Studying the Parkinson's disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study

in Analytical and Bioanalytical Chemistry (2022)

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease, with an increasing incidence in recent years due to the ageing population. Genetic mutations alone only explain <10% of PD ... [more ▼]

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease, with an increasing incidence in recent years due to the ageing population. Genetic mutations alone only explain <10% of PD cases, while environmental factors, including small molecules, may play a significant role in PD. In the present work, 22 plasma (11 PD, 11 control) and 19 feces samples (10 PD, 9 control) were analyzed by non-target high resolution mass spectrometry (NT-HRMS) coupled to two liquid chromatography (LC) methods (reversed phase (RP) and hydrophilic interaction liquid chromatography (HILIC)). A cheminformatics workflow was optimized using open software (MS-DIAL and patRoon) and open databases (all public MSP-formatted spectral libraries for MS-DIAL, PubChemLite for Exposomics and the LITMINEDNEURO list for patRoon). Furthermore, five disease-specific databases and three suspect lists (on PD and related disorders) were developed, using PubChem functionality to identifying relevant unknown chemicals. The results showed that non-target screening with the larger databases generally provided better results compared with smaller suspect lists. However, two suspect screening approaches with patRoon were also good options to study specific chemicals in PD. The combination of chromatographic methods (RP and HILIC) as well as two ionization modes (positive and negative) enhanced the coverage of chemicals in the biological samples. While most metabolomics studies in PD have focused on blood and cerebrospinal fluid, we found a higher number of relevant features in feces, such as alanine betaine or nicotinamide, which can be directly metabolized by gut microbiota. This highlights the potential role of gut dysbiosis in PD development. [less ▲]

Supporting non-target identification by adding hydrogen deuterium exchange MS/MS capabilities to MetFrag

in Analytical and bioanalytical chemistry (2019), 411(19), 4683-4700

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly popular for the non-targeted exploration of complex samples, where tandem mass spectrometry (MS/MS) is used ... [more ▼]

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is increasingly popular for the non-targeted exploration of complex samples, where tandem mass spectrometry (MS/MS) is used to characterize the structure of unknown compounds. However, mass spectra do not always contain sufficient information to unequivocally identify the correct structure. This study investigated how much additional information can be gained using hydrogen deuterium exchange (HDX) experiments. The exchange of “easily exchangeable” hydrogen atoms (connected to heteroatoms), with predominantly [M+D]+ ions in positive mode and [M-D]− in negative mode was observed. To enable high-throughput processing, new scoring terms were incorporated into the in silico fragmenter MetFrag. These were initially developed on small datasets and then tested on 762 compounds of environmental interest. Pairs of spectra (normal and deuterated) were found for 593 of these substances (506 positive mode, 155 negative mode spectra). The new scoring terms resulted in 29 additional correct identifications (78 vs 49) for positive mode and an increase in top 10 rankings from 80 to 106 in negative mode. Compounds with dual functionality (polar head group, long apolar tail) exhibited dramatic retention time (RT) shifts of up to several minutes, compared with an average 0.04 min RT shift. For a smaller dataset of 80 metabolites, top 10 rankings improved from 13 to 24 (positive mode, 57 spectra) and from 14 to 31 (negative mode, 63 spectra) when including HDX information. The results of standard measurements were confirmed using targets and tentatively identified surfactant species in an environmental sample collected from the river Danube near Novi Sad (Serbia). The changes to MetFrag have been integrated into the command line version available at http://c-ruttkies.github.io/MetFrag and all resulting spectra and compounds are available in online resources and in the Electronic Supplementary Material (ESM). [less ▲]

Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS.

in Analytical and bioanalytical chemistry (2018), 410(7), 1931-1941

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among ... [more ▼]

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification. [less ▲]

Monitoring metabolites consumption and secretion in cultured cells using ultra-performance liquid chromatography quadrupole-time of flight mass spectrometry (UPLC-Q-ToF-MS).

in Analytical and Bioanalytical Chemistry (2012), 402(3), 1183-98

Here we present an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) method for extracellular measurements of known and unexpected metabolites in parallel. The method was developed by ... [more ▼]

Here we present an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) method for extracellular measurements of known and unexpected metabolites in parallel. The method was developed by testing 86 metabolites, including amino acids, organic acids, sugars, purines, pyrimidines, vitamins, and nucleosides, that can be resolved by combining chromatographic and m/z dimensions. Subsequently, a targeted quantitative method was developed for 80 metabolites. The presented method combines a UPLC approach using hydrophilic interaction liquid chromatography (HILIC) and MS detection achieved by a hybrid quadrupole-time of flight (Q-ToF) mass spectrometer. The optimal setup was achieved by evaluating reproducibility and repeatability of the analytical platforms using pooled quality control samples to minimize the drift in instrumental performance over time. Then, the method was validated by analyzing extracellular metabolites from acute lymphoblastic leukemia cell lines (MOLT-4 and CCRF-CEM) treated with direct (A-769662) and indirect (AICAR) AMP activated kinase (AMPK) activators, monitoring uptake and secretion of the targeted compound over time. This analysis pointed towards a perturbed purine and pyrimidine catabolism upon AICAR treatment. Our data suggest that the method presented can be used for qualitative and quantitative analysis of extracellular metabolites and it is suitable for routine applications such as in vitro drug screening. [less ▲]