Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine.

OBJECTIVES: The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used. METHODS: Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data. RESULTS: The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs. CONCLUSIONS: The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Specific gut bacterial and fungal microbiota pattern in the first half of pregnancy is linked to the development of gestational diabetes mellitus in the cohort including obese women.

Aims: Gestation is linked to changes in gut microbiota composition and function. Since gestational diabetes mellitus (GDM) can develop at any time of the pregnancy, we stratified the women into four groups according to the time and test used for the diagnosis. We focused on the gut microbiota pattern in early pregnancy to detect changes which could be linked to later GDM development. Methods: We collected stool samples from 104 pregnant women including obese individuals (first trimester body mass index median was 26.73). We divided the women into four groups according to routine screening of fasting plasma glucose (FPG) levels and oral glucose tolerance test (oGTT) in the first and third trimesters, respectively. We processed the stool samples for bacterial 16S rRNA and fungal ITS1 genes sequencing by Illumina MiSeq approach and correlated the gut microbiota composition with plasma short-chain fatty acid levels (SCFA). Results: We found that gut bacterial microbiota in the first trimester significantly differs among groups with different GDM onset based on unweighted UniFrac distances (p=0.003). Normoglycemic women had gut microbiota associated with higher abundance of family Prevotellaceae, and order Fusobacteriales, and genus Sutterella. Women diagnosed later during pregnancy either by FGP levels or by oGTT had higher abundances of genera Enterococcus, or Erysipelotrichaceae UCG-003, respectively. We observed significant enrichment of fungal genus Mucor in healthy pregnant women whereas Candida was more abundant in the group of pregnant women with impaired oGTT. Using correlation analysis, we found that Holdemanella negatively correlated with Blautia and Candida abundances and that Escherichia/Shigella abundance positively correlated and Subdoligranulum negatively correlated with plasma lipid levels. Coprococcus, Akkermansia, Methanobrevibacter, Phascolarctobacterium and Alistipes positively correlated with acetate, valerate, 2-hydroxybutyrate and 2-methylbutyrate levels, respectively, in women with GDM. Conclusions: We conclude that there are significant differences in the gut microbiota composition between pregnant women with and without GDM already at the early stage of pregnancy in our cohort that included also overweight and obese individuals. Specific microbial pattern associated with GDM development during early pregnancy and its correlation to plasma lipid or SCFA levels could help to identify women in higher risk of GDM development.

Combined Targeted and Untargeted Profiling of HeLa Cells Deficient in Purine De Novo Synthesis.

Three genetically determined enzyme defects of purine de novo synthesis (PDNS) have been identified so far in humans: adenylosuccinate lyase (ADSL) deficiency, 5-amino-4-imidazole carboxamide-ribosiduria (AICA-ribosiduria), and deficiency in bifunctional enzyme phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccinocarboxamide synthase (PAICS). Clinical signs of these defects are mainly neurological, such as seizures, psychomotor retardation, epilepsy, autistic features, etc. This work aims to describe the metabolic changes of CRISPR-Cas9 genome-edited HeLa cells deficient in the individual steps of PDNS to better understand known and potential defects of the pathway in humans. High-performance liquid chromatography coupled with mass spectrometry was used for both targeted and untargeted metabolomic analyses. The statistically significant features from the untargeted study were identified by fragmentation analysis. Data from the targeted analysis were processed in Cytoscape software to visualize the most affected metabolic pathways. Statistical significance of PDNS intermediates preceding deficient enzymes was the highest (p-values 10 × 10-7-10 × 10-15) in comparison with the metabolites from other pathways (p-values of up to 10 × 10-7). Disturbed PDNS resulted in an altered pool of adenine and guanine nucleotides. However, the adenylate energy charge was not different from controls. Different profiles of acylcarnitines observed among deficient cell lines might be associated with a specific enzyme deficiency rather than global changes related to the PDNS pathway. Changes detected in one-carbon metabolism might reduce the methylation activity of the deficient cells, thus affecting the modification state of DNA, RNA, and proteins.