Metabolic phenotyping in phenylketonuria reveals disease clustering independently of metabolic control.

Phenylketonuria (PKU, MIM #261600) is one of the most common inborn errors of metabolism (IEM) with an incidence of 1:10000 in the European population. PKU is caused by autosomal recessive mutations in phenylalanine hydroxylase (PAH) and manifests with elevation of phenylalanine (Phe) in plasma and urine. Untreated PKU manifests with intellectual disability including seizures, microcephaly and behavioral abnormalities. Early treatment and good compliance result in a normal intellectual outcome in many but not in all patients. This study examined plasma metabolites in patients with PKU (n = 27), hyperphenylalaninemia (HPA, n = 1) and healthy controls (n = 32) by LC- MS/MS. We hypothesized that PKU patients would exhibit a distinct "submetabolome" compared to that of healthy controls. We further hypothesized that the submetabolome of PKU patients with good metabolic control would resemble that of healthy controls. Results from this study show: (i) Distinct clustering of healthy controls and PKU patients based on polar metabolite profiling, (ii) Increased and decreased concentrations of metabolites within and afar from the Phe pathway in treated patients, and (iii) A specific PKU-submetabolome independently of metabolic control assessed by Phe in plasma. We examined the relationship between PKU metabolic control and extended metabolite profiles in plasma. The PKU submetabolome characterized in this study represents the combined effects of dietary adherence, adjustments in metabolic pathways to compensate for defective Phe processing, as well as metabolic derangements that could not be corrected with dietary management even in patients classified as having good metabolic control. New therapeutic targets may be uncovered to approximate the PKU submetabolome to that of healthy controls and prevent long-term organ damage.

Analysis of S-Adenosylmethionine and S-Adenosylhomocysteine: Method Optimisation and Profiling in Healthy Adults upon Short-Term Dietary Intervention.

S-adenosylmethionine (SAM) is essential for methyl transfer reactions. All SAM is produced de novo via the methionine cycle. The demethylation of SAM produces S-adenosylhomocysteine (SAH), an inhibitor of methyltransferases and the precursor of homocysteine (Hcy). The measurement of SAM and SAH in plasma has value in the diagnosis of inborn errors of metabolism (IEM) and in research to assess methyl group homeostasis. The determination of SAM and SAH is complicated by the instability of SAM under neutral and alkaline conditions and the naturally low concentration of both SAM and SAH in plasma (nM range). Herein, we describe an optimised LC-MS/MS method for the determination of SAM and SAH in plasma, urine, and cells. The method is based on isotopic dilution and employs 20 µL of plasma or urine, or 500,000 cells, and has an instrumental running time of 5 min. The reference ranges for plasma SAM and SAH in a cohort of 33 healthy individuals (age: 19-60 years old; mean ± 2 SD) were 120 ± 36 nM and 21.5 ± 6.5 nM, respectively, in accordance with independent studies and diagnostic determinations. The method detected abnormal concentrations of SAM and SAH in patients with inborn errors of methyl group metabolism. Plasma and urinary SAM and SAH concentrations were determined for the first time in a randomised controlled trial of 53 healthy adult omnivores (age: 18-60 years old), before and after a 4 week intervention with a vegan or meat-rich diet, and revealed preserved variations of both metabolites and the SAM/SAH index.

Metabolic Profiling in Human Fibroblasts Enables Subtype Clustering in Glycogen Storage Disease.

Glycogen storage disease subtypes I and III (GSD I and GSD III) are monogenic inherited disorders of metabolism that disrupt glycogen metabolism. Unavailability of glucose in GSD I and induction of gluconeogenesis in GSD III modify energy sources and possibly, mitochondrial function. Abnormal mitochondrial structure and function were described in mice with GSD Ia, yet significantly less research is available in human cells and ketotic forms of the disease. We hypothesized that impaired glycogen storage results in distinct metabolic phenotypes in the extra- and intracellular compartments that may contribute to pathogenesis. Herein, we examined mitochondrial organization in live cells by spinning-disk confocal microscopy and profiled extra- and intracellular metabolites by targeted LC-MS/MS in cultured fibroblasts from healthy controls and from patients with GSD Ia, GSD Ib, and GSD III. Results from live imaging revealed that mitochondrial content and network morphology of GSD cells are comparable to that of healthy controls. Likewise, healthy controls and GSD cells exhibited comparable basal oxygen consumption rates. Targeted metabolomics followed by principal component analysis (PCA) and hierarchical clustering (HC) uncovered metabolically distinct poises of healthy controls and GSD subtypes. Assessment of individual metabolites recapitulated dysfunctional energy production (glycolysis, Krebs cycle, succinate), reduced creatinine export in GSD Ia and GSD III, and reduced antioxidant defense of the cysteine and glutathione systems. Our study serves as proof-of-concept that extra- and intracellular metabolite profiles distinguish glycogen storage disease subtypes from healthy controls. We posit that metabolite profiles provide hints to disease mechanisms as well as to nutritional and pharmacological elements that may optimize current treatment strategies.