Author Correction: Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism.
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ABSTRACT:
SUBMITTER: Cheng Y
PROVIDER: S-EPMC8494923 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Author Correction: Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism.
Cheng Yurong Y Schlosser Pascal P Hertel Johannes J Sekula Peggy P Oefner Peter J PJ Spiekerkoetter Ute U Mielke Johanna J Freitag Daniel F DF Schmidts Miriam M Kronenberg Florian F Eckardt Kai-Uwe KU Thiele Ines I Li Yong Y Köttgen Anna A
Nature communications 20211006 1
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Project description:Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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Project description:<p>We investigated the cumulative contribution of rare, exonic genetic variants on the concentration of 1,487 metabolites and 53,714 metabolite ratios in urine by performing gene-based tests based on 226,233 variants from up to 4,864 participants of the German Chronic Kidney Disease (GCKD) study. There were 128 significant associations (53 metabolite-gene and 75 metabolite ratio-gene pairs) involving 30 unique genes, 16 of which are known to underlie recessively inherited inborn errors of metabolism (IEMs). Across the 30 genes, 47% of individuals carried at least one rare missense, stop or splice variant. The 30 genes were strongly enriched for shared high expression in liver and kidney (OR=65, p-FDR=3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of whole-exome sequencing data in the UK Biobank allowed for linking genes to diseases that could plausibly be explained by the identified metabolites. In silico constraint-based modeling of knockouts of the implicated genes in a virtual whole-body, organ-resolved metabolic human correctly predicted the observed direction of metabolite changes in urine and blood, highlighting the potential of linking population genetics to modeling to validate associations and to predict metabolic consequences of yet unknown IEMs. Our study extends the map of genes influencing urine metabolite concentrations, reveals metabolic processes and connected health outcomes, and implicates novel candidate variants and genes for IEMs.</p><p><br></p><p>Further links;</p><p><a href='https://www.gckd.org/' rel='noopener noreferrer' target='_blank'>German Chronic Kidney Disease (GCKD)</a></p>
2021-03-09 | MTBLS284 | MetaboLights
Project description:The overall disease burden of pediatric infection is high, with widely varying clinical outcomes including death. Among the most vulnerable children, those with inborn errors of immunity, reduced penetrance and variable expressivity are common but poorly understood. There are several genetic mechanisms that influence phenotypic variation in inborn errors of immunity, as well as a body of knowledge on environmental influences and specific pathogen triggers. Critically, recent advances are illuminating novel nuances for fundamental concepts on disease penetrance, as well as raising new areas of inquiry. The last few decades have seen the identification of almost 500 causes of inborn errors of immunity, as well as major advancements in our ability to characterize somatic events, the microbiome, and genotypes across large populations. The progress has not been linear, and yet, these developments have accumulated into an enhanced ability to diagnose and treat inborn errors of immunity, in some cases with precision therapy. Nonetheless, many questions remain regarding the genetic and environmental contributions to phenotypic variation both within and among families. The purpose of this review is to provide an updated summary of key concepts in genetic and environmental contributions to phenotypic variation within inborn errors of immunity, conceptualized as including dynamic, reciprocal interplay among factors unfolding across the key dimension of time. The associated findings, potential gaps, and implications for research are discussed in turn for each major influencing factor. The substantial challenge ahead will be to organize and integrate information in such a way that accommodates the heterogeneity within inborn errors of immunity to arrive at a more comprehensive and accurate understanding of how the immune system operates in health and disease. And, crucially, to translate this understanding into improved patient care for the millions at risk for serious infection and other immune-related morbidity.
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Project description:AimsWe wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively.MethodsBiochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses.ResultsWe identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%.ConclusionsA single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.
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Project description:The current diagnostic work-up of inborn errors of metabolism (IEM) is rapidly moving toward integrative analytical approaches. We aimed to develop an innovative, targeted urine metabolomics (TUM) screening procedure to accelerate the diagnosis of patients with IEM. Urinary samples, spiked with three stable isotope-labeled internal standards, were analyzed for 258 diagnostic metabolites with an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) configuration run in positive and negative ESI modes. The software automatically annotated peaks, corrected for peak overloading, and reported peak quality and shifting. Robustness and reproducibility were satisfactory for most metabolites. Z-scores were calculated against four age-group-matched control cohorts. Disease phenotypes were scored based on database metabolite matching. Graphical reports comprised a needle plot, annotating abnormal metabolites, and a heatmap showing the prioritized disease phenotypes. In the clinical validation, we analyzed samples of 289 patients covering 78 OMIM phenotypes from 12 of the 15 society for the study of inborn errors of metabolism (SSIEM) disease groups. The disease groups include disorders in the metabolism of amino acids, fatty acids, ketones, purines and pyrimidines, carbohydrates, porphyrias, neurotransmitters, vitamins, cofactors, and creatine. The reporting tool easily and correctly diagnosed most samples. Even subtle aberrant metabolite patterns as seen in mild multiple acyl-CoA dehydrogenase deficiency (GAII) and maple syrup urine disease (MSUD) were correctly called without difficulty. Others, like creatine transporter deficiency, are illustrative of IEM that remain difficult to diagnose. We present TUM as a powerful diagnostic screening tool that merges most urinary diagnostic assays expediting the diagnostics for patients suspected of an IEM.
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