A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes

OBJECTIVES

Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA).

METHODS

Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring.

RESULTS

The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included.

CONCLUSIONS

Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Blood based metabolic markers of glioma from pre-diagnosis to surgery

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

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.

Characterization of Meat Metabolites and Lipids in Shanghai Local Pig Breeds Revealed by LC-MS-Based Method

The meat of local livestock breeds often has unique qualities and flavors. In this study, three Shanghai native pig breeds (MSZ, SWT, and SHB) exhibited better meat quality traits than globalized commercial pig breeds (DLY). Subsequently, metabolomic and lipidomic differences in the longissimus dorsi (L) and gluteus (T) muscles of the Shanghai native pig breeds and DLY pig breed were compared using liquid chromatography-mass spectrometry (LC-MS). The results demonstrated that the metabolites mainly consisted of (28.16%) lipids and lipid-like molecules, and (25.87%) organic acids and their derivatives were the two most dominant groups. Hundreds of differential expression metabolites were identified in every compared group, respectively. One-way ANOVA was applied to test the significance between multiple groups. Among the 20 most abundant differential metabolites, L-carnitine was significantly different in the muscles of the four pig breeds (p-value = 7.322 × 10-11). It was significantly higher in the L and T muscles of the two indigenous black pig breeds (MSZ and SWT) than in the DLY pigs (p-value < 0.001). Similarly, lipidomic analysis revealed the PA (18:0/18:2) was significantly more abundant in the muscle of these two black breeds than that in the DLY breed (p-value < 0.001). These specific metabolites and lipids might influence the meat quality and taste properties and lead to customer preferences. Therefore, this study provided insights into the characterization of meat metabolites and lipids in Shanghai native pig breeds.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Plasma metabolomic profile of adiposity and body composition in childhood: The Genetics of Glucose regulation in Gestation and Growth cohort

OBJECTIVE

This study identified metabolite modules associated with adiposity and body fat distribution in childhood using gold-standard measurements.

METHODS

We used cross-sectional data from 329 children at mid-childhood (age 5.3 ± 0.3 years; BMI 15.7 ± 1.5 kg/m2) from the Genetics of Glucose regulation in Gestation and Growth (Gen3G), a prospective pre-birth cohort. We quantified 1038 plasma metabolites and measured body composition using the gold-standard dual-energy x-ray absorptiometry (DXA), in addition to skinfold, waist circumference, and BMI. We applied weighted-correlation network analysis to identify a network of highly correlated metabolite modules. Spearman's partial correlations were applied to determine the associations of adiposity with metabolite modules and individual metabolites with false discovery rate (FDR) correction.

RESULTS

We identified a 'green' module of 120 metabolites, primarily comprised of lipids (mostly sphingomyelins and phosphatidylcholine), that showed positive correlations (all FDR p < 0.05) with DXA estimates of total and truncal fat (ρadjusted = 0.11-0.19), skinfold measures (ρadjusted = 0.09-0.26), and BMI and waist circumference (ρadjusted = 0.15 and 0.18, respectively). These correlations were similar when stratified by sex. Within this module, sphingomyelin (d18:2/14:0, d18:1/14:1)*, a sphingomyelin sub-specie that is an important component of cell membranes, showed the strongest associations.

CONCLUSIONS

A module of metabolites was associated with adiposity measures in childhood.

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors

BACKGROUND

Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis.

METHODS

We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model.

RESULTS

In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant.

CONCLUSION

Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Unveiling the Pharmacological Mechanisms of Davidiin's Anti-Diabetic Efficacy in Streptozotocin-Treated Rats: A Comprehensive Analysis of Serum Metabolome

Background

Polygonum capitatum Buch.-Ham. ex D. Don (P. capitatum), a traditional herb used in Miao medicine, is renowned for its heart-clearing properties. Davidiin, the primary bioactive component (approximately 1%), has been used to treat various conditions, including diabetes. Given its wide range of effects and the diverse biomolecular pathways involved in diabetes, there is a crucial need to study how davidiin interacts with these pathways to better understand its anti-diabetic properties.

Materials and Methods

Diabetic rats were induced using a high-fat diet and streptozotocin (STZ) administered intraperitoneally at 35 mg/kg. Out of these, 24 rats with blood glucose levels ≥ 11.1 mmol/L and fasting blood glucose levels ≥ 7.0 mmol/L were selected for three experimental groups. These groups were then treated with either metformin (gavage, 140 mg/kg) or davidiin (gavage, 90 mg/kg) for four weeks. After the treatment period, we measured body weight, blood glucose levels, and conducted untargeted metabolic profiling using UPLC-QTOF-MS.

Results

Davidiin has been shown to effectively treat diabetes by reducing blood glucose levels from 30.2 ± 2.6 mmol/L to 25.1 ± 2.4 mmol/L (P < 0.05). This effect appears stronger than that of metformin, which lowered glucose levels to 26.5 ± 2.6 mmol/L. The primary outcomes of serum metabolomics are significant changes in lipid and lipid-like molecular profiles. Firstly, davidiin may affect phosphatide metabolism by increasing levels of phosphatidylinositol and sphingosine-1-phosphate. Secondly, davidiin could influence cholesterol metabolism by reducing levels of glycocholic acid and glycochenodeoxycholic acid. Lastly, davidiin might impact steroid hormone metabolism by increasing hepoxilin B3 levels and decreasing prostaglandins.

Conclusion

Our study demonstrates that davidiin modulates various lipid-related metabolic pathways to exert its anti-diabetic effects. These findings offer the first detailed metabolic profile of davidiin's action mechanism, contributing valuable insights to the field of Traditional Chinese Medicine in the context of diabetes treatment.

Peptide set test: a peptide-centric strategy to infer differentially expressed proteins

MOTIVATION

The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.

RESULTS

We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.

AVAILABILITY AND IMPLEMENTATION

Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Nuclear magnetic resonance analysis of extra virgin olive oil: classification through secoiridoids

BACKGROUND

Extra virgin olive oil (EVOO), a natural product with a multidisciplinary role, has been and is continuing to be studied from several points of view. Among them, its chemical analysis is of major importance and several methods have been used. Nuclear magnetic resonance (NMR) spectroscopy has inherent advantages, among them monitoring the chemical constituents without the need for a separation technique and without, for instance, possible carry-over effects. Additionally, several magnetic resonance spectroscopic techniques can provide a novel powered insight into the nature and properties of a sample under study. Moreover, -omics procedure can reveal new information and can lead to the classification of populations under study. The main objective of the present work was the possible classification of the EVOO samples based on their aldehyde content using a proposed unreferenced 1 H-NMR spectroscopic quantification method combined with a metabolomic approach. Moreover, the study of the impact of such elevated aldehyde content on several spectra regions of importance in the proton NMR spectra led to the proposal of a possible new isomer indicator.

RESULTS

Univariate analysis of 12 EVOO samples showed that oleacein, oleocanthal, elenolic acid, hydroxytyrosol/hydroxytyrosol derivatives and tyrosol/tyrosol derivatives strongly differentiated two classes of EVOO: OEH (for high aldehyde EVOO content) and OE (for non-high aldehyde content). Moreover, we pointed out the 'impact' of such elevated secoiridoid and derivatives content, through their moieties' units, on a range of several resonances of the 1 H-NMR spectrum. The metabolomic approach demonstrated the classification of EVOO samples based on their secoiridoid and derivatives content. Multivariate analysis showed a strong influence on the discrimination of the EVOO classes based on the protons resonating at the aldehyde region of the 1 H-NMR spectrum; the aldehyde protons corresponding to 5S,4R-ligstrodial and 5S,4R-oleuropeindial, oleacein, oleocanthal, elenolic acid, p-HPEA-EA, 3,4-DHPEA-EA, 5S,4R- and 5S,4S-ligstrodial and the proton corresponding to a new compound were reported for the first time. This isomer compound, reported for the first time, could serve as a possible indicator for EVOO classification.

CONCLUSIONS

An unreferenced quantification method was proposed and EVOO samples were classified into two classes: OEH and OE, according to their aldehyde content, gaining thus probably higher nutrient and possible pharmacological value. Moreover, we point out the 'impact' of such elevated aldehyde content on several spectral regions of the 1 H spectrum. Finally, a new compound was detected in the OEH samples and is reported for the first time. This compound could possibly be an indicator. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Identification of organic chemical indicators for tracking pollution sources in groundwater by machine learning from GC-HRMS-based suspect and non-target screening data

In this study, the strong analytical power of gas chromatography coupled to a high resolution mass spectrometry (GC-HRMS) in suspect and non-target screening (SNTS) of organic micropollutants was combined with machine learning tools for proposing a novel and robust systematic environmental forensics workflow, focusing on groundwater contamination. Groundwater samples were collected from four different regions with diverse contamination histories (namely oil [OC], agricultural [AGR], industrial [IND], and landfill [LF]), and a total of 252 organic micropollutants were identified, including pharmaceuticals, personal care products, pesticides, polycyclic aromatic hydrocarbons, plasticizers, phenols, organophosphate flame retardants, transformation products, and others, with detection frequencies ranging from 3 % to 100 %. Amongst the SNTS identified compounds, a total of 51 chemical indicators (i.e., OC: 13, LF: 12, AGR: 19, IND: 7) which included level 1 and 2 SNTS identified chemicals were pinpointed across all sampling regions by integrating a bootstrapped feature selection method involving the bootfs algorithm and a partial least squares discriminant analysis (PLS-DA) model to determine potential prevalent contamination sources. The proposed workflow showed good predictive ability (Q2) of 0.897, and the suggested contamination sources were gasoline, diesel, and/or other light petroleum products for the OC region, anthropogenic activities for the LF region, agricultural and human activities for the AGR region, and industrial/human activities for the IND region. These results suggest that the proposed workflow can select a subset of the most diagnostic features in the chemical space that can best distinguish a specific contamination source class.

Strategy for Comprehensive Detection and Annotation of Gut Microbiota-Related Metabolites Based on Liquid Chromatography-High-Resolution Mass Spectrometry

Gut microbiota, widely populating the mammalian gastrointestinal tract, plays an important role in regulating diverse pathophysiological processes by producing bioactive molecules. Extensive detection of these molecules contributes to probing microbiota function but is limited by insufficient identification of existing analytical methods. In this study, a systematic strategy was proposed to detect and annotate gut microbiota-related metabolites on a large scale. A pentafluorophenyl (PFP) column-based liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method was first developed for high-coverage analysis of gut microbiota-related metabolites, which was verified to be stable and robust with a wide linearity range, high sensitivity, satisfactory recovery, and repeatability. Then, an informative database integrating 968 knowledge-based microbiota-related metabolites and 282 sample-sourced ones defined by germ-free (GF)/antibiotic-treated (ABX) models was constructed and subsequently used for targeted extraction and annotation in biological samples. Using pooled feces, plasma, and urine of mice for demonstration application, 672 microbiota-related metabolites were annotated, including 21% neglected by routine nontargeted peak detection. This strategy serves as a useful tool for the comprehensive capture of the intestinal flora metabolome, contributing to our deeper understanding of microbe-host interactions.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

A New Biomarker Profiling Strategy for Gut Microbiome Research: Valid Association of Metabolites to Metabolism of Microbiota Detected by Non-Targeted Metabolomics in Human Urine

The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.

Single-cell metabolic fingerprints discover a cluster of circulating tumor cells with distinct metastatic potential

Circulating tumor cells (CTCs) are recognized as direct seeds of metastasis. However, CTC count may not be the "best" indicator of metastatic risk because their heterogeneity is generally neglected. In this study, we develop a molecular typing system to predict colorectal cancer metastasis potential based on the metabolic fingerprints of single CTCs. After identification of the metabolites potentially related to metastasis using mass spectrometry-based untargeted metabolomics, setup of a home-built single-cell quantitative mass spectrometric platform for target metabolite analysis in individual CTCs and use of a machine learning method composed of non-negative matrix factorization and logistic regression, CTCs are divided into two subgroups, C1 and C2, based on a 4-metabolite fingerprint. Both in vitro and in vivo experiments demonstrate that CTC count in C2 subgroup is closely associated with metastasis incidence. This is an interesting report on the presence of a specific population of CTCs with distinct metastatic potential at the single-cell metabolite level.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Urine Metabolomics Exposes Anomalous Recovery after Maximal Exertion in Female ME/CFS Patients

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown etiology or effective treatments. Post-exertional malaise (PEM) is a key symptom that distinguishes ME/CFS patients. Investigating changes in the urine metabolome between ME/CFS patients and healthy subjects following exertion may help us understand PEM. The aim of this pilot study was to comprehensively characterize the urine metabolomes of eight female healthy sedentary control subjects and ten female ME/CFS patients in response to a maximal cardiopulmonary exercise test (CPET). Each subject provided urine samples at baseline and 24 h post-exercise. A total of 1403 metabolites were detected via LC-MS/MS by Metabolon® including amino acids, carbohydrates, lipids, nucleotides, cofactors and vitamins, xenobiotics, and unknown compounds. Using a linear mixed effects model, pathway enrichment analysis, topology analysis, and correlations between urine and plasma metabolite levels, significant differences were discovered between controls and ME/CFS patients in many lipid (steroids, acyl carnitines and acyl glycines) and amino acid subpathways (cysteine, methionine, SAM, and taurine; leucine, isoleucine, and valine; polyamine; tryptophan; and urea cycle, arginine and proline). Our most unanticipated discovery is the lack of changes in the urine metabolome of ME/CFS patients during recovery while significant changes are induced in controls after CPET, potentially demonstrating the lack of adaptation to a severe stress in ME/CFS patients.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion─the first description and evaluation of this strategy─and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two GC × GC data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Non-Targeted Metabolomics Approach Revealed Significant Changes in Metabolic Pathways in Patients with Chronic Traumatic Encephalopathy

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is frequently found in athletes and those who have experienced repetitive head traumas. CTE is associated with a variety of neuropathologies, which cause cognitive and behavioral impairments in CTE patients. However, currently, CTE can only be diagnosed after death via brain autopsy, and it is challenging to distinguish it from other neurodegenerative diseases with similar clinical features. To better understand this multifaceted disease and identify metabolic differences in the postmortem brain tissues of CTE patients and control subjects, we performed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based non-targeted metabolomics. Through multivariate and pathway analysis, we found that the brains of CTE patients had significant changes in the metabolites involved in astrocyte activation, phenylalanine, and tyrosine metabolism. The unique metabolic characteristics of CTE identified in this study were associated with cognitive dysfunction, amyloid-beta deposition, and neuroinflammation. Altogether, this study provided new insights into the pathogenesis of CTE and suggested appealing targets for both diagnosis and treatment for the disease.

Screening of Candidate Bioactive Secondary Plant Metabolite Ion-Features from Moringa oleifera Accessions Associated with High and Low Enteric Methane Inhibition from Ruminants

This study evaluated the relationship of secondary bioactive plant metabolite ion-features (MIFs) of Moringa oleifera accessions with antimethanogenesis to identify potential MIFs that were responsible for high and low methane inhibition from ruminants. Plant extracts from 12 Moringa accessions were evaluated at a 50 mg/kg DM feed for gas production and methane inhibition. Subsequently, the accessions were classified into low and high enteric methane inhibition groups. Four of twelve accessions (two the lowest and two the highest methane inhibitors), were used to characterize them in terms of MIFs. A total of 24 samples (12 from lower and 12 from higher methane inhibitors) were selected according to their methane inhibition potential, which ranged from 18% to 29%. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and untargeted metabolomics with univariate and multivariate statistical analysis with MetaboAnalyst were used in the study. Although 86 MIFs showed (p < 0.05) variation between higher and lower methane inhibition groups and lay within the detection ranges of the UPLC-MS column, only 14 were significant with the volcano plot. However, Bonferroni correction reduced the candidate MIFs to 10, and their R2-value with methane production ranged from 0.39 to 0.64. Eventually, MIFs 4.44_609.1462 and MIF 4.53_433.1112 were identified as bioactive MIFs associated with higher methane inhibition, whereas MIF 9.06_443.2317 and 15.00_487.2319 were associated with lower methane inhibition with no significant effect on in vitro organic matter digestibility of the feed. These MIFs could be used by plant breeders as potential markers to develop new M. oleifera varieties with high methane inhibition characteristics. However, further investigation on identifying the name, structure, and detailed biological activities of these bioactive metabolites needs to be carried out for future standardization, commercialization, and application as dietary methane mitigation additives.

1H NMR based urinary metabolites profiling dataset of canine mammary tumors

The identification of efficient and sensitive biomarkers for non-invasive tests is one of the major challenges in cancer diagnosis. To address this challenge, metabolomics is widely applied for identifying biomarkers that detect abnormal changes in cancer patients. Canine mammary tumors exhibit physiological characteristics identical to those in human breast cancer and serve as a useful animal model to conduct breast cancer research. Here, we aimed to provide a reliable large-scale metabolite dataset collected from dogs with mammary tumors, using proton nuclear magnetic resonance spectroscopy. We identified 55 metabolites in urine samples from 20 benign, 87 malignant, and 49 healthy control subjects. This dataset provides details of mammary tumor-specific metabolites in dogs and insights into cancer-specific metabolic alterations that share similar molecular characteristics.

Comprehensive two-dimensional gas chromatography mass spectrometry-based untargeted metabolomics to clarify the dynamic variations in the volatile composition of Huangjiu of different ages

Aging plays an important role in the formation of aroma characteristics of Huangjiu, a traditional Chinese alcoholic beverage. Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-qMS)-based untargeted metabolomics combined with a multivariate analysis was used to investigate the dynamic variations in the aroma profile of Huangjiu during aging process and to establish the relationship between the changing volatile metabolite profiles and the age-dependent sensory attributes. A total of 144 volatile metabolites were identified by GC×GC-qMS and 63 were selected as critical metabolites based on variable importance in projection values and p-values. Based on the results of principal component analysis, orthogonal partial least-squares discriminant analysis, and hierarchical clustering analysis, the samples of six different ages were divided into three groups: 1Y and 3Y samples, 5Y and 8Y samples, and 10Y and 15Y samples. The partial least-squares analysis results further revealed the relationship between the aromas attributes and variations of these volatile compounds. The high esters, aldehydes, and lactones contents contributed to the high intensities of the sweet and ester aroma attributes of the aged Huangjiu, while the high alcohols and ethyl esters contents contributed to the alcoholic and fruity aroma attributes of the newly brewed Huangjiu. These results improve our understanding of the chemical nature of the aroma characteristics of aged Huangjiu. PRACTICAL APPLICATION: Huangjiu is often labeled with its age as a measure of quality, which influences consumers' choice. Dynamic variations in volatile compounds of Huangjiu during aging and its contribution to the aroma characteristics of Huangjiu were figured out, which will assist the industry to produce better quality aged Huangjiu for consumers.

Plant Responses Underlying Timely Specialized Metabolites Induction of Brassica Crops

A large subset of plant stress-signaling pathways, including those related with chemical defense production, exhibit diurnal or circadian oscillations. However the extent to which diurnal or circadian time influences the stress mediated accumulation of plant specialized metabolites remains largely unknown. Because plant responses to physical stress (e.g., wounding) is considered a common component of mounting a response against a broad range of environmental stresses, including herbivory, we have utilized mechanical wounding as the stress stimulus to determine the direct contribution of time of day on the induced defenses of Brassica crops. We analyzed glucosinolates (GSLs) from leaves of broccoli (Brassica oleracea) and turnip greens (Brassica rapa) following exposure to mechanical wounding at dawn (ZT0), mid-day (ZT4), and dusk (ZT8). Several GSLs differentially accumulated and their changes depended upon the time of day at wounding was performed. This response varied considerably between species. In a parallel experiment, we investigated whether diurnal activation of Brassica phytochemicals in response to wounding might prime plants against herbivore attack. Results showed that maximal response of plant chemical defense against larvae of the generalist pest Mamestra brassicae occurred at ZT0 in broccoli and ZT8 in turnip greens. Metabolome analysis for global trends of time dependent compounds showed that sulfur-containing phytochemicals, GSL hydrolysis products, auxin-signaling components, and other metabolites activators of plant disease resistance (nicotinamide and pipecolate) had important contributions to the responses of M. brassicae feeding behavior in broccoli at morning. Overall, the findings in this study highlight a significant role for time of day in the wound stress responsive metabolome, which can in turn affect plant-herbivore interactions.

GC-MS based metabolite fingerprinting of serous ovarian carcinoma and benign ovarian tumor

The aim of this study is to identify urinary metabolomic profile of benign and malign ovarian tumors patients. Samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and metabolomic tools to define biomarkers that cause differentiation between groups. 7 metabolites were found to be different in patients with ovarian cancer (OC) and benign tumors (BT). R2Y and Q2 values were found to be 0.670 and 0.459, respectively. L-tyrosine, glycine, stearic acid, turanose and L-threonine metabolites were defined as prominent biomarkers. The sensitivity of the model was calculated as 90.72% and the specificity as 82.09%. In the pathway analysis, glutathione metabolism, aminoacyl-tRNA biosynthesis, glycine serine and threonine metabolic pathway, primary bile acid biosynthesis pathways were found to be important. According to the t-test, 29 metabolites were found to be significant in urine samples of OC patients and healthy controls (HC). R2Y and Q2 values were found to be 0.8170 and 0.749, respectively. These results showed that the model has high compatibility and predictive power. Benzoic acid, L-threonine, L-pyroglutamic acid, creatinine and 3,4-dihydroxyphenylacetic acid metabolites were determined as prominent biomarkers. The sensitivity of the model was calculated as 93.81% and the specificity as 98.59%. Glycine serine and threonine metabolic pathway, glutathione metabolism and aminoacyl-tRNA biosynthesis pathways were determined important in OC patients and HC. The R2Y, Q2, sensitivity and specificity values in the urine samples of BT patients and HC were found to be 0.869, 0.794, 91.75, 97.01% and 97.18%, respectively. L-threonine, L-pyroglutamic acid, benzoic acid, creatinine and pentadecanol metabolites were determined as prominent biomarkers. Valine, leucine and isoleucine biosynthesis and aminoacyl-tRNA biosynthesis were significant. In this study, thanks to the untargeted metabolomic approach and chemometric methods, every group was differentiated from the others and prominent biomarkers were determined.

Metabolomics of Synovial Fluid and Infrapatellar Fat Pad in Patients with Osteoarthritis or Rheumatoid Arthritis

Osteoarthritis (OA) and autoimmune-driven rheumatoid arthritis (RA) are inflammatory joint diseases with complex and insufficiently understood pathogeneses. Our objective was to characterize the metabolic fingerprints of synovial fluid (SF) and its adjacent infrapatellar fat pad (IFP) obtained during the same surgical operation from OA and RA knees. Non-targeted metabolite profiling was performed for 5 non-inflammatory trauma controls, 10 primary OA (pOA) patients, and 10 seropositive RA patients with high-resolution mass spectrometry-based techniques, and metabolites were matched with known metabolite identities. Groupwise differences in metabolic features were analyzed with the univariate Welch’s t-test and the multivariate linear discriminant analysis (LDA) and principal component analysis (PCA). Significant discrimination of metabolite profiles was discovered by LDA for both SF and IFP and by PCA for SF based on diagnosis. In addition to a few drug-derived substances, there were 16 and 13 identified metabolites with significant differences between the diagnoses in SF and IFP, respectively. The pathways downregulated in RA included androgen, bile acid, amino acid, and histamine metabolism, and those upregulated included biotin metabolism in pOA and purine metabolism in RA and pOA. The RA-induced downregulation of androgen and bile acid metabolism was observed for both SF and IFP. The levels of 11 lipid metabolites, mostly glycerophospholipids and fatty acid amides, were also altered by these inflammatory conditions. The identified metabolic pathways could be utilized in the future to deepen our understanding of the pathogeneses of OA and RA and to develop not only biomarkers for their early diagnosis but also therapeutic targets.

Shared and Specific Lung Microbiota with Metabolic Profiles in Bronchoalveolar Lavage Fluid Between Infectious and Inflammatory Respiratory Diseases

Background

Infiltration of the lower respiratory tract (LRT) microenvironment could be significantly associated with respiratory diseases. However, alterations in the LRT microbiome and metabolome in infectious and inflammatory respiratory diseases and their correlation with inflammation still need to be explored.

Methods

Bronchoalveolar lavage samples from 44 community-acquired pneumonia (CAP) patients, 29 connective tissue disease-associated interstitial disease (CTD-ILD) patients, and 30 healthy volunteers were used to detect microbiota and metabolites through 16S rRNA gene sequencing and untargeted high-performance liquid chromatography with mass spectrometry.

Results

The composition of the LRT microbial communities and metabolites differed in disease states. CAP patients showed a significantly low abundance and both diseases presented a depletion of some genera of the phylum Bacteroidetes, including Prevotella, Porphyromonas, and health-associated metabolites, such as sphingosine (d16:1), which were negatively correlated with infectious indicators. In contrast, Bacillus and Mycoplasma were both enriched in the disease groups. Streptococcus was specifically increased in CTD-ILD. In addition, co-elevated metabolites such as FA (22:4) and pyruvic acid represented hypoxia and inflammation in the diseases. Significantly increased levels of amino acids and succinate, as well as decreased itaconic acid levels, were observed in CAP patients, whereas CTD-ILD patients showed only a handful of specific metabolic alterations. Functions related to microbial lipid and amino acid metabolism were significantly altered, indicating the possible contributions of microbial metabolism. Dual omics analysis showed a moderate positive correlation between the microbiome and metabolome. The levels of L-isoleucine and L-arginine were negatively correlated with Streptococcus, and itaconic acid positively correlated with Streptococcus.

Conclusion

In the LRT microenvironment, shared and specific alterations occurred in CAP and CTD-ILD patients, which were associated with inflammatory and immune reactions, which may provide a new direction for future studies aiming to elucidate the mechanism, improve the diagnosis, and develop therapies for different respiratory diseases.

Vaginal microbiota of American Indian women and associations with measures of psychosocial stress

Molecular-bacterial vaginosis (BV) is characterized by low levels of vaginal Lactobacillus species and is associated with higher risk of sexually transmitted infections (STI). Perceived psychosocial stress is associated with increased severity and persistence of infections, including STIs. American Indians have the highest rates of stress and high rates of STIs. The prevalence of molecular-BV among American Indian women is unknown. We sought to evaluate measures of psychosocial stress, such as historic loss (a multigenerational factor involving slavery, forced removal from one's land, legally ratified race-based segregation, and contemporary discrimination) and their association with the vaginal microbiota and specific metabolites associated with BV, in 70 Northwestern Plains American Indian women. Demographics, perceived psychosocial stressors, sexual practices, and known BV risk factors were assessed using a modified version of the American Indian Service Utilization, Psychiatric Epidemiology, Risk and Protective Factors Project survey. Self-collected mid-vaginal swabs were profiled for bacterial composition by 16S rRNA gene amplicon sequencing and metabolites quantified by targeted liquid-chromatography mass spectrometry. Sixty-six percent of the participants were classified as having molecular-BV, with the rest being either dominated by L. crispatus (10%) or L. iners (24%). High levels of lifetime trauma were associated with higher odds of having molecular-BV (adjusted Odds Ratio (aOR): 2.5, 95% Credible Interval (CrI): 1.1-5.3). Measures of psychosocial stress, including historic loss and historic loss associated symptoms, were significantly associated with lifestyle and behavioral practices. Higher scores of lifetime trauma were associated with increased concentrations of spermine (aFC: 3.3, 95% CrI: 1.2-9.2). Historic loss associated symptoms and biogenic amines were the major correlates of molecular-BV. Historical loss associated symptoms and lifetime trauma are potentially important underlying factors associated with BV.

Discovery of metabolomic biomarkers for discriminating platinum-sensitive and platinum-resistant ovarian cancer by using GC-MS

This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients' urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Systematic selection of competing metabolomics methods in a metabolite-sensory relationship study

INTRODUCTION

The relationship between the chemical composition of food products and their sensory profile is a complex association confronting many challenges. However, new untargeted methodologies are helping correlate metabolites with sensory characteristics in a simpler manner. Nevertheless, in the pilot phase of a project, where only a small set of products are used to explore the relationships, choices have to be made about the most appropriate untargeted metabolomics methodology.

OBJECTIVE

To provide a framework for selecting a metabolite-sensory methodology based on: the quality of measurements, the relevance of the detected metabolites in terms of distinguishing between products or in terms of whether they can be related to the sensory attributes of the products.

METHODS

In this paper we introduce a systematic approach to explore all these different aspects driving the choice for the most appropriate metabolomics method.

RESULTS

As an example we have used a tomato soup project where the choice between two sampling methods (SPME and SBSE) had to be made. The results are not always consistently pointing to the same method as being the best. SPME was able to detect metabolites with a better precision, SBSE seemed to be able to provide a better distinction between the soups.

CONCLUSION

The three levels of comparison provide information on how the methods could perform in a follow up study and will help the researcher to make a final selection for the most appropriate method based on their strengths and weaknesses.

Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women

We aimed to identify serum metabolites related to abnormal glucose metabolism (AGM) among women with gestational diabetes mellitus (GDM). The study recruited 50 women diagnosed with GDM during mid-late pregnancy and 50 non-GDM matchees in a Singapore birth cohort. At the 5-year post-partum follow-up, we applied an untargeted approach to investigate the profiles of serum metabolites among all participants. We first employed OPLS-DA and logistic regression to discriminate women with and without follow-up AGM, and then applied area under the curve (AUC) to assess the incremental indicative value of metabolic signatures on AGM. We identified 23 candidate metabolites that were associated with postpartum AGM among all participants. We then narrowed down to five metabolites [p-cresol sulfate, linoleic acid, glycocholic acid, lysoPC(16:1) and lysoPC(20:3)] specifically associating with both GDM and postpartum AGM. The combined metabolites in addition to traditional risks showed a higher indicative value in AUC (0.92–0.94 vs. 0.74 of traditional risks and 0.77 of baseline diagnostic biomarkers) and R² (0.67–0.70 vs. 0.25 of traditional risks and 0.32 of baseline diagnostic biomarkers) in terms of AGM indication, compared with the traditional risks model and traditional risks and diagnostic biomarkers combined model. These metabolic signatures significantly increased the AUC value of AGM indication in addition to traditional risks, and might shed light on the pathophysiology underlying the transition from GDM to AGM.

Comparison of the metabolome in urine prior and eight weeks after radical prostatectomy uncovers pathologic and molecular features of prostate cancer

Prostate cancer (PCa) is associated with cellular metabolism alterations leading to changes of the metabolome. So far, studies investigating these alterations mainly focused on comparisons of metabolite profiles of PCa patients and healthy controls. In the present study we compared for the first time metabolite profiles in a significant number of paired urine samples collected before and eight weeks after radical prostatectomy (rPX) in 34 patients with PCa. Our comprehensive non-targeted liquid chromatographic-mass spectrometric metabolomics approach covered > 3000 metabolite ion masses. We annotated 23 metabolites showing significant changes eight weeks after rPX. While the levels of uridine and six acylcarnitines in urine were increased before surgery, lower levels were detected for 16 metabolites, like e.g. citrate, phenyl-lactic acid, choline, myo-inositol, emphasizing a relevant pathophysiological role of these biomarkers and the associated metabolic pathways. These results have important implications for potential use of metabolome analyses for detection of prostate cancer and related pathologic and molecular features.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BACKGROUND

The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

RESULTS

We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

CONCLUSIONS

SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Metabolite Biomarkers of Leishmania Antimony Resistance

Leishmania parasites cause leishmaniasis, one of the most epidemiologically important neglected tropical diseases. Leishmania exhibits a high ability of developing drug resistance, and drug resistance is one of the main threats to public health, as it is associated with increased incidence, mortality, and healthcare costs. The antimonial drug is the main historically implemented drug for leishmaniasis. Nevertheless, even though antimony resistance has been widely documented, the mechanisms involved are not completely understood. In this study, we aimed to identify potential metabolite biomarkers of antimony resistance that could improve leishmaniasis treatment. Here, using L. tropica promastigotes as the biological model, we showed that the level of response to antimony can be potentially predicted using ¹H-NMR-based metabolomic profiling. Antimony-resistant parasites exhibited differences in metabolite composition at the intracellular and extracellular levels, suggesting that a metabolic remodeling is required to combat the drug. Simple and time-saving exometabolomic analysis can be efficiently used for the differentiation of sensitive and resistant parasites. Our findings suggest that changes in metabolite composition are associated with an optimized response to the osmotic/oxidative stress and a rearrangement of carbon-energy metabolism. The activation of energy metabolism can be linked to the high energy requirement during the antioxidant stress response. We also found that metabolites such as proline and lactate change linearly with the level of resistance to antimony, showing a close relationship with the parasite's efficiency of drug resistance. A list of potential metabolite biomarkers is described and discussed.

Multimodal Theranostic Cyanine-Conjugated Gadolinium(III) Complex for In Vivo Imaging of Amyloid-β in an Alzheimer's Disease Mouse Model

Despite the wide use of magnetic resonance imaging (MRI) as a clinical diagnostic tool, there are still no clinically approved MRI contrast agents that can be applied for cerebral Alzheimer's disease (AD) biomarker imaging. We report here the design and development of the first amyloid-β (Aβ)-targeted, blood-brain barrier (BBB) penetrable theranostic Gd(DOTA)-cyanine dyad, which was synthesized by the conjugation of Gd(DOTA) complex and carbazole-based cyanine dye by the copper(I)-catalyzed azide-alkyne cycloaddition click reaction for imaging of Aβ in vivo and ex vivo in AD mouse models. This dyad, as a multimodal probe, possesses desirable multifunctional properties, including good biocompatibility, low cytotoxicity, high Aβ selectivity, strong fluorescence enhancement upon binding with Aβ species, good paramagnetic properties, high stability, good BBB penetrability, and fast elimination from the mouse. The longitudinal relaxivity (r₁) of the dyad was found to be 4.42 mM^-1 s^-1 at 3 T, suggesting it to be promising as a T₁-weighted MRI contrast agent. The probe has been successfully demonstrated to be able to be applied for one- and two-photon excited fluorescence and magnetic resonance (MR) imaging of Aβ in transgenic mouse models of AD. In addition, it can inhibit Aβ aggregation, protect against toxicity induced by Aβ, and suppress Aβ-induced reactive oxygen species (ROS) production. Our results demonstrate the highly promising theranostic capability of the dyad for diagnosis and therapy of AD and extraordinary potential for MRI of Aβ in humans.

Biogenic Amines Increase the Odds of Bacterial Vaginosis and Affect the Growth of and Lactic Acid Production by Vaginal Lactobacillus spp

Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-aged women, yet its etiology remains enigmatic. One clinical symptom of BV, malodor, is linked to the microbial production of biogenic amines (BA). Using targeted liquid chromatography mass spectrometry, we analyzed 149 longitudinally collected vaginal samples to determine the in vivo concentrations of the most common BAs and then assessed their relationship to BV and effect upon the growth kinetics of axenically cultured vaginal Lactobacillus species. Increases in cadaverine, putrescine, and tyramine were associated with greater odds of women transitioning from L. crispatus-dominated vaginal microbiota to microbiota that have a paucity of Lactobacillus spp. and from Nugent scores of 0 to 3 to Nugent scores of 7 to 10, consistent with BV. Exposure to putrescine lengthened the lag time and/or slowed the growth of all vaginal Lactobacillus spp. except L. jensenii 62G. L. iners AB107's lag time was lengthened by cadaverine but reduced in the presence of spermidine and spermine. The growth rate of L. crispatus VPI 3199 was slowed by cadaverine and tyramine, and strain-specific responses to spermine and spermidine were observed. BAs were associated with reduced production of d- and l-lactic acid by vaginal Lactobacillus spp., and this effect was independent of their effect upon Lactobacillus species growth. The exceptions were higher levels of d- and l-lactic acid by two strains of L. crispatus when grown in the presence of spermine. Results of this study provide evidence of a direct impact of common biogenic amines on vaginal Lactobacillus spp.IMPORTANCELactobacillus spp. are credited with providing the primary defense against gynecological conditions, including BV, most notably through the acidification of the vaginal microenvironment, which results from their production of lactic acid. The microbial production of BAs has been hypothesized to play a mechanistic role in diminishing Lactobacillus species-mediated protection, enabling the colonization and outgrowth of diverse anaerobic bacterial species associated with BV. Here, we demonstrate that in vivo increases in the most commonly observed BAs are associated with a loss of Lactobacillus spp. and the development of BV, measured by Nugent score. Further, we show that BAs formed by amino acid decarboxylase enzymes negatively affect the growth of type strains of the most common vaginal Lactobacillus spp. and separately alter their production of lactic acid. These results suggest that BAs destabilize vaginal Lactobacillus spp. and play an important and direct role in diminishing their protection of the vaginal microenvironment.

OptiMissP: A dashboard to assess missingness in proteomic data-independent acquisition mass spectrometry

Background

Missing values are a key issue in the statistical analysis of proteomic data. Defining the strategy to address missing values is a complex task in each study, potentially affecting the quality of statistical analyses.

Results

We have developed OptiMissP, a dashboard to visually and qualitatively evaluate missingness and guide decision making in the handling of missing values in proteomics studies that use data-independent acquisition mass spectrometry. It provides a set of visual tools to retrieve information about missingness through protein densities and topology-based approaches, and facilitates exploration of different imputation methods and missingness thresholds.

Conclusions

OptiMissP provides support for researchers’ and clinicians’ qualitative assessment of missingness in proteomic datasets in order to define study-specific strategies for the handling of missing values. OptiMissP considers biases in protein distributions related to the choice of imputation method and helps analysts to balance the information loss caused by low missingness thresholds and the noise introduced by selecting high missingness thresholds. This is complemented by topological data analysis which provides additional insight to the structure of the data and their missingness. We use an example in Chronic Kidney Disease to illustrate the main functionalities of OptiMissP.

A prospective study of maternal adiposity and glycemic traits across pregnancy and mid-childhood metabolomic profiles

BACKGROUND

Fetal exposure to maternal excess adiposity and hyperglycemia is risk factors for childhood adverse metabolic outcomes. Using data from a prospective pre-birth cohort, we aimed to further understand the prenatal determinants of fetal metabolic programming based on analyses of maternal adiposity and glycemic traits across pregnancy with childhood metabolomic profiles.

METHODS

This study included 330 mother-child pairs from the Gen3G cohort with information on maternal adiposity and glycemic markers at 5-16 (visit 1) and 24-30 (visit 2) weeks of pregnancy. At mid-childhood (4.8-7.2 years old), we collected fasting plasma and measured 1116 metabolites using an untargeted approach. We constructed networks of interconnected metabolites using a weighted-correlation network analysis algorithm. We estimated Spearman's partial correlation coefficients of maternal adiposity and glycemic traits across pregnancy with metabolite networks and individual metabolites, adjusting for maternal age, gravidity, race/ethnicity, history of smoking, and child's sex and age at blood collection for metabolite measurement.

RESULTS

We identified a network of 16 metabolites, primarily glycero-3-phosphoethanolamines (GPE) at mid-childhood that showed consistent negative correlations with maternal body mass index, waist circumference, and body-fat percentage at visits 1 and 2 (ρ_adjusted = -0.14 to -0.21) and post-challenge glucose levels at visit 2 (ρ_adjusted = -0.10 to -0.13), while positive correlations with Matsuda index (ρ_adjusted = 0.13). Within this identified network, 1-palmitoyl-2-decosahexaenoyl-GPE and 1-stearoyl-2-decosahexaenoyl-GPE appeared to be driving the associations. In addition, a network of 89 metabolites, primarily phosphatidylcholines, plasmalogens, sphingomyelins, and ceramides showed consistent negative correlations with insulin at visit 1 and post-challenge glucose at visit 2, while positive correlation with adiponectin at visit 2.

CONCLUSIONS

Prenatal exposure to maternal higher adiposity and hyperglycemic traits and lower insulin sensitivity markers were associated with a unique metabolomic pattern characterized by low serum phospho- and sphingolipids in mid-childhood.

Disposition of Proteins and Lipids in Synaptic Membrane Compartments Is Altered in Q175/Q7 Huntington's Disease Mouse Striatum

Dysfunction at synapses is thought to be an early change contributing to cognitive, psychiatric and motor disturbances in Huntington's disease (HD). In neurons, mutant Huntingtin collects in aggregates and distributes to the same sites as wild-type Huntingtin including on membranes and in synapses. In this study, we investigated the biochemical integrity of synapses in HD mouse striatum. We performed subcellular fractionation of striatal tissue from 2 and 6-month old knock-in Q175/Q7 HD and Q7/Q7 mice. Compared to striata of Q7/Q7 mice, proteins including GLUT3, Na+/K+ ATPase, NMDAR 2b, PSD95, and VGLUT1 had altered distribution in Q175/Q7 HD striata of 6-month old mice but not 2-month old mice. These proteins are found on plasma membranes and pre- and postsynaptic membranes supporting hypotheses that functional changes at synapses contribute to cognitive and behavioral symptoms of HD. Lipidomic analysis of mouse fractions indicated that compared to those of wild-type, fractions 1 and 2 of 6 months Q175/Q7 HD had altered levels of two species of PIP2, a phospholipid involved in synaptic signaling, increased levels of cholesterol ester and decreased cardiolipin species. At 2 months, increased levels of species of acylcarnitine, phosphatidic acid and sphingomyelin were measured. EM analysis showed that the contents of fractions 1 and 2 of Q7/Q7 and Q175/Q7 HD striata had a mix of isolated synaptic vesicles, vesicle filled axon terminals singly or in clusters, and ER and endosome-like membranes. However, those of Q175/Q7 striata contained significantly fewer and larger clumps of particles compared to those of Q7/Q7. Human HD postmortem putamen showed differences from control putamen in subcellular distribution of two proteins (Calnexin and GLUT3). Our biochemical, lipidomic and EM analysis show that the presence of the HD mutation conferred age dependent disruption of localization of synaptic proteins and lipids important for synaptic function. Our data demonstrate concrete biochemical changes suggesting altered integrity of synaptic compartments in HD mice that may mirror changes in HD patients and presage cognitive and psychiatric changes that occur in premanifest HD.

Use of metabolomics to identify strategies to improve and prolong ex vivo lung perfusion for lung transplants

BACKGROUND

Normothermic ex vivo lung perfusion (EVLP) allows for functional assessment of donor lungs; thus has increased the use of marginal lungs for transplantation. To extend EVLP for advanced organ reconditioning and regenerative interventions, cellular metabolic changes need to be understood. We sought to comprehensively characterize the dynamic metabolic changes of the lungs during EVLP, and to identify strategies to improve EVLP.

METHODS

Human donor lungs (n = 50) were assessed under a 4-hour Toronto EVLP protocol. EVLP perfusate was sampled at first (EVLP-1h) and fourth hour (EVLP-4h) of perfusion and were submitted for mass spectrometry-based untargeted metabolic profiling. Differentially expressed metabolites between the 2 timepoints were identified and analyzed from the samples of lungs transplanted post-EVLP (n = 42) to determine the underlying molecular mechanisms.

RESULTS

Of the total 312 detected metabolites, 84 were up-regulated and 103 were down-regulated at EVLP-4h relative to 1h (FDR adjusted p < .05, fold change ≥ |1.1|). At EVLP-4h, markedly decreased energy substrates were observed, accompanied by the increase in fatty acid β-oxidation. Concurrently, accumulation of amino acids and nucleic acids was evident, indicative of increased protein and nucleotide catabolism. The uniform decrease in free lysophospholipids and polyunsaturated fatty acids at EVLP-4h suggests cell membrane remodeling.

CONCLUSIONS

Untargeted metabolomics revealed signs of energy substrate consumption and metabolic by-product accumulation under current EVLP protocols. Strategies to supplement nutrients and to maintain homeostasis will be vital in improving the current clinical practice and prolonging organ perfusion for therapeutic application to further enhance donor lung utilization.

High-Throughput Microbore Ultrahigh-Performance Liquid Chromatography-Ion Mobility-Enabled-Mass Spectrometry-Based Proteomics Methodology for the Exploratory Analysis of Serum Samples from Large Cohort Studies

The deployment of proteomic analysis in clinical studies represents a significant opportunity to detect and validate biomarkers in translational medicine, improve disease understanding, and provide baseline information on population health. However, comprehensive proteome studies usually employ nanoscale chromatography and often require several hours of analysis/sample. Here, we describe a high-throughput liquid chromatography tandem mass spectrometry (LC/MS/MS) methodology using 1 mm scale chromatography requiring only 15 min/sample, coupled to ion mobility-enabled mass spectrometry. The short run time effected a 6-fold increase in productivity compared with nanoscale LC/MS. The method demonstrated excellent reproducibility with retention time coefficient of variations of less than 0.05% and peak area reproducibility ranging from 5 to 15%. The 1 mm system produced similar chromatographic peak capacity values to the nanoscale miniaturized system, detecting 90% of the Escherichia coli proteins identified by the 75 μm LC/MS system (albeit based on only 75% of the peptides found by the latter). Application to the analysis of serum samples from a human prostate cancer study group resulted in the identification of a total of 533 proteins revealing the differential expression of proteins linked to patients receiving hormone-radiotherapy or undergoing surgery.

Comparative analysis of statistical tools for oil palm phytochemical research

Recent advances in phytochemical analysis have allowed the accumulation of data for crop researchers due to its capacity to footprint and distinguish metabolites that are present within an organisms, tissues or cells. Apart from genotypic traits, slight changes either by biotic or abiotic stimuli will have significant impact on the metabolite abundances and will eventually be observed through physicochemical characteristics. Apposite data mining to interpret the mounds of phytochemical information from such a dynamic system is thus incumbent. In this investigation, several statistical software platforms ranging from exploratory and confirmatory technique of multivariate data analysis from four different statistical tools of COVAIN, SIMCA-P+, MetaboAnalyst and RIKEN Excel Macro were appraised using an oil palm phytochemical data set. As different software tool encompasses its own advantages and limitations, the insights gained from this assessment were documented to enlighten several aspects of functions and suitability for the adaptation of the tools into the oil palm phytochemistry pipeline. This comparative analysis will certainly provide scientists with salient notes on data assessment and data mining that will later allow the depiction of the overall oil palm status in-situ and ex-situ.

Early signature in the blood lipidome associated with subsequent cognitive decline in the elderly: A case-control analysis nested within the Three-City cohort study

BACKGROUND

Brain lipid metabolism appears critical for cognitive aging, but whether alterations in the lipidome relate to cognitive decline remains unclear at the system level.

METHODS

We studied participants from the Three-City study, a multicentric cohort of older persons, free of dementia at time of blood sampling, and who provided repeated measures of cognition over 12 subsequent years. We measured 189 serum lipids from 13 lipid classes using shotgun lipidomics in a case-control sample on cognitive decline (matched on age, sex and level of education) nested within the Bordeaux study center (discovery, n = 418). Associations with cognitive decline were investigated using bootstrapped penalized regression, and tested for validation in the Dijon study center (validation, n = 314).

FINDINGS

Among 17 lipids identified in the discovery stage, lower levels of the triglyceride TAG50:5, and of four membrane lipids (sphingomyelin SM40:2,2, phosphatidylethanolamine PE38:5(18:1/20:4), ether-phosphatidylethanolamine PEO34:3(16:1/18:2), and ether-phosphatidylcholine PCO34:1(16:1/18:0)), and higher levels of PCO32:0(16:0/16:0), were associated with greater odds of cognitive decline, and replicated in our validation sample.

INTERPRETATION

These findings indicate that in the blood lipidome of non-demented older persons, a specific profile of lipids involved in membrane fluidity, myelination, and lipid rafts, is associated with subsequent cognitive decline.

FUNDING

The complete list of funders is available at the end of the manuscript, in the Acknowledgement section.

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study

INTRODUCTION

Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.

OBJECTIVES

To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study.

METHODS

We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models.

RESULTS

Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.

CONCLUSIONS

This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.

TRIAL REGISTRATION NUMBER

NCT03335644.

Lipopolysaccharide perception in Arabidopsis thaliana: Diverse LPS chemotypes from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris trigger differential defence-related perturbations in the metabolome

Lipopolysaccharides (LPSs) are microbe-associated molecular pattern molecules (MAMPs) from Gram-negative bacterial pathogens that potentially contain three different MAMPs (the O-polysaccharide chain, the oligosaccharide core and lipid A). LPSs was purified from Burkholderia cepacia, Pseudomonas syringae and Xanthomonas campestris and electrophoretically profiled. Outcomes of the interactions of the three different LPS chemotypes with Arabidopsis thaliana, as reflected in the induced defence metabolites, profiled at 12 h and 24 h post elicitation, were investigated. Plants were pressure-infiltrated with LPS solutions and methanol-based extractions at different time points were performed for untargeted metabolomics using ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Multivariate data modelling and chemometric analysis were applied to generate interpretable biochemical information from the multidimensional data sets. The three LPSs triggered differential metabolome changes in the plants as apparent from chromatographically distinct MS chromatograms. Unsupervised and supervised multivariate data models exhibited time- and treatment-related variations, and revealed discriminating metabolite variables. Heat map models comparatively displayed the up-regulated pathways affecting the metabolomes and Venn diagrams indicated up-regulated and shared metabolites among the three LPS treatments. The altered metabolomes reflect the up-regulation of metabolites from not only the glucosinolate pathway, but also from the shikimate-phenylpropanoid-flavonoid -, terpenoid - and indolic/alkaloid pathways, as well as oxygenated fatty acids. Distinct phytochemical profiles, especially at the earlier time point, suggest differences in the perception of the three LPS chemotypes, associated with the molecular patterns within the tripartite lipoglycans.