Case Study

Improve Screening for Inborn Errors of Metabolism

This study leverages Metabolon’s technology to measure GABA-transaminase by profiling not only cerebrospinal fluid (CSF), but also plasma and urine.

The analysis of plasma or urine is less invasive and less expensive than a lumbar puncture and can be performed faster than CSF analysis. This could also lead to early detection of disease, which can result in expedited treatment.

The analysis of plasma or urine is less invasive and less expensive than a lumbar puncture and can be performed faster than CSF analysis. This could also lead to early detection of disease, which can result in expedited treatment.

Improve Screening for Inborn Errors of Metabolism

The Challenge: Improve Screening for Neurometabolic Diseases

Gamma-aminobutyric acid (GABA) transaminase deficiency, also known as 4-ABAT deficiency, is an inborn error of metabolism (IEM) caused by mutations in the ABAT gene. Clinically, GABA-transaminase deficiency presents with hypotonia, seizures, psychomotor retardation, and electroencephalogram (EEG) abnormalities and is often associated with death in childhood. Biochemically, the inhibition of GABA transaminase activity results in the accumulation of GABA in the cerebrospinal fluid (CSF), subsequently leading to elevated levels of 2-pyrrolidinone, succinimide, and succinamic acid. For that reason, the measurement of GABA in CSF is currently the main method of diagnosis. However, collecting an appropriate and necessary volume of CSF can be challenging, and rigorous sample handling and storage protocols are required to preserve CSF samples appropriately.

Metabolon Insight: Use Less-Invasive Sample Type in Rare Disease Screening

In this study, the Metabolon Global Discovery Panel was used to profile over 1,000 plasma, CSF, and urine samples from four subjects with GABA-transaminase deficiency, a standard reference pediatric population, and several subjects without variants in ABAT taking different medications that inhibit GABA metabolism (n = 93).1

The Solution: Metabolomics Data Detects GABA-transaminase Deficiency and Treatment Efficacy

Global metabolomics of plasma, urine, and CSF samples was performed on four subjects with GABA-transaminase deficiency and a normal pediatric population cohort. Metabolomics data revealed that the average 2-pyrrolidinone level was elevated by over 4.34 standard deviations in the plasma of subjects with GABA-transaminase deficiency relative to the normal reference population. Higher levels of 2-pyrrolidinone were also detected in the urine and CSF of subjects with GABA-transaminase deficiency. Succinimide was detected in all plasma and CSF samples from GABA-transaminase deficiency patients but was not detected in enough samples of the healthy reference control population to allow the calculation of Z– score reference ranges. Additionally, Z-scores for succinamic acid in plasma and CSF samples from GABA-transaminase deficiency patients ranged from 0.62 to 2.25.

This study also aimed to elucidate whether pharmacological treatments that affect GABA metabolism can inhibit GABA-transaminase. The goal was to improve the ability to accurately screen and diagnose GABA-transaminase deficiency. They found that compared to the reference population, 2-pyrrolidinone levels were significantly higher in patients treated with vigabatrin but not with topiramate, levetiracetam, baclofen, and valproate.

The Outcome: Metabolon Can Use Less-Invasive Sample Types to Screen for Rare Diseases and Inform Treatment

This study validates Metabolon’s ability to identify GABA-transaminase deficiency by profiling plasma and urine samples, not only via CSF. The analysis of plasma or urine is less invasive and less expensive than a lumbar puncture and can be performed faster than CSF analysis. This could also lead to early detection of disease, which can result in expedited treatment. In the future, this technology could track how patients respond to treatment to differentiate responders from non-responders. Monitoring 2-pyrrolidinone in patients with GABA-transaminase deficiency levels via targeted metabolomic profiling may inform the efficacy of a therapeutic intervention. These data show that metabolomics provides not only an informed approach to diagnosing GABA-transaminase deficiency but also distinguishes them from other sources of GABA/2-pyrrolidinone elevations that may otherwise confound targeted metabolomic analysis of GABA metabolism.

References

1. Kennedy AD, Pappan KL, Donti T, Delgado MR, Shinawi M, Pearson TS, et al. 2-Pyrrolidinone and Succinimide as Clinical Screening Biomarkers for GABA-Transaminase Deficiency: Anti-seizure Medications Impact Accurate Diagnosis. Front Neurosci. 2019;13:394. Epub 20190508. doi: 10.3389/fnins.2019.00394. PubMed PMID: 31133775; PubMed Central PMCID: PMC6517487.

References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.