Metabolomic Profiling of Asparagine Deprivation in Asparagine Synthetase Deficiency Patient-Derived Cells

Chang, Mario C.; Staklinski, Stephen J.; Malut, Vinay; Pierre, Geraldine L.; Kilberg, Michael S.; Merritt, Matthew E.

doi:10.3390/nu15081938

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…We identified suggestive causal associations for 6 metabolic products that inhibit OC development. Among them, asparagine is an essential natural amino acid that healthy cells utilize to maintain function and proliferation [ 48 ]. Its role as a targeted anticancer amino acid aligns with our findings [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

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Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

Liu,

Ding,

Liu

et al. 2024

J Ovarian Res

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Background The mechanisms and risk factors underlying ovarian cancer (OC) remain under investigation, making the identification of new prognostic biomarkers and improved predictive factors critically important. Recently, circulating metabolites have shown potential in predicting survival outcomes and may be associated with the pathogenesis of OC. However, research into their genetic determinants is limited, and there are some inadequacies in understanding the distinct subtypes of OC. In this context, we conducted a Mendelian randomization study aiming to provide evidence for the relationship between genetically determined metabolites (GDMs) and the risk of OC and its subtypes. Methods In this study, we consolidated genetic statistical data of GDMs with OC and its subtypes through a genome-wide association study (GWAS) and conducted a two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method served as the primary approach, with MR-Egger and weighted median methods employed for cross-validation to determine whether a causal relationship exists between the metabolites and OC risk. Moreover, a range of sensitivity analyses were conducted to validate the robustness of the results. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. False discovery rate (FDR) correction was applied to validate the findings. We also conducted a reverse MR analysis to validate whether the observed blood metabolite levels were influenced by OC risk. Additionally, metabolic pathway analysis was carried out using the MetaboAnalyst 5.0 software. Results In MR analysis, we discovered 18 suggestive causal associations involving 14 known metabolites, 8 metabolites as potential risk factors, and 6 as potential cancer risk reducers. In addition, three significant pathways, "caffeine metabolism," "arginine biosynthesis," and "citrate cycle (TCA cycle)" were associated with the development of mucinous ovarian cancer (MOC). The pathways "caffeine metabolism" and "alpha-linolenic acid metabolism" were associated with the onset of endometrioid ovarian cancer (OCED). Conclusions Our MR analysis revealed both protective and risk-associated metabolites, providing insights into the potential causal relationships between GDMs and the metabolic pathways related to OC and its subtypes. The metabolites that drive OC could be potential candidates for biomarkers.

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Section: Discussionmentioning

confidence: 99%

“…Among them, asparagine is an essential natural amino acid that healthy cells utilize to maintain function and proliferation [ 48 ]. Its role as a targeted anticancer amino acid aligns with our findings [ 48 ]. Betaine, another vital amino acid, has been shown to have chronic disease prevention potential [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

Liu,

Ding,

Liu

et al. 2024

J Ovarian Res

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Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

Staklinski,

Scheben,

Siepel

et al. 2023

Preprint

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AlphaMissense is a recently developed method that is designed to classify missense variants into pathogenic, benign, or ambiguous categories across the entire human proteome. Asparagine Synthetase Deficiency (ASNSD) is a developmental disorder associated with severe symptoms, including congenital microcephaly, seizures, and premature death. Diagnosing ASNSD relies on identifying mutations in the asparagine synthetase (ASNS) gene through DNA sequencing and determining whether these variants are pathogenic or benign. Pathogenic ASNS variants are predicted to disrupt the protein’s structure and/or function, leading to asparagine depletion within cells and inhibition of cell growth. AlphaMissense offers a promising solution for the rapid classification of ASNS variants established by DNA sequencing and provides a community resource of pathogenicity scores and classifications for newly diagnosed ASNSD patients. Here, we assessed AlphaMissense’s utility in ASNSD by benchmarking it against known critical residues in ASNS and evaluating its performance against a list of previously reported ASNSD-associated variants. We also present a pipeline to calculate AlphaMissense scores for any protein in the UniProt database. AlphaMissense accurately attributed a high average pathogenicity score to known critical residues within the two ASNS active sites and the connecting intramolecular tunnel. The program successfully categorized 78.9% of known ASNSD-associated missense variants as pathogenic. The remaining variants were primarily labeled as ambiguous, with a smaller proportion classified as benign. This study underscores the potential role of AlphaMissense in classifying ASNS variants in suspected cases of ASNSD, potentially providing clarity to patients and their families grappling with ongoing diagnostic uncertainty.

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Metabolomic Profiling of Asparagine Deprivation in Asparagine Synthetase Deficiency Patient-Derived Cells

Cited by 2 publications

References 39 publications

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

Utility of AlphaMissense predictions in Asparagine Synthetase deficiency variant classification

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