Synthetic hepcidin triggered hypoferremia in control but not iron-loaded wild type animals. Furthermore, it dramatically decreased hepatic and splenic ferroportin in -/-Hjv mice on standard or iron-deficient diet, but only triggered hypoferremia in the latter. Mechanistically, iron antagonized hepcidin responsiveness by inactivating IRPs in the liver and spleen to stimulate ferroportin mRNA translation. Prolonged LPS treatment eliminated ferroportin mRNA and permitted hepcidin-mediated hypoferremia in iron-loaded mice. Thus, de novo ferroportin synthesis is a critical determinant of serum iron and finetunes hepcidin-dependent functional outcomes. Our data uncover a crosstalk between hepcidin and IRE/IRP systems that controls tissue ferroportin expression and determines serum iron levels. Moreover, they suggest that hepcidin supplementation therapy is more efficient when combined with iron depletion.The iron hormone hepcidin is transcriptionally activated by iron or inflammation via distinct, partially overlapping pathways. We addressed how iron affects inflammatory hepcidin levels and the ensuing hypoferremic response. Dietary iron overload did not mitigate hepcidin induction in lipopolysaccharide (LPS)-treated wild type mice but prevented effective inflammatory hypoferremia. Likewise, LPS modestly decreased serum iron in hepcidin-deficient  Systemic iron balance is controlled by hepcidin, a peptide hormone that is produced by hepatocytes in the liver and operates in target cells by binding to the iron exporter ferroportin . This reHamp gene is transcriptionally induced by iron or inflammatory stimuli via BMP/SMAD  mRNA, the major ferroportin transcript that harbors an ‘iron responsive element’ (IRE) within its 5’ untranslated regions (5’ UTR) is controlled by ‘iron regulatory proteins’ (IRPs), IRP1 and IRP2. The IRE/IRP system accounts for coordinate post-transcriptional regulation of iron metabolism proteins in cells (Slc40a1(+IRE) and ferritin (Fth1 and Ftl1) mRNAs, inhibiting their translation. IRE/IRP interactions do not take place in iron-loaded cells, allowing de novo ferroportin and ferritin synthesis to promote iron efflux and storage, respectively. The impact of the IRE/IRP system on the regulation of tissue ferroportin and serum iron is not well understood.Hepcidin-dependent inhibition of ferroportin activity and expression is a major but not the sole contributor to inflammatory hypoferremia . This isin cells . In a ho-/-Hjv mice, we demonstrate that serum iron levels reflect regulation of ferroportin in the liver and spleen by multiple signals. We further show that effective hepcidin-mediated hypoferremia is antagonized by compensatory mechanisms aiming to prevent cellular iron overload. Our data uncovered a crosstalk between hepcidin and the IRE/IRP system that controls ferroportin expression in the liver and spleen, and thereby determines serum iron levels.The aim of this work was to elucidate mechanisms by which systemic iron overload affects hepcidin expression and downstream responses, especially under inflammatory conditions. Utilizing wild type and Hamp mRNA  for short (1 day), intermediate (1 week), or long (5 weeks) time intervals; other animals remained on standard (control) diet. As expected, mice on HID for 1 day manifested maximal increases in serum iron  and tranamp mRNA , with hiamp mRNA .Hamp mRNA in dietary iron-loaded mice  for 5 weeks, to achieve a broad spectrum of hepcidin regulation. Wild type mice on HID and -/-Hjv mice on control diet or IDD manifested similarly high serum iron and transferrin saturation  and Slc40a1(-IRE) -encoding Tfrc mRNA was largely unaffected by LPS, except for a reduction in -/-Hjv mice on IDD  . Both ofnduction , and wernduction . The LPS animals . These ee on IDD . The abo-/-Hjv mice , Socs3, Id1, and Bmp6 mRNAs  using a 32P-labelled IRE probe. The data in -/-Hjv mice, leaving IRP1 largely unaffected. IRE/IRP2 interactions are better visible in longer exposures (middle panels). EMSAs with tissue extracts previously treated with 2-mercaptoethanol (2-ME) were performed as loading controls  was ~1.5–twofold higher in the non-parenchymal cell fraction as compared to hepatocytes in both wild type and -/-Hjv mice  mRNA is the predominant ferroportin transcript in the mouse liver and spleen, as well as in hepatoma and macrophage cell lines  mRNA translation by polysome profile analysis. We focused on the liver because this organ contains the highest number of iron-recycling macrophages , Fth1 (positive control for iron regulation), and Actb (negative control) mRNAs within the different fractions was quantified by qPCR (Slc40a1(+IRE) (and Fth1) mRNA translation in wild type mice (note the shifts from monosomes to polysomes in Slc40a1(+IRE) (and Fth1) mRNA translation in -/-Hjv mice. We also attempted to obtain polysome profiles of Slc40a1(-IRE) mRNA but it was undetectable after fractionation. These data indicate that in mice subjected to iron overload, iron-stimulated ferroportin synthesis in the liver antagonizes hepcidin-mediated ferroportin degradation and prevents an appropriate hypoferremic response. Considering that levels of Slc40a1(+IRE) mRNA are elevated in iron-loaded wild type and -/-Hjv mice  (and Fth1) mRNA translation in the liver.Quantification of liver iron by ICP-MS  validatesee also . Iron reepletion . NeverthSlc40a1(+IRE) mRNA below detection levels (Slc40a1(-IRE) isoform (Slc40a1(+IRE) (ΔCt = 8.18), in agreement with published data  in cultured cells . To expl-/-Hjv mice with extreme differences in hepcidin expression. The unimpaired inflammatory induction of hepcidin in iron-loaded wild type mice correlated with significant drops in serum iron, but these appeared inversely proportional to the degree of systemic iron loading . Thus, L-/-Hjv mice on control diet, but not below baseline  mRNA translation in the liver of wild type mice. Conversely, relative dietary iron depletion inhibits Slc40a1(+IRE) mRNA translation in the liver of -/-Hjv mice, in line with the restoration of hepcidin-mediated hypoferremic response  mRNA. Direct evidence for activation of IRP responses in the liver and spleen to dietary iron manipulations is provided in Slc40a1(+IRE) mRNA translation in both fractions of hepatocytes and non-parenchymal liver cells. Presumably, this offers a compensatory mechanism to protect the cells from iron overload and iron-induced toxicity. On the other hand, this mechanism attenuates hepcidin responsiveness and promotes a state of hepcidin resistance, as higher amounts of hepcidin are required to achieve effective hypoferremia. Because hepcidin has a short plasma half-life, it is reasonable to predict that the use of more potent hepcidin analogs .Wild type C57BL/6J and isogenic -/- mice  were hou-/- mice . Where iBlood was collected via cardiac puncture. Serum was prepared by using micro Z-gel tubes with clotting activator (Sarstedt) and was kept frozen at −20°C until analysis. Serum iron, total iron binding capacity (TIBC) and, where indicated serum ferritin, were determined at the Biochemistry Department of the Montreal Jewish General Hospital using a Roche Hitachi 917 Chemistry Analyzer. Transferrin saturation was calculated from the ratio of serum iron and TIBC. Serum hepcidin was measured by using an ELISA kit .l . 5 μl of sample was loaded in a 96-well plate containing 195 μl of iron-free HEPES-buffered saline with or without 100 μM deferiprone. Microplates were read every 2 min at 37℃ over 40 min at 485/520 nm (ex/em). Final ΝΤΒΙ was calculated by comparing the oxidation rate of DHR in the presence or absence of the strong chelator deferiprone.NTBI was measured by adapting the method developed by Esposito et al . BrieflyDTHFPICIFCCGCCHRSKCGMCCKT) was synthesized at Ferring Research Institute, San Diego, CA. The linear peptide was assembled on Rink amide resin using Tribute peptide synthesizer and the peptide was cleaved from the resin with the TFA/TIS/EDT/H2O 91:3:3:3 (v/v/v/v) cocktail. The solvents were evaporated, and the crude peptide was precipitated with diethyl ether, reconstituted in 50% aqueous acetonitrile and lyophilized. The lyophilizate was dissolved in 30% aqueous acetonitrile at the concentration of 0.05 mM and the pH of the solution was adjusted to 7.8 with 6 M ammonium hydroxide. Folding was achieved within 4 hr using the cysteine/cystine redox (peptide/Cys/Cys2 1:6:6 molar ratio). The reaction mixture was acidified to pH 3, loaded onto HPLC prep column and purified in a TFA based gradient. The identity of the peptide was confirmed by mass spectrometry and by coelution with a commercially available sample .Human hepcidin (-ΔΔCt method (Rpl19). Data are reported as fold increases compared to samples from wild type mice on control diet.RNA was extracted from livers by using the RNeasy kit (Qiagen). cDNA was synthesized from 1 μg RNA by using the OneScript Plus cDNA Synthesis Kit . Gene-specific primers pairs  were valt method . Data we2, 0.1 mg/ml Cycloheximide, 1 tablet cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail (Roche), 200 U/mL Recombinant RNasin Ribonuclease Inhibitor (Promega), 2 mM DTT). Linear gradients were prepared in Polyallomer Centrifuge Tubes (Beckman Coulter). Tubes were marked using a gradient cylinder (BioComp), and 5% sucrose solution was added using a syringe with a layering needle (BioComp) until solution level reached the mark. Then, 50% sucrose solution was layered underneath the 5% solution until the interface between the two solutions reached the mark. Tubes were capped with rate zonal caps (BioComp) and linearized using a Gradient Master 108 (Biocomp). All reagents were nuclease-free and all solutions were kept on ice or at 4℃. Sample preparation was adapted from 2, 2 mM DTT, 1 mg/ml Cycloheximide, 200 U/ml Recombinant RNasin Ribonuclease Inhibitor [Promega], 1 tablet cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail [Roche] 0.5% [v/v] Triton X-100, 0.5% [v/v] Sodium Deoxycholate) and homogenized using Dounce homogenizers (60 movements with both loose and tight pestles) on ice. Samples were centrifuged at 4℃, 16,060 g for 4 min and supernatants were collected. Sample optical density was measured at 260 nM and samples were normalized to either the lowest value or 30 ODs. 450 μl of sucrose gradient was removed from the top and replaced with normalized sample. Tube weights were balanced by weight before centrifugation at 200,000 g for 2 hr at 4℃ in a SW 41 Ti rotor and a Beckman Optima L-60 Ultracentrifuge. Samples were fractionated using a BR-188 Density Gradient Fractionation System (Brandel). Immediately upon collection, 800 μl of samples were mixed with 1 ml of TRIzol and kept on ice before storage at –80℃. Polysomal RNA was processed according to the manufacturer’s protocol. mRNA distribution was analyzed as previously described (RNA was freshly prepared from frozen livers. Linear sucrose gradients were prepared the day before the experiment by using 5% (w/v) and 50% (w/v) sucrose solutions with 10 × gradient buffer  and dried overnight at 37°C. The slides were then loaded onto the Discovery XT Autostainer  for automated immunohistochemistry. Slides underwent deparaffinization and heat-induced epitope retrieval. Immunostaining was performed by using 1:500 diluted rabbit polyclonal antibodies against ferroportin  and an aTo visualize non-heme iron deposits, deparaffinized tissue sections were stained with Perls’ Prussian blue using the Accustain Iron Stain kit (Sigma).Total liver iron was quantified by using the ferrozine assay  or induc3 as DRC-gas was applied for non-interfered monitoring of the iron isotopes. A ‘PrinCe 706’ CE system  was employed for separation of iron species at +20 kV. Temperature settings for sample/buffer tray and capillary were set to 20°C. An uncoated capillary  was used for separation and hyphenation to the ICP–DRC-MS. A CE-ICP-MS interface  coupled to ICP-MS (CE-ICP-MS). Dynamic reaction cell (DRC) technology (ICP-DRC-MS) with NHnterface  was instStatistical analysis was performed by using the Prism GraphPad software (version 9.1.0). Lognormally distributed data including qPCR and ELISA results were first log transformed before analysis with ordinary two-way ANOVA (Tukey’s multiple comparisons test) for comparisons within same treatment groups (denoted by a or b on figures) or with multiple unpaired t tests using the Holm-Sidak method to compare effects between treatments. Normally distributed data was analyzed by two-way ANOVA using either Sidak’s method for comparisons between treatment groups or Tukey’s multiple comparisons test within treatments groups. Where indicated, pairwise comparisons were done with unpaired Student’s t test. Probability value p<0.05 was considered statistically significant. The authors present a manuscript aiming to understand how systemic iron overload counteracts the hypoferremic effects of a specific inflammatory stimulus, specifically focused on the role of mechanisms of ferroportin regulation to achieve hypoferremia during inflammation. This work is of interest to the community of researchers interested in the interaction of systemic iron regulation and inflammation and possibly ultimately clinicians managing iron disorders. This work also is of novel significance for translational purposes and could lead to the design of better therapeutics for iron related disorders and/or anemia of chronic inflammation. The current study demonstrates LPS and exogenous hepcidin can synergistically lead to hypoferremia even in iron overload conditions and provides data implicating ferroportin translation in contributing to the fully sequestering iron in cells involved in iron flows to induce hypoferremia. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting the paper "Coordinate regulation of liver ferroportin degradation and de novo synthesis determines serum iron levels in mice" for consideration by Comments to the Authors:eLife.We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by The main shortcomings of the presented work include a nebulously defined problem that makes for difficulty understanding; lack of clarity whether hepatocytes or macrophages are the specific cellular targets of hepcidin and LPS driving hypoferremia; incomplete analysis of dosing and timing of synthetic hepcidin to determine whether there is a proportion of systemic iron to overcoming hepcidin resistance; and lack of important endpoints of signaling pathways to hepcidin and IRP-IRE related changes as part of a more complete transcriptional regulation in addition to the translational regulation of FPN. The later point is aimed at the lack of a mechanistic understanding behind increased FPN synthesis. A complete list of reviewer comments can be found below.Reviewer #1 (Recommendations for the authors):Charlebois et al. present a well written manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction or exogenous hepcidin. Furthermore, there is an interesting clue that ferroportin can be translationally regulated by LPS to enhance iron egress and prevent robust hypoferremia. However, the authors do not reconcile how this work can be implemented, whether manipulating the nuanced distribution of iron  has significance, or whether this better understanding is important for management of infection in iron overloaded patients. Furthermore, a substantive part of the presented data is not central to the main story, and in some cases, the conclusions are overstated. In addition, a more robust evaluation of signaling pathways in the liver in response to LPS is missing and important. Finally, it is not clear whether hypoferremia would be expected to decrease susceptibility to pathogens.Charlebois et al. present a well written manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction or exogenous hepcidin. Furthermore, there is an interesting clue that ferroportin can be translationally regulated by LPS to enhance iron egress and prevent robust hypoferremia. However, the authors do not reconcile how this work can be implemented, whether manipulating the nuanced distribution of iron  has significance, or whether this better understanding is important for management of infection in iron overloaded patients. Furthermore, a substantive part of the presented data is not central to the main story, and in some cases, the conclusions are overstated. In addition, a more robust evaluation of signaling pathways in the liver in response to LPS is missing and important. Finally, it is not clear whether hypoferremia would be expected to decrease susceptibility to pathogens. Some additional questions and clarifications are delineated below.1) The authors consider hypoferremia to be serum iron below that of WT untreated mice on standard iron diet. This is not well explained. Why was this definition selected? In some cases, a statistically significant decrease in serum iron in response to an intervention was not meaningful as per the authors unless it resulted in hypoferremia to a certain degree . However, this is an unrealistic expectation that in iron loaded conditions that the same dose of LPS or hepcidin mimetic would lead to a proportionally larger decrease in iron than if the starting ferremia was much lower at baseline. This requires some explanation and corroboration.2) Serum hepcidin is induced in LPS treated iron loaded mice in Figure 1 but increased hepcidin did not prevent iron overload. The authors interpret this as a decrease in hepcidin responsiveness. However, it would be useful to evaluate the signaling to hepcidin regulation, namely SMAD1/5/8, STAT3, and MEK/ERK1/2.3) It is somewhat unclear why the authors are comparing WT iron loaded mice with Hjv ko mice and Hjv ko on an iron deficient diet. The effects in vivo of these composite changes make for inadequate comparisons. Please clarify in the introduction to preemptively elucidate why this is a sound comparison.4) The authors overstate the statistics  or call things "borderline" when they are not. Please edit throughout to make clear that differences that are not significant are unlikely to be "truly" different one from the other. The only way to make this argument is to increase the number of mice in the repeated analysis. For example, using qualifiers like "profoundly" suppressed  or "modestly" affected  is subjective when the statistics drive the argument making the "modest" effect without significant difference an overinterpretation of the results. Similarly, page 7, line 147 suggesting splenic FPN suppression "in all animals" when only HJV ko on SD is statistically significant is misleading.5) The authors spend a substantial amount of time recapitulating the already known elements regarding Hjv ko mice relative to WT and the effects of synthetic hepcidin. This focus detracts from the main points and all data that is not relevant to the main point should be moved to the supplementary material sections. This mainly pertains to Figure 1, 2, and 4.6) The most interesting and novel part of the manuscript is the effects on FPN translation in the setting of LPS treatment. In addition, the effect of LPS on expression of iron transporters and sensors is additionally novel and under-developed. Is there a precedent for this? Please expand in the Discussion section. The manuscript as a whole would benefit from refocusing effort toward the data in Figure 6 and 7.7) The authors claim that the results in WB in Figure 3 and 5 are a result of hepatocyte FPN expression despite using liver homogenates for these analyses while simultaneously suggesting the FPN signal in IHC is strongest in Kupffer cells in the liver. This can be resolved by analyzing the 2 populations separately using well-established liver digestion techniques. Otherwise, the first few sentences on page 7 are difficult to interpret objectively.8) Page 8, line 171, there is mention that synthetic hepcidin did not promote inflammation but no direct evidence of this is provided. Please add expression and signaling data for inflammatory endpoints in the liver.9) The main argument appears obvious, that the higher the iron load in the system, the more hepcidin is needed to induce hypoferremia due to the additional compensatory mechanisms that protect cells from iron toxicity. This should be made very clear in the abstract, introduction, and discussion.10) Figure 6 is interesting and novel. However, the polysome data is significant only for WT vs. Hjv ko which is likely multifactorial, not singularly the result of dietary iron loading as the authors suggest . Having a WT IDD control would be helpful here. In addition, the generalization that Hjv ko represents all forms of iron overload is an overstatement.11) Unclear in Figure 6B and 6C how the specific statistical comparison is important. Is it noted because it is the only observed difference between groups in this experiment or does it have specific importance? Please clarify?Reviewer #2 (Recommendations for the authors):The value of this study is the investigation of the relative role of iron overload and inflammation in the regulation of iron intake and iron distribution. In particular, the Authors investigated if and how inflammation can lead to hypoferremia in presence of iron overload. They analyze the expression of the proteins hepcidin and ferroportin in normal and HJV-KO animals (which are iron overloaded) in presence of administration of LPS, which triggers inflammation and increased expression of hepcidin. They showed that ferroportin is expressed at high levels under condition of iron overload and that administration of synthetic hepcidin is insufficient to trigger hypoferremia in iron-loaded animals. Hypoferremia was eventually achieved when LPS and synthetic hepcidin were administered concurrently. This can lead to design better therapeutics for iron related disorders.The paper is well written and clear. The primary claims of the Authors are supported by their data, although some additional experiment are required to complete these studies.Figure 7: The treatment with synthetic hepcidin (SH) is done only for one day. Although only the combination of LPS and SH shows a reduction in serum iron, long term affects were not evaluated. I think that a complete analysis of this approach should include longer studies to assess the effect on iron organ concentration comparing SH or LPS alone and the combination of the two reagents. Also, Figure 7 is not showing the effect of the various parameters using SH alone. . I suspect that SH alone, in the long term, may also be sufficient to induce hypoferremia and reduce iron overload. Targeting ferroportin + SH is likely to induce hypoferremia and reduce iron overload faster than administration of SH alone.In absence of appropriate mouse models, can the authors show, in a cellular model, that cells harboring the ferroportin gene in absence of the IRE sequence are less sensitive to export iron following iron administration, in presence or absence of SH?Reviewer #3 (Recommendations for the authors):The ability of animals to lower their extracellular iron concentration in response to inflammatory stimuli (hypoferremia of inflammation) is an important innate immune response that has been shown to mediate resistance to certain bacterial infections. Early studies of the molecular mechanism of hypoferremia noted that even mild iron overload, caused by excessive iron content of the common mouse diet, interfered with this response. The current study aimed to analyze the mechanism(s) behind this interference.The strengths of the study include:1) Two different models of iron overload were employed, a low hepcidin genetic model (Hjv-/-) and a high hepcidin iron-rich diet model (2% carbonyl iron) and a standard inflammatory stimulus 1 microgram/g of LPS. Both iron overload states greatly decreased the hypoferremic response, even though hepcidin was still induced by LPS.2) The authors show that the mechanism of relative resistance to hepcidin is in large part caused by increased ferroportin translation in iron-overloaded tissues that counteracts the ferroportin-degrading and ferroportin-blocking effect of hepcidin.3) The use of high-dose exogenous hepcidin as a probe of iron-overload-induced resistance to hepcidin is an important advance, with implications for the treatment of iron disorders.The main weaknesses of the study include:1) The paper is narrowly conceived and interpreted, making it mainly interesting to a very specialized readership. However, for this readership, the mechanistic insights are few and already anticipated.2) The paper does not define the cell type that drives the hypoferremic response in each situation or mediates the resistance to hepcidin in iron overload (hepatocytes vs macrophages).3) The evidence that iron overload also substantially raised ferroportin mRNA concentrations , likely by a transcriptional mechanism, is not developed or interpreted in the context of the changes in translation.4) Although the implication is that increased ferroportin translation in iron-overloaded tissues is mediated by the IRP-IRE system, and this is a reasonable hypothesis, the authors did not demonstrate this in their various models and conditions.5) The paper provides strong evidence that iron overload induces a state of relative resistance to hepcidin, including hepcidin induced by LPS injections. Using phrases that explicitly state this would make the paper easier to understand.6) Identifying the specific cellular targets of hepcidin and LPS (hepatocytes vs macrophages) that drive hypoferremia in the various conditions is not easy but it is important.7) The induction of ferroportin by iron overload has a transcriptional component whose importance should be analyzed and commented on.8) The role of IRE-IRP in the translational mechanism should be demonstrated.9) A dose-response analysis of hepcidin resistance using exogenous hepcidin would be more convincing than a single dosage study.eLife. Your revised article has been evaluated by Mone Zaidi (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "A crosstalk between hepcidin and IRE/IRP pathways controls ferroportin expression and determines serum iron levels in mice" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:The reviewers found merit in the work and appreciate the significant improvement in response to critiques of the original submission. However, the evaluation identified remaining weaknesses that prevent publication of this work in the present form. The main shortcomings of the presented work include a nebulously defined problem and suboptimal manuscript organization that makes for difficulty following, lack of clarity on the degree of effect of IRE mediated FPN expression, and the poor quality of the EMSA gels and IHC that hampers confirmation of the authors central conclusions. A complete list of reviewer comments can be found below. The requirements for further consideration would require the authors fully addressing the following:1) Significant further edits to the manuscript to clarify the rationale, temper the conclusions, and organize the Results section with a more clear interpretation/meaning/shortcomings of results presented.2) Because the EMSA gel and IHC are not quantitative and are the most important aspects underlying the central conclusions of the current work, either a more quantitative method to support the interpretation of these results or a modification of the conclusions is warranted.Reviewer #1 (Recommendations for the authors):Charlebois et al. present a well written and significantly improved manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction. Furthermore, there is new data demonstrating that iron counteracts the effects of induced hepcidin on post-translation ferroportin regulation by stabilizing Fpn(IRE+) mRNA to enhance its translation, thus enhancing iron egress and preventing robust hypoferremia in response to inflammation. The authors have added significant amounts of data and reorganized the manuscript to increase readability and support their conclusions. Some additional queries and clarifications remain.1) Page 7, line 133-139, this paragraph appears out of place. It would be useful to understand why these analyses were done and the meaning of the results. For example, it appears that the authors intended "to evaluate compensatory effects on iron trafficking genes in response to LPS." Such a statement could be added to explain why these genes specifically were analyzed and what the results indicate at the end of this paragraph. Also, it is unclear why the authors are specifying Fpn(IRE+) here. Is the Fpn(IRE-) result not the same in response to LPS? These results should be presented all together. Please edit.2) It would be much easier to read if the data comparing wt and Hjv-/- mice was presented separately from the data on LPS response. Again, much of the results on wt vs. Hjv-/- mice is already well established in the literature; as a consequence, only select comparisons are needed to support the specific points in LPS treated mice. Please consider moving all the wt vs Hjv-/- data to the supplement.3) Page 8, line 157, "modestly affect it in wt mice " is stated while there is no statistically significant difference reported on the figure itself. Please edit.4) Page 9, line 193-194, the authors report that synthetic hepcidin administration suppresses endogenous Hamp expression in the liver as a consequence of suppressed Tfr2. However, it is not clear what the underlying mechanism would be. If it were just hypoferremia, Tfr2 concentration would be altered between SD and HID in wt mice and between wt and Hjv-/- mice and no such difference occurs. This is conjecture and not central to the major theme of this manuscript. Consider removing or moving to the supplement.5) Figure 5A-5D require quantification as the loading controls are not homogeneous in most gels. Please edit to statistically corroborate claims. Also, high exposure gels do not add anything additional relative to low exposure gels. Please remove. In addition, there is still a problem with the non-parenchymal cells containing Kupffer cells as well as other cells and the claims should therefore be tempered as the findings are not specific for Kupffer cells. Finally, please add statistics to Figure 5E panel.6) Figure 6A is unclear. What do the authors mean about shift from monosome to polysome? Where is that evident? May be better to break this single panel with many images into individual panels. This is very difficult to interpret as it is and the figure legend is consistently cumbersome, not providing sufficient clarity to enable effective interpretation of the details. The big picture message is that FPN(IRE+) is as expected modulated by iron status in the liver and thus would be expected to counteract hepcidin action on FPN in iron loading. However, as in Figure 5, the concept of whether the effect is in hepatocytes, Kupffer cells, or other non-parenchymal cells is not clarified. Please comment and provide additional study limitations on this point in the Discussion section.7) What is the proportion of FPN(IRE+) vs FPN(IRE-) in hepatocytes and Kupffer cells? Has this previously been published? FPN(IRE+) in liver is more abundant that FPN(IRE-) but do we know if that is a consequence of changing the fraction of cell types in the liver? Is iron loading responsible for increasing the fraction of cells that express FPN(IRE+) or the amount of FPN(IRE+) expression per cell?8) Figure 7 indicates that additional hepcidin can overcome the protective effect of iron overload to offset the effect presumably via an IRE-mediated increase in FPN. However, this is obvious and anticipated and provides only a correlation and not direct evidence supporting the IRE-mediated FPN compensation in inflammation hypothesis. This is also important to note in the discussion as a limitation of the study.9) Second paragraph of the discussion  and also later  continue to provide the expectation that hypoferremia is a binary phenomena. However, iron loaded mice also had a relative hypoferremia. This is important as there is no expectation that a higher starting point would be expected to reach the same nadir; this would require that the sensitivity/potency of the regulation was increased. This is not realistic. I would again urge the authors to more clearly define hypoferremia as a relative not absolute threshold concept and edit the discussion in line with this expectation.Reviewer #2 (Recommendations for the authors):Overall, the study could be potentially impactful, and the authors provided a large amount of data, but I found it difficult to follow as the questions were not clearly laid out, and it was not always clear what questions the experiments were trying to address. I think improving the logical flow of the writing would vastly improve the quality of the manuscript.Figure 1 in particular was difficult to follow- the figure panels were not presented in order in the manuscript . Some rewriting is in order to clarify this section.While the authors carried out experiments on WT mice fed SD of HID, and Hjv -/- mice were fed SD or IDD, to achieve a broad spectrum of hepcidin regulation, it is difficult to make head to head comparisons between such a large number of variable and draw conclusions. "NTBI levels …seemed to decrease in Hjv-/- mice on IDD" -compared to what? This was not clear.Perls staining in Figure 1F - please use higher magnification- it's difficult to draw conclusions from these images.P.8 (line 169) In this assay LPS appeared to suppress splenic ferroportin -please state p value.p.9 (line 187), Strikingly, hepcidin administration was much more effective in relatively iron depleted Hjv -/- mice on IDD and lowered serum iron and transferrin saturation below baseline - I'm not sure I agree with the use of the word "strikingly" as these mice are iron deficient?Figure 3F, 3G and line 192-194 - synthetic hepcidin led to reduction of Hamp mRNA in WT mice on SD, possibly related to destabilization of Tfr2.However, there is an even more drastic decrease in Tfr2 in hepcidin treated mice in Hjv -/- mice on IDD but no change in Hamp mRNA-can the authors comment?Figure 4A - The authors draw conclusions on ferroportin localization based on IHC - IHC is not a quantitative method and the authors should find some other more quantitative method.Line 210 "substantially reduced it (ferroportin) in Hjv -/- mice to almost control levels" -what is the control?Figure 5 - given the quality of the EMSA gels, I'm not sure that the gels are quantitative (the lanes are blending into each other). It's very difficult to draw conclusions on IRP2.Reviewer #3 (Recommendations for the authors):The authors have properly responded to most issues raised by the reviewers but some of them remained unsolved. However, the data shown on FPN regulation rather suggest transcriptional regulation as a major driving forces  whereas IRP mediated regulation appears to be only of marginal importance. This needs to be clarified.In addition, the use of different iron diets in wt and Hjv-/- mice still needs more explanation  because different effects may be induced by iron availability independent of hepcidin signaling and Hjv functionality.In the discussion the relevance of that finding for iron regulation in the setting of inflammation should be better emphazised. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1 (Recommendations for the authors):Charlebois et al. present a well written manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction or exogenous hepcidin. Furthermore, there is an interesting clue that ferroportin can be translationally regulated by LPS to enhance iron egress and prevent robust hypoferremia. However, the authors do not reconcile how this work can be implemented, whether manipulating the nuanced distribution of iron  has significance, or whether this better understanding is important for management of infection in iron overloaded patients. Furthermore, a substantive part of the presented data is not central to the main story, and in some cases, the conclusions are overstated. In addition, a more robust evaluation of signaling pathways in the liver in response to LPS is missing and important. Finally, it is not clear whether hypoferremia would be expected to decrease susceptibility to pathogens.Charlebois et al. present a well written manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction or exogenous hepcidin. Furthermore, there is an interesting clue that ferroportin can be translationally regulated by LPS to enhance iron egress and prevent robust hypoferremia. However, the authors do not reconcile how this work can be implemented, whether manipulating the nuanced distribution of iron  has significance, or whether this better understanding is important for management of infection in iron overloaded patients. Furthermore, a substantive part of the presented data is not central to the main story, and in some cases, the conclusions are overstated. In addition, a more robust evaluation of signaling pathways in the liver in response to LPS is missing and important. Finally, it is not clear whether hypoferremia would be expected to decrease susceptibility to pathogens. Some additional questions and clarifications are delineated below.1) The authors consider hypoferremia to be serum iron below that of WT untreated mice on standard iron diet. This is not well explained. Why was this definition selected? In some cases, a statistically significant decrease in serum iron in response to an intervention was not meaningful as per the authors unless it resulted in hypoferremia to a certain degree . However, this is an unrealistic expectation that in iron loaded conditions that the same dose of LPS or hepcidin mimetic would lead to a proportionally larger decrease in iron than if the starting ferremia was much lower at baseline. This requires some explanation and corroboration.We have reduced the use of the term “hypoferremia” as “serum iron below that of wt untreated mice on standard diet” and replaced it with other terms, such as “…decrease serum iron” (p. 5), “drop in serum iron” (p. 8 and p. 15). The term “lack of hypoferremic response” has also been replaced with “relatively high circulating iron levels” (p. 8).2) Serum hepcidin is induced in LPS treated iron loaded mice in Figure 1 but increased hepcidin did not prevent iron overload. The authors interpret this as a decrease in hepcidin responsiveness. However, it would be useful to evaluate the signaling to hepcidin regulation, namely SMAD1/5/8, STAT3, and MEK/ERK1/2.We have measured markers of BMP/SMAD and IL-6/STAT3 signaling, the major hepcidin-inducing pathways, under all experimental settings. The new data are shown in Figures 1H-I and S6B-C.3) It is somewhat unclear why the authors are comparing WT iron loaded mice with Hjv ko mice and Hjv ko on an iron deficient diet. The effects in vivo of these composite changes make for inadequate comparisons. Please clarify in the introduction to preemptively elucidate why this is a sound comparison.On p. 6 we added a sentence stating that these dietary manipulations aimed “to achieve a broad spectrum of hepcidin regulation”. In fact, wt mice on high-iron diet and Hjv-/- mice of iron-deficient diet exhibit dramatic differences in hepcidin expression and have comparable liver iron load.4) The authors overstate the statistics  or call things "borderline" when they are not. Please edit throughout to make clear that differences that are not significant are unlikely to be "truly" different one from the other. The only way to make this argument is to increase the number of mice in the repeated analysis. For example, using qualifiers like "profoundly" suppressed  or "modestly" affected  is subjective when the statistics drive the argument making the "modest" effect without significant difference an overinterpretation of the results. Similarly, page 7, line 147 suggesting splenic FPN suppression "in all animals" when only HJV ko on SD is statistically significant is misleading.We have modified extensively the manuscript and adhered to these suggestions.5) The authors spend a substantial amount of time recapitulating the already known elements regarding Hjv ko mice relative to WT and the effects of synthetic hepcidin. This focus detracts from the main points and all data that is not relevant to the main point should be moved to the supplementary material sections. This mainly pertains to Figure 1, 2, and 4.We have moved old Figure 1 and parts of old Figures 2 and 4 to the supplementary material section .6) The most interesting and novel part of the manuscript is the effects on FPN translation in the setting of LPS treatment. In addition, the effect of LPS on expression of iron transporters and sensors is additionally novel and under-developed. Is there a precedent for this? Please expand in the Discussion section. The manuscript as a whole would benefit from refocusing effort toward the data in Figure 6 and 7.We have performed new experiments to address the role of the IRE/IRP system in translational regulation of ferroportin. The data are shown in new Figure 5 and discussed. The responses of some iron transporters to LPS are known. This is explained in the Discussion section and appropriate references are provided.7) The authors claim that the results in WB in Figure 3 and 5 are a result of hepatocyte FPN expression despite using liver homogenates for these analyses while simultaneously suggesting the FPN signal in IHC is strongest in Kupffer cells in the liver. This can be resolved by analyzing the 2 populations separately using well-established liver digestion techniques. Otherwise, the first few sentences on page 7 are difficult to interpret objectively.The proposed experiment has been performed and the data are shown in new Figure 5E.8) Page 8, line 171, there is mention that synthetic hepcidin did not promote inflammation but no direct evidence of this is provided. Please add expression and signaling data for inflammatory endpoints in the liver.The proposed experiment has been performed and the data are shown in new Figure S6B. Since Zip14 and Lcn2 are also inflammatory markers, the data in Figure SF-G provide additional support.9) The main argument appears obvious, that the higher the iron load in the system, the more hepcidin is needed to induce hypoferremia due to the additional compensatory mechanisms that protect cells from iron toxicity. This should be made very clear in the abstract, introduction, and discussion.We fully agree with this view and have changed the text accordingly, as requested (see for instance p. 4 and p. 16).10) Figure 6 is interesting and novel. However, the polysome data is significant only for WT vs. Hjv ko which is likely multifactorial, not singularly the result of dietary iron loading as the authors suggest . Having a WT IDD control would be helpful here. In addition, the generalization that Hjv ko represents all forms of iron overload is an overstatement.The IRE-binding experiments shown in new Figure 5 provide a framework to interpret the polysome profile data in Figure 6 We agree that statistical significance is not reached in the shift of Fpn mRNA to polysomes in wt mice fed a high iron diet; however, the trend is clear. We have performed 3 biological replicates of this technically challenging and laborious experiment and with the additional support of the IRE-binding experiments, we are confident that all results in Figure 6 are biologically significant. We have rephrased the text to avoid the generalization that Hjv ko represents all forms of iron overload.11) Unclear in Figure 6B and 6C how the specific statistical comparison is important. Is it noted because it is the only observed difference between groups in this experiment or does it have specific importance? Please clarify?We agree, the way the statistical analysis was presented was confusing and did not have any specific importance. This is fixed in the new Figure 6B and 6C.Reviewer #2 (Recommendations for the authors):The value of this study is the investigation of the relative role of iron overload and inflammation in the regulation of iron intake and iron distribution. In particular, the Authors investigated if and how inflammation can lead to hypoferremia in presence of iron overload. They analyze the expression of the proteins hepcidin and ferroportin in normal and HJV-KO animals (which are iron overloaded) in presence of administration of LPS, which triggers inflammation and increased expression of hepcidin. They showed that ferroportin is expressed at high levels under condition of iron overload and that administration of synthetic hepcidin is insufficient to trigger hypoferremia in iron-loaded animals. Hypoferremia was eventually achieved when LPS and synthetic hepcidin were administered concurrently. This can lead to design better therapeutics for iron related disorders.The paper is well written and clear. The primary claims of the Authors are supported by their data, although some additional experiment are required to complete these studies.Figure 7: The treatment with synthetic hepcidin (SH) is done only for one day. Although only the combination of LPS and SH shows a reduction in serum iron, long term affects were not evaluated. I think that a complete analysis of this approach should include longer studies to assess the effect on iron organ concentration comparing SH or LPS alone and the combination of the two reagents.We agree that the fact that treatments with synthetic hepcidin were done only for one day is a potential limitation. The suggestion to perform long-term treatments is excellent; however, the cost for these experiments is not permissive. Moreover, the plasma half-life of hepcidin is known to be short, and this notion has sparked the development of more potent hepcidin mimetics.Also, Figure 7 is not showing the effect of the various parameters using SH alone. .We have generated and embedded here an alternative Figure 7 that includes the hepcidin data shown in new Figure 3 . If the reviewer feels that this is more informative, we will replace the current Figure 7 with the alternative.I suspect that SH alone, in the long term, may also be sufficient to induce hypoferremia and reduce iron overload. Targeting ferroportin + SH is likely to induce hypoferremia and reduce iron overload faster than administration of SH alone.A stable hepcidin analogue would be an excellent tool to address the issue raised by the reviewer. In fact, in preliminary experiments we found that high doses a potent hepcidin mimetic drug can trigger effective hypoferremia in iron-loaded mice. Unfortunately, we cannot include these data here because they are not complete, and we have not received clearance yet. They will be part of a separate ongoing study. Nevertheless, we added in the Discussion the following sentence: “…it is reasonable to predict that the use of more potent hepcidin analogs will overcome the antagonistic effects of increased ferroportin mRNA translation under iron overload”.In absence of appropriate mouse models, can the authors show, in a cellular model, that cells harboring the ferroportin gene in absence of the IRE sequence are less sensitive to export iron following iron administration, in presence or absence of SH?This is a great suggestion that requires the generation of cell lines stably transfected with ferroportin with and without IRE. Because this approach would be time consuming and moreover, may have limited physiological relevance, we opted to focus on determining the IRP responses to dietary iron manipulations in the whole liver and spleen, as well as in isolated hepatocytes and liver non-parenchymal cells .Reviewer #3 (Recommendations for the authors):The ability of animals to lower their extracellular iron concentration in response to inflammatory stimuli (hypoferremia of inflammation) is an important innate immune response that has been shown to mediate resistance to certain bacterial infections. Early studies of the molecular mechanism of hypoferremia noted that even mild iron overload, caused by excessive iron content of the common mouse diet, interfered with this response. The current study aimed to analyze the mechanism(s) behind this interference.The strengths of the study include:1) Two different models of iron overload were employed, a low hepcidin genetic model (Hjv-/-) and a high hepcidin iron-rich diet model (2% carbonyl iron) and a standard inflammatory stimulus 1 microgram/g of LPS. Both iron overload states greatly decreased the hypoferremic response, even though hepcidin was still induced by LPS.2) The authors show that the mechanism of relative resistance to hepcidin is in large part caused by increased ferroportin translation in iron-overloaded tissues that counteracts the ferroportin-degrading and ferroportin-blocking effect of hepcidin.3) The use of high-dose exogenous hepcidin as a probe of iron-overload-induced resistance to hepcidin is an important advance, with implications for the treatment of iron disorders.The main weaknesses of the study include:1) The paper is narrowly conceived and interpreted, making it mainly interesting to a very specialized readership. However, for this readership, the mechanistic insights are few and already anticipated.We believe that the new manuscript provides critical mechanistic insights that strengthen the conclusions and increase the quality of this work.2) The evidence that iron overload also substantially raised ferroportin mRNA concentrations , likely by a transcriptional mechanism, is not developed or interpreted in the context of the changes in translation.This is a valid point, as the increase of ferroportin mRNA content in iron overload is consistent throughout the manuscript. We refer to iron-mediated transcriptional induction of ferroportin in the Introduction (p. 4), discuss the possibility in the context of our data on pages 7 and 13.3) Although the implication is that increased ferroportin translation in iron-overloaded tissues is mediated by the IRP-IRE system, and this is a reasonable hypothesis, the authors did not demonstrate this in their various models and conditions.4) The paper provides strong evidence that iron overload induces a state of relative resistance to hepcidin, including hepcidin induced by LPS injections. Using phrases that explicitly state this would make the paper easier to understand.This critical point is addressed in the new manuscript with the data in Figure 5.5) Identifying the specific cellular targets of hepcidin and LPS (hepatocytes vs macrophages) that drive hypoferremia in the various conditions is not easy but it is important.This critical issue is addressed with the experiments shown in new Figure 5D-E.6) The induction of ferroportin by iron overload has a transcriptional component whose importance should be analyzed and commented on.Please, see response to point 2.7) The role of IRE-IRP in the translational mechanism should be demonstrated.This critical issue is addressed with the experiments shown in new Figure 5A-D.8) A dose-response analysis of hepcidin resistance using exogenous hepcidin would be more convincing than a single dosage study.Please, see responses to point 1 and point 2 to reviewer 2.[Editors’ note: what follows is the authors’ response to the second round of review.]The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:Reviewer #1 (Recommendations for the authors):Charlebois et al. present a well written and significantly improved manuscript focused on exploring the mechanism underlying coregulation of hepcidin by iron and inflammation. While iron overload has previously been shown to prevent effective hypoferremic response to inflammation, the mechanisms thereof are incompletely understood. The current work presents a compelling story of the important finding that inflammation induced hepcidin expression can be made more efficient in inducing hypoferremia by coupling it with iron restriction. Furthermore, there is new data demonstrating that iron counteracts the effects of induced hepcidin on post-translation ferroportin regulation by stabilizing Fpn(IRE+) mRNA to enhance its translation, thus enhancing iron egress and preventing robust hypoferremia in response to inflammation. The authors have added significant amounts of data and reorganized the manuscript to increase readability and support their conclusions. Some additional queries and clarifications remain.1) Page 7, line 133-139, this paragraph appears out of place. It would be useful to understand why these analyses were done and the meaning of the results. For example, it appears that the authors intended "to evaluate compensatory effects on iron trafficking genes in response to LPS." Such a statement could be added to explain why these genes specifically were analyzed and what the results indicate at the end of this paragraph. Also, it is unclear why the authors are specifying Fpn(IRE+) here. Is the Fpn(IRE-) result not the same in response to LPS? These results should be presented all together. Please edit.We have extensively edited this paragraph providing a rational for the analysis, as well as a concluding sentence, as requested. In addition, we have added qPCR data on Fpn(-IRE) expression in new Figure 2K.2) It would be much easier to read if the data comparing wt and Hjv-/- mice was presented separately from the data on LPS response. Again, much of the results on wt vs. Hjv-/- mice is already well established in the literature; as a consequence, only select comparisons are needed to support the specific points in LPS treated mice. Please consider moving all the wt vs Hjv-/- data to the supplement.We have thoroughly considered this suggestion. However, we were not satisfied with the outcome of alternative presentations of the data. While we agree that comparisons between wt and Hjv-/- mice are redundant, we feel that it is important to show these data as reference to compare responses of these animals to dietary iron manipulations (without or with LPS or hepcidin treatment). Therefore, we opted to keep the original structure of the figures.3) Page 8, line 157, "modestly affect it in wt mice " is stated while there is no statistically significant difference reported on the figure itself. Please edit.We edited the text indicating the lack of statistical significance.4) Page 9, line 193-194, the authors report that synthetic hepcidin administration suppresses endogenous Hamp expression in the liver as a consequence of suppressed Tfr2. However, it is not clear what the underlying mechanism would be. If it were just hypoferremia, Tfr2 concentration would be altered between SD and HID in wt mice and between wt and Hjv-/- mice and no such difference occurs. This is conjecture and not central to the major theme of this manuscript. Consider removing or moving to the supplement.We removed these data to the supplement, as suggested.5) Figure 5A-5D require quantification as the loading controls are not homogeneous in most gels. Please edit to statistically corroborate claims. Also, high exposure gels do not add anything additional relative to low exposure gels. Please remove. In addition, there is still a problem with the non-parenchymal cells containing Kupffer cells as well as other cells and the claims should therefore be tempered as the findings are not specific for Kupffer cells. Finally, please add statistics to Figure 5E panel.We have now quantified the data in Figure 6A-6D (new numbering) as suggested; the data are shown in the right panels. We think that showing high exposure gels is necessary to demonstrate induction of IRP2. We have considerable experience with EMSA, as indicated by our technical publication on the topic in JoVE (ref. 27). In this paper , we analyzed liver and spleen extracts from wt, Hjv-/-, Irp1-/- and Irp2-/- mice, and show both short and long exposures of the gels to better visualize IRE/IRP1 and IRE/IRP2 interactions. Regarding the issue with non-parenchymal and Kupffer cells, we agree that some statements should be tempered. As suggested, we did that throughout the manuscript . We have now added statistics to Figure 6E panel as requested.6) Figure 6A is unclear. What do the authors mean about shift from monosome to polysome? Where is that evident? May be better to break this single panel with many images into individual panels. This is very difficult to interpret as it is and the figure legend is consistently cumbersome, not providing sufficient clarity to enable effective interpretation of the details. The big picture message is that FPN(IRE+) is as expected modulated by iron status in the liver and thus would be expected to counteract hepcidin action on FPN in iron loading. However, as in Figure 5, the concept of whether the effect is in hepatocytes, Kupffer cells, or other non-parenchymal cells is not clarified. Please comment and provide additional study limitations on this point in the Discussion section.We have modified Figure 7A (new numbering) by breaking the single panel as suggested. In addition, we revised the Figure legend, to make the description clear. Polysome profiling is a standard technique in the mRNA translation field. The shift from monosomes to polysomes is indicated in the graphs and quantified in the right panels. We agree that the relative contribution of hepatocytes, Kupffer cells and other non-parenchymal cells has not been fully clarified. As suggested, we comment on that in a new paragraph in the Discussion, highlighting limitations of the study (p. 17).7) What is the proportion of FPN(IRE+) vs FPN(IRE-) in hepatocytes and Kupffer cells? Has this previously been published? FPN(IRE+) in liver is more abundant that FPN(IRE-) but do we know if that is a consequence of changing the fraction of cell types in the liver? Is iron loading responsible for increasing the fraction of cells that express FPN(IRE+) or the amount of FPN(IRE+) expression per cell?The proportion of Fpn(+IRE) and Fpn(-IRE) has not been analyzed in freshly isolated hepatocytes and Kupffer cells. However, it has been analyzed in whole liver, as well as in hepatoma and macrophage cell lines. In all these settings, Fpn(+IRE) was shown to be predominant (ref. 21); we have indicated this on p. 12. It appears that Fpn(-IRE) is only enriched in intestinal epithelial cells (ref. 21).8) Figure 7 indicates that additional hepcidin can overcome the protective effect of iron overload to offset the effect presumably via an IRE-mediated increase in FPN. However, this is obvious and anticipated and provides only a correlation and not direct evidence supporting the IRE-mediated FPN compensation in inflammation hypothesis. This is also important to note in the discussion as a limitation of the study.We comment on this in the new Discussion section highlighting limitations of the study (p. 17).9) Second paragraph of the discussion  and also later  continue to provide the expectation that hypoferremia is a binary phenomena. However, iron loaded mice also had a relative hypoferremia. This is important as there is no expectation that a higher starting point would be expected to reach the same nadir; this would require that the sensitivity/potency of the regulation was increased. This is not realistic. I would again urge the authors to more clearly define hypoferremia as a relative not absolute threshold concept and edit the discussion in line with this expectation.We have modified the sentences as suggested. In addition, to clarify this point we added values of ratios of serum iron between untreated and LPS- or hepcidin-treated mice in Figures 2A and 4A.Reviewer #2 (Recommendations for the authors):Overall, the study could be potentially impactful, and the authors provided a large amount of data, but I found it difficult to follow as the questions were not clearly laid out, and it was not always clear what questions the experiments were trying to address. I think improving the logical flow of the writing would vastly improve the quality of the manuscript.Figure 1 in particular was difficult to follow- the figure panels were not presented in order in the manuscript . Some rewriting is in order to clarify this section.We have reorganized Figure 2 (new numbering) and also modified the respective section in the text, as suggested. We also did the same for Figure 1.While the authors carried out experiments on WT mice fed SD of HID, and Hjv -/- mice were fed SD or IDD, to achieve a broad spectrum of hepcidin regulation, it is difficult to make head to head comparisons between such a large number of variable and draw conclusions. "NTBI levels …seemed to decrease in Hjv-/- mice on IDD" -compared to what? This was not clear.We agree that the experimental setting is complex, but we could not find a better way to present the data . On p. 6 we now specify that “NTBI levels… seemed to decrease in Hjv-/- mice following IDD intake”.Perls staining in Figure 1F - please use higher magnification- it's difficult to draw conclusions from these images.We added a higher magnification image to the new figure , and we moved the lower magnification image to Figure 2-supplement 1.P.8 (line 169) In this assay LPS appeared to suppress splenic ferroportin -please state p value.The p value is now added on Figure 3D.p.9 (line 187), Strikingly, hepcidin administration was much more effective in relatively iron depleted Hjv -/- mice on IDD and lowered serum iron and transferrin saturation below baseline - I'm not sure I agree with the use of the word "strikingly" as these mice are iron deficient?We replaced “strikingly” with “notably”.Figure 3F, 3G and line 192-194 - synthetic hepcidin led to reduction of Hamp mRNA in WT mice on SD, possibly related to destabilization of Tfr2.However, there is an even more drastic decrease in Tfr2 in hepcidin treated mice in Hjv -/- mice on IDD but no change in Hamp mRNA-can the authors comment?We removed these data to the supplement, as also suggested by reviewer 1. Hamp mRNA is already undetectable in Hjv-/- mice on IDD, therefore hepcidin treatment is not expected to have any effect.Figure 4A - The authors draw conclusions on ferroportin localization based on IHC - IHC is not a quantitative method and the authors should find some other more quantitative method.We agree that IHC is not quantitative. We analyzed by Western and quantified relative ferroportin expression in hepatocytes and non-parenchymal liver cells in Figure 6E. We discuss limitations of the study to fully address these issues on p. 17.Line 210 "substantially reduced it (ferroportin) in Hjv -/- mice to almost control levels" -what is the control?We modified the sentence as “substantially reduced it (ferroportin) in Hjv -/- mice to almost wt levels”.Figure 5 - given the quality of the EMSA gels, I'm not sure that the gels are quantitative (the lanes are blending into each other). It's very difficult to draw conclusions on IRP2.As indicated in our response to Q5 of reviewer 1, we have considerable experience with EMSA. IRE/IRP2 bands are more difficult to visualize and often require longer exposures of the gel. We attach our technical publication in JoVE, where we analyzed liver and spleen extracts from wt and Hjv-/- mice. We hope that the reviewers can appreciate that the quality of the EMSAs in this work is similar to that in the JoVE paper.Reviewer #3 (Recommendations for the authors):The authors have properly responded to most issues raised by the reviewers but some of them remained unsolved. However, the data shown on FPN regulation rather suggest transcriptional regulation as a major driving forces  whereas IRP mediated regulation appears to be only of marginal importance. This needs to be clarified.The possible role of iron-dependent transcriptional induction of ferroportin is mentioned throughout the text. In response to this comment, we added a sentence in the “limitations” section of the Discussion (p. 17) stating that “The possible role of iron-dependent transcriptional induction of ferroportin in counterbalancing hepcidin actions requires further clarification”.In addition, the use of different iron diets in wt and Hjv-/- mice still needs more explanation  because different effects may be induced by iron availability independent of hepcidin signaling and Hjv functionality.We discuss this in the “limitations” section of the Discussion (p. 17).In the discussion the relevance of that finding for iron regulation in the setting of inflammation should be better emphazised.Again, we discuss on p. 17 as limitation of the study that “the physiological implications of translational regulation of ferroportin in the broader setting of inflammation and/or infection have not been explored”.


The endoplasmic Reticulum (ER) is fundamental to provide these necessary requirements for optimal neuronal function. Accumulation of misfolded proteins in the ER lumen, reactive oxygen species and exogenous stimulants like infections, chemical irritants and mechanical harm can induce ER stress, often followed by an ER stress response to reinstate cellular homeostasis. Imbedded between glial-, endothelial-, stromal-, and immune cells neurons are constantly in communication and influenced by their local environment. In this review, we discuss concepts of tissue homeostasis and innate immunity in the central and peripheral nervous system with a focus on its influence on ER stress, the unfolded protein response, and implications for health and disease.Neuronal cells are specialists for rapid transfer and translation of information. Their electrical properties relay on a precise regulation of ion levels while their communication  Cell and tissue homeostasis are the foundation of a physiological and healthy organism. Homeostasis means to balance and properly regulate all required components and interactions by actively maintaining certain quantitative characteristics of the system, known as regulated variables, within a desired range . This imOn an organismic level, innate and adaptive immunity are the main drivers for host defense. The innate immune system is evolutionally older and consists of myeloid cells  and NK-cells as well as the complement system. Pathogens invading the organism get recognized through conserved surface structures that are broadly shared by pathogens , endotoxins found on cell membranes from gram negative bacteria) and distinguishable from host molecules and therefore called pathogen-associated molecular patterns (PAMPs) that bind to pattern recognition receptors (PRRs) expressed by innate immune cells such as toll-like receptors (TLRs), C-Type lectin receptors (CLRs), NOD-like receptors (NLR & NLRPs) RIG-I-like receptors (RLRs) . This alMoreover, we and others discovered recently that there is another cell type involved in the recognition of harmful invaders like bacteria, fungi, and allergens or toxins. Peripheral sensory neurons have a big impact in the initiation of the local inflammation and contribute to the activation of innate and adaptive immune cells –10. By svia splicing of X-box binding protein 1 (XBP1), phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) that can attenuate general protein synthesis and allows translation of ATF4 mRNA to ATF4, which controls the transcription of genes involved in autophagy, apoptosis, amino acid metabolism and antioxidant responses  in myeloid cells in response to the intracellular bacterium Chlamydia trachomatis  –46. In fThe ER is the largest organelle in the cytoplasm of eukaryotic cells. It is composed of a dynamic network of stacked membrane sheets connected with helicoidal motifs  with an 2+ storage, keeping a concentration between 100-800µM. The typical cytosolic Ca2+ concentration is supposed to be around 100nM. In comparison, extracellular space has a Ca2+ concentration of around 2mM. If a cell gets activated through G-protein coupled receptors, phospholipase C (PLC) gets stimulated to cleave phosphatidylinositol 4,5 bisphosphate (PIP2) into diacyl-glycerol (DAG) and IP3, which can then bind to IP3R on the ER membrane leading to Ca2+ release from the ER lumen into the cytosol and transient increase in intracellular Ca2+levels  and can additionally enter the cell trough voltage- or ligand gated calcium channels from the extracellular space. The most important function and characteristic of neurons compared to other cell types is the excitability, the transport of information through action potentials as a result of a change in membrane potential. Therefore, it is of utmost importance for the neuron to regulate its ion levels and transfer ions as fast as possible to the right location. In the specific architecture of neurons the ER lumen is connected from the soma, through the whole axon to the synapsis, and thereby allows for fast shifts of ions, but also the transport of required neuropeptides and neurotransmitters for cell communication and signal transduction  water channels present in the astrocyte end-feet that form the glial limitans of the BBB. As a result, the CSF flow clears the parenchyma from debris and intestinal solutes along paravenous spaces into the subarachnoid space CSF  contains of the brain and the spinal cord. It is surrounded by three layers of meninges, the very thin pia mater that covers the parenchyma and follows supplying arteries, the arachnoid mater, that consist of many spider like columns that build the arachnoid space that is filled with cerebrospinal fluid (CSF) and the outer, very thick and strong dura mater that is connected to the skull and vertebrate bones, builds compartments through the falx cerebri and accommodates the venous sinuses. The meninges offer protection of the CNS parenchyma as does the CSF, that functions as a shock absorber in the arachnoid space and it provides buoyancy, thereby reducing the overall brain weight to protect it from damage. Besides these physical implications for the CNS, the CSF and the meninges are actively contributing to CNS immunity. Other than in the periphery, the CNS is under common, non-inflammatory conditions isolated from blood circulating immune cells and most chemicals through the blood brain barrier (BBB). The BBB is composed of endothelial cells connected through tight junctions, followed by multiple layers of smooth muscle cells and finally large astrocyte  end-feet. Many years there was the prominent concept of the immune-privilege of the brain, pointing to studies, where antigens  implanted only into the brain could not induce a systemic immune response, which was different, if the immune system was pre-primed to the same antigen in the periphery before , 82. It pace CSF , 97. Morentation . In factentation –101. In entation –104. Exaentation –107.low effector and CD4+FOXP3+ Treg cells and a reduction in CD4+ T-bethigh IFNγ producing cells. Moreover, a hematopoietic deficiency in CCR7 can induce aging-associated changes in meningeal T cells accompanied by a reduced glymphatic influx and cognitive impairment. Deletion of CCR7 in 5xFAD transgenic mice used as an Alzheimer´s disease model promotes neurovascular and microglial activation and an increase in Aβ deposition in the brain of these mice. Conversely, old mice treated with anti-CD25 antibodies (CD4+FOXP3+Treg cells are mainly CD25high expressing cells and are more responsive to anti-CD25 treatment than CD4+ and CD8+ CD25 low expressing effector T cells) showed a reduced meningeal CD4+FOXP3+ Treg cell response associated with an improved cognitive function  assembly to promote caspase enzymatic function in cleaving pro IL-1β and pro IL-18 into their active forms  approved anti-epileptic treatment carbamazepine are known to induce autophagy and proteasomal activation and could be shown to be neuroprotective in preclinical studies including amyotrophic lateral sclerosis (ALS) and Alzheimer´s disease –171. ProThe ER stress response including UPR and ERAD signaling are important for the maintenance of neuronal function itself, but also for host defense and tissue immunity . ChronicCP and EK conceptualized the work, wrote parts, reviewed and edited. CP additionally wrote major parts of the manuscript and designed the figures, tables, and screened literature. Both authors have read and agreed to the published version of the manuscript.This work was supported by the German Research Foundation RTG 2719 to EK and DFG PE2864/3-1 to CP. We acknowledge support for the Article Processing Charge by the German Research Foundation and the Open Access Publication Fund of the University of Greifswald.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In the last decade, extensive efforts have been made to identify biomarkers of biological age. DNA methylation levels of ELOVL fatty acid elongase 2 (ELOVL2) and the signal joint T-cell receptor rearrangement excision circles (sjTRECs) represent the most promising candidates. Although these two non-redundant biomarkers echo important biological aspects of the ageing process in humans, a well-validated molecular clock exploiting these powerful candidates has not yet been formulated. The present study aimed to develop a more accurate molecular clock in a sample of 194 Italian individuals by re-analyzing the previously obtained EVOLV2 methylation data together with the amount of sjTRECs in the same blood samples. The proposed model showed a high prediction accuracy both in younger individuals with an error of about 2.5 years and in older subjects where a relatively low error was observed if compared with those reported in previously published studies. In conclusion, an easy, cost-effective and reliable model to measure the individual rate and the quality of aging in human population has been proposed. Further studies are required to validate the model and to extend its use in an applicative context. In the last decade, extensive efforts have been made to identify biomarkers of the biological ageing. They were mainly based on molecular methods and include the analysis of mitochondrial DNA 4977 deletion accumulations , telomerAnother well-validated and important molecular marker of ageing is the signal joint T-cell receptor rearrangement excision circles (sjTRECs). sjTRECs are extra-chromosomal DNA by-products of the rearrangements of gene segments encoding the variable parts of TCR α and β chains during intra-thymic development. sjTREC is one particular TREC arising through an intermediate rearrangement in the TCRD/A locus in developing TCRαβ+ T lymphocytes . SeveralTo the best of our knowledge, only one study attempted to build a molecular clock for biological age prediction exploiting both sjTRECs and ELOVL2 biomarkers . The maiThe present study aimed to develop a more accurate molecular clock in a sample of the Italian population of different ages covering the whole span of adult life. In particular, we re-analyzed the EVOLV2 methylation data previously obtained in an Italian sample whose blood samples have been here analysed to quantify the amount of sjTRECs . Then, bThe dataset included 194 unrelated individuals (90 men and 104 women) with a mean age of 63.5 years. The recruitment of subjects older than 90 years was carried out between 2002 and 2005 through the population registries of Calabrian municipalities. Subjects in the age range 20–89 years were recruited between 2004 and 2007 as part of a survey aimed at monitoring the health status of this population segment in Calabria . Fully wPeripheral blood samples were collected in EDTA containing tubes from each human subject. DNA was extracted from buffy coats following standard procedures. Genomic DNA was obtained by phenol/chloroform purification and then stored at −20 °C until use. DNA concentration and purity were determined spectrophotometrically using NanoDrop™ One .ELOVL2 gene region was performed by bisulfite pyrosequencing. The detailed protocol also described by other authors was previously reported [Methylation analysis of nine consecutive CpGs in the reported .Real-time PCR conditions and primers for quantification of sjTREC were carried out according to the previously published technique . In briePearson correlation coefficient and linear regression were used for assessing the univariate association between biological markers and age. We used JADBio (v1.3.32), an automated machine learning platform for creating machine learning models able to predict age from methylation markers . JADBio All of the analyzed CpG sites showed a strong positive correlation with age indicating that they could all be good estimators of the chronological age .p < 0.001). For this reason, it will be used as an input predictor variable in the following developed models.From 2 = 0.617). These prediction values seem consistent with those of the previous reports [The sjTREC content exhibits a significant age-related decline. In particular, a linear regression analysis between individual age and sjTREC level showed a strong negative dependence and that the formulated model explained a large and highly statistically significant proportion of the total age variance  with Polynomial Kernel as the best performing models. Their predictive performances are reported in Model with ELOVL2 and sjTREC:https://app.jadbio.com/share/39e4c459-abd1-4765-887e-e93f18e4155dModel with ELOVL2:https://app.jadbio.com/share/8492f92b-39f7-48d7-99bc-79cbae478f37The same analyses were repeated considering only males or females subjects. Overall, the results remained consistent, with a decrease in predictive performance for the sex specific models probably due to a decreased sample size. Particularly, the Mean Absolute Error (MAE) for the model containing both ELOVL2 and sjTREC passes from 4.449 years to 4.733  and 4.709 . For the model with ELOVL2 solely, the reduction is from 4.954 years to 5.220  and 5.055 .ELOVL2 was used in combination with sjTREC. Predicted and chronological ages were highly correlated with a MAE of 4.440 (panel a). In line with other similar studies, older individuals (>60 years old) showed an increased difference between predicted and chronological age  compared to younger ones and their predicted age was rather underestimated (panel b) [panel b) ,43,44,45To assess the predictive capacities of the formulated models in the different stages of aging, the analyzed sample was grouped into six different age classes by intervals of 10 years .ELOVL2 was used in combination with sjTREC, the SVR model exhibited much improved accuracy, particularly in the older age-groups. In fact, models with or without sjTREC showed similar prediction performances for individuals younger than 60 years, while in older individuals the SVR model also including sjTREC content showed an improved accuracy ranging from 6 months for the 60–70 age-group to about 1.3 years for individuals older than 80 years.For both models, prediction accuracies decreased with advanced ages. However, the average methylation value of ELOVL2 and sjTREC quantification in blood samples of 194 individuals of different ages covering the whole span of adult life. We re-assessed the DNA methylation markers of ELOLV2 gene together with the analysis of sjTREC content in the blood. We found that the sjTREC levels and DNA methylation of the ELOVL2 gene are very useful markers for age prediction. In fact, an SVR model including these two non-redundant markers showed high prediction accuracy with a prediction error of about 4.4 years. Interestingly, this prediction error was extremely low in younger individuals with a MAE lower that 2.5 years and increased up to around 4.5 years in older subjects with the exception of very old individuals where the prediction error, in line with other literature data [We developed a machine learning model able to infer chronological age from the analysis of the methylation levels of ure data ,47, resuThe model we proposed presents several points of strength. (i) It is based on two very accurate markers of the ageing process. In fact, ELOVL2 represents one of the most widely used markers for age prediction and does not show tissue-specificity, as observed for the most part of the epigenetic markers so far identified. sjTREC is a very reliable biomarker of thymic activity whose decline represents one of main hallmark of the immunosenescence process. (ii) The strategies adopted for the model development were based on strongly robust methods. In fact, in a recent comparative evaluation over 360 datasets, methods implemented in JADBio demonstrated to be able to avoid overfitting, with the cross-validated predictive performances estimated by the system being in line with out-of-sample estimation on separated datasets . (iii) IThe limitations of our study deserve to be mentioned. The application of the proposed method is restricted to blood samples and body parts containing blood and is not possible for other body parts or fluids, such as semen or saliva, that do not contain T-cells in quantities required for sjTREC detection. Additional studies are therefore required to overcome this methodological limitation. Another point of weakness is the absence of information regarding longitudinal data that might be used for a further validation of the proposed model. However, since it is based on the joint use of the two markers whose individual accuracy to measure the rate of aging had already been largely demonstrated in previous studies, the impact on longitudinal outcomes such as mortality risk and loss of functional independence should be expected to be non-negligible.In conclusion, the data here reported may be of interest, because an easy, cost-effective and reliable model was proposed that might represent a new tool to measure the individual rate and the quality of aging in geriatric ages. Further studies are therefore required to validate the model, particularly in an applied context.


Pseudomonas aeruginosa) attachment was quantified and showed reduction as a function of the amount of PVA retained at the surface. The insights gained in this study help define the structure–function relationships of the particulate biomaterial architecture, supporting materials design with biofilm control.Micro- and nanoparticles are of great interest because of their potential for trafficking into the body for applications such as low-fouling coatings on medical devices, drug delivery in pharmaceutics and cell carriers in regenerative medicine strategies. Particle production often relies on the use of surfactants to promote stable droplet formation. However, the presence of residual surfactant has been shown to complicate the surface chemistry and resultant properties. When forming particles from polymerizable monomer droplets, these polymeric surfactant chains can become physically entangled in the particle surface. Due to the key role of the outermost layers of the surface in biomaterial interactions, the surface chemistry and its influence on cells needs to be characterized. This is the first study to assess surfactant retention on microfluidic produced particles and its effect on bacterial attachment; surfactant contaminated microparticles are compared with flat films which are surfactant-free. Polymeric microparticles with an average diameter of 76 ± 1.7 μm were produced by using a T-junction microfluidic system to form monomer droplets which were subsequently photopolymerized. Acrylate based monomer solutions were found to require 2 wt% PVA to stabilize droplet formation. ToF-SIMS was employed to assess the surface chemistry revealing the presence of PVA in a discontinuous layer on the surface of microparticles which was reduced but not removed by solvent washing. The effect of PVA on bacterial ( Pseudomonas aeruginosa on microfluidic produced particles was shown to reduce as a function of PVA concentration retained at the surface, enabling novel structure–function relationships of biomaterial architecture.The attachment of  Particles have wide applicability for use in a range of areas including drug delivery,6,7 tissue engineering,8,9 diagnostics10 and photonics.11 They can be produced by many techniques, with the most commonly used for polymers being precipitation,12 emulsion polymerization13 and most recently, microfluidic based methods.14,15 Droplet formation in microfluidic devices is capable of producing highly uniform droplets ranging from the nano- to the microscale from a variety of materials.16,17 Particle control has been demonstrated by Kim et al. to be feasible with microfluidics, ranging from control of shape to compartmentalization of different components.14 This technique has been shown to be suitable for manufacturing large quantities of particles,18 and has the potential to generate particles of diverse compositions when coupled with automated fluid handling and a library of UV polymerizable monomers. A drawback that is shared among many droplet generation methods is that surfactants are often required in order to stabilize the fluid–fluid interfaces of the droplets.19,20 Salman et al. have recently reported that poly (PVA) and a mixture of PVA with a biosurfactant reduces the formation of Staphylococcus aureus and Pseudomonas aeruginosa biofilms by up to 98% on glass and plastic plates, depending on the molecular weight of PVA used.21Over the last decade, materials discovery using high throughput screening has been applied to identify novel materials with unique properties that can be tailored for specific applications, such as maintaining stem cell pluripotency for tissue engineering applications or reducing bacterial attachment and biofilm formation for medical devices.22 Moreover, the adhesion of marine fouling organisms to surfaces such as ships' hulls poses a worldwide problem and is subject to on-going research.23,24 Microorganisms attach to surfaces and form biofilms i.e. populations of microbial cells enmeshed in a matrix consisting of extracellular polymers such as polysaccharides, proteins and nucleic acids. In such bacterial communities, bacteria are up to 1000 times more tolerant to antimicrobials and host defenses than individual planktonic bacterial cells.25 Addressing this at the earliest possible stage ideally requires that surfaces are engineered in such a way that they can prevent bacterial attachment and subsequent biofilm formation.Novel materials that can control bacterial attachment and biofilm formation are in demand to reduce device-centered infections in the fight against antimicrobial resistance (AMR).P. aeruginosa was investigated on acrylate-based microparticles using washing cycles to control the amount of PVA.In this paper, the production of monodisperse microparticles with an average diameter of 76 ± 1.7 μm was demonstrated using a simple microfluidic T-junction configuration by photopolymerizing acrylate based monomer solutions. First, we show the need for a surfactant, in this case 2 wt% PVA, for successful droplet formation. Then, the surface chemistry of the fabricated particles was assessed using Time-of-Flight secondary ion mass spectrometry (ToF-SIMS). The effect of PVA on the attachment of P. aeruginosa.2 The underlying mechanism by which the polyEGdPEA resists biofilm formation has still not been fully elucidated and is part of ongoing research. The chemical structure of the EGdPEA monomer is depicted in co-vinyl acetate) (PVA) solution (2 wt%), while the dispersed phase coming from the bottom is the respective monomer solution, in this case comprising of EGdPEA and 1 wt% of 2,2-dimethoxy-2-phenylacetophenone (DMPA) as photo-initiator for the subsequent polymerization of the monomer droplets to microparticles.Microparticles were successfully prepared from the monomer ethylene glycol dicyclopentenyl ether acrylate (EGdPEA) in a microfluidic approach using a glass T-junction chip with a feature size of 100 μm. EGdPEA was selected since it has been identified as a ‘hit’ homopolymer exhibiting low surface area coverage when incubated with PVA is able to stabilize the monomer solution droplets within the deionized water due to its amphiphilicity, hence the surfactant was required for successful droplet formation. Without a surfactant in the continuous phase, a constant stream of monomer solution was observed in the microfluidic channel. Moreover, employing a PVA concentration below 2 wt% failed to produce droplets or led to unstable droplet formation which resulted in a lack of both control and particle homogeneity. No data was gathered for a PVA concentration above 2 wt% because the scope was to reduce the amount of surfactant as much as possible.Using the methodology outlined, homogeneous microparticles with a coefficient of variation of 2.2% were obtained. via microfluidics was assessed using ToF-SIMS. This surface mass spectral technique is not inherently quantitative, but is good at providing a relative measure of the amount of a particular species in a similar sample down to sub-monolayer sensitivities by virtue of its 3 nm depth of analysis.26 To study the presence of PVA surfactant on the polymer particles, aliquots of particles were washed with varying the numbers of wash cycles. A wash cycle involved sonicating the particles in isopropanol for 5 min followed by another sonication in distilled water for 5 min. SIMS spectra of PVA powder as-received  is presented in A secondary ion representative of the EGdPEA polymer, C2H3O2− and C5H5−, the ToF-SIMS spectra were reconstructed as images to examine the distribution of PVA surfactant and EGdPEA on the surfaces. The overlay of PVA surfactant (red) and EGdPEA (blue) is shown for the thin film (i), particles as-produced (ii), after the first wash (iii) and after 10 washes (iv) in 28 The flat thin film revealed a homogeneous distribution of the two diagnostic ions across the surface. In contrast, the untreated EGdPEA particles (no washing) were almost fully covered by the PVA surfactant such that the proportion of the characteristic ion C5H5− to the PVA was only 0.05. PolyEGdPEA appeared only on a few spots in the image  with the image of the extensively washed particles (iv), a significant reduction in PVA was observed. Therefore, the reduction in PVA surfactant at the particle surfaces resulted in an increased detection of the cyclopentadienyl anion derived from the EGdPEA polymer Applying the characteristic ions, Che image  indicatiThe distribution of PVA surfactant was further analyzed on microparticles after the first wash because microfluidic produced particles were washed once with phosphate buffer saline (PBS) before being tested for bacterial attachment. As shown in A model proposing the PVA chain entanglement is illustrated in P. aeruginosa PAO1-N expressing the mCherry fluorescent protein for 24 h in 48-well microtitre plates with agitation (shaking at 60 rpm) and imaged using confocal microscopy. The number of bacterial cells attached was quantified using the mCherry fluorescence, exclusively on the surfaces of microparticles and flat films by using a custom MATLAB algorithm  cast on glass and plastic plates.21 For the microfluidic produced polyEGdPEA microparticles, a gradual increase in the bacterial cells attached was observed from zero up to three particle pre-wash cycles. However, the level of bacterial attachment remained unaltered after performing five wash cycles. The number of bacterial cells attached was inversely proportional to the amount of PVA surfactant determined on the particle surfaces using ToF-SIMS . A chain entanglement model was proposed to explain the residual PVA. A bulk PVA sample had 91% less The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.The authors declare no competing financial interest.RA-008-C8RA01491D-s001RA-008-C8RA01491D-s002RA-008-C8RA01491D-s003