Dataset Information

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

ABSTRACT: Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites (p < 0.05) in RBC metabolism using only measurements of these five biomarkers. The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. The ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.

SUBMITTER: Yurkovich JT

PROVIDER: S-EPMC5358888 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

Yurkovich James T JT Yang Laurence L Palsson Bernhard O BO

PLoS computational biology 20170306 3

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to acc ...[more]

PMID: 28264007

Similar Datasets

Project description:BackgroundDNA methylation is an epigenetic modification that plays an important role during mammalian development. Around birth in humans, the main site of red blood cell production moves from the fetal liver to the bone marrow. DNA methylation changes at the β-globin locus and a switch from fetal to adult hemoglobin production characterize this transition. Understanding this globin switch may improve the treatment of patients with sickle cell disease and β-thalassemia, two of the most common Mendelian diseases in the world. The goal of our study was to describe and compare the genome-wide patterns of DNA methylation in fetal and adult human erythroblasts.MethodsWe used the Illumina HumanMethylation 450 k BeadChip to measure DNA methylation at 402,819 CpGs in ex vivo-differentiated erythroblasts from 12 fetal liver and 12 bone marrow CD34+ donors.ResultsWe identified 5,937 differentially methylated CpGs that overlap with erythroid enhancers and binding sites for erythropoiesis-related transcription factors. Combining this information with genome-wide association study results, we show that erythroid enhancers define particularly promising genomic regions to identify new genetic variants associated with fetal hemoglobin (HbF) levels in humans. Many differentially methylated CpGs are located near genes with unanticipated roles in red blood cell differentiation and proliferation. For some of these new candidate genes, we confirm the correlation between DNA methylation and gene expression levels in red blood cell progenitors. We also provide evidence that DNA methylation and genetic variation at the β-globin locus independently control globin gene expression in adult erythroblasts.ConclusionsOur DNA methylome maps confirm the widespread dynamic changes in DNA methylation that occur during human erythropoiesis. These changes tend to happen near erythroid enhancers, further highlighting their importance in erythroid regulation and HbF production. Finally, DNA methylation may act independently of the transcription factor BCL11A to repress fetal hemoglobin production. This provides cues on strategies to more efficiently re-activate HbF production in sickle cell disease and β-thalassemia patients.

Project description:BackgroundA well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.MethodsThe surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.ResultsA prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.ConclusionsIn a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

Dataset Information

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

Publications

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets