A metabolomic map of Zellweger spectrum disorders reveals novel disease biomarkers

Wangler, Michael F.; Hubert, Leroy; Donti, Taraka; Ventura, Meredith J.; Miller, Marcus J.; Braverman, Nancy; Gawron, Kelly M.; Bose, Mousumi; Moser, Ann B.; Jones, Richard O.; Rizzo, William B.; Sutton, V. Reid; Sun, Qin; Kennedy, Adam D.; Elsea, Sarah H.

doi:10.1038/gim.2017.262

Cited by 41 publications

(52 citation statements)

References 38 publications

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“…This approach resulted in an overall diagnostic rate of 12.3% (21/170 = 12.3% [ Table 1 ]). 10 , 13 , 16 – 18 …”

Section: Resultsmentioning

confidence: 99%

“…We have previously described the use of untargeted metabolomics in the screening and diagnosis of a variety of inborn errors of metabolism (IEMs) 9 and the identification of novel disease biomarkers. 10 , 11 We have also demonstrated that untargeted metabolomics may be useful in the diagnosis of individuals at either end of a phenotypic spectrum, as in the case of several individuals with adenylosuccinate lyase deficiency 12 and in patients with GABA-transaminase deficiency 13 . Here, metabolomic data were not only able to suggest a clinical diagnosis but also informed the choice of targeted molecular testing for additional confirmation.…”

Section: Introductionmentioning

confidence: 95%

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Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Alaimo

Glinton

Liu

et al. 2020

Genetics in Medicine

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Purpose: A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. We aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both whole exome sequencing and untargeted metabolomics within the same clinical laboratory. Methods: Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities which could be used to support their interpretation using current ACMG guidelines. Results: Metabolomic data contributed to the interpretation variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the re-classification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population. Conclusion: Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

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“…This approach resulted in an overall diagnostic rate of 12.3% (21/170 = 12.3% [ Table 1 ]). 10 , 13 , 16 – 18 …”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Alaimo

Glinton

Liu

et al. 2020

Genetics in Medicine

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“…The accumulation of putrescine is associated with another inborn error of metabolism, cystinuria 40 . The concentration of hexadecanedioate is elevated in the Peroxisome Biogenesis Disorders-Zellweger Syndrome Spectrum disorders 41 . Biotinidase deficiency is an inborn error of metabolism that affects the endogenous recycling of biotin, causing neurological and cutaneous symptoms 42 .…”

Section: Maternal Factors Affecting Inborn Errors Of Metabolism (Iemsmentioning

confidence: 99%

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Zhao¹,

Ge²,

Li³

et al. 2020

Sci Rep

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Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (ieMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. this study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H-NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. the results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs. Gestational diabetes mellitus (GDM) has attracted worldwide public health concern due to its adverse maternal, fetal and neonatal outcomes. GDM is a serious pregnancy complication with various risk factors 1 , including overweight, obesity, a family history of diabetes, advanced maternal age, etc. GDM has been linked with an increased risk of inborn errors of metabolism (IEMs) in offspring 2,3. IEMs are caused by inherited genetic defects and can be influenced by environmental stimuli 4. Accumulating evidence suggests that maternal hyperglycemia is associated

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“…Urine, plasma and CSF metabolomic assays have been employed, revealing significant findings on clinically analysed samples [58,[85][86][87][88]. These insights include new diagnostic biomarkers [87][88][89], a broader range of phenotype in adenylosuccinate lyase deficiency [90], insights around pre-analytical factors [91], primary metabolic variations from drug-related changes [92][93][94] and a better understanding of the underlying pathobiological mechanism of disease [95][96][97].…”

Section: Biomarker Discoverymentioning

confidence: 99%

Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

Mordaunt

Cox

Fuller

2020

IJMS

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Early diagnosis of inborn errors of metabolism (IEM)—a large group of congenital disorders—is critical, given that many respond well to targeted therapy. Newborn screening programs successfully capture a proportion of patients enabling early recognition and prompt initiation of therapy. For others, the heterogeneity in clinical presentation often confuses diagnosis with more common conditions. In the absence of family history and following clinical suspicion, the laboratory diagnosis typically begins with broad screening tests to circumscribe specialised metabolite and/or enzyme assays to identify the specific IEM. Confirmation of the biochemical diagnosis is usually achieved by identifying pathogenic genetic variants that will also enable cascade testing for family members. Unsurprisingly, this diagnostic trajectory is too often a protracted and lengthy process resulting in delays in diagnosis and, importantly, therapeutic intervention for these rare conditions is also postponed. Implementation of mass spectrometry technologies coupled with the expanding field of metabolomics is changing the landscape of diagnosing IEM as numerous metabolites, as well as enzymes, can now be measured collectively on a single mass spectrometry-based platform. As the biochemical consequences of impaired metabolism continue to be elucidated, the measurement of secondary metabolites common across groups of IEM will facilitate algorithms to further increase the efficiency of diagnosis.

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A metabolomic map of Zellweger spectrum disorders reveals novel disease biomarkers

Abstract: Untargeted metabolomics is effective in detecting mild to intermediate cases of PBD-ZSD. Surprisingly, dramatic reductions in plasma sphingomyelin are a consistent feature of the PBD-ZSD metabolome. The use of metabolomics in PBD-ZSD can provide insight into novel biomarkers of disease.

Cited by 41 publications

References 38 publications

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

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