DINGO: increasing the power of locus discovery in maternal and fetal genome-wide association studies of perinatal traits.

Perinatal traits are influenced by fetal and maternal genomes. We investigate the performance of three strategies to detect loci in maternal and fetal genome-wide association studies (GWASs) of the same quantitative trait: (i) the traditional strategy of analysing maternal and fetal GWASs separately; (ii) a two-degree-of-freedom test which combines information from maternal and fetal GWASs; and (iii) a one-degree-of-freedom test where signals from maternal and fetal GWASs are meta-analysed together conditional on estimated sample overlap. We demonstrate that the optimal strategy depends on the extent of sample overlap, correlation between phenotypes, whether loci exhibit fetal and/or maternal effects, and whether these effects are directionally concordant. We apply our methods to summary statistics from a recent GWAS meta-analysis of birth weight. Both the two-degree-of-freedom and meta-analytic approaches increase the number of genetic loci for birth weight relative to separately analysing the scans. Our best strategy identifies an additional 62 loci compared to the most recently published meta-analysis of birth weight. We conclude that whilst the two-degree-of-freedom test may be useful for the analysis of certain perinatal phenotypes, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWASs only partially overlap.

Menstrual blood loss is an independent determinant of hemoglobin and ferritin levels in premenopausal blood donors.

INTRODUCTION: To prevent blood donors from developing iron deficiency (ferritin <15 µg/L) and subsequent anemia (hemoglobin <120 g/L), blood services rely on information about known risk factors, including the donor's sex and age. For example, while Finnish women are able to donate whole blood with a minimum donation interval of 91 days, women in the 18 to 25-year-old age group are recommended to donate no more than once per year. Menstrual blood loss is not accounted for in blood donation interval recommendations, despite being a known risk factor of iron deficiency. We aim to investigate to what extent menstrual bleeding is associated with ferritin and hemoglobin levels in female blood donors, and quantify the association of other menstruation-related variables not currently accounted for by blood services (i.e., use of hormonal contraception, heavy menstrual bleeding) with iron deficiency or anemia. MATERIAL AND METHODS: The study population consisted of 473 premenopausal and 491 postmenopausal Dutch whole blood donors. Exclusion criteria were current pregnancy, BMI ≥50, ferritin ≥200, pictorial blood assessment chart (PBAC) ≥400, and age <18 or ≥70 years. Menstrual blood loss was quantified using a PBAC, a semiquantitative method to evaluate the number of used menstrual products and the degree of staining. We identified predictors of log(ferritin)/hemoglobin and iron deficiency/anemia using Bayesian linear and logistic regression models and quantified the average percentage of variance in log(ferritin) and hemoglobin explained by the covariates. RESULTS: Menstrual blood loss accounted for most of the explained variance in hemoglobin (8%) and second only to the number of days since last donation for ferritin (8%). Heavy menstrual bleeding (PBAC ≥150, OR = 3.56 [1.45-8.85], prevalence 13%) was associated with anemia, and use of levonorgestrel-releasing intrauterine device was negatively associated with iron deficiency (OR = 0.06 [0.01-0.44]). After statistical control for menstrual blood loss, age was not associated with iron status. CONCLUSIONS: Menstrual blood loss and blood donation were the most important determinants of iron status in premenopausal women. Thus, results suggest that accounting for menstrual blood loss in donation interval guidelines may benefit blood donors.

Iron status in Dutch and Finnish blood donor and general populations: A cross-cohort comparison study.

BACKGROUND AND OBJECTIVES: Blood donors are at risk of developing iron deficiency (ID) (ferritin <15 µg/L, World Health Organization definition). Blood services implement different strategies to mitigate this risk. Although in Finland risk group-based iron supplementation is in place, no iron supplementation is provided in the Netherlands. We aim to describe differences in ferritin levels and ID prevalence in donor and general populations in these countries. MATERIALS AND METHODS: Six cohorts, stratified based on sex, and for women age, in the Netherlands and Finland were used to evaluate differences in ferritin levels and ID between donor populations (Donor InSight-III and FinDonor 10,000) and general populations (Prevention of Renal and Vascular End-Stage Disease [PREVEND], FinRisk 1997 and Health 2000) and newly registered Dutch donors. Multivariable logistic regression was used to quantify associations of various explanatory factors with ID. RESULTS: In total, 13,443 Dutch and 13,933 Finnish subjects were included. Donors, except for women aged ≤50 years old in Finland, had lower median ferritin levels compared with the general population and new donors. Dutch regular blood donors had higher or similar prevalence of ID as compared with the Dutch general population, including new donors. In contrast, Finnish donors showed similar prevalence of ID compared with the general population, except for a markedly lower prevalence in ≤50-year-old women who routinely receive iron supplements when donating. CONCLUSION: Iron status in blood donors differs from that in the general population. The Finnish blood service donor management policy, for example, iron supplementation for risk groups, seemingly protects young female blood donors from developing ID.

Predicting haemoglobin deferral using machine learning models: Can we use the same prediction model across countries?

BACKGROUND AND OBJECTIVES: Personalized donation strategies based on haemoglobin (Hb) prediction models may reduce Hb deferrals and hence costs of donation, meanwhile improving commitment of donors. We previously found that prediction models perform better in validation data with a high Hb deferral rate. We therefore investigate how Hb deferral prediction models perform when exchanged with other blood establishments. MATERIALS AND METHODS: Donation data from the past 5 years from random samples of 10,000 donors from Australia, Belgium, Finland, the Netherlands and South Africa were used to fit random forest models for Hb deferral prediction. Trained models were exchanged between blood establishments. Model performance was evaluated using the area under the precision-recall curve (AUPR). Variable importance was assessed using SHapley Additive exPlanations (SHAP) values. RESULTS: Across the validation datasets and exchanged models, the AUPR ranged from 0.05 to 0.43. Exchanged models performed similarly within validation datasets, irrespective of the origin of the training data. Apart from subtle differences, the importance of most predictor variables was similar in all trained models. CONCLUSION: Our results suggest that Hb deferral prediction models trained in different blood establishments perform similarly within different validation datasets, regardless of the deferral rate of their training data. Models learn similar associations in different blood establishments.

Blood donor biobank as a resource in personalised biomedical genetic research.

Health questionnaires and donation criteria result in accumulation of highly selected individuals in a blood donor population. To understand better the usefulness of a blood donor-based biobank in personalised disease-associated genetic studies, and for possible personalised blood donation policies, we evaluated the occurrence and distributions of common and rare disease-associated genetic variants in Finnish Blood Service Biobank. We analysed among 31,880 blood donors the occurrence and geographical distribution of (i) 53 rare Finnish-enriched disease-associated variants, (ii) mutations assumed to influence blood donation: four Bernard-Soulier syndrome and two hemochromatosis mutations, (iii) type I diabetes risk genotype HLA-DQ2/DQ8. In addition, we analysed the level of consanguinity in Blood Service Biobank. 80.3% of blood donors carried at least one (range 0-9 per donor) of the rare variants, many in homozygous form, as well. Donors carrying multiple rare variants were enriched in Eastern Finland. Haemochromatosis mutation HFE C282Y homozygosity was 43.8% higher than expected, whereas mutations leading to Bernard-Soulier thrombocytopenia were rare. The frequency of HLA-DQ2/DQ8 genotype was slightly lower than expected. First-degree consanguinity was higher in Blood Service Biobank than in the general population. We demonstrate that despite donor selection, the Blood Service Biobank is a valuable resource for personalised medical research and for genotype-selected samples from unaffected individuals. The geographical genetic substructure of Finland enables efficient recruitment of donors carrying rare variants. Furthermore, we show that blood donor biobank material can be utilised for personalised blood donation policies.

The value of genetic data from 665,460 individuals in managing iron deficiency anaemia and suitability to donate blood.

BACKGROUND AND OBJECTIVES: Although the genetic determinants of haemoglobin and ferritin have been widely studied, those of the clinically and globally relevant iron deficiency anaemia (IDA) and deferral due to hypohaemoglobinemia (Hb-deferral) are unclear. In this investigation, we aimed to quantify the value of genetic information in predicting IDA and Hb-deferral. MATERIALS AND METHODS: We analysed genetic data from up to 665,460 participants of the FinnGen, Blood Service Biobank and UK Biobank, and used INTERVAL (N = 39,979) for validation. We performed genome-wide association studies (GWASs) of IDA and Hb-deferral and utilized publicly available genetic associations to compute polygenic scores for IDA, ferritin and Hb. We fitted models to estimate the effect sizes of these polygenic risk scores (PRSs) on IDA and Hb-deferral risk while accounting for the individual's age, sex, weight, height, smoking status and blood donation history. RESULTS: Significant variants in GWASs of IDA and Hb-deferral appear to be a small subset of variants associated with ferritin and Hb. Effect sizes of genetic predictors of IDA and Hb-deferral are similar to those of age and weight which are typically used in blood donor management. A total genetic score for Hb-deferral was estimated for each individual. The odds ratio estimate between first decile against that at ninth decile of total genetic score distribution ranged from 1.4 to 2.2. CONCLUSION: The value of genetic data in predicting IDA or suitability to donate blood appears to be on a practically useful level.

Simulated effects of ferritin screening on C-reactive protein levels in recruited blood donors.

BACKGROUND AND OBJECTIVES: Ferritin is commonly measured to evaluate iron stores in the body. Some countries have added or considered adding ferritin lower bounds to donor eligibility criteria. Ferritin is also elevated by inflammation. The main goal of this study is to estimate how different ferritin cut-offs would affect the proportion of donors with a C-reactive protein (CRP) level over 3 mg/L, which is the decision limit of the highest chronic cardiovascular risk. MATERIALS AND METHODS: To simulate recruitment of new blood donors, we selected participants from two Finnish general population cohorts, namely FINRISK 1997 (n = 5369) and Health 2000 (n = 3278), that would likely fulfil the selection criteria of blood donation. We then calculated the proportion of individuals with high-sensitivity CRP values above 3 mg/L, over a range of ferritin values. RESULTS: We found that for several ferritin cut-offs the proportion of potential donors with CRP > 3 mg/L would rise by a statistically significant amount. The trend was significant and similar for all subgroups but weaker for non-menstruating women as well as men. CONCLUSION: Our results show that screening a population of potential blood donors with ferritin cut-offs raises the number of people with CRP > 3 mg/L within the blood donor population.

The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral.

BACKGROUND AND OBJECTIVES: On-site haemoglobin deferral for blood donors is sometimes necessary for donor health but demotivating for donors and inefficient for the blood bank. Deferral rates could be reduced by accurately predicting donors' haemoglobin status before they visit the blood bank. Although such predictive models have been published, there is ample room for improvement in predictive performance. We aim to assess the added value of ferritin levels or genetic markers as predictor variables in haemoglobin deferral prediction models. MATERIALS AND METHODS: Support vector machines with and without this information (the full and reduced model, respectively) are compared in Finland and the Netherlands. Genetic markers are available in the Finnish data and ferritin levels in the Dutch data. RESULTS: Although there is a clear association between haemoglobin deferral and both ferritin levels and several genetic markers, predictive performance increases only marginally with their inclusion as predictors. The recall of deferrals increases from 68.6% to 69.9% with genetic markers and from 79.7% to 80.0% with ferritin levels included. Subgroup analyses show that the added value of these predictors is higher in specific subgroups, for example, for donors with minor alleles on single-nucleotide polymorphism 17:58358769, recall of deferral increases from 73.3% to 93.3%. CONCLUSION: Including ferritin levels or genetic markers in haemoglobin deferral prediction models improves predictive performance. The increase in overall performance is small but may be substantial for specific subgroups. We recommend including this information as predictor variables when available, but not to collect it for this purpose only.

DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning.

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

An international comparison of haemoglobin deferral prediction models for blood banking.

BACKGROUND AND OBJECTIVES: Blood banks use a haemoglobin (Hb) threshold before blood donation to minimize donors' risk of anaemia. Hb prediction models may guide decisions on which donors to invite, and should ideally also be generally applicable, thus in different countries and settings. In this paper, we compare the outcome of various prediction models in different settings and highlight differences and similarities. MATERIALS AND METHODS: Donation data of repeat donors from the past 5 years of Australia, Belgium, Finland, the Netherlands and South Africa were used to fit five identical prediction models: logistic regression, random forest, support vector machine, linear mixed model and dynamic linear mixed model. Only donors with five or more donation attempts were included to ensure having informative data from all donors. Analyses were performed for men and women separately and outcomes compared. RESULTS: Within countries and overall, different models perform similarly well. However, there are substantial differences in model performance between countries, and there is a positive association between the deferral rate in a country and the ability to predict donor deferral. Nonetheless, the importance of predictor variables across countries is similar and is highest for the previous Hb level. CONCLUSION: The limited impact of model architecture and country indicates that all models show similar relationships between the predictor variables and donor deferral. Donor deferral is found to be better predictable in countries with high deferral rates. Therefore, such countries may benefit more from deferral prediction models than those with low deferral rates.

FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

A robust autonomous method for blood demand forecasting.

BACKGROUND: Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issues that discourage their use in blood supply chain optimization: there is no single approach that works everywhere, and there are no guarantees that any favorable method performance continues into the future. STUDY DESIGN AND METHODS: We design a novel autonomous forecasting system to solve the aforementioned issues. We show how possible changes in blood demand could affect prediction performance using partly synthetic demand data. We use these data then to investigate the performances of different method selection heuristics. Finally, the performances of the heuristics and single method approaches were compared using historical demand data from Finland and the Netherlands. The development code is publicly accessible. RESULTS: We find that a shift in the demand signal behavior from stochastic to seasonal would affect the relative performances of the methods. Our autonomous system outperforms all examined individual methods when forecasting the synthetic demand series, exhibiting meaningful robustness. When forecasting with real data, the most accurate methods in Finland and in the Netherlands are the autonomous system and the method average, respectively. DISCUSSION: Optimal use of method selection heuristics, as with our autonomous system, may overcome the need to constantly supervise forecasts in anticipation of changes in demand while being sufficiently accurate in the absence of such changes.

Prediction and impact of personalized donation intervals.

BACKGROUND AND OBJECTIVES: Deferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. MATERIALS AND METHODS: Using donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors. RESULTS: We discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply. CONCLUSION: We find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike.

Low ferritin levels appear to be associated with worsened health in male repeat blood donors.

BACKGROUND AND OBJECTIVES: Frequent blood donation depletes iron stores of blood donors. Iron depletion may lead to anaemia, but the health effects of iron depletion without anaemia in healthy blood donors are not well understood. We studied in the FinDonor cohort whether worsening of self-rated health of blood donors during the study period was associated with biomarkers for iron levels or other self-reported changes in lifestyle. MATERIALS AND METHODS: We included 1416 participants from the cohort who answered an 89-item questionnaire on their health and lifestyle during their enrolment visit and again at the end of the study. We performed multivariate logistic regression to test if blood donation-related factors affected the probability of reporting worsened health. To set these findings into a more holistic context of health, we subsequently analysed all other questionnaire items with a data-driven exploratory analysis. RESULTS: We found that donation frequency in men and post-menopausal women and ferritin level only in men was associated negatively with worsened health between questionnaires. In the exploratory analysis, stable physical condition was the only questionnaire item that was associated negatively with worsened health in both women and men. CONCLUSION: Our results suggest that low ferritin level is associated with worsened health even in non-anaemic repeat donors, although we find that when health is analysed more holistically, ferritin and other factors primarily related to blood donation lose their importance.

Succession of physiological stages hallmarks the transcriptomic response of the fungus Aspergillus niger to lignocellulose.

BACKGROUND: Understanding how fungi degrade lignocellulose is a cornerstone of improving renewables-based biotechnology, in particular for the production of hydrolytic enzymes. Considerable progress has been made in investigating fungal degradation during time-points where CAZyme expression peaks. However, a robust understanding of the fungal survival strategies over its life time on lignocellulose is thereby missed. Here we aimed to uncover the physiological responses of the biotechnological workhorse and enzyme producer Aspergillus niger over its life time to six substrates important for biofuel production. RESULTS: We analysed the response of A. niger to the feedstock Miscanthus and compared it with our previous study on wheat straw, alone or in combination with hydrothermal or ionic liquid feedstock pretreatments. Conserved (substrate-independent) metabolic responses as well as those affected by pretreatment and feedstock were identified via multivariate analysis of genome-wide transcriptomics combined with targeted transcript and protein analyses and mapping to a metabolic model. Initial exposure to all substrates increased fatty acid beta-oxidation and lipid metabolism transcripts. In a strain carrying a deletion of the ortholog of the Aspergillus nidulans fatty acid beta-oxidation transcriptional regulator farA, there was a reduction in expression of selected lignocellulose degradative CAZyme-encoding genes suggesting that beta-oxidation contributes to adaptation to lignocellulose. Mannan degradation expression was wheat straw feedstock-dependent and pectin degradation was higher on the untreated substrates. In the later life stages, known and novel secondary metabolite gene clusters were activated, which are of high interest due to their potential to synthesize bioactive compounds. CONCLUSION: In this study, which includes the first transcriptional response of Aspergilli to Miscanthus, we highlighted that life time as well as substrate composition and structure (via variations in pretreatment and feedstock) influence the fungal responses to lignocellulose. We also demonstrated that the fungal response contains physiological stages that are conserved across substrates and are typically found outside of the conditions with high CAZyme expression, as exemplified by the stages that are dominated by lipid and secondary metabolism.

Venous sample is superior to repeated skin-prick testing in blood donor haemoglobin second-line screening.

BACKGROUND: Blood donor haemoglobin concentration (Hb) is commonly measured from a skin-prick sample. However, the skin-prick sample is prone to preanalytical error and variation, which may lead to false deferrals due to low Hb. STUDY DESIGN AND METHODS: We assessed the efficacy of two second-line screening models for the evaluation of blood donors failing the initial skin-prick test. In the venous model (n = 305), Hb was measured from a venous sample at the donation site. In the skin-prick model (n = 331), two additional skin-prick samples were measured. All on-site Hb measurements were performed with HemoCue Hb201+ (HemoCue AB) point-of-care (POC) device. Hb in the venous samples was later also determined with a hematology analyzer (Sysmex XN, Sysmex Co.) to obtain the donor's correct Hb. A questionnaire evaluated Blood Service nurses' preferences regarding Hb assessment. RESULTS: Significantly less donors were deferred from donation with venous model (40%) than with skin-prick model (51%; chi-square test P = 0·004). Only two donors (0·7%) were incorrectly accepted in the venous model. Further, Blood Service nurses preferred venous model over skin-prick model. After the study, the venous model was implemented nationwide, and in the first two months after implementation, the deferral rate due to low Hb decreased from 2·7% to 1·9%. CONCLUSION: A venous sample for blood donor Hb second-line screening significantly decreased low Hb deferrals compared to repeated skin-prick testing without compromising donor safety. Valuable donations can be recovered by implementing a practical second-line screening model based on venous sampling.

FinDonor 10 000 study: a cohort to identify iron depletion and factors affecting it in Finnish blood donors.

BACKGROUND AND OBJECTIVES: There is increasing evidence that frequent blood donation depletes the iron stores of some blood donors. The FinDonor 10 000 study was set up to study iron status and factors affecting iron stores in Finnish blood donors. In Finland, iron supplementation for at-risk groups has been in place since the 1980s. MATERIAL AND METHODS: A total of 2584 blood donors (N = 8003 samples) were recruited into the study alongside standard donation at three donation sites in the capital region of Finland between 5/2015 and 12/2017. All participants were asked to fill out a questionnaire about their health and lifestyle. Blood samples were collected from the sample pouch of whole blood collection set, kept in cool temperature and processed centrally. Whole blood count, CRP, ferritin and sTFR were measured from the samples, and DNA was isolated for GWAS studies. RESULTS: Participant demographics, albeit in general similar to the general blood donor population in Finland, indicated some bias towards older and more frequent donors. Participation in the study increased median donation frequency of the donors. Analysis of the effect of time lag from the sampling to the analysis and the time of day when sample was drawn revealed small but significant time-dependent changes. CONCLUSION: The FinDonor cohort now provides us with tools to identify potential donor groups at increased risk of iron deficiency and factors explaining this risk. The increase in donation frequency during the study suggests that scientific projects can be used to increase the commitment of blood donors.

The effect of donation activity dwarfs the effect of lifestyle, diet and targeted iron supplementation on blood donor iron stores.

The iron status of blood donors is a subject of concern for blood establishments. The Finnish Red Cross Blood Service addresses iron loss in blood donors by proposing systematic iron supplementation for demographic at-risk donor groups. We measured blood count, ferritin and soluble transferrin receptor (sTfR) and acquired lifestyle and health information from 2200 blood donors of the FinDonor 10000 cohort. We used modern data analysis methods to estimate iron status and factors affecting it with a special focus on the effects of the blood service's iron supplementation policy. Low ferritin (< 15 µg/L), an indicator of low iron stores, was present in 20.6% of pre-menopausal women, 10.6% of post-menopausal women and 6% of men. Anemia co-occurred with iron deficiency more frequently in pre-menopausal women (21 out of 25 cases) than in men (3/6) or post-menopausal women (1/2). In multivariable regression analyses, lifestyle, dietary, and blood donation factors explained up to 38% of the variance in ferritin levels but only ~10% of the variance in sTfR levels. Days since previous donation were positively associated with ferritin levels in all groups while the number of donations during the past 2 years was negatively associated with ferritin levels in pre-menopausal women and men. FRCBS-provided iron supplementation was negatively associated with ferritin levels in men only. Relative importance analyses showed that donation activity accounted for most of the explained variance in ferritin levels while iron supplementation explained less than 1%. Variation in ferritin levels was not significantly associated with variation in self-reported health. Donation activity was the most important factor affecting blood donor iron levels, far ahead of e.g. red-meat consumption or iron supplementation. Importantly, self-reported health of donors with lower iron stores was not lower than self-reported health of donors with higher iron stores.

A community-driven reconstruction of the Aspergillus niger metabolic network.

BACKGROUND: Aspergillus niger is an important fungus used in industrial applications for enzyme and acid production. To enable rational metabolic engineering of the species, available information can be collected and integrated in a genome-scale model to devise strategies for improving its performance as a host organism. RESULTS: In this paper, we update an existing model of A. niger metabolism to include the information collected from 876 publications, thereby expanding the coverage of the model by 940 reactions, 777 metabolites and 454 genes. In the presented consensus genome-scale model of A. niger iJB1325 , we integrated experimental data from publications and patents, as well as our own experiments, into a consistent network. This information has been included in a standardized way, allowing for automated testing and continuous improvements in the future. This repository of experimental data allowed the definition of 471 individual test cases, of which the model complies with 373 of them. We further re-analyzed existing transcriptomics and quantitative physiology data to gain new insights on metabolism. Additionally, the model contains 3482 checks on the model structure, thereby representing the best validated genome-scale model on A. niger developed until now. Strain-specific model versions for strains ATCC 1015 and CBS 513.88 have been created containing all data used for model building, thereby allowing users to adopt the models and check the updated version against the experimental data. The resulting model is compliant with the SBML standard and therefore enables users to easily simulate it using their preferred software solution. CONCLUSION: Experimental data on most organisms are scattered across hundreds of publications and several repositories.To allow for a systems level understanding of metabolism, the data must be integrated in a consistent knowledge network. The A. niger iJB1325 model presented here integrates the available data into a highly curated genome-scale model to facilitate the simulation of flux distributions, as well as the interpretation of other genome-scale data by providing the metabolic context.