Abstract Hereditary xerocytosis (HX; also known as dehydrated stomatocytosis) is a rare red blood cell (RBC) disorder associated with hemolysis and iron overload. Gain-of-function mutations in either PIEZO1 or KCNN4 lead to disturbed RBC ion homeostasis. PIEZO1-HX and KCNN4-HX are mechanistically different and have distinct phenotypes, yet these differences are poorly understood. Here, we study RBC hydration and metabolism in 12 patients with PIEZO1-HX, 3 patients with KCNN4-HX, and 10 controls, with particular focus on the key glycolytic enzyme pyruvate kinase (PK). In PIEZO1-HX, RBC PK activity is relatively decreased (PK-to-hexokinase ratio, 4.7 vs 8.4 in controls), along with a decrease in PK thermostability (43.6% vs 77.8%) and PK protein levels. KCNN4-HX RBCs show a less pronounced decrease in activity (PK-to-hexokinase ratio, 6.3) and thermostability (64.7%), with no decrease in protein levels. Untargeted metabolomics demonstrated distinct differences in various metabolites, including carnitines, in PIEZO1-HX vs KCNN4-HX or controls. Interestingly, hydration (Ohyper) of PIEZO1-HX RBCs correlated negatively with the ratio of adenosine triphosphate to 2,3-diphosphoglycerate (r = –0.839). To evaluate whether enhancing glycolysis by PK activation could improve hydration, we treated RBCs ex vivo with the PK activator tebapivat. This treatment increased PK activity (>40%) in both disorders, but it did not negate dehydration in most PIEZO1-HX RBCs. In contrast, Ohyper did improve in KCNN4-HX (>10 mOsm/kg increase). Our results indicate significant metabolic differences between PIEZO1-HX and KCNN4-HX and different responses to ex vivo PK activation. Our findings increase the understanding of the differences between PIEZO1-HX and KCNN4-HX and provide evidence for a potential response to PK activator treatment.

Diamond–Blackfan anemia (DBA) is one of the inherited bone marrow failure syndromes marked by erythroid hypoplasia. Underlying variants in ribosomal protein (RP) genes account for 80% of cases, thereby classifying DBA as a ribosomopathy. In addition to RP genes, extremely rare variants in non-RP genes, including GATA1, the master transcription factor in erythropoiesis, have been reported in recent years in patients with a DBA-like phenotype. Subsequently, a pivotal role for GATA-1 in DBA pathophysiology was established by studies showing the impaired translation of GATA1 mRNA downstream of the RP haploinsufficiency. Here, we report on a patient from the Dutch DBA registry, in which we found a novel hemizygous variant in GATA1 (c.220+2T>C), and an Iranian patient with a previously reported variant in the initiation codon of GATA1 (c.2T>C). Although clinical features were concordant with DBA, the bone marrow morphology in both patients was not typical for DBA, showing moderate erythropoietic activity with signs of dyserythropoiesis and dysmegakaryopoiesis. This motivated us to re-evaluate the clinical characteristics of previously reported cases, which resulted in the comprehensive characterization of 18 patients with an inherited GATA-1 defect in exon 2 that is presented in this case-series. In addition, we re-investigated the bone marrow aspirate of one of the previously published cases. Altogether, our observations suggest that DBA caused by GATA1 defects is characterized by distinct phenotypic characteristics, including dyserythropoiesis and dysmegakaryopoiesis, and therefore represents a distinct phenotype within the DBA disease spectrum, which might need specific clinical management.

In Diamond-Blackfan anaemia (DBA), iron overload (IO) is common in transfusion-dependent patients, yet has also been reported in non-transfusion-dependent patients. We explored the incidence of IO in transfusion-dependent and non-transfusion-dependent DBA patients. We observed hepatic IO in 65% of patients analysed with MRI, including three patients that were only treated with transfusions in the past. Whereas overall ferritin levels and liver iron content correlated, ferritin levels did not reflect total body iron adequately. Our data suggest that transfusion burden in the past plays an important role in IO in DBA, and should be taken into account during follow up.

Rare hereditary anemias (RHA) represent a group of disorders characterized by either impaired production of erythrocytes or decreased survival (i.e., hemolysis). In RHA, the regulation of iron metabolism and erythropoiesis is often disturbed, leading to iron overload or worsening of chronic anemia due to unavailability of iron for erythropoiesis. Whereas iron overload generally is a well-recognized complication in patients requiring regular blood transfusions, it is also a significant problem in a large proportion of patients with RHA that are not transfusion dependent. This indicates that RHA share disease-specific defects in erythroid development that are linked to intrinsic defects in iron metabolism. In this review, we discuss the key regulators involved in the interplay between iron and erythropoiesis and their importance in the spectrum of RHA.

BACKGROUND Status dystonicus (SD) is a severe episode of generalized dystonia, potentially complicated by respiratory and metabolic disruption. Triggers can be infection, medication, or metabolic disturbance. The prognosis is variable and mortality is approximately 10%. CASE DESCRIPTION An 18 month old girl presented to the ER with clinical suspicion of a febrile status epilepticus and was evaluated according to APLS principles. Eventually, a SD became apparent, with generalized dystonic features at examination. Most likely, the episode was provoked by a single dose of metoclopramide. Her clinical state improved rapidly, possibly aided by administration of biperiden. CONCLUSION Treatment of SD encompasses elimination or treatment of the trigger, stabilization of vital functions, possible administration of sedatives and dystonia specific medication. Metoclopramide holds a relatively high risk for extrapyramidal complications (1-10%) and dystonia (0.1-1.5%), even within therapeutic range. The use of anti-emetics with less alarming side effect profiles, for example ondansetron, is recommended.

Abstract Introduction: Diamond Blackfan anemia (DBA) is an inherited bone marrow failure syndrome (IBMFS) characterized by hypoplastic anemia, congenital malformations and an increased risk to develop malignancies.Until now, treatment of DBA consists of red blood cell (RBC) transfusions, glucocorticoids (GC) and allogeneic hematopoietic stem cell transplantation in a selection of patients. Whereas RBC transfusions are the main cause of IO, elevated iron parameters have also been reported in non-transfusion-dependent DBA patients. Here we investigated the incidence and severity of IO in a well-described cohort of transfusion-dependent and non-transfusion-dependent DBA patients in order to gain more insight in the regulation of iron metabolism in DBA, and to provide clinical guiding to improve the diagnosis and management of IO in DBA. Methods: In this retrospective, observational study we have included twenty-nine pediatric and adult DBA patients for whom at least one serum ferritin level and/or MRI result was available. Ten patients (34%) were classified as transfusion-dependent (TD) (ten or more transfusions during the twelve months prior to evaluation). Non-transfusion-dependent (NTD) patients (66%) were treated with either GC, incidental transfusions or received no treatment. Transfusion burden (transfusion history) was assessed via medical records. Serum ferritin levels ≥250 ng/mL in males and ≥150 ng/mL in females were considered to be elevated. Results of MRI were expressed as liver iron content (LIC) and as cardiac T2* in milliseconds (ms). LIC ≥3 mg/g indicates significant hepatic IO, and LIC ≥7 mg/g is associated with clinical morbidity. Cardiac T2* ≤20 ms indicates significant cardiac IO. Results: In 15/29 (52%) MRI analysis of IO was performed. Hepatic IO (LIC >3 mg/g) was present in 9/29 (31%) of DBA patients, of which 8/9 (89%) had moderate to severe IO (LIC>7mg/g), despite the fact that all but one were treated with chelation therapy. Overall serum ferritin levels and LIC correlated significantly (r=0.7907, p<0.001), and all TD patients with LIC ≥7 mg/g had serum ferritin levels ³400 ng/mL, however, none of the patients had a serum ferritin >1000 ng/mL (Figure 1A). Interestingly, in the NTD group, hepatic IO was present in 2/7 patients (29%), who both only had mildly elevated serum ferritin levels (263 ng/mL and 277 ng/mL) and were not treated with iron chelation therapy. Based on total transfusion burden since birth, patients were classified in distinct groups: nine patients who received ³10 transfusions during life (9/10) were diagnosed with hepatic IO, whilst none of the patients who received <10 transfusions were diagnosed with hepatic IO. Both mean serum ferritin levels and mean LIC values were significantly higher in patients with ³10 transfusions compared to all with <10 transfusions (Figure 1B-C). Discussion: We demonstrate that IO is common in DBA yet can be easily overlooked in NTD patients that were treated with transfusions in the past. While serum ferritin levels significantly correlated with LIC values, this parameter cannot be used exclusively to screen for IO or titrate iron chelation therapy. We conclude that in clinical practice, biochemical parameters in combination with transfusion history justify a low-threshold to perform an MRI-based evaluation of IO, and to start adequate chelation therapy. Figure 1 Figure 1. Disclosures Van Beers: Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Novartis: Research Funding; RR Mechatronics: Research Funding. Wijk: Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Axcella health: Research Funding; Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding.

The diagnostic evaluation and clinical characterization of rare hereditary anemia (RHA) is to date still challenging. In particular, there is little knowledge of the broad metabolic impact of many of the molecular defects underlying RHA. In this study we explored the potential of untargeted metabolomics to diagnose a relatively common type of RHA: pyruvate kinase deficiency (PKD). In total, 1,903 unique metabolite features were identified in dried blood spot samples from 16 PKD patients and 32 healthy controls. A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95% CI: 0.981-0.999) and an accurate class assignment was achieved for all newly added control (n=13) and patient samples, (n=6) with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines. In general, the application of untargeted metabolomics in dried blood spots is a novel functional tool that holds promise for the diagnostic stratification and studies on the disease pathophysiology in RHA.

SummaryThe diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging because of a broad phenotypic variability and the lack of functional screening tests. In this study, we explored the potential of untargeted metabolomics to diagnose DBA. In dried blood spot samples from 18 DBA patients and 40 healthy controls, a total of 1752 unique metabolite features were identified. This metabolic fingerprint was incorporated into a machine‐learning algorithm, and a binary classification model was constructed using a training set. The model showed high performance characteristics (average accuracy 91·9%), and correct prediction of class was observed for all controls (n = 12) and all but one patient (n = 4/5) from the validation or ‘test’ set (accuracy 94%). Importantly, in patients with congenital dyserythropoietic anaemia (CDA) – an erythroid disorder with overlapping features – we observed a distinct metabolic profile, indicating the disease specificity of the DBA fingerprint and underlining its diagnostic potential. Furthermore, when exploring phenotypic heterogeneity, DBA treatment subgroups yielded discrete differences in metabolic profiles, which could hold future potential in understanding therapy responses. Our data demonstrate that untargeted metabolomics in dried blood spots is a promising new diagnostic tool for DBA.

Hereditary spherocytosis (HS) is the most common cause of hereditary chronic hemolytic anemia in people of northern European descent (1:2000–3000).1 Characterized by impaired red blood cell (RBC) membrane integrity due to disruption of the (vertical) association between the cytoskeleton and the plasma membrane, RBCs from HS patients show enhanced membrane loss and thereby surface area. As a result RBCs become spheroidal with decreased deformability, leading to premature splenic sequestration.2 HS is highly heterogeneous both molecularly and phenotypically. Mutations have been identified in many of the major proteins of the cytoskeleton or RBC membrane. Clinically, a broad phenotypic spectrum is recognized, ranging from asymptomatic or well-compensated anemia to severe forms requiring regular blood transfusions and splenectomy. Moreover, genotype-phenotype correlations are incompletely understood. While RBC membrane proteins have been implicated in cellular metabolism, little is known about the metabolic consequences of defective membrane-cytoskeleton integrity in HS.3 Here we investigated the metabolic impact of the membrane defect in HS using untargeted metabolomics in dried blood spots (DBS). Materials and Methods are described in Section S1 (https://links.lww.com/HS/A158). We report a metabolic fingerprint for HS that provides promising leads for understanding clinical heterogeneity in patients as well as leads for further study into pathophysiological mechanisms associated with decreased membrane and cytoskeleton integrity. In our study, we compared DBS of HS patients (n = 35) with healthy controls (HCs, n = 50). Clinical and laboratory characteristics and baseline comparison are summarized in Supplemental Table 1 (https://links.lww.com/HS/A158). The dataset comprised Z scores for 1770 unique metabolite features corresponding to 3565 metabolite annotations. The variation in metabolic fingerprints between both groups was analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), in which the number of features is reduced by combining them into fewer explanatory variables. PLS-DA takes group label into account (HS or HC) to maximize the variance between groups whereas PCA does not. The PCA revealed evident separation between patients and controls. As expected, the separation was more pronounced upon PLS-DA. Both PCA and PLS-DA indicated a distinct metabolic profile for the investigated groups, with close clustering for controls and a higher level of heterogeneity in HS patients (Figure 1A and B). Metabolites contributing most to the separation of groups in PLS-DA are reflected by high Variable Importance in Projection scores. These metabolites include multiple polyamines (spermidine, spermine, N1-acetylspermidine, putrescine), (acyl)carnitines (propionylcarnitine, oleoylcarnitine, stearoylcarnitine, L-acetylcarnitine, L-palmitoylcarnitine, linoelaidylcarnitine, L-carnitine) and the glycolytic intermediates 2,3-diphosphoglycerid acid (2,3-DPG) and glyceraldehyde 3-phosphate (GA3P) (Figure 1C; Supplemental Table 2, https://links.lww.com/HS/A158). Furthermore, t test analysis identified corresponding metabolites as significantly different between patients and controls (Figure 1D; Supplemental Table 2, https://links.lww.com/HS/A158).Figure 1.: Metabolic profile in DBS of HS patients. (A), PCA plot and (B) PLS-DA plot of HS and HCs displayed with 95% confidence regions. Both analyses reduce dimensionality to identify the overall variation by combining all metabolite features (weighted) into new variables. These new variables are principal components. The first 2 principal components capture most of the variation in the dataset, expressed as percentage of total variation within a dataset, and are displayed on the x- and y-axis. Each dot represents a patient or control sample, colored according to group label. The position of these dots is based on the metabolite profile of 1770 unique Z scores. Samples that have a similar metabolic profile cluster more closely. The difference between PCA and PLS-DA is that PLS-DA takes group label into account to find the optimal separation between groups. (C), The metabolites that contribute the most to separation in PLS-DA are identified by a high VIP score. The 30 features that contribute the most to separation of patients and HC are displayed. (D), Heatmap of 30 most significant features identified by t test (P < 0.000003). Each colored cell in the heatmap corresponds to the autoscaled Z score per metabolite feature (rows). The columns of the heatmap are colored by group label. The heatmap was created with hierarchical Ward’s linkage clustering using Euclidean distances. The dendrogram represents clustering of samples and metabolite features. Figures were created using MetaboAnalyst. A comprehensive overview of P values and isomers is displayed in Supplemental Table 2 (https://links.lww.com/HS/A158). DBS = dried blood spot; HC = healthy control; HS = hereditary spherocytosis; PCA = principal component analysis; PLS-DA = partial least squares discriminant analysis; VIP = Variable Importance in Projection.Based on the observation of significantly decreased 2,3-DPG and GA3P in both PLS-DA and t test, the glycolytic pathway was explored in detail. Apart from a decrease in 2,3-DPG and GA3P, none of the glycolytic intermediates were significantly altered, and no substantial differences were observed in pyruvate and lactate, or glucose 6-phosphate as respective end- and starting points of glycolysis (Supplemental Figure 1, https://links.lww.com/HS/A158). Interestingly, previous studies have shown that glycolytic enzymes associate with the RBC membrane3,4 and others have reported specifically on decreased pyruvate kinase (PK) activity in HS patients as a consequence of loss of membrane-bound PK.5,6 Here, we only found 2,3 DPG and GA3P (and/or features with the same respective mass to charge ratio; Supplemental Table 2, https://links.lww.com/HS/A158) to be significantly decreased in HS patients. This suggests that although glycolytic enzymes might be affected to some extent by decreased RBC-membrane integrity, glycolysis in general appears not strongly affected. While the metabolic profile was obtained from whole blood, it is highly likely that the observed glycolytic disturbances mainly reflect RBCs, since 2,3-DPG is specific to the RBC. In addition, no correlation was observed for 2,3-DPG and GA3P with any of the full blood count parameters. While the mechanism underlying the decrease in 2,3-DPG in HS remains to be determined, it is intriguing, as 2,3-DPG is an important regulator of hemoglobin oxygen affinity. It binds with greater affinity to deoxygenated hemoglobin than to oxygenated hemoglobin and decreases the p50, thereby promoting the release of oxygen to tissues. While the reported increase of 2,3 DPG in hereditary anemias like pyruvate kinase deficiency (PKD)7 and beta-thalassemia minor8 is assumed to be clinically beneficial for patients, the robust decrease that we observe in HS patients could also be of clinical importance. In this respect, the reported fatigue-related impairment of quality of life, and “complaints of fatigue” being reported as critically factored into the decision for splenectomy in a substantial number of HS patients despite relatively mild to moderate hemolysis, might be well worth further exploring.9,10 Further exploration of red cell metabolism revealed decreased glutathione, but no other metabolites of glutathione metabolism were altered (data not shown). In addition, the pentose phosphate pathway and its respective metabolites appeared unaffected (data not shown). Furthermore, investigation of amino acids and carnitines revealed a significant decrease in aspartate, and significant increases in glycine, L-valine, L-carnitine and L-acetylcarnitine (data not shown). Putrescine, spermidine and spermine, players of arginine and polyamine metabolism, were significantly elevated in HS patients compared to controls (Supplemental Figure 2, https://links.lww.com/HS/A158). To explore whether the heterogeneity in HS metabolic profiles related to clinical phenotype, additional PCA and PLS-DA were performed. Clinical phenotypes were classified as mild, moderate or severe, based on the severity of anemia and degree of compensation for hemolysis, according to the modified criteria originally proposed by Eber et al.11 The PLS-DA plot showed that mildly affected HS patients clustered most closely to controls, followed by moderately affected patients, whereas the metabolic profile from severely affected individuals differed most from controls (Figure 2A and B). As the clinical phenotype in HS is highly heterogeneous and severity categories depend upon 4 parameters only (hemoglobin, reticulocyte count, bilirubin and splenectomy), the distinction is arbitrary and subject to fluctuation over time. This is likely reflected by the heterogeneity in the “moderately affected” HS-group (Supplementary Table 1, https://links.lww.com/HS/A158). Nevertheless, a clear distinction is observed between mildly or even moderately affected HS patients and severely affected HS patients.Figure 2.: Metabolic profile in relation to clinical severity phenotypes. (A), PCA plot and (B) PLS-DA plot distinguishing clinical severity. (C), Top 20 features contributing to the separation of patients and controls in PLS-DA, reflected by VIP-scores. For almost all metabolites a correlation with clinical severity is observed, reflected by an increasing or decreasing color gradient. (D), Z scores of spermidine, N1-Acetylspermidine, L-Acetylcarnitine and Propionylcarnitine based on clinical severity for control (n = 50), HS-mild (n = 15), HS-moderate (n = 12) and HS-severe (n = 8) in a boxplot with Tukey whiskers. HS = hereditary spherocytosis; PCA = principal component analysis; PLS-DA = partial least squares discriminant analysis; VIP = Variable Importance in Projection.Metabolites that contributed the most to the distinction between HC and the clinical severity of HS included polyamines, (acyl)carnitines, 2,3-DPG and creatine (Figure 2C). Increased clinical severity was associated with increased Z scores of spermidine, N1-Acetylspermidine, L-Acetylcarnitine and propionylcarnitine (Figure 2D). In addition, analysis of these metabolites in relation to full blood count parameters revealed significant correlations for spermidine, N1-acetylspermidine, L-acetylcarnitine and propionylcarnitine with RBC and reticulocyte counts (Supplemental Figure 3, https://links.lww.com/HS/A158). Together with the distinct profiles for clinical severity subgroups, the gradient that was demonstrated for top metabolites indicates that clinical phenotypes correlate with the metabolic profiles of HS-patients. While these findings need to be validated in a larger cohort of patients, our approach offers new perspectives for a better understanding of the complex genotype-phenotype associations in HS. Lastly, we explored the correlation of the top 20 features of PLS-DA with functional features of RBC deformability as established with osmotic gradient ektacytometry. For the maximum deformability of RBCs, reflected by the maximum elongation index (EI max), an inverse correlation with Z scores of spermidine (r = –0.37; P = 0.04), N1-Acetylspermidine (r = –0.47; P = 0.008), L-Acetylcarnitine (r = –0.42; P = 0.02), and propionylcarnitine (r = –0.36; P = 0.05) (Supplemental Figure 3, https://links.lww.com/HS/A158) was observed. Further correlations with functional features of RBC deformability were observed between propionylcarnitine and the total area under the curve (r = –0.37; P = 0.04) and between L-carnitine and O hyper (r = 0.37; P = 0.04, plots not shown), suggesting that increases in distinctive metabolites were associated with decreased RBC hydration and deformability. These findings were supported by the relation between the top metabolites and change in deformability (ΔEI) as measured with the cell membrane stability test in a subset of HS patients (n = 8), where we observed a strong correlation for propionylcarnitine and ΔEI (r = 0.74; P = 0.05). Previously, the polyamines spermidine, spermine and putrescine have been reported to decrease erythrocyte membrane deformability and stabilize the membrane skeleton of resealed ghosts loaded with polyamines.12 The inverse correlation between spermidine and N1-Acetylspermidine and the maximal deformability (EI max) of RBCs in HS patients, although not very strong, as well as the relation with clinical phenotypes we observe in this study, provides supporting evidence for the hypothesis that apart from an in vitro-effect in ghosts, the concentration of polyamines is associated with in vivo red cell deformability in patients. Whether this contributes to the decreased deformability seen in HS, or is a compensatory mechanism, remains to be determined. The observed correlations between polyamines and reticulocyte count, and inverse correlations with hemoglobin and erythrocyte count, but not with leukocytes and platelets, suggests that these alterations are RBC-specific. Interestingly, previous studies demonstrated significant correlations between younger and older RBCs, with lower levels of polyamines in the older red cells, suggesting the possibility of using red cell polyamines as an indicator of the activity of the bone marrow in anemic states.13 The increase of polyamines we observed previously in PKD,14 another hemolytic anemia with a hyper-regenerative bone marrow, further supports these findings. As concentrations of polyamines have also been reported to be increased in RBC’s and DBS in sickle cell disease and PKD,14,15 we anticipate that unraveling the underlying mechanisms and consequences of altered polyamine metabolism may contribute not only to a better understanding of HS pathophysiology but for rare hereditary hemolytic anemia in general. In summary, we report on a metabolic fingerprint that offers for the first time a comprehensive overview of metabolic disturbances in HS and includes altered levels of polyamines and (acyl)carnitines. These metabolic disturbances correlate with RBC characteristics (in particular deformability) and, in addition, to clinical severity. We also identified significant decreases in glycolytic intermediates 2,3-diphosphoglyceric acid and glyceraldehyde-3-phosphate. We demonstrate that untargeted metabolomics can be instrumental in investigating the phenotypic heterogeneity in patients and our results provide promising leads for further study into the pathophysiological mechanisms that determine the phenotypic expression of HS. Lastly, this comprehensive characterization of metabolic disturbances in HS, might serve as a starting point for the development of new therapeutic strategies. Acknowledgments We thank Nienke van Unen and Fini de Gruyter for their technical support in Bio-informatics. Disclosures EJvB and RvW perform consultancy and receive research funding from Agios Pharmaceuticals. All the other authors have no conflicts of interest to disclose. Sources of funding This study was supported in part by research funding from MetaKids (Grant No. 2017-075) to JJMJ.

Background: Diamond-Blackfan anemia (DBA) is a rare inherited bone marrow failure syndrome (IBMFS) marked by erythroid hypoplasia, reticulocytopenia and macrocytosis, and associated with congenital malformations and an increased risk of developing malignancies. From a clinical and molecular perspective this disease is highly heterogeneous, and no clear genotype-phenotype correlations can be found. Since the majority of molecular defects have been found in ribosomal protein (RP) genes, DBA is regarded a "ribosomopathy". While the molecular basis has been studied intensively, the pathophysiology of DBA is still not fully understood. One of the major unresolved issues is how RP gene mutations result in the specific erythroid defect seen in DBA, with macrocytic erythrocytes and increased adenosine deaminase activity. In addition, the highly heterogeneous and variable clinical presentation and disease course, even in patients with similar molecular defects, remains enigmatic. Hence, in order to investigate the cellular defect, and increase our understanding of disease pathophysiology and clinical heterogeneity, novel tools are needed. In this study, we explore the potential of untargeted metabolomics on dried blood spots and report for the first time a metabolic fingerprint for DBA. Aims: Defining a metabolic signature for DBA in order to: 1. Increase our understanding of cellular determinants of impaired ribosome biogenesis, and 2. Extend the toolbox for diagnostic evaluation. Methods: Untargeted metabolic profiling was performed on DBS samples obtained from 18 DBA patients and 45 healthy controls using direct infusion high resolution mass spectrometry following a previously established approach1. Statistical analysis was performed in MetaboAnalyst and predictive modeling was executed within R-software. Results: In total, 1917 unique metabolite features were identified in DBS samples from patients and controls. Multivariate analysis yielded distinct metabolic profiles, reflected by natural separation detected by principal component analysis (Figure 1A), and emphasized by clear distinction with partial least square discriminant analysis (Figure 1B). This 'metabolic fingerprint' was incorporated into a machine learning algorithm, and subsequently a binary classification (or prediction) model was constructed by randomly dividing patient and controls into 'training' (32 HC, 13 DBA) or 'test' set (13 HC, 5 DBA). Accurate class assignment was achieved for all patients and controls in the training set (Figure 1C). Prominent metabolites in the fingerprint and classification algorithm were a.o. menadione (a vitamin K precursor), 4-hydroxyproline (collagen component) and methylmalonylcarnitine (an acylcarnitine). In addition a large number of interesting metabolites were identified that could provide novel starting points for studying/understanding downstream effects of RP defects in DBA and therapeutic mechanisms (Figure 1D). Conclusion: In this study we performed untargeted metabolomics on dried blood spots from a substantial cohort of DBA patients and report for the first time a metabolic fingerprint of this disease. By incorporating this fingerprint in a machine learning algorithm we underlined the diagnostic potential of this approach. Moreover, the metabolites identified in this fingerprint, provide promising starting points for further studies to increase our insights in disease pathophysiology, including the mechanism involved in elevated eADA activity, as well as the development of new therapeutic strategies. References: 1. de Sain-van der Velden MGM, van der Ham M, Gerrits J, et al. Quantification of metabolites in dried blood spots by direct infusion high resolution mass spectrometry. Anal Chim Acta. 2017;979:45-50. Disclosures Wijk: Agios Pharmaceuticals Inc.: Research Funding; RR mechatronics: Research Funding.

Background: The group of rare hereditary anemias includes a large variety of intrinsic defects of the red blood cell, as well as erythropoiesis. They include hemolytic anemias (e.g. enzyme deficiencies), hemoglobinopathies, hypoplastic anemias (e.g. Diamond-Blackfan Anemia, DBA), and dyserythropoietic anemias. As a result of the rapid developments in genetic testing and the subsequent increased knowledge of molecular defects underlying hereditary anemias, our understanding of the pathophysiology of rare anemias has increased during the last decade. However, in a substantial number of patients, the clinical phenotype does not fit classical criteria of a disease, response to therapy is less than expected, or a molecular defect cannot be found. In addition, in patients with well-described molecular defects, there is often no clear genotype-phenotype correlation. In order to better understand the underlying pathophysiological mechanisms driving ineffective erythropoiesis in patients and to improve their classification and clinical evaluation, novel functional tests are needed. Metabolomics is the large-scale, unbiased study of metabolites and their interactions within a biological system, directly reflecting the underlying biochemical activity and state of cells. Metabolomics can be used to identify novel disease biomarkers, study deregulated cellular pathways, and to determine the cellular responses to therapeutic interventions. In this study we demonstrate that dried blood spots (DBS) can be used as a minimal invasive and validated technical approach to perform large scale metabolomics in a variety of rare hereditary anemias. Methods: DBS samples from >100 patients suffering from a variety of rare anemiaswere collected during regular hospital visits. Quantification of metabolites was performed by direct infusion high resolution mass spectrometry (DI-HRMS) followed by an untargetedmetabolomics pipeline. For annotation, the Human Metabolome Database (HMDB) was used. Results were compared with DBS samples of 70 healthy adult controls and 35 pediatric patients negatively screened for metabolic diseases Results: For each patient sample, Z-scores were calculated for all mass peaks annotated with metabolites (HMDB, 3930). Mass peak, intensity and corresponding Z-scores were compared with two distinct groups of controls (∆Z-scores): pediatric patients who were screened for metabolic diseases but were found negative, and healthy adult controls. For data interpretation, two strategies were used. First, by untargeted statistical analysis in Metabo-analyst, we identified metabolites (and/or isomers) that showed either increased or decreased intensity. For the second strategy we specifically focused on red blood cell metabolic pathways, including glycolysis, the pentose phosphate pathway, ascorbate and glutathione metabolism, arginine and polyamine metabolism, and erythrocyte membrane turnover and transport. We corrected for a potential hematocrit effect and performed subgroup analyses correcting for reticulocyte counts. Our preliminary data indicate potential biomarkers for distinct disease entities, including altered polyamine metabolism (DBA, SCD), glycolysis (DBA, HS), and aberrant arginine metabolism (SCD) (Figure 1). Further in-depth pathway analyses, and targeted validation of biomarker profiles are currently being performed. Conclusion: Untargeted metabolomics using dried blood spots provides a novel functional tool to identify disease biomarkers and common and distinct deregulated cellular pathways. This will improve diagnostic evaluation and clinical management of patients with rare hereditary anemias, contribute to a better understanding of disease pathophysiology, and aid in the development of therapeutic strategies. Disclosures van Beers: Agios Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Research Funding; RR Mechatronics: Research Funding. van Wijk:RR Mechatronics: Research Funding; Agios Pharmaceuticals: Consultancy, Research Funding.

Introduction: Diamond-Blackfan anemia (DBA) is characterized by hypoplastic anemia, congenital anomalies, and a predisposition for malignancies. Most of our understanding of this disorder stems from molecular studies combined with extensive data input from international patient registries. Objectives: To create an overview of the pediatric DBA population in the Netherlands. Methods: Forty-three patients diagnosed with DBA from all Dutch university pediatric hospitals were included in this study, and their clinical and genetic characteristics were collected from patient records. Results: Congenital malformations were present in 24 of 43 patients (55.8%). An underlying genetic defect was identified in 26 of 43 patients (60.5%), the majority of which were found in the RPS19 gene (12 of 43, 27.9%) with 1 patient carrying a mutation in a novel DBA candidate gene, RPL9. In 31 of 35 (88.6%) patients, an initial response to glucocorticoid treatment was observed. Six patients (14.0%) underwent hematopoietic stem cell transplantation, and eleven patients (11 of 43, 25.6%) became treatment-independent spontaneously. Conclusion: In agreement with previous reports, the Dutch pediatric DBA population is both clinically and genetically heterogeneous. National and international registries, together with more extensive genetic testing, are crucial to increase our understanding of genotype and phenotype correlations of this intriguing disorder.

Diamond-Blackfan anemia (DBA) is a rare congenital erythroblastopenia and inherited bone marrow failure syndrome that affects approximately seven individuals in every million live births. In addition to anemia, about 50% of all DBA patients suffer from various physical malformations of the face, hands, heart, or urogenital region. The disorder is almost exclusively driven by haploinsufficient mutations in one of several ribosomal protein (RP) genes, although for ∼30% of diagnosed patients no mutation is found in any of the known DBA-linked genes. Because DBA is such a rare disease with a particularly wide range of clinical phenotypes and molecular signatures, the development of collaborative efforts such as the ERARE-funded European DBA consortium (EuroDBA) has become imperative for DBA research. EuroDBA was founded in 2012 and brings together dedicated clinical and biological researchers of DBA from France, Italy, the Netherlands, Germany, Israel, Poland, and Turkey to achieve a number of goals including the consolidation of data in patient registries, establishment of minimal diagnostic criteria, and projects aimed at more fully describing the different mutations linked to DBA. This review will cover the history of the EuroDBA registries, the methods used by EuroDBA in the diagnosis of DBA, and how the consortium has successfully worked together towards the discovery of new DBA-linked genes and the better understanding their pathophysiological effects.

An at first sight seemingly coherent, global medical workforce, with clearly recognizable specialities, subspecialties and primary care doctors, appears at a closer look quite variable. Even within the most progressive countries as to the development of medical education, with educators who regularly meet at conferences and share major journals about medical education, the differences in structures and regulations are big. This contribution focuses on the preparation, admission policy, duration, examinations, and national competency frameworks in postgraduate speciality training in Germany, the USA, Canada, the UK, Australia and the Netherlands. While general objectives for postgraduate training programs have not been very clear, only recently competency-frameworks, created in a limited number of countries, serve harmonize objectives. This process appears to be a challenge and the recent creation of milestones for the reporting on progress of individual trainees (in the US and in Canada in different ways) and the adoption of en-trustable professional activities, a most recent concept that is quickly spreading internationally as a framework for teaching and assessing in the clinical workplace is an interesting and hopeful development, but time will tell whether true harmonization across countries will happen.