QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data

Analysis of alkyl-anthraquinone derivatives from hydrogen peroxide industrial process, using LC × LC-HRMS with shifting gradients

The characterization of industrial working solutions containing numerous structurally related compounds and isomers requires the use of two-dimensional liquid chromatography coupled with high-resolution mass spectrometry (LC × LC-HRMS). The separation of alkyl-anthraquinone derivatives produced during hydrogen peroxide production was achieved by coupling a biphenyl and a C18 in the first and second dimensions, combined with the use of continuous shifting gradients in the second dimension (2D). The use of shifting gradients offers a significant advantage over regular gradients, with a 20% increase in occupancy and better separation of isomers eluted within the same modulation. Additionally, MS spectra were improved by reducing ion suppression in the source. The analysis of the industrial solutions produced a list of 226 peaks with 75 different molar masses, thereby confirming the presence of many isomers. The excellent repeatability of the retention times (within ±0.28 s in the second dimension) enabled the method to be used to study the differences between production batches, which were divided into two groups according to their measured hydrogen peroxide yield. Using multivariate statistical tools on the area of each peak, a total of 191 variables, including 11 clusters of insufficiently resolved isomers, were studied by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). A simple Wilcoxon test performed on the most pertinent variables was able to confirm that 28 peaks could be used to monitor the industrial process. This work highlighted the efficacy of combining a very powerful separation technique with the implementation of two simple statistical tests to rapidly identity a set of discriminant markers in a highly complex industrial mixture.

Metabolomics-Driven Insights into Biomarkers for Poor Ovarian Response: A Narrative Review

Poor ovarian response (POR) remains a significant challenge in the field of assisted reproductive technology (ART), as the quantity and quality of oocytes retrieved directly influence embryo implantation, clinical pregnancy, and live birth rates. Metabolomics has become a valuable tool for elucidating the molecular mechanisms underlying diminished ovarian reserve (DOR) and POR. This review aims to synthesize findings from metabolomic studies examining metabolite expression patterns in serum and follicular fluid samples from women with POR. A literature search was performed using the Medline/PubMed and Scopus databases, employing keywords related to metabolomics and POR. In total, nine studies met the inclusion criteria for this review. These studies identified several metabolites with differential expression in serum and follicular fluid samples between women with normal ovarian response and those with POR. Although the metabolomic profiles varied significantly among studies, consistent alterations in prostaglandin related metabolites were observed in two of the nine studies reviewed. These findings suggest that, pending further validation, these metabolites may serve as potential biomarkers for ovarian response. Metabolomics has significantly advanced our understanding of the mechanisms underlying ovarian function and holds promise for identifying effective biomarkers that could improve the prediction and management of POR.

Chromatography-Based Metabolomics as a Tool in Bioorganic Research of Honey

This review presents the latest research on chromatography-based metabolomics for bioorganic research of honey, considering targeted, suspect, and untargeted metabolomics involving metabolite profiling and metabolite fingerprinting. These approaches give an insight into the metabolic diversity of different honey varieties and reveal different classes of organic compounds in the metabolic profiles, among which, key metabolites such as biomarkers and bioactive compounds can be highlighted. Chromatography-based metabolomics strategies have significantly impacted different aspects of bioorganic research, including primary areas such as botanical origins, honey origin traceability, entomological origins, and honey maturity. Through the use of different tools for complex data analysis, these strategies contribute to the detection, assessment, and/or correlation of different honey parameters and attributes. Bioorganic research is mainly focused on phytochemicals and their transformation, but the chemical changes that can occur during the different stages of honey formation remain a challenge. Furthermore, the latest user- and environmentally friendly sample preparation methods and technologies as well as future perspectives and the role of chromatography-based metabolomic strategies in honey characterization are discussed. The objective of this review is to summarize the latest metabolomics strategies contributing to bioorganic research onf honey, with emphasis on the (i) metabolite analysis by gas and liquid chromatography techniques; (ii) key metabolites in the obtained metabolic profiles; (iii) formation and accumulation of biogenic volatile and non-volatile markers; (iv) sample preparation procedures; (v) data analysis, including software and databases; and (vi) conclusions and future perspectives. For the present review, the literature search strategy was based on the PRISMA guidelines and focused on studies published between 2019 and 2024. This review outlines the importance of metabolomics strategies for potential innovations in characterizing honey and unlocking its full bioorganic potential.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Profiling metabolome of mouse embryonic cerebrospinal fluid following maternal immune activation

The embryonic cerebrospinal fluid (eCSF) plays an essential role in the development of the central nervous system (CNS), influencing processes from neurogenesis to lifelong cognitive functions. An important process affecting eCSF composition is inflammation. Inflammation during development can be studied using the maternal immune activation (MIA) mouse model, which displays altered cytokine eCSF composition and mimics neurodevelopmental disorders including autism spectrum disorder (ASD). The limited nature of eCSF as a biosample restricts its research and has hindered our understanding of the eCSF's role in brain pathologies. Specifically, investigation of the small molecule composition of the eCSF is lacking, leaving this aspect of eCSF composition under-studied. We report here the eCSF metabolome as a resource for investigating developmental neuropathologies from a metabolic perspective. Our reference metabolome includes comprehensive MS1 and MS2 datasets and evaluates two mouse strains (CD-1 and C57Bl/6) and two developmental time points (E12.5 and E14.5). We illustrate the reference metabolome's utility by using untargeted metabolomics to identify eCSF-specific compositional changes following MIA. We uncover MIA-relevant metabolic pathways as differentially abundant in eCSF and validate changes in glucocorticoid and kynurenine pathways through targeted metabolomics. Our resource can guide future studies into the causes of MIA neuropathology and the impact of eCSF composition on brain development.

Proteo-metabolomics and patient tumor slice experiments point to amino acid centrality for rewired mitochondria in fibrolamellar carcinoma

Fibrolamellar carcinoma (FLC) is a rare, lethal, early-onset liver cancer with a critical need for new therapeutics. The primary driver in FLC is the fusion oncoprotein, DNAJ-PKAc, which remains challenging to target therapeutically. It is critical, therefore, to expand understanding of the FLC molecular landscape to identify druggable pathways/targets. Here, we perform the most comprehensive integrative proteo-metabolomic analysis of FLC. We also conduct nutrient manipulation, respirometry analyses, as well as key loss-of-function assays in FLC tumor tissue slices from patients. We propose a model of cellular energetics in FLC pointing to proline anabolism being mediated by ornithine aminotransferase hyperactivity and ornithine transcarbamylase hypoactivity with serine and glutamine catabolism fueling the process. We highlight FLC's potential dependency on voltage-dependent anion channel (VDAC), a mitochondrial gatekeeper for anions including pyruvate. The metabolic rewiring in FLC that we propose in our model, with an emphasis on mitochondria, can be exploited for therapeutic vulnerabilities.

Probing erythrocytes as sensitive and reliable sensors of metabolic disturbances in the crosstalk between childhood obesity and insulin resistance: findings from an observational study, in vivo challenge tests, and ex vivo incubation assays

BACKGROUND

Although insulin resistance (IR) is among the most frequent and pathogenically relevant complications accompanying childhood obesity, its role in modulating and exacerbating obesity pathophysiology has not yet been completely clarified.

METHODS

To get deeper insights into the interplay between childhood obesity and IR, we leveraged a comprehensive experimental design based on a combination of observational data, in vivo challenge tests (i.e., oral glucose tolerance test), and ex vivo assays (i.e., incubation of erythrocytes with insulin) using a population comprising children with obesity and IR, children with obesity without IR, and healthy controls, from whom plasma and erythrocyte samples were collected for subsequent metabolomics analysis.

RESULTS

Children with concomitant IR showed exacerbated metabolic disturbances in the crosstalk between endogenous, microbial, and environmental determinants, including failures in energy homeostasis, amino acid metabolism, oxidative stress, synthesis of steroid hormones and bile acids, membrane lipid composition, as well as differences in exposome-related metabolites associated with diet, exposure to endocrine disruptors, and gut microbiota. Furthermore, challenge tests and ex vivo assays revealed a deleterious impact of IR on individuals' metabolic flexibility, as reflected in blunted capacity to regulate homeostasis in response to hyperinsulinemia, at both systemic and erythroid levels.

CONCLUSIONS

Thus, we have demonstrated for the first time that metabolite alterations in erythrocytes represent reliable and sensitive biomarkers to disentangle the metabolic complexity of IR and childhood obesity. This study emphasizes the crucial need of addressing inter-individual variability factors, such as the presence of comorbidities, to obtain a more accurate understanding of obesity-related molecular mechanisms.

Identifying metabotypes of insulin resistance severity in children with metabolic syndrome

BACKGROUND

Insulin resistance is a frequent precursor of typical obesity and metabolic syndrome complications. However, accurate diagnosis remains elusive because of its pathophysiological complexity and heterogeneity. Herein, we have explored the utility of insulin secretion dynamics in response to an oral glucose tolerance test as a surrogate marker to identify distinct metabotypes of disease severity.

METHODS

The study population consisted of children with obesity and insulin resistance, stratified according to the post-challenge insulin peak timing (i.e., early, middle, and late peak), from whom fasting and postprandial plasma and erythrocytes were collected for metabolomics analysis.

RESULTS

Children with late insulin peak manifested worse cardiometabolic health (i.e., higher blood pressure, glycemia, and HOMA-IR scores) than early responders. These subjects also showed more pronounced changes in metabolites mirroring failures in energy homeostasis, oxidative stress, metabolism of cholesterol and phospholipids, and adherence to unhealthy dietary habits. Furthermore, delayed insulin peak was associated with impaired metabolic flexibility, as reflected in compromised capacity to regulate mitochondrial energy pathways and the antioxidant defense in response to glucose overload.

CONCLUSIONS

Altogether, these findings suggest that insulin resistance could encompass several phenotypic subtypes characterized by graded disturbances in distinctive metabolic derangements occurring in childhood obesity, which serve as severity predictive markers.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Parental obesity predisposes to exacerbated metabolic and inflammatory disturbances in childhood obesity within the framework of an altered profile of trace elements

BACKGROUND

Family history of obesity is known to increase the odds of developing childhood obesity in the offspring, but its influence in underlying molecular complications remains unexplored.

SUBJECTS/METHODS

Here, we investigated a population-based cohort comprising children with obesity, with and without parental obesity (PO+, N = 20; PO-, N = 29), and lean healthy children as controls (N = 30), from whom plasma and erythrocyte samples were collected to characterize their multi-elemental profile, inflammatory status, as well as carbohydrate and lipid metabolisms.

RESULTS

We found parental obesity to be associated with unhealthier outcomes in children, as reflected in increased blood insulin levels and reduced insulin sensitivity, unfavorable lipid profile, and pro-inflammatory milieu. This was accompanied by moderate alterations in the content of trace elements, including increased copper-to-zinc ratios and iron deficiency in circulation, as well as metal accumulation within erythrocytes.

CONCLUSIONS

Therefore, we hypothesize that family history of obesity could be an important risk factor in modulating the characteristic multi-elemental alterations behind childhood obesity, which in turn could predispose to boost related comorbidities and metabolic complications.