Disturbed phospholipid metabolism in serine biosynthesis defects revealed by metabolomic profiling

Glinton, Kevin E.; Benke, Paul J.; Lines, Matthew A.; Geraghty, Michael T.; Chakraborty, Pranesh; Al-Dirbashi, Osama Y.; Jiang, Yiguo; Kennedy, Adam D.; Grotewiel, Mike; Sutton, V. Reid; Elsea, Sarah H.; El‐Hattab, Ayman W.

doi:10.1016/j.ymgme.2017.12.009

Cited by 44 publications

(40 citation statements)

References 36 publications

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“…The gene facilitates serine's role in sphingolipid metabolism 68 . Disturbances in this pathway are hypothesized to be central in the development the neuronal symptoms 69 , suggesting a link between AARS expression and the serine pathway.…”

Section: Methods Supplementarymentioning

confidence: 99%

Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Võsa¹,

Claringbould²,

Westra³

et al. 2018

Preprint

494

733

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SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.To identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.We observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

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Section: Methods Supplementarymentioning

confidence: 99%

Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Võsa¹,

Claringbould²,

Westra³

et al. 2018

Preprint

494

733

View full text Add to dashboard Cite

show abstract

“…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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“…Patients with 3-phosphoglycerate dehydrogenase deficiency showed concurrent results for plasma serine by both methods (Table S2: Cases F17, F18). Additionally, GUM revealed low levels of plasma sphingomyelin in these patients, a potential biomarker for the disorder 16 .…”

Section: Clinical Utility Study a Complete Description Of The Diagnomentioning

confidence: 90%

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

Almontashiri

Zha

Kim

et al. 2020

Sci Rep

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Global untargeted metabolomics (GUM) has entered clinical diagnostics for genetic disorders. We compared the clinical utility of GUM with traditional targeted metabolomics (TM) as a screening tool in patients with established genetic disorders and determined the scope of GUM as a discovery tool in patients with no diagnosis under investigation. We compared TM and GUM data in 226 patients. The first cohort (n = 87) included patients with confirmed inborn errors of metabolism (IEM) and genetic syndromes; the second cohort (n = 139) included patients without diagnosis who were undergoing evaluation for a genetic disorder. In patients with known disorders (n = 87), GUM performed with a sensitivity of 86% (95% CI: 78-91) compared with TM for the detection of 51 diagnostic metabolites. The diagnostic yield of GUM in patients under evaluation with no established diagnosis (n = 139) was 0.7%. GUM successfully detected the majority of diagnostic compounds associated with known IEMs. The diagnostic yield of both targeted and untargeted metabolomics studies is low when assessing patients with non-specific, neurological phenotypes. GUM shows promise as a validation tool for variants of unknown significance in candidate genes in patients with non-specific phenotypes.

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Disturbed phospholipid metabolism in serine biosynthesis defects revealed by metabolomic profiling

Cited by 44 publications

References 36 publications

Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis

Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

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