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The identification of disease biomarkers plays a crucial role in developing diagnostic strategies for inborn errors of metabolism and understanding their pathophysiology. A primary metabolite that accumulates in the inborn error phenylketonuria is phenylalanine, however its levels do not always directly correlate with clinical outcomes. Here we combine infrared ion spectroscopy and NMR spectroscopy to identify the Phe-glucose Amadori rearrangement product as a biomarker for phenylketonuria. Additionally, we find analogous amino acid-glucose metabolites formed in the body fluids of patients accumulating methionine, lysine, proline and citrulline. Amadori rearrangement products are well-known intermediates in the formation of advanced glycation end-products and have been associated with the pathophysiology of diabetes mellitus and ageing, but are now shown to also form under conditions of aminoacidemia. They represent a general class of metabolites for inborn errors of amino acid metabolism that show potential as biomarkers and may provide further insight in disease pathophysiology., Rianne van Outersterp et al. combine mass spectrometry, NMR, and infrared ion spectroscopy to identify amino acid-hexose conjugates in the blood plasma from patients with metabolic disorders such as phenylketonuria (PKU). These conjugates, or Amadori rearrangement products, are generally not detectable in blood samples from unaffected individuals, and may therefore represent disease biomarkers.

Timely diagnosis is essential for patients with neurometabolic disorders to enable targeted treatment. Next‐Generation Metabolic Screening (NGMS) allows for simultaneous screening of multiple diseases and yields a holistic view of disturbed metabolic pathways. We applied this technique to define a cerebrospinal fluid (CSF) reference metabolome and validated our approach with patients with known neurometabolic disorders. Samples were measured using ultra‐high‐performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry followed by (un)targeted analysis. For the reference metabolome, CSF samples from patients with normal general chemistry results and no neurometabolic diagnosis were selected and grouped based on sex and age (0‐2/2‐5/5‐10/10‐15 years). We checked the levels of known biomarkers in CSF from seven patients with five different neurometabolic disorders to confirm the suitability of our method for diagnosis. Untargeted analysis of 87 control CSF samples yielded 8036 features for semiquantitative analysis. No sex differences were found, but 1782 features (22%) were different between age groups (q

The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed “next-generation metabolic screening” (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the “various forms of chromatography mass spectrometry (XCMS)” software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term “open the metabolome” analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs. Electronic supplementary material The online version of this article (10.1007/s10545-017-0131-6) contains supplementary material, which is available to authorized users.

A microcapillary column switching high-performance liquid chromatography (HPLC) system was developed for the separation of major histocompatibility complex (MHC) class I associated peptides. Combination of the column switching system with electrospray ionization mass spectrometry (ESIMS) enabled the detection and identification of the peptides at low-femtomole levels. Sample volumes of 30-50 microL were injected and concentrated onto a short, 100-micron-i.d. precolumn. The precolumn was coupled to a 100-micron-i.d. reversed-phase analytical HPLC column via a six-port valve. Peptides were separated on the analytical column using an ESI-compatible mobile phase at a flow rate of 0.5 microL/min. Peptides were eluted directly into the ESI source of either a magnetic sector MS or an ion trap MS. Peptides associated with human leukocyte antigen A*0201 molecules were determined in immunoaffinity-purified extracts from either measles virus infected cells or uninfected cells by microcapillary column switching HPLC-ESIMS. The approach toward detection of virus-specific peptides we used was based on the comparison of ion chromatograms obtained from the LC-MS analysis of extracts from virally infected cells and their uninfected counterparts. In this way, the molecular mass of peptides unique to virus infected cells was obtained. The utility of the system is demonstrated by the identification of a candidate epitope. Microcapillary column switching HPLC was used along with ESI ion trap tandem MS to identify the naturally processed viral peptide KLWESPQEI. This peptide was found to derive from the measles virus nonstructural protein C. The approach described here provides a versatile and sensitive method for the direct identification of viral peptides associated with MHC class I molecules.

The performance of mass spectrometric (MS) detection and UV detection in combination with reversed-phase liquid chromatography without and with the use of coupled column RPLC (LC-LC) has been compared for the trace analysis of phenylurea herbicides in environmental waters. The selected samples of this comparative study originated from an inter-laboratory study. For both detection modes, a 50 mm x 4.6 mm I.D. column and a 100 mm x 4.6 mm I.D. column packed with 3 microm C18 were used as the first (C-1) and second (C-2) column, respectively. Atmospheric pressure chemical ionization mass spectrometry was performed on a magnetic sector instrument. The LC-LC-MS analysis was carried out on-line by means of direct large volume (11.7 ml) injection (LVI). The performance of both on-line (LVI, 4 ml of sample) and off-line LC-LC-UV (244 nm) analysis was investigated. The latter procedure consisted of a solid-phase extraction (SPE) of 250 ml of water sample on a 500 mg C18 cartridge. The comparative study showed that LC-LC-MS is more selective then LC-LC-UV and, in most cases, more sensitive. The LVI-LC-LC-MS approach combines direct quantification and confirmation of most of the analytes down to a level of 0.01 microg/l in water samples in less then 30 min. As regards LC-LC-UV, the off-line method appeared to be a more viable approach in comparison with the on-line procedure. This method allows the screening of phenylurea's in various types of water samples down to a level of at least 0.05 microg/l. On-line analysis with LVI provided marginal sensitivity (limits of detection of about 0.1 microg/l) and selectivity was sometimes less in case of surface water samples. Both the on-line LVI-LC-LC-MS method and the LC-LC-UV method using off-line SPE were validated by analysing a series of real-life reference samples. These samples were part of an inter-laboratory test and contained residues of herbicides ranging from 0.02 to 0.8 microg/l. Beside good correlation between the methods the data agreed very well with the true values of the samples.