software (Version 3.6.3). K-means method was then used to group serum IgE levels into discrete and stable clusters of time series data from 6 months to 4 years of age.Serum samples were collected and measured at 6 months, and 1, 1.5, 2, 3, and 4 years of age. As described in our previous studyD. pteronyssinus and D. farinae) as described previously23. Allergen sensitization was defined as values ≥ 0.35 kU/L24.Allergen-specific IgE was determined using a commercial assay for IgE  for a mix of the two most common food allergens (egg white and milk) and inhalant allergens in Taiwan  software was used for statistical analysis of data, and GraphPad Prism software  was used to represent data graphically. Statistical hypothesis tests were two-tailed with a significance level of 0.05.Supplementary Information.


BACE1 is the rate-limiting enzyme involved in the production and deposition of β-amyloid (Aβ). Since neurotoxic Aβ plays a critical role in Alzheimer’s disease (AD) pathogenesis, BACE1 has emerged as a key target for preventing AD. In the present study, the potential of sulforaphane, an isothiocyanate found in cruciferous vegetables, as a BACE1 inhibitor has been investigated. Sulforaphane exhibited six times more potent activity against BACE1 compared to well-known positive controls including resveratrol and quercetin. Sulforaphane presented selective and non-competitive BACE1 inhibitory activity with low off-target inhibition of BACE2 and other aspartic and serine proteases. In addition, sulforaphane presented negative binding energy, suggesting that the compound had a high affinity for BACE1. It interacted with locations other than the active binding sites of BACE1 through van der Waals forces. Overall, sulforaphane appeared to be a promising candidate with potent and selective BACE1 inhibitory properties that play an important role in AD prevention. Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive declines and memory impairments. AD is the second most common cause of death among those aged 70 and older worldwide . The dysβ-site amyloid precursor protein cleaving enzyme 1 (BACE1), the main form of β-secretase, is a membrane-bound aspartyl protease expressed mainly in the central nervous system (CNS). In addition, BACE1 is the rate-limiting enzyme responsible for the production of Aβ from APP and both the expression level and activity of this enzyme are aberrantly elevated in the brains of patients with AD . It has Sulforaphane (isothiocyanato-4-(methylsulfinyl)-butane) is a sulfur-rich compound found in cruciferous vegetables including broccoli and cabbage. Sulforaphane is not only a well-known chemoprotective compound but also demonstrated to have a variety of biological activities including antioxidant, antidiabetic, anti-inflammatory, and neuroprotective effects ,9,10,11.Sulforaphane (≥98% purity), resveratrol, and quercetin were purchased from Sigma-Aldrich . The aspartic proteases such as pepsin, cathepsin D, and their substrates were also purchased from Sigma-Aldrich . BACE1 fluorescence resonance energy transfer (FRET) kit was obtained from Thermo Fisher Scientific, Inc. . BACE2 and synthetic peptide substrate were purchased from Enzo Life Sciences .The fluorescence resonance energy transfer (FRET) assay for BACE1 and BACE2 was conducted following the supplier’s instructions, with minor modifications . BrieflyInhibition mode and kinetic parameters including Ki, Km, and Vmax were evaluated by Dixon plot and Lineweaver–Burk plot. The Dixon plot was acquired in the presence of various substrate concentrations, which was intended to determine the mode of enzyme inhibition and Ki value for the enzyme–inhibitor complex. The Lineweaver–Burk plot was used to calculate Vmax and Km and from the absence or presence of sulforaphane . SigmaPlot™ was used to analyze enzyme kinetics.To evaluate enzyme selectivity, aspartic proteases including pepsin and cathepsin D were assessed with their substrates. In addition, serine proteases such as trypsin, chymotrypsin, and elastase assays were conducted.Docking analysis was conducted on the conformation of the BACE1-sulforaphane with Autodock Vina program for predicting binding free energy and poses . Pck software was employed to find the binding pocket residues of enzymes. The default box size was set to 30 Å × 30 Å × 30 Å. The structures of BACE1 and sulforaphane were retrieved from the Protein Data Bank (PDB ID: 2wjo) and PubChem (CID: 5350), respectively.All data were presented as the mean ± standard deviation (SD) of three independent experiments. Statistical significance was determined by Duncan’s multiple range tests using the SAS system .50 value of 2.80 ± 0.19 μM. According to the Dixon and Lineweaver–Burk plots, sulforaphane suppressed BACE1 in non-competitive mode with a Ki value of 3.1 μM, which demonstrated that the compound interacted at sites other than the catalytic sites of BACE1 (50 18.10 ± 0.03 and 15.04 ± 0.87 μM). Previously published natural BACE1 inhibitors with various structural types, such as catechins, flavonoids, stilbenes, tannins, coumarins, chromones, and others, exhibit inhibitory activity, with IC50 values ranging from >200 μM to 1.6 μM [As shown in of BACE1 . Sulforao 1.6 μM , which s50, 0.24 μM), did not proceed to phase II clinical trials because of its off-target activity against cathepsin D [To prevent cross-inhibition side effects, it is important to confirm that sulforaphane is selective over other off-target enzymes. Sulforaphane did not show significant inhibition against serine proteases such as trypsin, chymotrypsin, and elastase that play important roles in various normal physiological processes including digestion, embryonic development, inflammatory responses, and immunity . The comhepsin D . The preFor further exploration of the BACE1–sulforaphane interaction, computational docking simulation was employed. As illustrated in According to our present study, sulforaphane, as a non-competitive BACE1 inhibitor, had high selectivity for BACE1 as compared to the other aspartyl and serine proteases, indicating that the compound was a safe natural inhibitor of side effects. Many natural BACE1 inhibitors showed non-competitive modes, such as green tea catechins, stilbenoids, coumarins, citrus flavonoids, and ellagitannin ,22,23,24Sulforaphane has received considerable recent attention because of its neuroprotective effect in several previous in vitro and in vivo studies. In vitro studies showed that sulforaphane decreased neuronal toxicity induced by Aβ, tau protein, methylglyoxal, and hydrogen peroxide ,26,27. IThe blood–brain barrier (BBB) is constituted by neurovascular units that contain endothelial cells advocated by the neuroglia . The resWhen consumed, sulforaphane is conjugated with glutathione (GSH) and sequentially metabolized to form several metabolic conjugates including sulforaphane-GSH, sulforaphane- cysteine, and sulforaphane-N-acetylcysteine, which were found in the CNS of mice . Further50) and the median lethal dose (LD50) of sulforaphane were 191.58 mg/kg and 212.67 mg/kg, respectively. In addition, several clinical studies proved the safety and tolerance of the compound [Sulforaphane from cruciferous vegetables is considered to be safe and nontoxic. Based on the results from toxicity studies, the median toxic dose (TDcompound ,39,40. AAlthough the pathological mechanisms of AD have not been exactly clarified yet, various evidence supports the notion that BACE1 is the rate-limiting enzyme in Aβ production and the critical pathogenic events that lead to AD. The present study revealed that sulforaphane is a novel BACE1 inhibitor with high potency and selectivity which, together with its previously proven safety and good BBB penetration property, could be used as a promising candidate in AD prevention.


This cohort study describes the burden of coronavirus disease 2019 among people detained by US Immigration and Customs Enforcement compared with the US population. Practices undertaken by ICE, such as detainment, deportation, and searches, adversely affect the physical and mental health of those who are undocumented.The New York Times, with denominator data from the American Community Survey (eMethods in the STROBE) reporting guidelines.Data concerning ICE and COVID-19 for this cohort study were obtained from the COVID Prison Project from May 5 to September 15, 2020, with the ICE mean daily population (MDP) serving as the denominator. General population COVID-19 data were obtained from We used baseline facility MDP to calculate cumulative case rates per 1000 persons detained by ICE over time and compared this with cumulative case rates in the general population. We also used ICE testing and case data to calculate the percentage tested and test positivity rate (percentage of tests returned with positive results). For county-level comparisons, we used year-to-date MDP from September 12, 2020, to calculate cumulative and current case rates for ICE designated facilities. County cumulative case rates in the general population were also calculated. Risk ratios compared cumulative case rates in facilities and the corresponding county. Analysis was performed using STATA, version 15 (StataCorp LLC) and Excel (Microsoft Corporation).One hundred sixty-seven facilities housed people detained by ICE, most of which were facilities that primarily housed non-ICE detainees . Immigration and Customs Enforcement reported at least 1 COVID-19 case in 96 facilities. Using baseline MDP, the overall September 15 cumulative case rate was 214 per 1000 people (5810 cases among 27 189 people); ICE reported 6 deaths. There were 28 designated ICE facilities that exclusively housed people detained by ICE, including 4 family residential centers.As testing rates reported by ICE increased, case rates increased, and test positivity rates decreased . However6 With a mean stay of 38 days, it is difficult to assess mortality and testing rates given high population churn. We report crude rates because age data for ICE detainees are not available. It is likely that the age structure is younger than that of the general population. Facility staff were excluded. There are potential differences in facility responses to COVID-19 (https://www.freedomforimmigrants.org/map). Ultimately, it is imperative that expeditious action is taken to protect people housed in ICE detention facilities from COVID-19 by reducing the number of people detained and terminating raids, transfers, and deportation flights.Cumulative case rates among people detained by ICE are higher than those of the US population and dwarf those of surrounding communities. However, this study has limitations. This analysis depends on ICE reporting; thus, cases may actually be higher.


The uncertain response of marine terminating outlet glaciers to climate change at time scales beyond short-term observation limits models of future sea level rise. At temperate tidewater margins, abundant subglacial meltwater forms morainal banks  or ice-contact deltas that reduce water depth, stabilizing grounding lines and slowing or reversing glacial retreat. Here we present a radiocarbon-dated record from Integrated Ocean Drilling Program (IODP) Site U1421 that tracks the terminus of the largest Alaskan Cordilleran Ice Sheet outlet glacier during Last Glacial Maximum climate transitions. Sedimentation rates, ice-rafted debris, and microfossil and biogeochemical proxies, show repeated abrupt collapses and slow advances typical of the tidewater glacier cycle observed in modern systems. When global sea level rise exceeded the local rate of bank building, the cycle of readvances stopped leading to irreversible retreat. These results support theory that suggests sediment dynamics can control tidewater terminus position on an open shelf under temperate conditions delaying climate-driven retreat. Tidewater glaciers in fjords can advance/retreat independent of climate due to stabilization by sediments at their termini. We show that an Alaskan paleo-ice stream behaved similarly on an open shelf, suggesting that increased sediment flux may delay catastrophic retreat of outlet glaciers in a warming world. Similarly, in Greenland the stability of calving outlet glaciers is not uniform but is influenced by local conditions particular to their marine termini6, as well as by thinning and acceleration attributed to climate change7. Both theory9 and field studies11 demonstrate that sediment yield from temperate glacial erosion and sediment transport can reduce water depth and restrict calving by building up morainal banks and ice-contact deltas. This behavior, which may last for centuries to millennia, comprises the advance phase of the tidewater glacier cycle (TGC) when calving is low while the morainal bank builds at the grounding line13. Iceberg flux may be eliminated if the bank rises above the sea surface and grows into an ice-contact delta. A delta or morainal bank, however, eventually destabilizes when meltwater-controlled erosion creates an overdeepened, reverse sloping bed and a terminus grounded in progressively deeper water, initiating retreat13. Catastrophic collapse produces an ice-rafted debris mass accumulation rate (IRD MAR) peak due to increased calving rates lasting decades until it reaches a stable position in shallower water.In the present climate regime, Alaskan tidewater glaciers exhibit regionally asynchronous behavior with ~33% advancing14), the Cordilleran ice sheet discharged to the NE Pacific via ice streams through eight shelf-crossing sea valleys on the southern Alaskan margin18. Today, Bering Glacier is grounded on land, seaward of the St. Elias Mountains and Bagley Icefield and comprises 15% of the glacier ice in Alaska19   except that it opens directly into the Gulf of Alaska  reconstructions for the LGM26, sedimentary architecture in the trough16, and high accumulation rates of glacimarine mud28 indicate that BIS existed under a temperate climate regime as a grounded tidewater ice cliff without a floating ice tongue or shelf29.Bounded by steep sidewalls, the morphology of the 25-km-wide, 55-km-long Bering Trough is similar to Alaskan fjords, where the TGC was first described 18. Here, we focus on the upper 116 m  Site U1421  was drilled to 695.72 m CCSF-A (meters core composite depth below sea floor) at 721 m water depth on the continental slope32  derived IRD MAR, counts of diatoms, planktic and benthic foraminifera, and organic geochemical data  clast-rich diamicton or mud with abundant clasts with high-frequency peaks and troughs in IRD MAR averaging >240 g cm−2 kyr−1  and 18.7–17.6 ka cal BP (cycles 8–12)  compared with Facies I, and consists of clast-rich diamicton and mud with dispersed clasts with average IRD MAR of ~100 g cm−2 kyr−1  indicate warming of the NE Pacific coeval to the deposition of the Facies II units at Site U1421 , an upwelling indicator often found along continental slopes in the North Pacific, Thalassionema nitzschioides, a diatom associated with high nutrient waters in the North Pacific, and Shionodiscus trifultus, a diatom common in regions with melting sea ice. Several diatoms that are associated with sea ice are also present, including Fragilariopsis cylindrus, Thalassiosira antarctica RS, and Ehrenbergiulva spp.38. Sea-ice diatoms decrease in abundance throughout Facies III  even when regional SSTs were warmer and eustatic sea level was rising after ~21 ka  images  appear similar to the observed initial retreat from the trough51.Tidewater glacier advance during sea-ice build up (ice mélange) is well known in Greenlandds Figs.  and  5. 6. At the tidewater ice cliff, warm seawater can promote undercutting and collapse, thus accelerating iceberg calving52 and triggering glacier retreat. Also, sea level rise may increase buoyancy forces on the terminus and alter basin geometry53. Temperate tidewater glaciers can enhance their stability by building submarine morainal banks at the terminus, which both reduce buoyancy forces and insulate the ice from warm erosive seawater13. SST began to steadily rise ~17 ka, exceeding those previously observed in the LGM46; however, BIS appears to switch from the TGC and begin its terminal retreat earlier, around 17.6 ka cal BP. This timing suggests that the trigger for terminal retreat may be related to rapid sea level rise, increased subglacial meltwater erosion   in order to exclude biogenic components and burrow fills, which do not have an ice-rafted origin. MARs were calculated by multiplying by the sedimentation rates and dry bulk densities, which were obtained from discrete shipboard measurements30 (Supplementary Data Ice-rafted debris is quantified by weighing the coarse sand fraction (250 µm–2 mm) following the method of Krissek59). Radiocarbon analyses were performed at Australian National University at the Single Stage Accelerator Mass Spectrometry (SSAMS) Lab in the Research School of Earth Sciences60. Instrument reproducibility was tracked over the course of the project via the analysis of 30 unleached aliquots of the 18,199 ± 8 BP TIRI/FIRI turbidite standard61; individual dates averaged 18,210 ± 50 BP (1−σ) and ranged from 18,110 to 18,300 BP with individual reported errors of between 45 and 70 14C years.Sediment samples from Site U1421 were sieved at 150 µm and picked for benthic and planktic foraminifera, with care taken to avoid infaunal benthic species . The age model was further constrained via three lithologic transitions at 6.01, 52.078, and 58.078 m CCSF-A, such that 14C-resolved sedimentation rates within a given depositional unit were extended to its boundaries. Sedimentation rates were then calculated from this age model at binned 1000-year intervals  profiles were collected by the U.S. Geological Survey in conjunction with the University of Washington in September 197466. The sources for the survey were simultaneously firing, dual 45/45 cubic inch GI guns, which are ideal due to their suppression of the bubble pulse. The shot interval was 25 m and the signal was recorded using a 750-m-long, 60 channel hydrophone streamer with a 1 ms sampling interval. Processing steps completed at the University of Texas Institute for Geophysics included: bandpass filtering, normal moveout correction, trace regularization, f-k filtering, water-bottom muting, stacking, and finite-difference migration66. Resultant vertical resolution is ~5 m with 1500–2000 m of penetration in sediments.18.Seismic line GOA2505 was collected as part of the National Science Foundation, Ocean Drilling Program-funded site survey aboard the R/V Maurice Ewing in the summer of 2004N. pachyderma  and benthic foraminifera (Uvigerina peregrina and Epistominella pacifica) were picked from the >150 µm size fraction. Isotope analyses were performed by the Oregon State University College of Oceanic and Atmospheric Sciences Stable Isotope Laboratory using a Kiel III carbonate preparation device connected to a Thermo-Finnigan MAT‐252 mass spectrometer. Data were corrected using an internal lab standard (Wiley—expected δ18O = −7.20 permil vs VPDB). The international standard NBS19 was run as a check standard (expected δ18O = −2.20 permil vs VPDB). Results were compared with the LR04 benthic stack67 . SPT was diluted to 2.38 g cm−3 and added to the treated sediments. Sediments were centrifuged for 20 min at 2500 rpm. The supernatant, which contained primarily diatoms, was collected and the process repeated twice more. The three vials of supernatant for each sample were then combined into one centrifuge tube, 15 ml of DI water was added to reduce the density of the liquid, and this mixture was centrifuged again for 20 min at 2500 rpm. After decanting and discarding the supernatant, this process was also repeated twice more. The discarded supernatant and discarded dense sediments were checked to be sure no diatoms were present using a Nikon Eclipse Ni compound microscope at ×400 magnification. None was found. The concentrated diatoms were then diluted with sodium hexametaphosphate, a deflocculant, and made into quantitative diatom slides70. When possible, at least 300 diatoms were identified to the species level at ×1000 magnification from at least three transects across each slide71. In cases with apparent low diatom abundance, diatoms were counted from three transects and the number of diatoms per gram of sediment was calculated using the formula by Scherer70. If there were fewer than 25,000 diatoms per gram of sediment, the sample was considered barren and diatoms were not identified to the species level for that sample  content was determined using a carbon–sulfur determinator (ELTRA CS 800) after the removal of carbonates by adding 500 µl 12N hydrochloric acid to the sediment. Total nitrogen (Ntot) content determined by a CNS analyzer  was used to calculate atomic C/N ratios to distinguish between algae and land-plant origins of organic matter72. For biomarker analyses, sediments were extracted by means of ultrasonication using a dichloromethane:methanol mixture . Sediments were extracted three times and the combined total lipid extract was fractionated using silica open-column chromatography to obtain the polar lipids . For quantification, androstanol was added to the sediment as internal standard prior to extraction. Sterols were derivatized using N,O-bis(trimethylsilyl)trifluoroacetamide  and analyzed using a gas chromatograph  coupled to a mass selective detector . The identification of dinosterol  was based on comparison of its retention time and mass spectra with that of a reference compound run on the same instrument. Data are presented in Supplementary Data Bulk sediment total organic carbon  or absence (0) of each microtexture on the list and frequency percent was calculated for each sample. PCA confirms the microtexture associations for our Alaska samples  analysis was used to verify quartz composition before collecting a photomicrograph. We complied a checklist based on the literature that included microtextures typical of glacial crushing and assumed direct iceberg transport including high relief, conchoidal fracture, fracture faces, arcuate steps, straight steps, and angular (sharp) outlinesSupplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9