Quantification of Stable Isotope Traces Close to Natural Enrichment in Human Plasma Metabolites Using Gas Chromatography-Mass Spectrometry

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched plant material in the administered food product, and hence, an even weaker 13C enrichment in downstream plasma metabolites. Therefore, we developed an analytical workflow to determine weak 13C enrichments of diverse plasma metabolites with conventional gas chromatography-mass spectrometry (GC-MS). The limit of quantification was increased by optimizing (1) the metabolite extraction from plasma, (2) the GC-MS measurement, and (3) most importantly, the computational data processing. We applied our workflow to study the catabolic dynamics of 13C-enriched wheat bread in three human subjects. For that purpose, we collected time-resolved human plasma samples at 16 timepoints after the consumption of 13C-labeled bread and quantified 13C enrichment of 12 metabolites (glucose, lactate, alanine, glycine, serine, citrate, glutamate, glutamine, valine, isoleucine, tyrosine, and threonine). Based on isotopomer specific analysis, we were able to distinguish catabolic profiles of starch and protein hydrolysis. More generally, our study highlights that conventional GC-MS equipment is sufficient to detect isotope traces below 1% if an appropriate data processing is integrated

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases

LacaScore: a novel plasma sample quality control tool based on ascorbic acid and lactic acid levels

Introduction Metabolome analysis is complicated by the continuous dynamic changes of metabolites in vivo and ex vivo. One of the main challenges in metabolomics is the robustness and reproducibility of results, partially driven by pre-analytical variations. Objectives The objective of this study was to analyse the impact of pre-centrifugation time and temperature, and to determine a quality control marker in plasma samples. Methods Plasma metabolites were measured by gas chromatography-mass spectrometry (GC–MS) and analysed with the MetaboliteDetector software. The metabolites, which were the most labile to pre-analytical variations, were further measured by enzymatic assays. A score was calculated for their use as quality control markers. Results The pre-centrifugation temperature was shown to be critical in the stability of plasma samples and had a significant impact on metabolite concentration profiles. In contrast, pre-centrifugation delay had only a minor impact. Based on the results of this study, whole blood should be kept on wet ice and centrifuged within maximum 3 h as a prerequisite for preparing EDTA plasma samples fit for the purpose of metabolome analysis. Conclusions We have established a novel blood sample quality control marker, the LacaScore, based on the ascorbic acid to lactic acid ratio in plasma, which can be used as an indicator of the blood pre-centrifugation conditions, and hence the suitability of the sample for metabolome analyses. This method can be applied in research institutes and biobanks, enabling assessment of the quality of their plasma sample collections

Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease

The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized. Methods: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation and computing receiver operating characteristic (ROC) curves. Results: In the metabolomics data, the baseline comparison between cases and controls as well as the followup assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures. Conclusion: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation

Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease

The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized. Methods: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation and computing receiver operating characteristic (ROC) curves. Results: In the metabolomics data, the baseline comparison between cases and controls as well as the followup assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures. Conclusion: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease

An archaeal compound as a driver of Parkinson’s disease pathogenesis

Patients with Parkinson’s disease (PD) exhibit differences in their gut microbiomes compared to healthy individuals. Although differences have most commonly been described in the abundances of bacterial taxa, changes to viral and archaeal populations have also been observed. Mechanistic links between gut microbes and PD pathogenesis remain elusive but could involve molecules that promote α-synuclein aggregation. Here, we show that 2-hydroxypyridine (2-HP) represents a key molecule for the pathogenesis of PD. We observe significantly elevated 2-HP levels in faecal samples from patients with PD or its prodrome, idiopathic REM sleep behaviour disorder (iRBD), compared to healthy controls. 2-HP is correlated with the archaeal species Methanobrevibacter smithii and with genes involved in methane metabolism, and it is detectable in isolate cultures of M. smithii. We demonstrate that 2-HP is selectively toxic to transgenic α-synuclein overexpressing yeast and increases α-synuclein aggregation in a yeast model as well as in human induced pluripotent stem cell derived enteric neurons. It also exacerbates PD-related motor symptoms, α-synuclein aggregation, and striatal degeneration when injected intrastriatally in transgenic mice overexpressing human α-synuclein. Our results highlight the effect of an archaeal molecule in relation to the gut-brain axis, which is critical for the diagnosis, prognosis, and treatment of PD.

Development of biospecimen quality control tools and disease diagnostic markers by metabolic profiling

In metabolomics-based biomarker studies, the monitoring of pre-analytical variations is crucial and requires quality control tools to enable proper sample quality evaluation. In this dissertation work, biospecimen research and machine learning algorithms are applied (1) to develop sample quality assessment tools and (2) to develop disease-specific diagnostic models. In this regard, a novel plasma sample quality assessment tool, the LacaScore, is presented. The LacaScore plasma quality assessment is based on the plasma levels of ascorbic acid and lactic acid. The biggest challenge in metabolomics analyses is that the sample quality is often not known. The presented tool enhances the knowledge and importance of the monitoring of pre-analytical variations, such as pre-centrifugation time and temperature, prior to sample analysis in the emerging field of metabolomics. Based on the LacaScore, decisions on the suitability/fit-for-purpose of a given sample or sample cohort can be made. In this dissertation work, the knowledge on sample quality was applied in a biomarker discovery study based on cerebrospinal fluid (CSF) from early-stage Parkinson’s disease (PD) patients. To date, no markers for the diagnosis of Parkinson’s disease are available. In this work, a non-targeted GC-MS approach is presented and shows significant changes in the metabolic profile in CSF from early-stage PD patients compared to matched healthy control subjects. Based on these findings, a biomarker signature for the prediction of earlystage PD has been developed by the application of sophisticated machine learning algorithms. This disease-specific signature is composed of metabolites involved in inflammation, glycosylation/glycation and oxidative stress response. In summary, this dissertation illustrates the importance of sample quality monitoring in biomarker studies that are often limited by small amounts of human body fluids. The monitoring of sample quality enhances the robustness and reproducibility of biomarker discovery studies. In addition, proper data analysis and powerful machine learning algorithms enable the generation of potential disease diagnosis biomarker signatures

Method Validation for Preparing Serum and Plasma Samples from Human Blood for Downstream Proteomic, Metabolomic, and Circulating Nucleic Acid-Based Applications

Background: Formal method validation for biospecimen processing in the context of accreditation in laboratories and biobanks is lacking. Serum and plasma processing protocols were validated for fitness-for-purpose in terms of key downstream endpoints, and this article demonstrates methodology for biospecimen processing method validation. Methods: Serum and plasma preparation from human blood was optimized for centrifugation conditions with respect to microparticle counts. Optimal protocols were validated for methodology and reproducibility in terms of acceptance criteria based on microparticle counts, DNA and hemoglobin concentration, and metabolomic and proteomic profiles. These parameters were also used to evaluate robustness for centrifugation temperature (4°C versus room temperature [RT]), deceleration (low, medium, high) and blood stability (after a 2-hour delay). Results: Optimal protocols were 10-min centrifugation for serum and 20-min for plasma at 2000 g, medium brake, RT. Methodology and reproducibility acceptance criteria were met for both protocols except for reproducibility of plasma metabolomics. Overall, neither protocol was robust for centrifugation at 4°C versus RT. RT gave higher microparticles and free DNA yields in serum, and fewer microparticles with less hemolysis in plasma. Overall, both protocols were robust for fast, medium, and low deceleration, with a medium brake considered optimal. Pre-centrifugation stability after a 2-hour delay was seen at both temperatures for hemoglobin concentration and proteomics, but not for microparticle counts. Conclusions: We validated serum and plasma collection methods suitable for downstream protein, metabolite, or free nucleic acid-based applications. Temperature and pre-centrifugation delay can influence analytic results, and laboratories and biobanks should systematically record these conditions in the scope of accreditation

Method validation for preparing urine samples for downstream proteomic and metabolomic applications.

BACKGROUND: Formal validation of methods for biospecimen processing in the context of accreditation in laboratories and biobanks is lacking. A protocol for processing of a biospecimen (urine) was validated for fitness-for-purpose in terms of key downstream endpoints. METHODS: Urine processing was optimized for centrifugation conditions on the basis of microparticle counts at room temperature (RT) and at 4 degrees C. The optimal protocol was validated for performance (microparticle counts), and for reproducibility and robustness for centrifugation temperature (4 degrees C vs. RT) and brake speed (soft, medium, hard). Acceptance criteria were based on microparticle counts, cystatin C and creatinine concentrations, and the metabolomic profile. RESULTS: The optimal protocol was a 20-min, 12,000 g centrifugation at 4 degrees C, and was validated for urine collection in terms of microparticle counts. All reproducibility acceptance criteria were met. The protocol was robust for centrifugation at 4 degrees C versus RT for all parameters. The protocol was considered robust overall in terms of brake speeds, although a hard brake gave significantly fewer microparticles than a soft brake. CONCLUSIONS: We validated a urine processing method suitable for downstream proteomic and metabolomic applications. Temperature and brake speed can influence analytic results, with 4 degrees C and high brake speed considered optimal. Laboratories and biobanks should ensure these conditions are systematically recorded in the scope of accreditation