A crosstalk between hepcidin and IRE/IRP pathways controls ferroportin expression and determines serum iron levels in mice

  1. Edouard Charlebois
  2. Carine Fillebeen
  3. Angeliki Katsarou
  4. Aleksandr Rabinovich
  5. Kazimierz Wisniewski
  6. Vivek Venkataramani
  7. Bernhard Michalke
  8. Anastasia Velentza
  9. Kostas Pantopoulos  Is a corresponding author
  1. Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Canada
  2. Ferring Research Institute Inc, United States
  3. Department of Medicine II, Hematology/Oncology, University Hospital Frankfurt, Germany
  4. Institute of Pathology, University Medical Center Göttingen (UMG), Germany
  5. Helmholtz Zentrum München GmbH – German Research Center for Environmental Health, Research Unit Analytical BioGeoChemistry, Germany

Abstract

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 Hjv-/- mice, model of hemochromatosis. 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.

Editor's evaluation

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.

https://doi.org/10.7554/eLife.81332.sa0

Introduction

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 (Camaschella et al., 2020; Katsarou and Pantopoulos, 2020). This results in ferroportin internalization and lysosomal degradation but also directly inhibits ferroportin function by occluding its iron export channel (Aschemeyer et al., 2018; Billesbølle et al., 2020). Ferroportin is highly expressed in duodenal enterocytes and tissue macrophages, which are instrumental for dietary iron absorption and iron recycling from senescent erythrocytes, respectively. Ferroportin is also expressed in hepatocytes, where excess iron is stored and can be mobilized on demand. Hepcidin-mediated ferroportin inactivation inhibits iron entry into plasma. This is a critical homeostatic response against iron overload, but also an innate immune response against infection (Ganz and Nemeth, 2015). Thus, hepcidin expression is induced when systemic iron levels are high to prevent dietary iron absorption or under inflammatory conditions to promote iron retention within ferroportin-expressing cells and render the metal unavailable to extracellular pathogens.

The hepcidin-encoding Hamp gene is transcriptionally induced by iron or inflammatory stimuli via BMP/SMAD (Wang and Babitt, 2019) or IL-6/STAT3 (Schmidt, 2015) signaling, respectively. These pathways crosstalk at different levels. For instance, the BMP co-receptor hemojuvelin (HJV), a potent enhancer of iron-dependent BMP/SMAD signaling, is also essential for the inflammatory induction of hepcidin. Thus, Hjv-/- mice, a model of juvenile hemochromatosis characterized by severe iron overload and hepcidin deficiency (Huang et al., 2005), exhibit blunted inflammatory induction of hepcidin and fail to mount a hypoferremic response following LPS treatment or infection with E. coli (Fillebeen et al., 2018). Excess iron inhibits hepcidin induction via the BMP/SMAD and IL-6/STAT3 signaling pathways in cultured cells (Charlebois and Pantopoulos, 2021; Yu et al., 2021), but the in vivo relevance of these findings is not known.

Hepcidin-dependent inhibition of ferroportin activity and expression is a major but not the sole contributor to inflammatory hypoferremia (Guida et al., 2015; Deschemin and Vaulont, 2013). This is related to the fact that ferroportin expression is regulated by additional transcriptional and post-transcriptional mechanisms (Drakesmith et al., 2015). Thus, ferroportin transcription is induced by iron (Aydemir et al., 2009) and suppressed by inflammatory signals (Ludwiczek et al., 2003), while translation of Slc40a1(+IRE) 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 (Wang and Pantopoulos, 2011; Muckenthaler et al., 2008). In a homeostatic response to iron deficiency, IRPs bind to the IRE within the 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.

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 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.

Results

Dietary iron overload does not prevent further inflammatory Hamp mRNA induction in LPS-treated wild type mice, but mitigates hepcidin responsiveness

In an exploratory experiment, wild type mice were subjected to dietary iron loading by feeding a high-iron diet (HID) 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 (Figure 1A) and transferrin saturation (Figure 1B). They retained physiological liver iron content (LIC; [Figure 1C]) and serum ferritin (Figure 1D), a reflection of LIC. Serum iron and transferrin saturation plateaued after longer HID intake, while LIC and serum ferritin gradually increased to peak at 5 weeks. The dietary iron loading promoted gradual upregulation of serum hepcidin (Figure 1E) and liver Hamp mRNA (Figure 1F), with highest values at 5 weeks. This could not prevent chronic dietary iron overload, in agreement with earlier findings (Corradini et al., 2011; Daba et al., 2013).

Dietary iron loading does not disrupt inflammatory hepcidin induction in LPS-treated wild type mice but blunts hepcidin-mediated hypoferremia.

Nine-week-old male mice (n=12–14 per group) were fed control diet or high-iron diet (HID) for 1 day, 1 week, or 5 weeks prior to sacrifice. Half of the mice were injected intraperitoneally with saline and the other half with 1 µg/g LPS 4 hr before sacrifice. Sera were collected by cardiac puncture and analyzed for: (A) iron, (B) transferrin saturation, (D) ferritin, and (E) hepcidin. Livers were dissected and processed for biochemical analysis of: (C) liver iron content (LIC) by the ferrozine assay and (F) Hamp mRNA by qPCR. The dotted line in (A) and (B) indicates baseline serum iron and transferrin saturation, respectively, from mice on control diet. Data (A–E) are presented as the mean ± SEM and in (F) as geometric mean ± SD. Statistically significant differences (p<0.05) over time compared to values from saline- or LPS-treated control mice are indicated by a or b, respectively.

LPS triggered appropriate hepcidin induction and a robust hypoferremic response in control mice. Interestingly, LPS-induced inflammation resulted in further proportional increase in hepcidin and Hamp mRNA in dietary iron-loaded mice (Figure 1E–F). This was accompanied by significant drops in serum iron and transferrin saturation (Figure 1A–B). However, values did not reach the nadir of LPS-treated control animals and were increasing in mice on HID for longer periods, despite significant hepcidin accumulation. These data suggest that hepatic iron overload does not prevent inflammatory induction of hepcidin; however, it impairs its capacity to decrease serum iron.

Uncoupling inflammatory hepcidin induction from hypoferremic response in wild type and Hjv-/- mice following dietary iron manipulations

To further explore the potential of hepcidin to promote hypoferremia under iron overload, wild type and Hjv-/- mice, a model of hemochromatosis, were subjected to dietary iron manipulations. Wild type mice were fed control diet or HID, and Hjv-/- mice were fed control diet or an iron-deficient diet (IDD) 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 (Figure 2A–B). Serum non-transferrin bound iron (NTBI) levels appeared modestly elevated in the dietary and genetic iron overload models and seemed to decrease in Hjv-/- mice following IDD intake (Figure 2C). LIC was substantially reduced in Hjv-/- mice in response to IDD but also compared to wildtype mice on HID (Figure 2D). The quantitative LIC data were corroborated histologically by Perls staining (Figure 2E and Figure 2—figure supplement 1A). Dietary iron loading increased splenic iron in wild type mice and confirmed that Hjv-/- mice fail to retain iron in splenic macrophages (Figure 2—figure supplement 1B). As expected, serum hepcidin (Figure 2F) and liver Hamp mRNA (Figure 2G) were maximally induced in HID-fed wild type mice and were low in Hjv-/- mice on control diet, and further suppressed to undetectable levels following IDD intake.

Figure 2 with 1 supplement see all
Iron overload blunts hepcidin responsiveness to LPS-induced inflammation.

Four-week-old male wild type mice (n=12–14 per group) were placed on high-iron diet (HID) for 5 weeks. Conversely, age- and sex-matched isogenic Hjv-/- mice (n=12–14 per group) were placed on iron-deficient diet (IDD) for 5 weeks to prevent excessive iron overload. Other animals from both genotypes were kept on control diet. Half of the mice were injected with saline and the other half with 1 µg/g LPS; all animals were sacrificed 4 hr later. Sera were collected by cardiac puncture and analyzed for: (A) iron, (B) transferrin saturation, (C) non-transferrin bound iron (NTBI), and (F) hepcidin. Livers were dissected and processed for LIC quantification by the ferrozine assay (D) and for histological detection of iron deposits by Perls’ staining (E; magnification: 20 ×). Livers were also used for qPCR analysis of following mRNAs: (G) Hamp, (H) Id1, (I) Socs3, (J) Slc40a1(+IRE), (K) Slc40a1(-IRE), (L) Slc11a2, (M) Slc39a14, (N) Lcn2, and (O) Tfrc. The dotted line in (A) and (B) indicates baseline serum iron and transferrin saturation, respectively, of wild type mice on control diet. Values in (A) represent ratios of serum iron levels between untreated and LPS-treated mice. Data in (A–F) are presented as the mean ± SEM while in (G–O) are presented as geometric mean ± SD. Statistically significant differences (p<0.05) compared to values from saline- or LPS-treated wild type control mice are indicated by a or b, respectively.

LPS reduced serum iron and transferrin saturation in hyperferremic wild type mice on HID and Hjv-/- mice on control diet or IDD, but not below the baseline of wild type mice on control diet, the only animals that developed a robust hypoferremic response (Figure 2A–B); see also ratios of serum iron levels between untreated and LPS-treated mice in Figure 2A. The LPS treatment was associated with significant accumulation of hepcidin (Figure 2F) and induction of Hamp mRNA (Figure 2G) in all experimental groups, while NTBI (Figure 2C) and LIC (Figure 2D) were unaffected. Notably, LPS-treated wild type mice on HID and Hjv-/- mice on IDD exhibited dramatic differences in Hamp mRNA but similar blunted hypoferremic responses to the acute inflammatory stimulus. Thus, the profound hepcidin induction in iron-loaded wild type mice cannot decrease serum iron below that of iron-depleted Hjv-/- mice with negligible hepcidin, which indicates reduced hepcidin responsiveness. In support of this interpretation, Id1 and Socs3 mRNAs (Figure 2H–I), which are markers of BMP/SMAD and IL-6/STAT3 signaling, respectively, were appropriately induced by dietary iron loading or LPS treatment in wild type mice. Thus, the major hepcidin signaling pathways were intact under these experimental conditions.

Serum iron levels are also controlled by hepcidin-independent mechanisms (Guida et al., 2015; Deschemin and Vaulont, 2013). To explore their possible contribution in our experimental setting, we analyzed expression of genes involved in iron transport in the liver, an organ that contributes to iron sequestration during inflammation. Ferroportin is encoded by two alternatively spliced transcripts, Slc40a1(+IRE) and Slc40a1(-IRE) (Zhang et al., 2009). Both of them were significantly increased in the liver of iron-loaded wild type mice on HID and Hjv-/- mice on control diet, which is consistent with transcriptional induction (Aydemir et al., 2009), and were strongly suppressed by LPS (Figure 2J–K). The LPS treatment induced Slc11a2, Slc39a14 and Lcn2 mRNAs in all animals (Figure 2L–N). These encode the divalent metal transporter DMT1, the NTBI transporter Zip14 and the siderophore-binding protein Lcn2, respectively; Lcn2 mRNA induction was dramatic. The transferrin receptor 1 (Tfr1)-encoding Tfrc mRNA was largely unaffected by LPS, except for a reduction in Hjv-/- mice on IDD (Figure 2O). The above data indicate that LPS-induced inflammation triggers transcriptional responses favoring reduced iron efflux from the liver and increased uptake of NTBI by liver cells.

To assess the downstream function of hepcidin, we analyzed tissue ferroportin levels. Immunohistochemical staining of liver sections revealed strong ferroportin expression in Kupffer cells, predominantly in periportal areas, under all experimental conditions (Figure 3A and Figure 3—figure supplement 1). Hepatocellular ferroportin staining is also evident in the iron overload models, mostly in periportal hepatocytes (Figure 3—figure supplement 1), and in line with recent data (Katsarou et al., 2021). LPS triggered redistribution and decreased expression of ferroportin in Kupffer cells from wild type but not Hjv-/- mice (Figure 3—figure supplement 1), as reported in Fillebeen et al., 2018.

Figure 3 with 2 supplements see all
Effects of LPS on hepatic and splenic ferroportin of iron-manipulated wild type and Hjv-/- mice.

Livers and spleens from mice described in Figure 2 were dissected and processed for immunohistochemical and biochemical analysis of ferroportin. Immunohistochemical staining of ferroportin in liver (A) and spleen (C) sections (magnification for liver is 20 × and for spleen 5 ×). Western blot for ferroportin and β-actin in liver (B) and spleen (D) extracts from four representative mice in each condition. Blots were quantified by densitometry and ferroportin/β-actin ratios are shown on the right. Densitometric data are presented as the mean ± SEM. Statistically significant differences (p<0.05) compared to values from saline- or LPS-treated wild type control mice are indicated by a or b, respectively. Statistics in bold were performed using unpaired Student’s t test. HID: high-iron diet; IDD: iron-deficient diet.

Figure 3—source data 1

Western blot quantifications.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data1-v2.xlsx
Figure 3—source data 2

Raw unlabeled Western blot ferroportin Figure 3D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data2-v2.jpg
Figure 3—source data 3

Raw unlabeled Western blot ferroportin Figure 3D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data3-v2.jpg
Figure 3—source data 4

Raw unlabeled Western blot β-actin Figure 3D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data4-v2.jpg
Figure 3—source data 5

Raw unlabeled Western blot β-actin Figure 3D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data5-v2.jpg
Figure 3—source data 6

Raw unlabeled Western blot ferroportin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data6-v2.jpg
Figure 3—source data 7

Raw unlabeled Western blot β-actin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data7-v2.jpg
Figure 3—source data 8

Raw unlabeled Western blot ferroportin and β-actin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data8-v2.jpg
Figure 3—source data 9

Raw unlabeled Western blot ferroportin Figure 3B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data9-v2.jpg
Figure 3—source data 10

Raw unlabeled Western blot β-actin Figure 3B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data10-v2.jpg
Figure 3—source data 11

Raw labeled Western blot β-actin Figure 3B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data11-v2.bmp
Figure 3—source data 12

Raw labeled Western blot ferroportin Figure 3B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data12-v2.bmp
Figure 3—source data 13

Raw labeled Western blot ferroportin and β-actin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data13-v2.bmp
Figure 3—source data 14

Raw labeled Western blot ferroportin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data14-v2.bmp
Figure 3—source data 15

Raw labeled Western blot β-actin Figure 3B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data15-v2.bmp
Figure 3—source data 16

Raw labeled Western blot ferroportin Figure 3D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data16-v2.bmp
Figure 3—source data 17

Raw labeled Western blot ferroportin Figure 3D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data17-v2.bmp
Figure 3—source data 18

Raw labeled Western blot β-actin Figure 3D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data18-v2.bmp
Figure 3—source data 19

Raw labeled Western blot β-actin Figure 3D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig3-data19-v2.bmp

We further analyzed ferroportin in liver homogenates by Western blotting. Levels of biochemically detectable liver ferroportin differed substantially between wild type and Hjv-/- mice. Thus, they were relatively low in the former and highly induced in the latter (Figure 3B), independently of iron load. The differences were more dramatic compared to those observed by immunohistochemistry (Figure 3A and Figure 3—figure supplement 1). Conceivably, the strong ferroportin signal in Western blots from Hjv-/- liver homogenates reflects high ferroportin expression in hepatocytes, which are the predominant cell population and make up ~80% of the liver cell mass (Schulze et al., 2019). Yet, hepatocellular ferroportin is less visible by immunohistochemistry because the signal is substantially weaker compared to that in Kupffer cells (see also Figure 6E). Interestingly, the LPS treatment visibly suppressed total liver ferroportin in Hjv-/- mice on control diet but not IDD, and appeared to modestly reduce it in wild type mice (Figure 3B); albeit without statistical significance. These data are consistent with negative regulation of ferroportin by residual LPS-induced hepcidin in Hjv-/- mice on control diet, which could explain the small drop in serum iron and transferrin saturation under these acute inflammatory conditions, as reported in Fillebeen et al., 2018. However, liver ferroportin remained detectable and apparently functional, as it did not allow significant iron sequestration and dramatic drop in serum iron. Notably, persistence of relatively high serum iron is also evident in LPS-treated wild type mice on HID, despite maximal hepcidin and minimal liver ferroportin levels.

Next, we analyzed ferroportin in the spleen, an organ with erythrophagocytic macrophages that plays an important role in body iron traffic (Kurotaki et al., 2015). Immunohistochemical analysis shows that LPS reduced ferroportin in red pulp splenic macrophages from wild type mice on control diet, but this effect was less evident in wild type mice on HID and in Hjv-/- mice on control diet or IDD (Figure 3C and Figure 3—figure supplement 2). Western blot analysis shows a stronger ferroportin signal in splenic extracts from Hjv-/- animals (Figure 3D), consistent with immunohistochemistry. However, in this assay, LPS suppressed splenic ferroportin in wild type animals and in Hjv-/- mice on control diet, but not IDD. This could be a result of residual hepcidin upregulation (Figure 2F–G), while the lack of significant splenic ferroportin suppression in Hjv-/- mice on IDD may denote hepcidin insufficiency. In any case, the relatively high circulating iron levels in dietary iron-loaded and LPS-treated wild type mice indicate continuous iron efflux to plasma despite hepcidin excess.

Insufficient hepcidin leads to blunted hypoferremic response in iron overload

We used human synthetic hepcidin to address whether the failure of mouse models of iron overload to mount an appropriate hypoferremic response to acute inflammation is caused by endogenous hepcidin insufficiency or other mechanisms. Wild type and Hjv-/- mice subjected to dietary iron manipulations received 2.5 μg/g synthetic hepcidin every two hours for a total of four intraperitoneal injections. Each dose corresponds to ~200-fold excess over endogenous circulating hepcidin in wild type animals. The treatment caused hypoferremia in wild type mice on control diet but not on HID, where the decrease in serum iron was significant but well above baseline of untreated wild type controls (Figure 4A–B); see also ratios of serum iron levels between untreated and hepcidin-treated mice in Figure 4A. Likewise, synthetic hepcidin significantly decreased serum iron but failed to cause dramatic hypoferremia in hepcidin-deficient Hjv-/- mice on control diet. Notably, hepcidin administration was much more effective in relatively iron-depleted Hjv-/- mice on IDD, and lowered serum iron and transferrin saturation below baseline. The treatments significantly reduced NTBI in Hjv-/- mice on control diet, with a trend in mice on IDD (Figure 4C) but did not affect LIC or splenic iron content (SIC) under any experimental conditions (Figure 4D–E and Figure 4—figure supplement 1). Serum iron represents <2% of total tissue iron and, therefore, its acute fluctuations are not expected to dramatically alter LIC or SIC.

Figure 4 with 3 supplements see all
Iron depletion of Hjv-/- mice improves the efficacy of synthetic hepcidin to promote hypoferremia.

Four-week-old wild type male mice (n=12–14 per group) were placed on HID for 5 weeks. Conversely, age- and sex-matched isogenic Hjv-/- mice (n=12–14 per group) were placed on IDD for 5 weeks to prevent excessive iron overload. Other animals from both genotypes were kept on standard diet. Half of the mice were injected every 2 hr for a total of 4 injections with saline, and the other half with 2.5 µg/g synthetic hepcidin. Sera were collected by cardiac puncture and analyzed for: (A) iron, (B) transferrin saturation, and (C) non-transferrin bound iron (NTBI). Livers and spleens were dissected and processed for analysis of: (D) liver iron content (LIC) and (E) splenic iron content (SIC) by the ferrozine assay. (F) qPCR analysis of liver Hamp mRNA. The dotted line in (A) and (B) indicates baseline serum iron and transferrin saturation, respectively, of wild type mice on control diet. Values in (A) represent ratios of serum iron levels between untreated and hepcidin-treated mice. Data in (A–E) are presented as the mean ± SEM and in (F) as geometric mean ± SD. Statistically significant differences (p<0.05) compared to values from saline- or hepcidin-treated wild type control mice are indicated by a or b, respectively. HID: high-iron diet; IDD: iron-deficient diet.

Synthetic hepcidin led to a significant reduction of endogenous Hamp mRNA in wild type mice on control diet (Figure 4F), as earlier reported (Laftah et al., 2004). Conceivably, this is related to destabilization of the Hamp inducer transferrin receptor 2 (Tfr2) in the liver (Figure 4—figure supplement 2), a known response to hypoferremia (Johnson and Enns, 2004). Synthetic hepcidin did not promote inflammation, iron perturbations or alterations in BMP/SMAD signaling in the liver, as judged by the unaltered expression of hepatic Slc40a1(+IRE), Socs3, Id1, and Bmp6 mRNAs (Figure 4—figure supplement 3A-D). Moreover, synthetic hepcidin did not affect Slc11a2, Slc39a14, Lcn2, or Tfrc mRNAs (Figure 4—figure supplement 3E-H), which encode iron transporters; Slc39a14 and Lcn2 are also inflammatory markers.

Next, we analyzed liver ferroportin by immunohistochemistry. Figure 5A and Figure 5—figure supplement 1 show that exogenous hepcidin decreased ferroportin signal intensity in all animal groups to varying degrees. The hepcidin effect was particularly noticeable in Hjv-/- hepatocytes (see low magnification images in Figure 5—figure supplement 1). Kupffer cells seemed to retain some ferroportin in all groups except Hjv-/- mice on IDD. Interestingly, while synthetic hepcidin decreased ferroportin signal intensity in Kupffer cells, it did not alter intracellular ferroportin distribution as would be expected based on the data in LPS-treated wild type mice (Figure 5A).

Figure 5 with 2 supplements see all
Effects of synthetic hepcidin on hepatic and splenic ferroportin of iron-manipulatedwild type and Hjv-/- mice.

Livers and spleens from mice described in Figure 4 were dissected and processed for immunohistochemical and biochemical analysis of ferroportin. Immunohistochemical staining of ferroportin in liver (A) and spleen (C) sections (magnification for liver is 20 × and for spleen 10 ×). Western blot for ferroportin and β-actin in liver (B) and spleen (D) extracts from four representative mice in each condition. Blots were quantified by densitometry and ferroportin/β-actin ratios are shown on the right. Densitometric data are presented as the mean ± SEM. Statistically significant differences (p<0.05) compared to values from saline- or hepcidin-treatedwild type control mice are indicated by a or b, respectively. HID: high-iron diet; IDD: iron-deficient diet.

Figure 5—source data 1

Western blot quantifications.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data1-v2.xlsx
Figure 5—source data 2

Raw unlabeled Western blot ferroportin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data2-v2.jpg
Figure 5—source data 3

Raw unlabeled Western blot β-actin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data3-v2.jpg
Figure 5—source data 4

Raw unlabeled Western blot ferroportin Figure 5B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data4-v2.jpg
Figure 5—source data 5

Raw unlabeled Western blot ferroportin Figure 5B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data5-v2.jpg
Figure 5—source data 6

Raw unlabeled Western blot ferroportin Figure 5D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data6-v2.jpg
Figure 5—source data 7

Raw unlabeled Western blot.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data7-v2.jpg
Figure 5—source data 8

Raw unlabeled Western blot ferroportin Figure 5D (c).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data8-v2.jpg
Figure 5—source data 9

Raw unlabeled Western blot β-actin Figure 5D (c).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data9-v2.jpg
Figure 5—source data 10

Raw unlabeled Western blot β-actin Figure 5B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data10-v2.jpg
Figure 5—source data 11

Raw unlabeled Western blot β-actin Figure 5B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data11-v2.jpg
Figure 5—source data 12

Raw labeled Western blot β-actin Figure 5B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data12-v2.bmp
Figure 5—source data 13

Raw labeled Western blot β-actin Figure 5B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data13-v2.bmp
Figure 5—source data 14

Raw labeled Western blot ferroportin Figure 5B (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data14-v2.bmp
Figure 5—source data 15

Raw labeled Western blot ferroportin Figure 5B (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data15-v2.bmp
Figure 5—source data 16

Raw labeled Western blot β-actin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data16-v2.bmp
Figure 5—source data 17

Raw labeled Western blot ferroportin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data17-v2.bmp
Figure 5—source data 18

Raw labeled Western blot β-actin Figure 5D (c).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data18-v2.bmp
Figure 5—source data 19

Raw labeled Western blot ferroportin Figure 5D (c).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data19-v2.bmp
Figure 5—source data 20

Raw labeled Western blot β-actin Figure 5D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data20-v2.bmp
Figure 5—source data 21

Raw labeled Western blot ferroportin Figure 5D (b).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data21-v2.bmp
Figure 5—source data 22

Raw labeled Western blot ferroportin and β-actin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data22-v2.bmp
Figure 5—source data 23

Raw unlabeled Western blot ferroportin and β-actin Figure 5D (a).

https://cdn.elifesciences.org/articles/81332/elife-81332-fig5-data23-v2.jpg

Western blotting confirmed that total liver ferroportin is highly induced in Hjv-/- mice (Figure 5B). Again, the signal intensity can be attributed to proteins expressed in hepatocytes. The treatment with synthetic hepcidin did not significantly affect liver ferroportin in wild type mice (either on control diet or HID), but substantially reduced it in Hjv-/- mice, to almost wild type levels. The effect appeared more pronounced in Hjv-/- mice on IDD; nevertheless, ferroportin remained detectable.

Splenic ferroportin was reduced in all animal groups following hepcidin treatment, with stronger effects visualized by immunohistochemistry in wild type mice on control diet and Hjv-/- mice on IDD (Figure 5C and Figure 5—figure supplement 2). At the biochemical level, ferroportin expression was again much stronger in the spleen of Hjv-/- mice (Figure 5D). Synthetic hepcidin did not significantly affect splenic ferroportin in wild type mice but dramatically reduced it in all Hjv-/- mice.

Taken together, our data suggest that synthetic hepcidin overcomes endogenous hepcidin deficiency in Hjv-/- mice. However, it only triggers hypoferremia in these animals following relative iron depletion. On the other hand, in iron-loaded wild type mice with already high endogenous hepcidin, excess synthetic hepcidin fails to promote hypoferremia.

Dietary iron manipulations are sensed by IRPs in the liver and spleen of wild type and Hjv-/- mice

The IRE/IRP system orchestrates homeostatic adaptation to cellular iron supply (Wang and Pantopoulos, 2011; Muckenthaler et al., 2008). To evaluate the responses of IRPs in the whole liver and spleen to the above-described dietary iron manipulations, we analyzed tissue extracts from wild type and Hjv-/- mice by an electrophoretic mobility shift assay (EMSA) using a 32P-labelled IRE probe. The data in Figure 6A–B show that HID intake tended to decrease the IRE-binding activities of IRP1 and IRP2 in both the liver and spleen of wild type mice (statistical significance is only reached in the liver); densitometric quantification of IRE/IRP1 and IRE/IRP2 complexes is shown on the right. Conversely, IDD intake significantly induced the IRE-binding activity of IRP2 in the liver and spleen of 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 (Fillebeen et al., 2014) and are shown in the bottom panels.

Dietary iron manipulations trigger IRP responses in the liver and spleen, as well as in primary hepatocytes and non-parenchymal liver cells of wild type and Hjv-/- mice.

Whole liver (A), whole spleen (B), isolated hepatocytes (C) or isolated non-parenchymal liver cells (D) from the mice described in Figure 4 were analyzed for IRE-binding activity by EMSA with a 32P-labelled IRE probe in the absence (top) or presence (bottom) of 2% mercaptoethanol (2-ME). Two or three representative samples from each condition are shown. The positions of IRE/IRP1 and IRE/IRP2 complexes are indicated by arrows. Shorter and longer exposures of the autoradiograms are shown in the left and middle panels, respectively. Relative band intensities were quantified by densitometry and shown on the right panels. (E) Isolated hepatocytes and isolated non-parenchymal liver cells were analyzed by Western blotting for expression of ferroportin and β-actin. Blots were quantified by densitometry and ferroportin/β-actin ratios are shown on the right. Densitometric data are presented as the mean ± SEM. Statistically significant differences (p<0.05) in values from wild type mice on control diet are indicated by a, from wild type mice on HID by b, and from Hjv-/- mice on control diet by c. HID: high-iron diet; IDD: iron-deficient diet; IRE: iron-responsive element; IRP: iron regulatory protein; EMSA: electrophoretic mobility shift assay.

Figure 6—source data 1

EMSA quantification.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data1-v2.xlsx
Figure 6—source data 2

Western quantification.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data2-v2.xlsx
Figure 6—source data 3

Raw unlabeled liver EMSA long exposure Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data3-v2.jpg
Figure 6—source data 4

Raw unlabeled liver EMSA long exposure 2-ME Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data4-v2.jpg
Figure 6—source data 5

Raw unlabeled liver EMSA short exposure Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data5-v2.jpg
Figure 6—source data 6

Raw unlabeled liver EMSA short exposure 2-ME Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data6-v2.jpg
Figure 6—source data 7

Raw unlabeled spleen EMSA long exposure Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data7-v2.jpg
Figure 6—source data 8

Raw unlabeled spleen EMSA long exposure 2-ME Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data8-v2.jpg
Figure 6—source data 9

Raw unlabeled spleen EMSA short exposure Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data9-v2.jpg
Figure 6—source data 10

Raw unlabeled spleen EMSA short exposure BME Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data10-v2.jpg
Figure 6—source data 11

Raw unlabeled Western blot β-actin Figure 6E.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data11-v2.jpg
Figure 6—source data 12

Raw unlabeled Western blot ferroportin Figure 6E.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data12-v2.jpg
Figure 6—source data 13

Raw unlabeled hepatocytes EMSA short exposure Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data13-v2.jpg
Figure 6—source data 14

Raw unlabeled hepatocytes EMSA short exposure 2-ME Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data14-v2.jpg
Figure 6—source data 15

Raw unlabeled hepatocytes EMSA long exposure Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data15-v2.jpg
Figure 6—source data 16

Raw unlabeled hepatocytes EMSA long exposure 2-ME Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data16-v2.jpg
Figure 6—source data 17

Raw unlabeled non-parenchymal cells EMSA short exposure Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data17-v2.jpg
Figure 6—source data 18

Raw unlabeled non-parenchymal cells EMSA short exposure 2-ME Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data18-v2.jpg
Figure 6—source data 19

Raw unlabeled non-parenchymal cells EMSA long exposure Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data19-v2.jpg
Figure 6—source data 20

Raw unlabeled non-parenchymal cells EMSA long exposure 2-ME Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data20-v2.jpg
Figure 6—source data 21

Raw labeled liver EMSA long exposure Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data21-v2.bmp
Figure 6—source data 22

Raw labeled liver EMSA long exposure 2-ME Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data22-v2.bmp
Figure 6—source data 23

Raw labeled liver EMSA short exposure Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data23-v2.bmp
Figure 6—source data 24

Raw labeled liver EMSA short exposure 2-ME Figure 6A.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data24-v2.bmp
Figure 6—source data 25

Raw labeled spleen EMSA short exposure Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data25-v2.bmp
Figure 6—source data 26

Raw labeled spleen EMSA short exposure 2-ME Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data26-v2.bmp
Figure 6—source data 27

Raw labeled spleen EMSA long exposure Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data27-v2.bmp
Figure 6—source data 28

Raw labeled spleen EMSA long exposure 2-ME Figure 6B.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data28-v2.bmp
Figure 6—source data 29

Raw labeled hepatocytes EMSA short exposure Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data29-v2.bmp
Figure 6—source data 30

Raw labeled hepatocytes EMSA short exposure 2-ME Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data30-v2.bmp
Figure 6—source data 31

Raw labeled hepatocytes EMSA long exposure Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data31-v2.bmp
Figure 6—source data 32

Raw labeled hepatocytes EMSA long exposure 2-ME Figure 6C.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data32-v2.bmp
Figure 6—source data 33

Raw labeled non-parenchymal cells EMSA short exposure Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data33-v2.bmp
Figure 6—source data 34

Raw labeled non-parenchymal cells EMSA short exposure 2-ME Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data34-v2.bmp
Figure 6—source data 35

Raw labeled non-parenchymal cells EMSA long exposure Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data35-v2.bmp
Figure 6—source data 36

Raw labeled non-parenchymal cells EMSA long exposure 2-ME Figure 6D.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data36-v2.bmp
Figure 6—source data 37

Raw labeled Western blot ferroportin and β-actin Figure 6E.

https://cdn.elifesciences.org/articles/81332/elife-81332-fig6-data37-v2.bmp

To clarify which cell types of the liver account for the responses of IRPs to dietary iron, separate EMSAs were performed using extracts from isolated hepatocytes or non-parenchymal liver cells. The data in Figure 6C–D uncover that IRP1 and IRP2 in both liver cell populations from wild type and Hjv-/- mice are sensitive to dietary iron loading or restriction, respectively. The EMSA analysis of non-parenchymal liver cells, which contain Kupffer cells among others, showed a high experimental variability (Figure 6D). Nevertheless, the overall results are consistent with those obtained with splenic extracts, which contain red pulp macrophages (Figure 6B).

Relative expression of ferroportin in hepatocytes and non-parenchymal liver cells from wild type and Hjv-/- mice

We determined the relative abundance of ferroportin in hepatocytes and non-parenchymal liver cells from wild type and Hjv-/- mice on control diet by Western blotting. As expected, ferroportin expression (normalized to β-actin) was ~1.5–twofold higher in the non-parenchymal cell fraction as compared to hepatocytes in both wild type and Hjv-/- mice (Figure 6E). In comparison across genotypes, ferroportin expression was ~2-fold higher in hepatocytes and ~50% higher in non-parenchymal cells from Hjv-/- vs wild type mice.

Iron-dependent regulation of ferroportin mRNA translation in the liver

Having established that dietary iron manipulations trigger IRP responses in the liver and spleen, we hypothesized that the functional outcomes of exogenous hepcidin may not merely depend on its capacity to degrade tissue ferroportin but also on iron-dependent ferroportin regeneration via de novo synthesis. Slc40a1(+IRE) mRNA is the predominant ferroportin transcript in the mouse liver and spleen, as well as in hepatoma and macrophage cell lines (Zhang et al., 2009), and is considered as a target of IRPs.

Thus, we assessed the effects of dietary iron on whole liver Slc40a1(+IRE) mRNA translation by polysome profile analysis. We focused on the liver because this organ contains the highest number of iron-recycling macrophages (Krenkel and Tacke, 2017) and can also export iron to plasma from ferroportin-expressing parenchymal cells. Liver extracts from wild type mice on control diet or HID, and Hjv-/- mice on control diet or IDD were fractionated on sucrose gradients to separate translationally inactive light monosomes from translating heavy polysomes (Figure 7A). The relative distribution of Slc40a1(+IRE), Fth1 (positive control for iron regulation), and Actb (negative control) mRNAs within the different fractions was quantified by qPCR (Figure 7B–D). Dietary iron loading stimulated Slc40a1(+IRE) (and Fth1) mRNA translation in wild type mice (note the shifts from monosomes to polysomes in Figure 7B–C). Conversely, dietary iron depletion inhibited 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 (Figure 2J and Figure 4—figure supplement 3), it is possible that increased de novo ferroportin synthesis is further enhanced by transcriptional induction.

Iron regulation of Slc40a1(+IRE) mRNA translation in the mouse liver.

Four-week-old wild type male mice (n=10–14 per group) were placed on high-iron diet (HID) for 5 weeks. Conversely, age- and sex-matched isogenic Hjv-/- mice (n=10–14 per group) were placed on iron-deficient diet (IDD) for 5 weeks to prevent excessive iron overload. Other animals from both genotypes were kept on control diet. At the endpoint, the mice were sacrificed, and livers were used for polysome profile analysis and iron assays. (A) Recording of absorbance at 254 nm of representative samples. Fraction numbers and direction of the gradient are indicated. (B–D) Liver polysome profiles from n=3 mice in each experimental group. Distribution of (B) Slc40a1(+IRE), (C) Fth1 and (D) Actb mRNAs among light monosomal and heavy polysomal fractions (separated by dashed line) was analyzed by qPCR. Bar graph comparisons of pooled fractions are shown on the right. Numbers indicate the fold change compared towild type mice oncontrol diet. (E and F) Analysis of total iron (E), and redox iron speciation (F) in the liver by CE-ICP-MS. Data are presented as the mean ± SEM. Statistical analysis in (A) was performed by two-way ANOVA and in (B, C) by one-way ANOVA. Statistically significant differences (p<0.05) compared to values from wild type mice on control are indicated by a.

Quantification of liver iron by ICP-MS (Figure 7E) validated iron loading of wild type mice by HID, and relative iron depletion of Hjv-/- mice by IDD intake, respectively (see also Figure 2D). Iron redox speciation analysis by CE-ICP-MS revealed a profound increase in Fe2+/Fe3+ ratios in livers of Hjv-/- mice on control diet, which was corrected by dietary iron depletion (Figure 7F). Nevertheless, there was no difference in Fe2+/Fe3+ ratios among the livers of wild type mice on control diet or HID, and Hjv-/- mice on IDD. We conclude that a relative increase in total iron content, rather than excessive accumulation of redox active Fe2+ drives Slc40a1(+IRE) (and Fth1) mRNA translation in the liver.

Restoration of effective hypoferremic response under iron overload following maximal Slc40a1 mRNA suppression

We reasoned that complete inactivation of ferroportin mRNA would restore hepcidin-induced hypoferremia despite iron overload. An 8 hr treatment of mice with LPS suppressed liver Slc40a1(+IRE) mRNA below detection levels (Figure 8A), as reported (Fillebeen et al., 2018). The same holds true for the Slc40a1(-IRE) isoform (Figure 8B), which was 290 times less abundant in control mouse livers compared to Slc40a1(+IRE) (ΔCt = 8.18), in agreement with published data (Zhang et al., 2009). We went on to examine the effects of synthetic hepcidin on serum iron under these conditions of maximal Slc40a1 mRNA suppression. Importantly, the prolonged LPS treatment decreased serum iron in wild type mice on HID below the control baseline (Figure 8C). Furthermore, when combined with synthetic hepcidin, it promoted an effective hypoferremic response in wild type mice on HID and Hjv-/- mice on control diet (or IDD; Figure 8C–D) and tended to decrease NTBI (Figure 8E). These data strongly suggest that the expression of actively translating Slc40a1 mRNA in iron-exporting tissues under systemic iron overload mitigates the hepcidin-induced drop in serum iron.

Elimination of ferroportin mRNA by prolonged LPS treatment potentiates hepcidin-induced hypoferremia in mouse models of iron overload.

Four-week-old wild type male mice (n=10–14 per group) were placed on high-iron diet (HID) for 5 weeks. Conversely, age- and sex-matched isogenic Hjv-/- mice (n=10–14 per group) were placed on iron-deficient diet (IDD) for 5 weeks to prevent excessive iron overload. Other animals from both genotypes were kept on control diet. (A and B) Half of the mice were injected with saline and the other half with 1 µg/g LPS and sacrificed after 8 hr. Livers were dissected and processed for qPCR analysis of Slc40a1(+IRE) (A) and Slc40a1(-IRE) (B) mRNAs. (C–E) All mice were injected with 1 µg/g LPS. Half of the animals were subsequently injected with saline, and the other half with 2.5 µg/g synthetic hepcidin every 2 hr for a total of 4 injections. At the endpoint the mice were sacrificed. Sera were collected by cardiac puncture and analyzed for: (C) iron, (D) transferrin saturation, and (E) non-transferrin bound iron (NTBI). The dotted line in (C) and (D) indicates baseline serum iron and transferrin saturation, respectively, of wild type mice on control diet. Data are presented as (A–B) geometric mean ± SD or (C–E) mean ± SEM. Statistically significant differences (p<0.05) compared to values from saline-, LPS- or hepcidin-treated wild type mice on control diet are indicated by a, b or c, respectively.

Discussion

We sought to analyze how iron overload affects hepcidin-mediated inflammatory responses. We and others reported that excess iron inhibits the major hepcidin signaling pathways (BMP/SMAD and IL-6/STAT3) in cultured cells (Charlebois and Pantopoulos, 2021; Yu et al., 2021). To explore the physiological relevance of these findings, wild type mice were subjected to variable degrees of dietary iron loading and then treated with LPS. All iron-loaded mice could further upregulate hepcidin in response to LPS-induced acute inflammation (Figure 1). This is consistent with other relevant findings (Enculescu et al., 2017) and apparently contradicts the in vitro data. While experimental iron loading of cultured cells is rapid, dietary iron loading of mice is gradual (Daba et al., 2013) and most of the excess iron is effectively detoxified within ferritin, which is highly induced (Kent et al., 2015). By contrast, the suppression of hepcidin preceded ferritin induction in cultured cells (Charlebois and Pantopoulos, 2021), which may explain the discrepancy with the in vivo data.

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 (Figure 1). Thus, LPS-treated mice on 5 weeks of HID developed relative hypoferremia but could not further reduce serum iron below the baseline of untreated mice on control diet. This can be attributed to mechanisms antagonizing hepcidin action. To explore how iron modulates the capacity of hepcidin to trigger inflammatory hypoferremia, we established conditions of iron overload using wild type and Hjv-/- mice with extreme differences in hepcidin expression. Figures 2 and 3 demonstrate that iron overload prevents effective inflammatory hypoferremia independently of hepcidin and tissue ferroportin levels.

We used a~200-fold excess of synthetic hepcidin to directly assess its capacity to divert iron traffic in iron-loaded mice. Hepcidin injection caused hypoferremia in wild type mice on control diet and significantly reduced serum iron in wild type mice on HID and Hjv-/- mice on control diet, but not below baseline (Figure 4). Thus, synthetic hepcidin failed to drastically drop serum iron levels in iron overload models with either high or low endogenous hepcidin. Importantly, synthetic hepcidin promoted robust hypoferremia in relatively iron-depleted Hjv-/- mice on IDD, with undetectable endogenous hepcidin. It should be noted that synthetic hepcidin had similar effects on tissue ferroportin among wild type or Hjv-/- mice, regardless of iron diet (Figure 5). It reduced the intensity of the ferroportin signal in Kupffer cells and splenic macrophages of wild type mice without significantly affecting biochemically detectable total protein levels. In addition, it dramatically reduced the total ferroportin in the liver and spleen of Hjv-/- mice. However, in all experimental settings, there was residual tissue ferroportin, which appears to be functionally significant.

We reasoned that at steady-state, tissue ferroportin may consist of fractions of newly synthesized protein and protein that is en route to hepcidin-mediated degradation. Conceivably, the former may exhibit more robust iron export activity, at least before its iron channel gets occluded by hepcidin. Increased de novo synthesis of active ferroportin could explain why synthetic hepcidin cannot drastically drop serum iron levels under iron overload. In fact, Figure 7 demonstrates that dietary iron overload augments Slc40a1(+IRE) 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 (Figure 4).

Our data are consistent with translational control of liver ferroportin expression via the IRE/IRP system and do not exclude the possibility for an additional contribution of iron-dependent transcriptional regulation of Slc40a1(+IRE) mRNA. Direct evidence for activation of IRP responses in the liver and spleen to dietary iron manipulations is provided in Figure 6. While translational control of ferritin in tissues is established (Wilkinson and Pantopoulos, 2014), regulation of ferroportin by the IRE/IRP system is less well characterized and has hitherto only been documented in cell models (Lymboussaki et al., 2003; Nairz et al., 2015), the mouse duodenum (Galy et al., 2013), and the rat liver (Garza et al., 2020). Moreover, the physiological relevance of this mechanism remained speculative. The data in Figures 6 and 7 show that the IRE/IRP system is operational and controls 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 (Katsarou and Pantopoulos, 2018) will overcome the antagonistic effects of increased ferroportin mRNA translation under iron overload.

The critical role of de novo ferroportin synthesis in fine-tuning hepcidin-dependent functional outcomes is also highlighted in Figure 8. Thus, synthetic hepcidin was highly effective as a promoter of hypoferremia in dietary iron-loaded wild type mice when administered together with LPS. LPS is known to suppress Slc40a1 mRNA in cell lines (Ludwiczek et al., 2003) and mouse tissues, with a nadir in the liver reached at 8 hr (Fillebeen et al., 2018). The recovery of hepcidin effectiveness in mouse models of iron overload was only possible when Slc40a1 mRNA was essentially eliminated. Under these conditions, LPS treatment alone was sufficient to decrease serum iron in dietary iron-loaded wild type mice below baseline.

Tissue iron uptake may be another important determinant of the hypoferremic response to inflammation. LPS did not affect Tfrc mRNA levels in the liver (Figure 2O), which argues against increased uptake of transferrin-bound iron via Tfr1. On the other hand, LPS induced Slc39a14, Slc11a2, and Lcn2 mRNAs (Figure 2L–N). Zip14 is the NTBI transporter accounting for hepatocellular iron overload in hemochromatosis (Jenkitkasemwong et al., 2015) and is upregulated by inflammatory cues in hepatocytes (Liuzzi et al., 2005). DMT1 is dispensable for NTBI uptake by hepatocytes (Wang and Knutson, 2013), and its inflammatory induction might promote iron acquisition by macrophages (Ludwiczek et al., 2003; Wardrop and Richardson, 2000). Nevertheless, considering that the fraction of NTBI represents <2% of total serum iron even in the iron overload models (Figure 2A and C), it is implausible that NTBI uptake by Zip14 and/or DMT1 substantially contributes to inflammatory hypoferremia. Lcn2 is an acute phase protein that can sequester intracellular iron bound to catecholate siderophores (Xiao et al., 2017), and is more likely to transport iron to tissues during infection. In any case, synthetic hepcidin did not affect expression of iron transporters (Figure 4—figure supplement Figure 4—figure supplement 3E-H). This excludes the possibility for a synergistic effect on LPS-induced tissue iron uptake that could promote effective hypoferremia in the iron overload models.

Our study has some limitations. While the data highlight the importance of translational regulation of liver ferroportin as a determinant of serum iron, they do not accurately dissect the specific role of ferroportin expressed in hepatocytes and Kupffer cells; the latter were not separated from other non-parenchymal cells in biochemical assays. The involvement of the IRE/IRP system has been established indirectly, while the relative contributions of IRP1 and IRP2 in the mechanism are not fully defined. The possible role of iron-dependent transcriptional induction of ferroportin in counterbalancing hepcidin actions requires further clarification. The use of diets with variable iron content may have triggered responses to iron availability independent of hepcidin signaling and Hjv functionality. Finally, the physiological implications of translational regulation of ferroportin in the broader setting of inflammation and/or infection have not been explored.

In conclusion, our data reveal a crosstalk between the hepcidin pathway and the IRE/IRP system in the liver and spleen for the control of tissue ferroportin and serum iron levels. Furthermore, they suggest that application of hepcidin therapeutics for treatment of iron overload disorders should be combined with iron depletion strategies to mitigate Slc40a1 mRNA translation and increase hepcidin efficacy. Future work is expected to clarify whether optimizing the hypoferremic response to inflammation under systemic iron overload decreases susceptibility to pathogens.

Materials and methods

Animals

Wild type C57BL/6J and isogenic Hjv-/- mice (Gkouvatsos et al., 2014) were housed in macrolone cages (up to 5 mice/cage, 12:12 hr light-dark cycle: 7 am–7 pm; 22 ± 1°C, 60 ± 5% humidity). The mice were fed either a standard control diet (200 ppm iron), an iron-deficient diet (2–6 ppm iron) or a high-iron diet (2% carbonyl iron) (Fillebeen et al., 2019). Where indicated, mice were injected intraperitoneally with 1 μg/g LPS (serotype 055:B5; Sigma-Aldrich) or subcutaneously with 2.5 µg/g synthetic hepcidin; control mice were injected with phosphate-buffered saline. At the endpoints, animals were sacrificed by CO2 inhalation and cervical dislocation. Experimental procedures were approved by the Animal Care Committee of McGill University (protocol 4966).

Serum biochemistry

Request a detailed protocol

Blood 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 (HMC-001; Intrinsic LifeSciences).

Quantification of serum non-transferrin bound iron (NTBI)

Request a detailed protocol

NTBI was measured by adapting the method developed by Esposito et al (Esposito et al., 2003). Briefly, iron samples of known concentration were created by mixing 70 mM nitrilotriacetate (NTA) (pH = 7.0) with 20 mM ferrous ammonium sulfate. Fe2+ was allowed to oxidize to Fe3+ in ambient air for at least 30 min and then the solution was diluted to 0.2 mM before further serial dilutions to create a ladder. 5 μl of ladder was loaded in a 96-well plate containing 195 μl plasma-like medium with or without 100 μM deferiprone. The composition of the plasma-like medium was: 40 mg/ml bovine serum albumin, 1.2 mM sodium phosphate dibasic, 120 μM sodium citrate, 10 mM sodium bicarbonate in iron-free HEPES-buffered saline (HEPES 20 mM, NaCl 150 mM, treated with Chelex-100 chelating resin [Bio-Rad, Hercules, CA], 0.5 mM ΝΤΑ, 40 μM ascorbic acid, 50 μM dihydrorhodamine, pH = 7.4). 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.

Hepcidin synthesis

Request a detailed protocol

Human hepcidin (DTHFPICIFCCGCCHRSKCGMCCKT) 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 (Peptide International, #PLP-3771-PI).

Quantitative real-time PCR (qPCR)

Request a detailed protocol

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 (Applied Biological Materials Inc). Gene-specific primers pairs (Supplementary file 1) were validated by dissociation curve analysis and demonstrated amplification efficiency between 90–110%. SYBR Green (Bioline) and primers were used to amplify products under following cycling conditions: initial denaturation 95°C 10 min, 40 cycles of 95°C 5 s, 58°C 30 s, 72°C 10 s, and final cycle melt analysis between 58–95°C. Relative mRNA expression was calculated by the 2-ΔΔCt method (Livak and Schmittgen, 2001). Data were normalized to murine ribosomal protein L19 (Rpl19). Data are reported as fold increases compared to samples from wild type mice on control diet.

Polysome fractionation

Request a detailed protocol

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 (200 mM HEPES pH = 7.6, 1 M KCl, 50 mM MgCl2, 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 Liang et al., 2018. Briefly, livers were flash frozen upon collection. Roughly 30–80 mg of tissue was crushed using a mortar and pestle in the presence of liquid nitrogen to prevent thawing. Tissues were lysed in up to 1 ml of hypotonic lysis buffer (5 mM Tris-Hcl pH = 7.5, 1.5 mM KCl, 2.5 mM MgCl2, 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 (Panda et al., 2017).

Electrophoretic mobility shift assay (EMSA)

Request a detailed protocol

IRE-binding activities from liver and spleen were analyzed by EMSA using a radioactive 32P-labelled IRE probe, according to established procedures (Fillebeen et al., 2014). EMSAs were also performed in extracts from hepatocytes and non-parenchymal cells, which were separated by using a 2-step collagenase perfusion technique, as previously described (Fillebeen et al., 2018).

Western blotting

Request a detailed protocol

Livers were washed with ice-cold PBS and dissected into pieces. Aliquots were snap frozen at liquid nitrogen and stored at −80°C. Protein lysates were obtained as described (Katsarou et al., 2021). Lysates containing 40 μg of proteins were analyzed by SDS-PAGE on 9–13% gels and proteins were transferred onto nitrocellulose membranes (BioRad). The blots were blocked in non-fat milk diluted in tris-buffered saline (TBS) containing 0.1% (v/v) Tween-20 (TBS-T), and probed overnight with antibodies against ferroportin (Ross et al., 2017; 1:1000 diluted monoclonal rat anti-mouse 1C7, kindly provided by Amgen Inc), β-actin (1:2000 diluted; Sigma), Tfr2 (1:1000 diluted rabbit polyclonal; Alpha Diagnostics), or Tfr1 (1:1000 diluted mouse monoclonal, Invitrogen). Following a 3 × wash with TBS-T, the membranes were incubated with peroxidase-coupled secondary antibodies for 1 hr. Immunoreactive bands were detected by enhanced chemiluminescence with the Western Lightning ECL Kit (Perkin Elmer).

Immunohistochemistry

Request a detailed protocol

Tissue specimens were fixed in 10% buffered formalin and embedded in paraffin. Samples from three different mice for each experimental condition were cut at 4 µm, placed on SuperFrost/Plus slides (Fisher) and dried overnight at 37°C. The slides were then loaded onto the Discovery XT Autostainer (Ventana Medical System) for automated immunohistochemistry. Slides underwent deparaffinization and heat-induced epitope retrieval. Immunostaining was performed by using 1:500 diluted rabbit polyclonal antibodies against ferroportin (Maffettone et al., 2010) and an appropriate detection kit (Omnimap rabbit polyclonal HRP, #760–4311 and ChromoMap-DAB #760–159; Roche). Negative controls were performed by the omission of the primary antibody. Slides were counterstained with hematoxylin for 4 min, blued with Bluing Reagent for 4 min, removed from the autostainer, washed in warm soapy water, dehydrated through graded alcohols, cleared in xylene, and mounted with Permount (Fisher). Sections were analyzed by conventional light microscopy and quantified by using the Aperio ImageScope software (Leica Biosystems; Fillebeen et al., 2018).

Perls Prussian blue staining

Request a detailed protocol

To visualize non-heme iron deposits, deparaffinized tissue sections were stained with Perls’ Prussian blue using the Accustain Iron Stain kit (Sigma).

Quantification of liver iron content (LIC)

Request a detailed protocol

Total liver iron was quantified by using the ferrozine assay (Daba et al., 2013) or inductively coupled plasma mass spectrometry (ICP-MS; Michalke et al., 2019).

Iron speciation analysis

Request a detailed protocol

Iron redox speciation analysis in the liver was performed by capillary electrophoresis (CE) coupled to ICP-MS (CE-ICP-MS). Dynamic reaction cell (DRC) technology (ICP-DRC-MS) with NH3 as DRC-gas was applied for non-interfered monitoring of the iron isotopes. A ‘PrinCe 706’ CE system (PrinCe Technologies B.V., Emmen, Netherlands) 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 (100 cm × 50 µm ID; CS-Chromatographie Service GmbH, Langerwehe, Germany) was used for separation and hyphenation to the ICP–DRC-MS. A CE-ICP-MS interface (Michalke et al., 2019; Michalke et al., 2020) was installed which provided the electrical connection between CE capillary end and outlet electrode. The self-aspiration mode allowed for best flow rate adjustment and avoided suction flow. Electrolytes for sample stacking and electrophoretic separation were 10% HCl = leading electrolyte, 0.05 mM HCl = terminating electrolyte and 50 mM HCl = background electrolyte. The Fe2+/Fe3+ ratio was calculated from quantitative determined concentrations of Fe-species.

Statistics

Statistical 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.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file.

References

Decision letter

  1. Yelena Ginzburg
    Reviewing Editor; Icahn School of Medicine at Mount Sinai, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States

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.]

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 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.

Comments to the Authors:

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 eLife.

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 (without decreasing iron overload systemically) 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 (without decreasing iron overload systemically) 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 (e.g. Figure 2a-2b). 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 (e.g. page 5, like 93: "slightly elevated" where there is no statistically significant changes) 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 (page 7, line 133) or "modestly" affected (page 7, line 134) 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 (page 10, line 207). 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. (this data is somehow presented in figure 4, but it would be better compared the same animals side by side). 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 (Figure 2H), 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.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

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 eLife. Your revised article has been evaluated by Mone Zaidi (Senior Editor) and a Reviewing Editor.

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 (Figure 2B)" 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(Figure 3G). 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 (page14, line 304) and also later (page 16, line 342) 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 (eg, 1D was described after 1E and 1F). 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 (Figure 1) 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 (for that rationale and in regard to the interpretation of the data) 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.

https://doi.org/10.7554/eLife.81332.sa1

Author response

[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 (without decreasing iron overload systemically) 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 (without decreasing iron overload systemically) 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 (e.g. Figure 2a-2b). 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 (e.g. page 5, like 93: "slightly elevated" where there is no statistically significant changes) 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 (page 7, line 133) or "modestly" affected (page 7, line 134) 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 (Figures S1, S2, S6A-C).

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 (page 10, line 207). 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. (this data is somehow presented in figure 4, but it would be better compared the same animals side by side).

We have generated and embedded here an alternative Figure 7 that includes the hepcidin data shown in new Figure 3 (old Figure 4). 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 (new Figure 5).

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 (Figure 2H), 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 (Figure 2B)" 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(Figure 3G). 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 (attached at the end of this rebuttal letter), 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 (see p. 4, p. 11 and p. 17). 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 (page14, line 304) and also later (page 16, line 342) 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 (eg, 1D was described after 1E and 1F). 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 (see also response to Q2 of reviewer 1). 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 (now Figure 2E), 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 (Figure 1) 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 (for that rationale and in regard to the interpretation of the data) 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”.

https://doi.org/10.7554/eLife.81332.sa2

Article and author information

Author details

  1. Edouard Charlebois

    Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Montreal, Canada
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  2. Carine Fillebeen

    Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Montreal, Canada
    Contribution
    Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  3. Angeliki Katsarou

    Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Aleksandr Rabinovich

    Ferring Research Institute Inc, San Diego, United States
    Contribution
    Resources
    Competing interests
    Was an employee of Ferring Research Institute Inc
  5. Kazimierz Wisniewski

    Ferring Research Institute Inc, San Diego, United States
    Contribution
    Resources
    Competing interests
    Was an employee of Ferring Research Institute Inc
  6. Vivek Venkataramani

    1. Department of Medicine II, Hematology/Oncology, University Hospital Frankfurt, Frankfurt, Germany
    2. Institute of Pathology, University Medical Center Göttingen (UMG), Göttingen, Germany
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  7. Bernhard Michalke

    Helmholtz Zentrum München GmbH – German Research Center for Environmental Health, Research Unit Analytical BioGeoChemistry, Neuherberg, Germany
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  8. Anastasia Velentza

    Ferring Research Institute Inc, San Diego, United States
    Contribution
    Resources
    Competing interests
    Was an employee of Ferring Research Institute Inc
  9. Kostas Pantopoulos

    Lady Davis Institute for Medical Research, Jewish General Hospital and Department of Medicine, McGill University, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    kostas.pantopoulos@mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2305-0057

Funding

Canadian Institutes of Health Research (PJT-159730)

  • Kostas Pantopoulos

Fonds de Recherche du Québec - Santé

  • Edouard Charlebois

Deutsche Forschungsgemeinschaft (SPP 2306)

  • Vivek Venkataramani

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr. Naciba Benlimame and Lilian Canetti for assistance with histology and immunohistochemistry. This work was supported by a grant from the Canadian Institutes of Health Research (CIHR; PJT-159730). EC was funded by a fellowship from the Natural Sciences and Engineering Research Council of Canada (NSERC) and is currently a recipient of a fellowship from the Fonds de recherche du Québec – Santé (FRQS). The work of VV and BM was financially supported by the Deutsche Forschungsgemeinschaft (DFG) through the Priority Program “Ferroptosis: from Molecular Basics to Clinical Applications” (SPP 2306).

Ethics

All experimental procedures were approved by the Animal Care Committee of McGill University (protocol 4966).

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Yelena Ginzburg, Icahn School of Medicine at Mount Sinai, United States

Publication history

  1. Preprint posted: November 1, 2021 (view preprint)
  2. Received: June 23, 2022
  3. Accepted: August 30, 2022
  4. Accepted Manuscript published: September 6, 2022 (version 1)
  5. Version of Record published: September 22, 2022 (version 2)

Copyright

© 2022, Charlebois et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 290
    Page views
  • 149
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Edouard Charlebois
  2. Carine Fillebeen
  3. Angeliki Katsarou
  4. Aleksandr Rabinovich
  5. Kazimierz Wisniewski
  6. Vivek Venkataramani
  7. Bernhard Michalke
  8. Anastasia Velentza
  9. Kostas Pantopoulos
(2022)
A crosstalk between hepcidin and IRE/IRP pathways controls ferroportin expression and determines serum iron levels in mice
eLife 11:e81332.
https://doi.org/10.7554/eLife.81332

Further reading

    1. Cell Biology
    Jia Chen, Daniel St Johnston
    Research Article Updated

    In the adult Drosophila midgut, basal intestinal stem cells give rise to enteroblasts that integrate into the epithelium as they differentiate into enterocytes. Integrating enteroblasts must generate a new apical domain and break through the septate junctions between neighbouring enterocytes, while maintaining barrier function. We observe that enteroblasts form an apical membrane initiation site (AMIS) when they reach the septate junction between the enterocytes. Cadherin clears from the apical surface and an apical space appears between above the enteroblast. New septate junctions then form laterally with the enterocytes and the AMIS develops into an apical domain below the enterocyte septate junction. The enteroblast therefore forms a pre-assembled apical compartment before it has a free apical surface in contact with the gut lumen. Finally, the enterocyte septate junction disassembles and the enteroblast/pre-enterocyte reaches the gut lumen with a fully formed brush border. The process of enteroblast integration resembles lumen formation in mammalian epithelial cysts, highlighting the similarities between the fly midgut and mammalian epithelia.

    1. Cell Biology
    2. Neuroscience
    Jinye Dai, Kif Liakath-Ali ... Thomas C Südhof
    Research Article

    At CA1→subiculum synapses, alternatively spliced neurexin-1 (Nrxn1SS4+) and neurexin-3 (Nrxn3SS4+) enhance NMDA-receptors and suppress AMPA-receptors, respectively, without affecting synapse formation. Nrxn1SS4+ and Nrxn3SS4+ act by binding to secreted cerebellin-2 (Cbln2) that in turn activates postsynaptic GluD1 receptors. Whether neurexin-Cbln2-GluD1 signaling has additional functions besides regulating NMDA- and AMPA-receptors, and whether such signaling performs similar roles at other synapses, however, remains unknown. Here, we demonstrate using constitutive Cbln2 deletions in mice that at CA1→subiculum synapses, Cbln2 performs no additional developmental roles besides regulating AMPA- and NMDA-receptors. Moreover, low-level expression of functionally redundant Cbln1 did not compensate for a possible synapse-formation function of Cbln2 at CA1→subiculum synapses. In exploring the generality of these findings, we examined the prefrontal cortex where Cbln2 was recently implicated in spinogenesis, and the cerebellum where Cbln1 is known to regulate parallel-fiber synapses. In the prefrontal cortex, Nrxn1SS4+-Cbln2 signaling selectively controlled NMDA-receptors without affecting spine or synapse numbers, whereas Nrxn3SS4+-Cbln2 signaling had no apparent role. In the cerebellum, conversely, Nrxn3SS4+-Cbln1 signaling regulated AMPA-receptors, whereas now Nrxn1SS4+-Cbln1 signaling had no manifest effect. Thus, Nrxn1SS4+- and Nrxn3SS4+-Cbln1/2 signaling complexes differentially control NMDA- and AMPA-receptors in different synapses in diverse neural circuits without regulating synapse or spine formation.