Long-Term Metabolomics Reference Material

Untargeted Metabolomics for Inborn Errors of Metabolism: Development and Evaluation of a Sustainable Reference Material for Correcting Inter-Batch Variability

BACKGROUND

Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

METHODS

The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

RESULTS

The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

CONCLUSIONS

Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

Role of UDP-Glycosyltransferase (ugt) Genes in Detoxification and Glycosylation of 1-Hydroxyphenazine (1-HP) in Caenorhabditis elegans

Caenorhabditis elegans is a useful model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins, but the mechanisms of phase II detoxification are understudied. 1-Hydroxyphenazine (1-HP), a toxin produced by the bacterium Pseudomonas aeruginosa, kills C. elegans. We previously showed that C. elegans detoxifies 1-HP by adding one, two, or three glucose molecules in N2 worms. Our current study evaluates the roles that some UDP-glycosyltransferase (ugt) genes play in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP than reference strains N2 or PD1074. Our data also show that ugt-23 knockout mutants produce reduced amounts of the trisaccharide sugars, while the ugt-49 knockout mutants produce reduced amounts of all 1-HP derivatives except for the glucopyranosyl product compared to the reference strains. We characterized the structure of the trisaccharide sugar phenazines made by C. elegans and showed that one of the sugar modifications contains an N-acetylglucosamine (GlcNAc) in place of glucose. This implies broad specificity regarding UGT function and the role of genes other than ogt-1 in adding GlcNAc, at least in small-molecule detoxification.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Omic-Scale High-Throughput Quantitative LC-MS/MS Approach for Circulatory Lipid Phenotyping in Clinical Research

Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera (n = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Enhancing cell resistance for production of mixed microbiological reference materials with Salmonella and coliforms by freeze-drying

The reference material (RM) is a technical requirement for the quality assurance of analytical results and proficiency tests or interlaboratory comparisons. Microbiological RMs are most available in the dehydrated form, mainly by freeze-drying, and maintaining bacterial survival after preparation is a challenge. Thus, obtaining the most resistant cells is essential. Considering that bacteria present cross-response to dehydration after being submitted to an array of stress conditions, this study aimed to evaluate the influence of growth conditions on enterobacteria for the production of mixed microbiological RMs by freeze-drying in skim milk powder. Salmonella enterica serovar Enteritidis, Cronobacter sakazakii, Escherichia coli, and Citrobacter freundii were grown in a minimal medium with 0.5 M NaCl and 0 to 5.0 mM of manganese sulfate (MnSO4) until stationary phase. Salmonella Enteritidis presented an increased resistance to dehydration in the presence of Mn, while C. sakazakii was the most resistant to freeze-drying and further storage for 90 days. Mixed microbiological RMs were produced by freeze-drying and containing Salmonella Enteritidis and coliforms in skim milk powder with 100 mM of trehalose and the Salmonella survival rate was 91.2 to 93.6%. The mixed RM was stable after 30 days at -20 °C, and Salmonella and coliforms were detected by different methods being, the Rambach Agar the best for the bacterial differentiation. The results showed that the culture conditions applied in this study resulted in bacterial cells being more resistant to dehydration, freeze-drying, and stabilization for the production of mixed microbiological RMs more stable and homogeneous.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Yeast-based reference materials for quantitative metabolomics

We introduce a new concept of yeast-derived biological matrix reference material for metabolomics research relying on in vivo synthesis of a defined biomass, standardized extraction followed by absolute quantification with isotope dilution. The yeast Pichia pastoris was grown using full control- and online monitoring fed-batch fermentations followed by fast cold methanol quenching and boiling ethanol extraction. Dried extracts served for the quantification campaign. A metabolite panel of the evolutionarily conserved primary metabolome (amino acids, nucleotides, organic acids, and metabolites of the central carbon metabolism) was absolutely quantified by isotope dilution utilizing uniformly labeled ¹³C-yeast-based internal standards. The study involved two independent laboratories employing complementary mass spectrometry platforms, namely hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) and gas chromatography-tandem mass spectrometry (GC–MS/MS). Homogeneity, stability tests (on a panel of >70 metabolites over a period of 6 months), and excellent biological repeatability of independent fermentations over a period of 2 years showed the feasibility of producing biological reference materials on demand. The obtained control ranges proved to be fit for purpose as they were either superior or comparable to the established reference materials in the field.