Synergistic intracellular iron chelation combinations: mechanisms and conditions for optimizing iron mobilization

Iron chelators are increasingly combined clinically but the optimal conditions for cellular iron mobilization and mechanisms of interaction are unclear. Speciation plots for iron(III) binding of paired combinations of the licensed iron chelators desferrioxamine (DFO), deferiprone (DFP) and deferasirox (DFX) suggest conditions under which chelators can combine as ‘shuttle’ and ‘sink’ molecules but this approach does not consider their relative access and interaction with cellular iron pools. To address this issue, a sensitive ferrozine‐based detection system for intracellular iron removal from the human hepatocyte cell line (HuH‐7) was developed. Antagonism, synergism or additivity with paired chelator combinations was distinguished using mathematical isobologram analysis over clinically relevant chelator concentrations. All combinations showed synergistic iron mobilization at 8 h with clinically achievable concentrations of sink and shuttle chelators. Greatest synergism was achieved by combining DFP with DFX, where about 60% of mobilized iron was attributable to synergistic interaction. These findings predict that the DFX dose required for a half‐maximum effect can be reduced by 3·8‐fold when only 1 μmol/l DFP is added. Mechanisms for the synergy are suggested by consideration of the iron‐chelate speciation plots together with the size, charge and lipid solubilities for each chelator. Hydroxypyridinones with low lipid solubilities but otherwise similar properties to DFP were used to interrogate the mechanistic interactions of chelator pairs. These studies confirm that synergistic cellular iron mobilization requires one chelator to have the physicochemical properties to enter cells, chelate intracellular iron and subsequently donate iron to a second ‘sink’ chelator

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies

Empirical tests of trait–function relationships are crucial for advancing trait‐based restoration: a response to Merchant et al. (2023)

Trait-based restoration strategies are gaining significant attention in the scientific community. A recent article in Restoration Ecology by Merchant et al. outlined four reasons why traits are underused in restoration practice. In their response to the paper, Gornish et al. highlighted examples of how practitioners do, in fact, use traits in restoration and made recommendations for researchers to better engage with practitioners to leverage existing knowledge. Here, we clarify a preeminent challenge for either perspective: that we continue to lack the empirical data needed to develop and apply the effective trait-based tools envisioned by many researchers. Long-term, spatially replicated studies designed to address context-dependency are needed to address critical knowledge gaps. Co-developing projects with practitioners not only fosters more realistic and relatable study designs but also increases the likelihood of adopting new methods, enabling long-term research that advances theory while improving local outcomes through more accurate trait-based predictions

Immunophenotypic analysis of cell cycle status in acute myeloid leukaemia: relationship to cytogenetics, genotype and clinical outcome

Cell cycle status may play an important role in directing patient therapy. We therefore determined the cell cycle status of leukaemic cells by immunophenotypic analysis of bone marrow trephine biopsies from 181 patients with acute myeloid leukaemia (AML) and correlated the results with biological features and clinical outcome. There was considerable heterogeneity between patients. The presenting white cell count significantly correlated with the proportion of non-quiescent cells (P < 0·0001), of cycling cells beyond G1 (P < 0·0001) and the speed of cycling (P < 0·0001). Profiles in acute promyelocytic leukaemia (APL) differed from non-APL and were consistent with more differentiated cells with reduced proliferative potential, but no significant differences were observed between non-APL cytogenetic risk groups. NPM1 mutations but not FLT3 internal tandem duplication (FLT3ITD ) were significantly associated with a higher proportion of cells beyond G1 (P = 0·002) and faster speed of cycling (P = 0·003). Resistance to standard cytosine arabinoside and daunorubicin induction chemotherapy was significantly related to a slower speed of cycling (P = 0·0002), as was a higher relapse rate (P = 0·05), but not with the proportion of non-quiescent cells or actively cycling cells. These results show a link between the cycling speed of AML cells and the response to chemotherapy, and help to identify a group with a very poor prognosis

Luspatercept stimulates erythropoiesis, increases iron utilization, and redistributes body iron in transfusion-dependent thalassemia

Luspatercept, a ligand-trapping fusion protein, binds select TGF-β superfamily ligands implicated in thalassemic erythropoiesis, promoting late-stage erythroid maturation. Luspatercept reduced transfusion burden in the BELIEVE trial (NCT02604433) of 336 adults with transfusion-dependent thalassemia (TDT). Analysis of biomarkers in BELIEVE offers novel physiological and clinical insights into benefits offered by luspatercept. Transfusion iron loading rates decreased 20% by 1.4 g (~7 blood units; median iron loading rate difference: −0.05 ± 0.07 mg Fe/kg/day, p< .0001) and serum ferritin (s-ferritin) decreased 19.2% by 269.3 ± 963.7 μg/L (p < .0001), indicating reduced macrophage iron. However, liver iron content (LIC) did not decrease but showed statistically nonsignificant increases from 5.3 to 6.7 mg/g dw. Erythropoietin, growth differentiation factor 15, soluble transferrin receptor 1 (sTfR1), and reticulocytes rose by 93%, 59%, 66%, and 112%, respectively; accordingly, erythroferrone increased by 51% and hepcidin decreased by 53% (all p < .0001). Decreased transfusion with luspatercept in patients with TDT was associated with increased erythropoietic markers and decreasing hepcidin. Furthermore, s-ferritin reduction associated with increased erythroid iron incorporation (marked by sTfR1) allowed increased erythrocyte marrow output, consequently reducing transfusion needs and enhancing rerouting of hemolysis (heme) iron and non-transferrin-bound iron to the liver. LIC increased in patients with intact spleens, consistent with iron redistribution given the hepcidin reduction. Thus, erythropoietic and hepcidin changes with luspatercept in TDT lower transfusion dependency and may redistribute iron from macrophages to hepatocytes, necessitating the use of concomitant chelator cover for effective iron management

Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan.

BACKGROUND: There is a need to standardise non-invasive measurements of liver iron concentrations (LIC) so clear inferences can be drawn about body iron levels that are associated with hepatic and extra-hepatic complications of iron overload. Since the first demonstration of an inverse relationship between biopsy LIC and liver magnetic resonance (MR) using a proof-of-concept T2* sequence, MR technology has advanced dramatically with a shorter minimum echo-time, closer inter-echo spacing and constant repetition time. These important advances allow more accurate calculation of liver T2* especially in patients with high LIC. METHODS: Here, we used an optimised liver T2* sequence calibrated against 50 liver biopsy samples on 25 patients with transfusional haemosiderosis using ordinary least squares linear regression, and assessed the method reproducibility in 96 scans over an LIC range up to 42 mg/g dry weight (dw) using Bland-Altman plots. Using mixed model linear regression we compared the new T2*-LIC with R2-LIC (Ferriscan) on 92 scans in 54 patients with transfusional haemosiderosis and examined method agreement using Bland-Altman approach. RESULTS: Strong linear correlation between ln(T2*) and ln(LIC) led to the calibration equation LIC = 31.94(T2*)-1.014. This yielded LIC values approximately 2.2 times higher than the proof-of-concept T2* method. Comparing this new T2*-LIC with the R2-LIC (Ferriscan) technique in 92 scans, we observed a close relationship between the two methods for values up to 10 mg/g dw, however the method agreement was poor. CONCLUSIONS: New calibration of T2* against liver biopsy estimates LIC in a reproducible way, correcting the proof-of-concept calibration by 2.2 times. Due to poor agreement, both methods should be used separately to diagnose or rule out liver iron overload in patients with increased ferritin

Oral ferroportin inhibitor vamifeport for improving iron homeostasis and erythropoiesis in β-thalassemia: current evidence and future clinical development

Introduction: In β-thalassemia, imbalanced globin synthesis causes reduced red blood cell survival and ineffective erythropoiesis. Suppressed hepcidin levels increase ferroportin-mediated iron transport in enterocytes, causing increased iron absorption and potentially iron overload. Low hepcidin also stimulates ferroportin-mediated iron release from macrophages, increasing transferrin saturation (TSAT), potentially forming non-transferrin-bound iron, which can be toxic. Modulating the hepcidin–ferroportin axis is an attractive strategy to improve ineffective erythropoiesis and limit the potential tissue damage resulting from iron overload. There are no oral β-thalassemia treatments that consistently ameliorate anemia and prevent iron overload. / Areas covered: The preclinical and clinical development of vamifeport (VIT-2763), a novel ferroportin inhibitor, was reviewed. PubMed, EMBASE and ClinicalTrials.gov were searched using the search term ‘VIT-2763ʹ. / Expert opinion: Vamifeport is the first oral ferroportin inhibitor in clinical development. In healthy volunteers, vamifeport had comparable safety to placebo, was well tolerated and rapidly decreased iron levels and reduced TSAT, consistent with observations in preclinical models. Data from ongoing/planned Phase II studies are critical to define its potential in β-thalassemia and other conditions associated with iron overabsorption and/or ineffective erythropoiesis. If vamifeport potentially increases hemoglobin and reduces iron-related parameters, it could be a suitable treatment for non-transfusion-dependent and transfusion-dependent β-thalassemia

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract

Linking changes in species composition and biomass in a globally distributed grassland experiment

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonisation and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e. local extinctions) were balanced by gains (i.e. colonisation). Under fertilisation, the decline in species richness resulted from increased species loss and decreases in species gained. Biomass increase under fertilisation resulted mostly from species that persist and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained or persisting.EEA Santa CruzFil: Ladouceur, Emma. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Ladouceur, Emma. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Ladouceur, Emma. University of Leipzig. Department of Biology; AlemaniaFil: Ladouceur, Emma. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Blowes, Shane A. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Blowes, Shane A. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Chase, Jonathan M. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Chase, Jonathan M. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Clark, Adam T. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Clark, Adam T. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Clark, Adam T. Karl-Franzens University of Graz. Institute of Biology; Austria.Fil: Garbowski, Magda. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Garbowski, Magda. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Alberti, Juan. Universidad Nacional de Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Laboratorio de Ecología. Mar del Plata; Argentina.Fil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Arnillas, Carlos Alberto. University of Toronto. Department of Physical and Environmental Sciences; Canadá.Fil: Bakker, Jonathan D. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Barrio, Isabel C. Agricultural University of Iceland. Faculty of Environmental and Forest Sciences; IslandiaFil: Bharath, Siddharth. Atria University; India.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Harpole, Stanley. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Harpole, Stanley. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaMartin Luther University Halle-Wittenberg. Institute of Computer Science; Alemani