Metabolomics

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  • Niek F. de JongeNiek F. de Jonge
  • Kevin MildauKevin Mildau
  • David MeijerDavid Meijer
  • [...]
  • Justin J. J. van der HooftJustin J. J. van der Hooft
Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.
 
Extraction and computational representation of features. The complexity of features is related to the amount of information used (solid lines) or assumed (dashed lines) to produce them. In annotation, matching is based on computationally definable characteristics which depend on feature complexity. Examples of characteristics are illustrated in the bottom row. Colored circles indicate chemically distinct protons or signals derived from them (in the 2D case). Statistical Total Correlation Spectroscopy (STOCSY) or similar correla-
  • Michael T. JudgeMichael T. Judge
  • Timothy M. D. EbbelsTimothy M. D. Ebbels
Background Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for ¹ H 1-dimensional ( ¹ H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. Aim of review This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. Key scientific concepts of review We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.
 
Forest plot of metabolomic biomarkers. Correlation coefficient r and 95% confidence intervals are presented as error bars
Forest plot of lipoprotein subclasses and relative lipoprotein lipid concentrations. Correlation coefficient r and 95% confidence intervals are presented as error bars
Background Previous study has shown that dyslipidemia is common in patients with Sickle cell disease (SCD) and is associated with more serious SCD complications. Methods This study investigated systematically dyslipidemia in SCD using a state-of-art nuclear magnetic resonance (NMR) metabolomics platform, including 147 pediatric cases with SCD and 1234 controls without SCD. We examined 249 metabolomic biomarkers, including 98 biomarkers for lipoprotein subclasses, 70 biomarkers for relative lipoprotein lipid concentrations, plus biomarkers for fatty acids and phospholipids. Results Specific patterns of hypolipoproteinemia and hypocholesterolemia in pediatric SCD were observed in lipoprotein subclasses other than larger VLDL subclasses. Triglycerides are not significantly changed in SCD, except increased relative concentrations in lipoprotein subclasses. Decreased plasma FFAs (including total-FA, SFA, PUFA, Omega-6, and linoleic acid) and decreased plasma phospholipids were observed in SCD. Conclusion This study scrutinized, for the first time, lipoprotein subclasses in pediatric patients with SCD, and identified SCD-specific dyslipidemia from altered lipoprotein metabolism. The findings of this study depict a broad panorama of lipid metabolism and nutrition in SCD, suggesting the potential of specific dietary supplementation of the deficient nutrients for the management of SCD.
 
Experimental design transformed from Pasiakos et al., 2017
Total testosterone concentrations between testosterone and placebo conditions. * p < 0.01, **p < 0.001. Time points represent after 14-d of the free-living, controlled feeding (CON), 14- (SED14) and day 28-d (SED28) of severe energy deficit, 14-d of free-living, ad libitum feeding (FL), and weight-regain (WR)
PCA scores plot during controlled feeding (Phase 1), after 14- and 28-days of severe energy deficit (Phase 2), and after 14-days of free-living and weight regain (Phase 3)
Heatmap of significant group x time interactions in response to severe energy deficit and testosterone administration. Data were clustered by Ward’s clustering with the Euclidean distance. Time points represent after 14-d of the free-living, controlled feeding (CON), 14- (SED14) and day 28-d (SED28) of severe energy deficit, 14-d of free-living, ad libitum feeding (FL), and weight-regain (WR). AC acylcarnitine metabolites, AC,DC acylcarnitine, dicarboxylate metabolites, AP Acetylated Peptides, AS Androgenic Steroids, BFA branch fatty acid, BM Benzoate metabolites, CA Ceramides, CH Chemical, CM creatine metabolites, FCP Food Component/Plant, GAA Gamma-glutamyl Amino Acid, GSTM Glycine, Serine and Threonine metabolites, HCER Hexosylceramides, HIS Histidine metabolites, HPM Hemoglobin and Porphyrin metabolites, LIV Leucine, Isoleucine and Valine metabolites, LM Lysine metabolites, LP Lysophospholipid, MCFA Medium Chain Fatty Acid, MCST Methionine, Cysteine, SAM and Taurine metabolites, MG Monoacylglycerol, MUFA Long Chain Monounsaturated Fatty Acid, NNM Nicotinate and Nicotinamide metabolites, PAM Polyamine metabolites, PBAM Primary Bile Acid metabolites, PCM Partially Characterized Molecules, PE Phosphatidylethanolamine, PHE Phenylalanine metabolites, PLM Phospholipid metabolites, PM Pentose metabolites, PN Vitamin B6 metabolites, PUFA Long Chain Polyunsaturated Fatty Acid, PYM Pyrimidine metabolites, SFA Long Chain Saturated Fatty Acid, TRP Tryptophan metabolites, TY Tyrosine metabolites, UAP Urea cycle; Arginine and Proline metabolites
Heatmap of Pearson’s correlation coefficients between changes in analytes and changes in fat mass (top) and lean mass (bottom) after severe energy-deficit
Introduction Testosterone administration attenuates reductions in total body mass and lean mass during severe energy deficit (SED). Objectives This study examined the effects of testosterone administration on the serum metabolome during SED. Methods In a double-blind, placebo-controlled clinical trial, non-obese men were randomized to receive 200-mg testosterone enanthate/wk (TEST) (n = 24) or placebo (PLA) (n = 26) during a 28-d inpatient, severe exercise- and diet-induced energy deficit. This study consisted of three consecutive phases. Participants were free-living and provided a eucaloric diet for 14-d during Phase 1. During Phase 2, participants were admitted to an inpatient unit, randomized to receive testosterone or placebo, and underwent SED for 28-d. During Phase 3, participants returned to their pre-study diet and physical activity habits. Untargeted metabolite profiling was conducted on serum samples collected during each phase. Body composition was measured using dual-energy X-ray absorptiometry after 11-d of Phase 1 and after 25-d of Phase 2 to determine changes in fat and lean mass. Results TEST had higher (Benjamini–Hochberg adjusted, q < 0.05) androgenic steroid and acylcarnitine, and lower (q < 0.05) amino acid metabolites after SED compared to PLA. Metabolomic differences were reversed by Phase 3. Changes in lean mass were associated (Bonferroni-adjusted, p < 0.05) with changes in androgenic steroid metabolites (r = 0.42–0.70), acylcarnitines (r = 0.37–0.44), and amino acid metabolites (r = − 0.36–− 0.37). Changes in fat mass were associated (p < 0.05) with changes in acylcarnitines (r = − 0.46–− 0.49) and changes in urea cycle metabolites (r = 0.60–0.62). Conclusion Testosterone administration altered androgenic steroid, acylcarnitine, and amino acid metabolites, which were associated with changes in body composition during SED.
 
Dynamic serum metabolomic profile. A Multilevel simultaneous component score plot discriminating different follow-up panels. B Multilevel simultaneous component score plot discriminating NCRT-sensitive and NCRT-resistant patients
Soft time trend clusters of 451 serum metabolites. Yellow or green colored lines correspond to metabolites with low membership value, red- and purple-colored lines correspond to metabolites with high membership value. A Decreasing cluster. B U-shape cluster. C Rapidly-decreasing cluster. D Rapidly-increasing cluster. E Inverse U-shape cluster. F Increasing cluster
Differential metabolic biomarkers to predict NCRT-sensitivity among locally advanced rectal cancer patients. A Violin Plot of 8 differential metabolic biomarkers. P-value was calculated by the Wilcoxon rank sum test for each time point among 8 differential metabolic biomarkers. *P-value < 0.05, **P-value < 0.01. B ROC analysis of prediction model for 8 metabolic biomarkers. C Summary of pathway analysis
Introduction Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT. Objectives This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients. Methods Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models. Results Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively. Conclusion This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.
 
Experimental design: study flow-chart. NAFLD: Non-alcoholic fatty liver disease; T2DM: Type two diabetes mellitus; HS-SPME: headspace solid-phase microextraction; GC-MS: gas chromatography-mass spectrometry; VOCs: volatile organic compounds
Score scatter plot of the PCA model based on urinary VOCs. The second (PC2) and the third (PC3) component explaining the 11.0% and the 9.2% of the total variance, respectively, are used to represent the urine samples of the 39 selected subjects. The convex hull of the NAFLD group and that of the T2DM group are reported as black lines
Boxplot showing the distribution of 3-heptanone (K10)
IntroductionAccumulating evidence have shown a significant correlation between urinary volatile organic compounds (VOCs) profile and the manifestation of several physiological and pathological states, including liver diseases. Previous studies have investigated the urinary metabolic signature as a non-invasive tool for the early discrimination between non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), which nowadays represents one of the most important challenges in this context, feasible only by carrying out liver biopsy.Objectives The aim of the study was to investigate the differences in the urinary VOCs profiles of non-alcoholic fatty liver disease (NAFLD) patients, diabetes mellitus (T2DM) subjects and NAFLD/T2DM patients.Methods Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS) was applied to profile the urinary VOCs. Urine samples were analysed both under acid and alkaline conditions, to obtain a range of urinary volatiles with different physicochemical properties.ResultsUrinary VOCs profiles of 13 NAFLD patients, 13 T2DM subjects and 13 NAFLD/T2DM patients were investigated by multivariate and univariate data analysis techniques which allowed to identify 21 volatiles under alkaline conditions able to describe the NAFLD/T2DM group concerning the other two groups.Conclusion Our results suggest that VOCs signatures can improve the knowledge of the pathological condition where NAFLD coexists with T2DM and discovering new features that are not simply the sum of the two diseases. These preliminary findings may be considered as hypothesis-generating, to be clearly confirmed by larger prospective investigations.
 
A Number of complete (green) and incomplete (red) datasets collected from EBI MetaboLights (in total 468 datasets). B Number of MetaboLights datasets, where no (green), one, two, three or more than three (red) piece of information are missing. C Number of complete (green) and incomplete (red) datasets collected from NIH Metabolomics Workbench (in total 1033 datasets). D Number of Metabolomics Workbench datasets, where no (green), one, two, three or more than three (red) piece of information are missing
Correct and complete notation using the example of study MTBLS291 from MetaboLights with additional isolation of the text blocks on the column, eluents and additives and the gradient using regular expressions via regexr.com
Introduction The structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC–MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup. Objectives In contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories. Methods We acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient. Results We found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients. Conclusion Reporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.
 
Volcano plot with log2 fold change (FC) in the x-axis and –log10 of p values on the y axis. The lines indicate FC > 1.3. Box whisker plot showing the interquartile range of the significant metabolites before (red) and after EEN treatment (green)
* Entities significant after FDR correction for multiple testing
Scores plot and Splot from the Ortogonal partial least square discriminant analysis (OPLS DA) (Left) and bar chart with of the most important features in the model with cut off of 3.6
Bar chart showing features correlated with wPCDAI scores with Pearson correlation coefficient cut off of -0.5 for the negatively correlated features and 0.3 for the positive
Results of univariate analysis using t-test
Background and aims Exclusive enteral nutrition is recommended as a first-line treatment in active pediatric Crohn’s Disease, but its mechanism of action is still not clear. We aimed to assess alterations in the metabolic profile of newly diagnosed pediatric Crohn’s Disease patients before and during exclusive enteral nutrition therapy. Methods Plasma samples from 14 pediatric Crohn’s Disease patients before and after 3–4 weeks on exclusive enteral nutrition were analyzed using mass spectrometry. T-test, fold change and orthogonal partial least squares discriminant analysis were used for mining significant features. Correlation analysis was performed between the annotated features and the weighted pediatric Crohn’s disease activity index using Pearson r distance. Results Among the 13 compounds which decreased during exclusive enteral nutrition, most are related to diet, while one is a bacterial metabolite, Bacteriohopane-32,33,34,35-tetrol. The phosphatidic acid metabolite PA(15:1/18:0) was significantly reduced and correlated with the weighted pediatric Crohn’s disease activity index. Lipids increased during exclusive enteral nutrition therapy included phosphatidylethanolamines; PE(24:1/24:1), PE(17:2/20:2) and one lactosylceramide; LacCer(d18:1/14:0). Conclusion Food additives and other phytochemicals were the major metabolites, which decreased following the exclusion of a regular diet during exclusive enteral nutrition. An alteration in bacterial biomarkers may reflect changes in intestinal microbiota composition and metabolism. Thus, metabolomics provides an opportunity to characterize the molecular mechanisms of dietary factors triggering Crohn’s Disease activity, and the mechanisms of action of exclusive enteral nutrition, thereby providing the basis for the development and evaluation of improved intervention strategies for prevention and treatment.
 
Representative example of a molecular family level annotation from spectral library searching that matches to hexenoylcarnitine. The MS/MS spectrum contains several diagnostic fragments and neutral losses that make it possible to assign it to the acylcarnitines molecular family, as indicated on the molecular structures (Yan et al., 2020). However, routine spectral library matching cannot distinguish between the 14 potential stereo- and regioisomers, resulting in a level 3 annotation. This highlights the need for new strategies to communicate the results from spectral library searching, as narrowing down to the molecular family, even when the exact molecular identity is unknown, can often already be valuable for biological interpretation. Top is the experimental observed MS/MS spectrum, with a precursor m/z deviation of 11.6 ppm compared to the calculated m/z of the protonated ions
Advances in spectral libraries for LC–MS/MS based untargeted metabolomics. a The GNPS community spectral libraries (non-commercial only) have grown from 23,790 MS/MS spectra in 2014 to 586,647 MS/MS spectra in 2022 (September 2022). Concurrently, the number of library spectra that matched to public data has grown from 4,727 MS/MS spectra in 2014 to 127,405 MS/MS spectra in 2022 (22% of the publicly available library spectra have matches to experimental MS/MS spectra in public data). b Fueled by growing spectral libraries, the MS/MS spectrum annotation rate for the GNPS continuous identification mode as part of living data (M. Wang et al., 2016), which periodically reanalyses all public datasets on GNPS/MassIVE with the latest spectral libraries, has increased from 2% of MS/MS spectra on average in 2014 to 13% in 2022
Distribution of ion adducts in public spectral libraries. The majority of positive ion mode MS/MS spectra in MoNA (a) and GNPS (b) are protonated, while other adducts, in-source fragments, multiply charged species, and multimers are minimally represented. c Ion identity molecular networking was used to extract novel reference MS/MS spectra that exhibit overall broader coverage of different adducts, multimers, and in-source fragments (Schmid et al., 2021). Note that these ion forms are found with a predefined inclusion list, rather than a comprehensive search for all ion forms that might be present in untargeted metabolomics data of a biological sample
Spectral entropy distributions for the GNPS, MoNA, and NIST20 spectral libraries. GNPS consists of 497,137 MS/MS spectra from the “ALL_GNPS_NO_PROPOGATED” library (downloaded on 2022–09-08), MoNA contains 145,361 MS/MS spectra from the “LC–MS/MS Spectra” collection (downloaded on 2022–09-08), and NIST20 consists of 1,026,712 MS/MS spectra (high-resolution MS/MS collection). Spectra were processed by removing noise peaks below 1% of the base peak intensity and normalizing fragment intensities to sum to one. a There is a strong relationship between spectral entropy and the number of fragment ions (Spearman correlation 0.963). b Although the NIST20 library contains smaller molecules than GNPS and MoNA, the difference in entropy distributions cannot be directly explained by the weight of the molecules (Spearman correlation 0.095)
Background Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments.Aim of reviewWe describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries.Key scientific concepts of reviewThis review focuses on the current state of spectral libraries for untargeted LC–MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
 
Introduction Plant cell walls play an important role in providing physical strength and defence against abiotic stress. Rice brittle culm (bc) mutants are a strength-decreased mutant because of abnormal cell walls, and it has been reported that the causative genes of bc mutants affect cell wall composition. However, the metabolic alterations in each organ of bc mutants have remained unknown. Objectives To evaluate the metabolic changes in rice bc mutants, comparative analysis of the primary metabolites was conducted. Methods The primary metabolites in leaves, internodes, and nodes of rice bc mutants and wild-type control were measured using CE- and LC-MS/MS. Multivariate analyses using metabolomic data was performed. Results We found that mutations in each bc mutant had different effects on metabolism. For example, higher oxalate content was observed in bc3 and bc1 bc3 mutants, suggesting that surplus carbon that was not used for cell wall components might be used for oxalate synthesis. In addition, common metabolic alterations such as a decrease of sugar nucleotides in nodes were found in bc1 and Bc6, in which the causative genes are involved in cellulose accumulation. Conclusion These results suggest that metabolic analysis of the bc mutants could elucidate the functions of causative gene and improve the cell wall components for livestock feed or bioethanol production.
 
Growth kinetics and immunoblotting analysis. A the in vitro growth curve of test strains under 10 μM MgSO4 (low Mg²⁺) conditions in the presence of 0.2% (w/v) l-arabinose; B the in vitro growth curve of test strains under 10 mM MgSO4 (High Mg²⁺) conditions in the presence of 0.2% (w/v) l-arabinose; C the immunoblotting results of TT-92 (ΔaraBAD::phoP-HA△phoP TT-1) in presence (line 1) or absence (line 2) of 0.2% (w/v) l-arabinose
The scatter plot of KEGG pathway enrichment on metabolites with statistical difference between strains. A TT-81 (PhoP-OFF) VS TT-80 (PhoP-N); B TT-81 (PhoP-OFF) VS TT-82 (PhoP-ON); C TT-82 (PhoP-ON) VS TT-80 (PhoP-N). The horizontal axis represented the topological analysis results; the bigger the circle size, the higher the centrality of the metabolites involved in the corresponding pathway. The vertical axis showed the result of enrichment analysis; the darker the circle color, the more significant are the changes of metabolites in the corresponding pathway
The relative expression of target genes between strains. For each strain, three independent biological samples were prepared and measured
The histogram of GO classification regarding the biological process (BP), molecular function (MF) and cellular component (CC), respectively. A TT-81 (PhoP-OFF) VS TT-80 (PhoP-N); B TT-81 (PhoP-OFF) VS TT-82 (PhoP-ON); C TT-82 (PhoP-ON) VS TT-80 (PhoP-N)
Characterization of LPS and ATP measurement. A SDS-PAGE analysis. Lane 1: the LPS of TT-80 (PhoP-N); Lane 2: the LPS of TT-82 (PhoP-ON); Lane 3: the LPS of TT-81 (PhoP-OFF). B ATP production in TT-80 (PhoP-N), TT-81 (PhoP-OFF) and TT-82 (PhoP-ON). p < 0.05 was considered as significant difference. Same letter represents no significant difference and vice versa
Introduction Previous reports revealed the role played by Salmonella PhoP–PhoQ system in virulence activation, antimicrobial tolerance and intracellular survival, the impact of PhoP–PhoQ on cell metabolism has been less extensively described. Objectives The aim of this study is to address whether and how the PhoP–PhoQ system affects the cell metabolism of Salmonella. Methods We constructed a Salmonella phoP deletion mutant strain TT-81 (PhoP-OFF), a Salmonella PhoP constitutively expressed strain TT-82 (PhoP-ON) and a wild-type Salmonella PhoP strain TT-80 (PhoP-N), using P22-mediated generalized transduction or λ Red-mediated targeted mutagenesis. We then measured the in vitro growth kinetics of all test strains and determined their metabolomic and transcriptomic profiles using gas chromatography coupled with tandem mass spectrometry (GC–MS/MS) and RNA-seq technique, respectively. Results Low-Mg²⁺ conditions impaired the growth of the phoP deletion mutant strain TT-81 (PhoP-OFF) dramatically. 42 metabolites in the wild-type PhoP strain TT-80 (PhoP-N) and 28 metabolites in the PhoP constitutively expressed strain TT-82 (PhoP-ON) changed by the absence of phoP. In contrast, the level of 19 compounds in TT-80 (PhoP-N) changed comparing to the PhoP constitutively expressed strain TT-82 (PhoP-N). The mRNA level of 95 genes in TT-80 (PhoP-N) changed when phoP was disrupted, wherein 78 genes downregulated and 17 genes upregulated. 106 genes were determined to be differentially expressed between TT-81 (PhoP-OFF) and TT-82 (PhoP-ON). While only 16 genes were found to differentially expressed between TT-82 (PhoP-ON) and TT-80 (PhoP-N). Conclusion Our findings confirmed the impact of PhoP–PhoQ system on lipopolysaccharide (LPS) modification, energy metabolism, and the biosynthesis or transport of amino acids. Most importantly, we demonstrated that the turnover of a given metabolite could respond differentially to the level of phoP. Taken together, the present study provided new insights into the adaptation of Salmonella to the host environment and helped to characterize the impact of the PhoP–PhoQ system on the cell metabolism.
 
(continued)
Principal component analysis scores plot for all experimental and quality control samples
Multivariate models for wild-type (NWT) versus PKCδ deficient mice (NKO) after gene deletion. a Partial least-squares discriminant analysis (PLS-DA) scores plot of the first three latent variables (LV). Percentages represent the proportion of variation explained in the observed data (X) and the group assignment (Y) by the specific LV. b Principal component analysis (PCA) scores plot for the first three principal components (PC). Percentages in brackets represent the proportion of variation explained in the observed data by the specific PC
A scatter plot of log scaled p-values from independent samples t-tests for each compound against Cohen’s d-value where the sign is indicative of up- or down-regulating effect. P-values have not been adjusted for multiple testing
Figure created with https://biorender.com/
Introduction PKCδ is ubiquitously expressed in mammalian cells and its dysregulation plays a key role in the onset of several incurable diseases and metabolic disorders. However, much remains unknown about the metabolic pathways and disturbances induced by PKC deficiency, as well as the metabolic mechanisms involved. Objectives This study aims to use metabolomics to further characterize the function of PKC from a metabolomics standpoint, by comparing the full serum metabolic profiles of PKC deficient mice to those of wild-type mice. Methods The serum metabolomes of PKCδ knock-out mice were compared to that of a wild-type strain using a GCxGC-TOFMS metabolomics research approach and various univariate and multivariate statistical analyses. Results Thirty-seven serum metabolite markers best describing the difference between PKCδ knock-out and wild-type mice were identified based on a PCA power value > 0.9, a t-test p-value < 0.05, or an effect size > 1. XERp prediction was also done to accurately select the metabolite markers within the 2 sample groups. Of the metabolite markers identified, 78.4% (29/37) were elevated and 48.65% of these markers were fatty acids (18/37). It is clear that a total loss of PKCδ functionality results in an inhibition of glycolysis, the TCA cycle, and steroid synthesis, accompanied by upregulation of the pentose phosphate pathway, fatty acids oxidation, cholesterol transport/storage, single carbon and sulphur-containing amino acid synthesis, branched-chain amino acids (BCAA), ketogenesis, and an increased cell signalling via N-acetylglucosamine. Conclusion The charaterization of the dysregulated serum metabolites in this study, may represent an additional tool for the early detection and screening of PKCδ-deficiencies or abnormalities.
 
The distribution of SUV indices and MTV of the 35 tumor samples. Box plots show median, 25th percentile, and 75th percentile of data, with minimum and maximum represented by whiskers
Metabolite concentrations in colorectal cancer tissue superimposed on a metabolic pathway map that included glycolysis, TCA cycle, and amino acids. Columns, average concentration (nmol/g tissue) of tumor tissues based on low-FDG and high-FDG metabolic tumor volume; bars, SD. All P values were evaluated using the Mann–Whitney U test. *, P < 0.05; **, P < 0.01; ***, P < 0.001
Introduction Advances in metabolomics have significantly improved cancer detection, diagnosis, treatment, and prognosis. Objectives To investigate the relationship between metabolic tumor volume (MTV) using 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) positron emission tomography (PET)/ computed tomography (CT) and metabolomics data in patients with colorectal cancer (CRC). Methods The metabolome in tumor tissues was analyzed using capillary electrophoresis time-of-flight mass spectrometry in 33 patients with newly diagnosed CRC who underwent FDG PET/CT before treatment and had tumor tissue post-surgery. Based on the FDG PET data, MTV was calculated and was dichotomized according to the median value, and tumors were divided into low-MTV and high-MTV tumors. Metabolomics data were compared between the low-MTV and high-MTV tumors. Results The levels of most glycolysis-related metabolites were not different between low-MTV and high-MTV tumors. The level of component of the initial part of the tricarboxylic acid (TCA) cycle, citrate, was significantly lower in the high-MTV tumor than in the low-MTV tumor. The TCA intermediate succinate level was significantly higher in the high-MTV tumor than in the low-MTV tumor. In contrast, the TCA intermediate fumarate level was significantly lower in the high-MTV tumor than in the low-MTV tumor. The levels of many amino acids were significantly higher in the high-MTV tumor than in the low-MTV tumor. Conclusions Although preliminary, these results suggest that tumors with high FDG metabolism in CRC may obtain more energy by using a reverse reaction of the TCA cycle and amino-acid metabolism. However, further research is required to clarify this relationship.
 
PCA score plot of metabolic fingerprints in the negative mode: a prespermiating males (PSM) vs. spermiating males (SM); b preovulatory females (POF) vs. ovulatory females (OF); c OF vs. SM; d POF vs. PSM. Blue circles: PSM; purple circles: SM; yellow circles: QC males; pink circles: POF; red circles: OF; green circles: QC females. QC: quality control samples were pooled from each sample in the group (detailed see Methods “2.3.1”)
Putative bile acid synthetic pathway map comparing plasma metabolites in mature male and female sea lamprey. The metabolites in bold letters represent the bile acids quantified by targeted analysis in sea lamprey plasma. The number below each box denotes the fold change for the respective metabolite and the red color indicates upregulation (SM > OF). OF ovulatory females, SM spermiating males
Introduction Adult sea lamprey (Petromyzon marinus) cease feeding and migrate to spawning streams where males build nests, undergo final sexual maturation, and subsequently produce and release large quantities of bile acid pheromones that attract mature females. These animals are predicted to rearrange their metabolic pathways drastically to support their reproductive strategies, presenting advantageous opportunities to examine how sex and the maturation processes affect metabolism. Objectives The objective is to investigate the metabolic differences between sexes and maturation states in sea lamprey that support changes in physiological functions. Methods We compared plasma metabolomes of spawning and prespawning sea lamprey in both sexes using both non-targeted and targeted metabolomics approaches using UPLC/MS–MS with electrospray ionization in both positive and negative modes. The data were processed using Progenesis QI, Compound Discoverer and XCMS softwares for alignment, peak picking, and deconvolution of the peaks. Principle component analyses (PCA) and partial least squares discriminant analyses (PLS-DA) were performed using SIMCA and Metaboanalyst softwares to identify discriminating features, followed by fragmentation matching with extensive database search and pathway mapping. Results The pheromonal bile acid biosynthesis was upregulated significantly in males compared to females. Spermiating males further upregulated bile acid biosynthesis by altering amino acid metabolisms, upregulating cofactors and nucleotide metabolisms, but downregulating carbohydrate and energy metabolisms. Conclusion Plasma metabolomes are sex- and maturation-dependent and reflect the special metabolic demands at each life stage and reproductive strategy.
 
Heatmap with the metabolites of control HaCaT and treated with 0.10 mM and 0.30 mM of DCBQ-OH.
Heatmap with the metabolites of control HaCaT and treated with 0.05 mM and 0.075 mM of DCBQ.
Introduction The 2,6-dichloro-1,4-benzoquinone (DCBQ) and its derivative 2,6-dichloro-3-hydroxy-1,4-benzoquinone (DCBQ-OH) are disinfection by-products (DBPs) and emerging pollutants in the environment. They are considered to be of particular importance as they have a high potential of toxicity and they are likely to be carcinogenic. Objectives In this study, human epidermal keratinocyte cells (HaCaT) were exposed to the DCBQ and its derivative DCBQ-OH, at concentrations equivalent to their IC20 and IC50, and a study of the metabolic phenotype of cells was performed. Methods The perturbations induced in cellular metabolites and their relative content were screened and evaluated through a metabolomic study, using 1H-NMR and MS spectroscopy. Results Changes in the metabolic pathways of HaCaT at concentrations corresponding to IC20 and IC50 of DCBQ-OH involved the activation of cell membrane α-linolenic acid, biotin, and glutathione and deactivation of glycolysis/gluconeogenesis at IC50. The changes in metabolic pathways at IC20 and IC50 of DCBQ were associated with the activation of inositol phosphate, pertaining to the transfer of messages from the receptors of the membrane to the interior as well as with riboflavin. Deactivation of biotin metabolism was recorded, among others. The cells exposed to DCBQ exhibited a concentration-dependent decrease in saccharide concentrations. The concentration of steroids increased when cells were exposed to IC20 and decreased at IC50. Although both chemical factors stressed the cells, DCBQ led to the activation of transporting messages through phosphorylated derivatives of inositol. Conclusion Our findings provided insights into the impact of the two DBPs on human keratinocytes. Both chemical factors induced energy production perturbations, oxidative stress, and membrane damage.
 
Canopy a and tuber morphology b of the early-maturing line CE3130 and the late-maturing line CE3027. Photos were taken on the plants 2 months after grafting. The size of the yellow ruler is 50 cm. Tubers were harvested at the end of the vegetation period of the scion. 30, non-grafted CE3130; 30/30, homo-grafted CE3130; 30/27; heterograft: CE3130 scion/CE3027 rootstock; 27, non-grafted control CE3027; 27/27, homo-grafted CE3027; 27/30, hetero-graft: CE3027 scion/CE3130 rootstock
Differences in metabolite composition of tubers of CE3027 and CE3130 potato plants and their homo- and hetero-grafts. a PCA score plot, b dendrogram illustrating the hierarchical clustering of tuber types based on metabolite composition. Tubers were derived from the second experiment described in Materials and methods sub-Sect. 2.2. Data were obtained from 5 groups of tubers per type. Each group contained 5 slices with 2–3 cm in diameter cut from 5 tubers. The labels are as in Fig. 1
VIP score plot showing the top 34 (score > 1.5) most important metabolite features identified by PLS-DA. The statistical analysis was carried out with the same dataset as in Fig. 2. The labels are as in Fig. 1
The heatmap of the top 60 metabolites differentiating CE3027, CE3027/CE3027, CE3027/CE3130, CE3130, CE3130/CE3130 and CE3130/CE3027 tubers. The statistical analysis was carried out with the same dataset as in Fig. 2. The labels are as in Fig. 1
Relative abundances of nine metabolites present in different amounts in CE3027 and CE3130 tubers and in the tubers of grafted plants. The box plot includes the median (50% of all values above and 50% below), the first quartile (25% of all values below the quartile), the third quartile (25% of all values above the quartile), and whiskers indicating the highest and the lowest value of the data set. Outliers are dotted. The statistics is based on one-way ANOVA with a post hoc Tukey’s HSD test at adjusted p-value of 0.05. The labels are as in Fig. 1
Introduction Earliness of tuberisation and the quality of potato tubers are important traits in potato breeding. The qualitative traits rely on the metabolite profile of tubers, which are storage organs and net importers of assimilates. Thus, the quality of tubers largely depends on the metabolites transported from leaves to developing tubers. Objectives To test the influence of canopy on the quality of tubers by metabolite profiling of tubers of an early- and a late-maturing potato line and their grafts. Methods Potatoes were grown under greenhouse conditions, grafted and the tubers harvested at the end of the scions’ vegetation period. Metabolite profiling of freshly harvested tubers was performed using gas chromatography coupled with mass spectrometry. Statistical analyses were applied to determine the significant differences between the different tubers. Results 99 metabolites were identified and an additional 181 peaks detected in chromatograms, out of which 186 were polar and 94 non-polar compounds. The concentrations of 113 metabolites were significantly different in the tubers from the early-maturing CE3130 and the late-maturing CE3027 line. Hetero-grafting resulted in considerable changes in the metabolite content of tubers. Especially, the effect of CE3027 on the metabolite composition of tubers formed on CE3130 rootstocks was readily apparent. Nevertheless, many compounds were present at similar levels in the tubers of hetero-grafted plants as was found in the tubers of their scion counterparts. Conclusion Hetero-grafting resulted in many compounds at similar concentrations in rootstock tubers as in scion tubers suggesting that these are transported from the source leaves to tubers.
 
Spearman’s correlations between PFAS and lipid classes (A), bile acids (B) polar metabolites (C), including values from all 22 NOD mouse blood serum samples. Significant correlations marked with *p < 0.05, **p < 0.01 and ***p < 0.001. Positive correlation showed in blue and negative in red
A and B PCA plots of metabolic profiles in NOD mice after exposure to a POP mixture for control (green), low exposure (blue) and high exposure (red) groups C Heatmap of 50 significantly-altered metabolites (ANOVA, p < 0.05) with the most contrasting patterns. Samples are sorted by the exposure group and metabolites clustered based on Ward’s clustering algorithm.
Spearman’s correlations between bile acids and lipid classes in NOD mouse blood serum samples. Significant correlations marked with *p < 0.05, **p < 0.01 and ***p < 0.001
Pathway analysis diagram representing, significantly-elevated metabolites/lipid classes in red (p < 0.05) and pink (0.05 < p < 0.1), as well as downregulated metabolite groups in blue (p < 0.05) and light blue (0.05 < p < 0.1), in mice after exposure to POPs Metabolites showing no significant changes are marked in light gray
Introduction Autoimmune disorders such as type 1 diabetes (T1D) are believed to be caused by the interplay between several genetic and environmental factors. Elucidation of the role of environmental factors in metabolic and immune dysfunction leading to autoimmune disease is not yet well characterized. Objectives Here we investigated the impact of exposure to a mixture of persistent organic pollutants (POPs) on the metabolome in non-obese diabetic (NOD) mice, an experimental model of T1D. The mixture contained organochlorides, organobromides, and per- and polyfluoroalkyl substances (PFAS). Methods Analysis of molecular lipids (lipidomics) and bile acids in serum samples was performed by UPLC-Q-TOF/MS, while polar metabolites were analyzed by GC-Q-TOF/MS. Results Experimental exposure to the POP mixture in these mice led to several metabolic changes, which were similar to those previously reported as associated with PFAS exposure, as well as risk of T1D in human studies. This included an increase in the levels of sugar derivatives, triacylglycerols and lithocholic acid, and a decrease in long chain fatty acids and several lipid classes, including phosphatidylcholines, lysophosphatidylcholines and sphingomyelins. Conclusion Taken together, our study demonstrates that exposure to POPs results in an altered metabolic signature previously associated with autoimmunity.
 
Introduction Postmenopausal women with osteoporosis (PMOP) are prone to fragility fractures. Osteoporosis is associated with alterations in the levels of specific circulating metabolites. Objectives To analyze the metabolic profile of individuals with PMOP and identify novel metabolites associated with bone mineral density (BMD). Methods We performed an unsupervised metabolomics analysis of plasma samples from participants with PMOP and of normal controls (NC) with normal bone mass. BMD values for the lumber spine and the proximal femur were determined using dual-energy X-ray absorptiometry. Principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were performed for metabolomic profile analyses. Metabolites with P < 0.05 in the t-test, VIP > 1 in the PLS-DA model, and SNR > 0.3 between the PMOP and NC groups were defined as differential abundant metabolites (DAMs). The SHapley additive explanations (SHAP) method was utilized to determine the importance of permutation of each DAM in the predictive model between the two groups. ROC analysis and correlation analysis of metabolite relative abundance and BMD/T-scores were conducted. KEGG pathway analysis was used for functional annotation of the candidate metabolites. Results Overall, 527 annotated molecular markers were extracted in the positive and negative total ion chromatogram (TIC) of each sample. The PMOP and NC groups could be differentiated using the PLS-DA model. Sixty-eight DAMs were identified, with most relative abundances decreasing in the PMOP samples. SHAP was used to identify 9 DAM metabolites as factors distinguishing PMOP from NC. The logistic regression model including Triethanolamine, Linoleic acid, and PC(18:1(9Z)/18:1(9Z)) metabolites demonstrated excellent discrimination performance (sensitivity = 97.0, specificity = 96.6, AUC = 0.993). The correlation analysis revealed that the abundances of Triethanolamine, PC(18:1(9Z)/18:1(9Z)), 16-Hydroxypalmitic acid, and Palmitic acid were significantly positively correlated with the BMD/T score (Pearson correlation coefficients > 0.5, P < 0.05). Most candidate metabolites were involved in lipid metabolism based on KEGG functional annotations. Conclusion The plasma metabolomic signature of PMOP patients differed from that of healthy controls. Marker metabolites may help provide information for the diagnosis, therapy, and prevention of PMOP. We highlight the application of feature selection approaches in the analysis of high-dimensional biological data.
 
a, b OPLS-DA score plots and volcano plots (red and blue colors indicate an increase and a decrease of metabolite level, respectively.) of two groups for the urine based on LC–MS analysis; c The total metabolic spectrum data analysis of two groups for the urine based on RF (The red dotted line is the control group, and the green dotted line is the injury group). d Heat map of the biomarkers associated with iodoxanol-related early renal injury (red and blue colors indicate an increase and a decrease of metabolite level, respectively)
Schematic diagram of the disturbed metabolic pathways related to this study
Levels of differential metabolites based on UHPLC/Q-Orbitrap MS and RF results
a–c PCA Score plots, OPLS-DA Score plots and Permutations test for urine based on ¹H-NMR analysis; d The corresponding loading plots derived from the ¹H-NMR spectra of urine
a ROC curve analysis of 6 differential metabolites and their combinations; b ROC curve analysis of the established multiple-metabolites predictive model, renal function, glomerular function and tubular function; c PCA score plot of 6 different metabolites’peak areas on the two groups of urine samples in the validation cohort; d Hotelling's T2 of PCA score plot
Background & aims There are some problems, such as unclear pathological mechanism, delayed diagnosis, and inaccurate therapeutic target of Contrast-induced acute kidney injury (CI-AKI). It is significantly important to find biomarkers and therapeutic targets that can indicate renal injury in the early stage of CI-AKI. This study aims to establish a multiple-metabolites model to predict preliminary renal injury induced by iodixanol and explore its pathogenesis. Methods Both UHPLC/Q-Orbitrap-MS and ¹H-NMR methods were applied for urine metabolomics studies on two independent cohorts who suffered from a preliminary renal injury caused by iodixanol, and the multivariate statistical analysis and random forest (RF) algorithm were used to process the related date. Results In the discovery cohort (n = 169), 6 metabolic markers (leucine, indole, 5-hydroxy-L-tryptophan, N-acetylvaline, hydroxyhexanoycarnine, and kynurenic acid) were obtained by the cross-validation between the RF and liquid chromatography-mass spectrometry (LC–MS). Secondly, the 6 differential metabolites were confirmed by comparison of standard substance and structural identification of ¹H-NMR. Subsequently, the multiple-metabolites model composed of the 6 biomarkers was validated in a validation cohort (n = 165). Conclusions The concentrations of leucine, indole, N-acetylvaline, 5-hydroxy-L-tryptophan, hydroxyhexanoycarnitine and kynurenic acid in urine were proven to be positively correlated with the degree of renal injury induced by iodixanol. The multiple-metabolites model based on these 6 biomarkers has a good predictive ability to predict early renal injury caused by iodixanol, provides treatment direction for injury intervention and a reference for reducing the incidence of clinical CI-AKI further.
 
Principal Component Analysis (PCA) of metabolite composition of plasma obtained from mice kept on an isoflavone or phyto-free diet. 876 metabolites were obtained from untargeted metabolomics across all samples
Hierarchical clustering of significantly altered metabolites in the plasma of mice on either isoflavone or phyto-free diet. Significantly altered metabolites were identified using the Wilcoxon test (p < .05, q < 0.10). Metabolites were further clustered based on the Euclidean distance and Ward’s linkage clustering algorithm. Each cell color represents the metabolite average intensity on a normalized scale from decreased (blue) to increased (red) abundance in each row
Metabolites increased in mice on an isoflavone diet compared to phyto-free diet. All values are normalized. Wilcoxon Rank Sum test utilized for statistical significance (* p < .05, ** p < .01, *** p < .001)
Metabolites decreased in mice on an isoflavone diet compared to phyto-free diet. All values are normalized. Wilcoxon Rank Sum test utilized for statistical significance (* p < .05, ** p < .01, *** p < .001)
IntroductionPhytoestrogens found in soy, fruits, peanuts, and other legumes, have been identified as metabolites capable of providing beneficial effects in multiple pathological conditions due to their ability to mimic endogenous estrogen. Interestingly, the health-promoting effects of some phytoestrogens, such as isoflavones, are dependent on the presence of specific gut bacteria. Specifically, gut bacteria can metabolize isoflavones into equol, which has a higher affinity for endogenous estrogen receptors compared to dietary isoflavones. We have previously shown that patients with multiple sclerosis (MS), a neuroinflammatory disease, lack gut bacteria that are able to metabolize phytoestrogen. Further, we have validated the importance of both isoflavones and phytoestrogen-metabolizing gut bacteria in disease protection utilizing an animal model of MS. Specifically, we have shown that an isoflavone-rich diet can protect from neuroinflammatory diseases, and that protection was dependent on the ability of gut bacteria to metabolize isoflavones into equol. Additionally, mice on a diet with isoflavones showed an anti-inflammatory response compared to the mice on a diet lacking isoflavones. However, it is unknown how isoflavones and/or equol mediates their protective effects, especially their effects on host metabolite levels.Objectives In this study, we utilized untargeted metabolomics to identify metabolites found in plasma that were modulated by the presence of dietary isoflavones.ResultsWe found that the consumption of isoflavones increased anti-inflammatory monounsaturated fatty acids and beneficial polyunsaturated fatty acids while reducing pro-inflammatory glycerophospholipids, sphingolipids, phenylalanine metabolism, and arachidonic acid derivatives.Conclusion Isoflavone consumption alters the systemic metabolic landscape through concurrent increases in monounsaturated fatty acids and beneficial polyunsaturated fatty acids plus reduction in pro-inflammatory metabolites and pathways. This highlights a potential mechanism by which an isoflavone diet may modulate immune-mediated disease.
 
Selection of studies
Heat map of the main metabolites in each study, their super class, the biological medium from which they were sampled and the type of response with which the metabolites were associated. A Fist 31 metabolites; B Other 30 metabolites
Overview of salivary biofluid pathway analysis. The metabolic pathway analysis highlighted the main pathways. Aminoacyl-tRNA biosynthesis, Butanoate metabolism, Pyruvate metabolism, Glycolysis/Gluconeogenesis, Alanine, aspartate and glutamate metabolism, Glyoxylate and dicarboxylate metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis
Background Periodontitis is resulted from a complex interaction between genetics and epigenetics, microbial factors, and the host response. Metabolomics analyses reflect both the steady-state physiological equilibrium of cells or organisms as well as their dynamic metabolic responses to environmental stimuli. Aim of review This systematic review of the literature aimed to assess which low molecular weight metabolites are more often found in biological fluids of individuals with periodontitis compared to individuals with gingivitis or periodontal health. Key scientific concepts of review All the included studies employed untargeted analysis. One or more biological fluids were analyzed, including saliva (n = 14), gingival crevicular fluid (n = 6), mouthwash (n = 1), serum (n = 3) and plasma (n = 1). Fifty-six main metabolites related to periodontitis have been identified in at least two independent studies by NMR spectroscopy or MS-based metabolomics. Saliva was the main biological fluid sampled. It is noteworthy that 14 metabolites of the 56 detected were identified as main metabolites in all studies that sampled the saliva. The majority of metabolites found consistently among studies were amino acids, organic acids and derivates: acetate, alanine, butyrate, formate, GABA, lactate, propionate, phenylalanine and valine. They were either up- or down-regulated in the studies or this information was not mentioned. The main metabolic pathway was related to phenylalanine, tyrosine and tryptophan biosynthesis. Metabolites more frequently found in individuals with periodontitis were related to both the host and to microorganism responses. Future studies are needed, and they should follow some methodological standards to facilitate their comparison.
 
IntroductionMetabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.Objectives In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.Methods Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography–mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30–120 and 30–60 min of delayed freezing were analyzed.Results190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.Conclusion Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.
 
Introduction Coronavirus disease 2019 (COVID-19) is strongly linked to dysregulation of various molecular, cellular, and physiological processes that change abundance of different biomolecules including metabolites that may be ultimately used as biomarkers for disease progression and severity. It is important at early stage to readily distinguish those patients that are likely to progress to moderate and severe stages. Objectives This study aimed to investigate the utility of saliva and plasma metabolomic profiles as a potential parameter for risk stratifying COVID-19 patients. Method LC–MS/MS-based untargeted metabolomics were used to profile the changes in saliva and plasma metabolomic profiles of COVID-19 patients with different severities. Results Saliva and plasma metabolites were screened in 62 COVID-19 patients and 18 non-infected controls. The COVID-19 group included 16 severe, 15 moderate, 16 mild, and 15 asymptomatic cases. Thirty-six differential metabolites were detected in COVID-19 versus control comparisons. SARS-CoV-2 induced metabolic derangement differed with infection severity. The metabolic changes were identified in saliva and plasma, however, saliva showed higher intensity of metabolic changes. Levels of saliva metabolites such as sphingosine and kynurenine were significantly different between COVID-19 infected and non-infected individuals; while linoleic acid and Alpha-ketoisovaleric acid were specifically increased in severe compared to non-severe patients. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid, and positively correlated with l-Tryptophan and l-Kynurenine. Conclusion Saliva disease-specific and severity-specific metabolite could be employed as potential COVID-19 diagnostic and prognostic biomarkers. Graphical abstract
 
Linear regression of urobilin fluorescence to urine volume and urobilin fluorescence to rat weight
pH of urine samples after collection and prepped samples after spectrum acquisition. UC—is the pH of the raw urine collected, Non-is the pH of the non-normalized samples from both groups after lyophilization and addition of DSS in D2O, UrbN – is the pH of the samples of both groups after normalization to urobilin, lyophilization and addition of DSS in D2O
Selected metabolites: a alpha-glucose, b alanine, c alpha-ketoglutarate, d creatinine, e citric acid, f allantoin g lactic acid h taurine, i succinic acid, j formic acid. NoNcntrl are the 1200 µL samples from control diet group. Urbcntrl are the samples normalized to urobilin concentration from the control diet group. Nonexp are the 1200 µL samples from moderate sucrose diet group. Urbexp are the samples normalized to urobilin concentration from the moderate sucrose diet group
Graph: 1a alpha-glucose control diet, 1b alpha-glucose experimental diet, 2a creatinine control diet, 2b creatinine experimental diet, 3a formic acid control diet, and 3b formic acid experimental diet. NoNcntrl are the 1200 µL samples from control diet group. Urbcntrl are the samples normalized to urobilin concentration from the control diet group. Nonexp are the 1200 µL samples from moderate sucrose diet group. Urbexp are the samples normalized to urobilin concentration from the moderate sucrose diet group
Introduction Metabolomics is a multi-discipline approach to systems biology that provides a snapshot of the metabolic status of a cell, tissue, or organism. Metabolomics uses mass spectroscopy (MS) and nuclear magnetic resonance (NMR) to analyze biological samples for low molecular weight metabolites. Objective Normalize urine sample pre-acquisition to perform a targeted quantitative analysis of selected metabolites in rat urine. Methods Urine samples were provided from rats on a control diet (n = 10) and moderate sucrose diet (n = 8) collected in a metabolic cage during an eight hour fast. Urine from each sample was prepared by two different methods. One sample was a non-normalized sample of 1200 µL and the second sample was a variable volume-normalized to the concentration of urobilin in a standard sample of urine. The urobilin concentration in all samples was determined by fluorescence. Ten metabolites for each non-normalized and normalized urine sample were quantified by integration to an internal standard of DSS. Results Both groups showed an improvement in pH range going from non-normalized to normalized samples. In the group on the control diet, eight metabolites had significant improvement in range, while the remaining two metabolites had insignificant improvement in range comparing the non-normalized sample to the normalized sample. In the group on the moderate sucrose diet all ten metabolites showed significant improvement in range going from non-normalized to normalized samples. Conclusions These findings describe a pre-acquisition method of urine normalization to adjust for differences in hydration state of each organism. This results in a narrower concentration range in a targeted analysis.
 
The workflow for the study design and analytical pipeline
The OPLS-DA score graph and 3D graph of the PD group and the PD-RLS group in the serum, the OPLS-DA S-plot graph, the model verification graph A and C are positive mode score graphs, B and D are negative mode score graphs, E is positive mode S-plot graph, F is negative mode S-plot graph; G is positive mode model verification graph, H is negative mode model verification graph)
A a: ROC curves of four differential metabolites in the Discovery set b: ROC curve diagram of combined diagnosis of four differential metabolites B Box plots of four metabolites in the Discovery set C Differential metabolites heat map analysis in the Validation set D Correlation analysis of laboratory test indexes and differential metabolites in PD patients with RLS. Color intensity represents the magnitude of correlation. Red, positive correlations, Blue, negative correlations
The association diagram of metabolites and disease mechanism
Background Restless legs syndrome (RLS) is a neuromotor disorder, and dialysis patients are more likely to develop RLS. RLS often causes sleep disorders, anxiety and depression in patients. It will increase the risk of death and severely affect the life of patients. At present, RLS has not received enough recognition and attention, and the misdiagnosis rate can reach more than 10%. Methods The discovery set selected 30 peritoneal dialysis (PD) patients and 27 peritoneal dialysis patients with RLS (PD-RLS). A metabolomics method based on ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometric method (UPLC-Q-TOF/MS) was used to analyze the differential metabolites of the two groups. 51 PD patients and 51 PD-RLS patients were included in the validation set. The receiver operating characteristic (ROC) analysis was used to evaluate the early diagnostic biomarkers, and the correlation between the differential metabolites and laboratory test indexes was analyzed to explore the biological function of the differential metabolites. Results Through the integrated analysis, four metabolites can be used as markers for the diagnosis of PD-RLS, including Hippuric acid, Phenylacetylglutamine, N,N,N-Trimethyl-L-alanyl-L-proline betaine and Threonic acid. Through ROC analysis, it is found that they can be used as a metabolic biomarker panel, and the area under the curve of this combination is more than 0.9, indicating that the panel has good diagnostic and predictive ability. Conclusion Metabolomics based on UPLC-Q-TOF/MS technology can effectively identify the potential biomarkers, and provide a theoretical basis for the early diagnosis, prevention and treatment on PD-RLS.
 
NMR-based non targeted analysis of plasma, serum, and urine PA metabolic profile. PLS-DA score plots of the three first components of the statistically significant comparisons among children. a¹H CPMG NMR plasma spectra of 50 PA versus the 48 controls; b¹H CPMG NMR serum of 51 PA versus the 47 controls; c¹H 1D NOESY NMR urine of 51 PA and 45 control. Children with PA are presented with turquoise, while control children are with purple spheres
¹H NMR signals in superimposition (left) and boxplots (right) of plasma metabolites derived by univariate analysis. Left figures: Spectral superimposition of plasma ¹H NMR peaks for each examined metabolite (y-axis: relative intensity (a.u.); x-axis: δ ¹Η (ppm)). Red and green ¹H NMR spectral peaks correspond to the control and PA group, respectively. Right figures: Boxplots derived by univariate analysis for each one of the examined plasma metabolites (y-axis: relative intensity (a.u.); x-axis: group title, boxplot dots: each dot represent the.¹H NMR spectrum of each child in different color)
3D PLS scores plot of 40 PA urine metabolic profiles. ¹H 1D NOESY NMR spectra distributed according to the BA (years) of the corresponding child. Urine metabolome of children with PA and BA < 1.5 years are presented in blue, while urine metabolome of children with PA and BA ≥ 1.5 years are in red
Statistically significant blood plasma, serum and urine metabolites according to the VIP scores of PLS-DA (VIP > 1)
Introduction Premature adrenarche (PA) for long time was considered a benign condition but later has been connected to various diseases in childhood and adulthood which remains controversial. Objective To investigate the effect of premature adrenarche on the metabolic phenotype, and correlate the clinical and biochemical data with the metabolic profile of children with PA. Methods Nuclear magnetic resonance (NMR)-based untargeted and targeted metabolomic approach in combination with multivariate and univariate statistical analysis applied to study the metabolic profiles of children with PA. Plasma, serum, and urine samples were collected from fifty-two children with Idiopathic PA and forty-eight age-matched controls from the division of Pediatric Endocrinology of the University Hospital of Patras were enrolled. Results Metabolomic results showed that plasma and serum glucose, myo-inositol, amino acids, a population of unsaturated lipids, and esterified cholesterol were higher and significantly different in PA children. In the metabolic profiles of children with PA and age-matched control group a gradual increase of glucose and myo-inositol levels was observed in serum and plasma, which was positively correlated their body mass index standard deviation score (BMI SDS) values respectively. Urine ¹H NMR metabolic fingerprint of PA children showed positive correlation and a clustering-dependent relationship with their BMI and bone age (BA) respectively. Conclusion This study provides evidence that PA driven metabolic changes begin during the childhood and PA may has an inductive role in a BMI–driven increase of specific metabolites. Finally, urine may be considered as the best biofluid for identification of the PA metabolism as it reflects more clearly the PA metabolic fingerprint.
 
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
 
Metabolites significantly associated with TNF-α, IL-1β, and MIF. LysoPC: lysophosphatidylcholine; TNF-α: tumor necrosis factor-alpha; IL-1β: interleukin-1 beta; MIF: macrophage migration inhibitory factor; CI: confidence interval. Beta coefficient and 95% confidence interval were obtained by multivariable linear regression adjusting for age, sex, body mass index, and osteoarthritis status
Metabolism indicators significantly associated with TNF-α, IL-1β, and MIF. GSH: glutathione; PLA2: phospholipase A2; lysoPC: lysophosphatidylcholine; LCFA: long-chain fatty acid; SFA: saturated fatty acid; TNF-α: tumor necrosis factor-alpha; IL-1β: interleukin-1 beta; MIF: macrophage migration inhibitory factor; CI: confidence interval. Beta coefficient and 95% confidence interval were obtained by multivariable linear regression adjusting for age, sex, body mass index, and osteoarthritis status
GSH metabolic pathways. 3-PS: 3-phosphoserine; 5-OP: 5-oxoproline; α-KG: alpha-ketoglutarate; γ-Glu-AA: gamma-glutamyl amino acid; AATs: amino acid transporters; CBS: cystathionine beta-synthase; CSE: cystathionine gamma-lyase; Cys: cysteine; Cys-Gly: cysteinylglycine; DP: dipeptidase; GCL: glutamate cysteine ligase; GDH1: glutamate dehydrogenase 1; GGCT: gamma-glutamylcyclotransferase; GGT: gamma-glutamyl transpeptidase; Gln: glutamine; GLS: glutaminase; Glu: glutamate; Glu-Cys: gamma-glutamylcysteine; Gly: glycine; GPx: glutathione peroxidase; GR: glutathione disulfide reductase; GSH: glutathione; GSS: glutathione synthetase; GSSG: glutathione disulfide; GST: glutathione S-transferases; GSX: glutathione S-conjugate; MRP: multidrug resistance-associated protein; OPase: 5-oxoprolinase; PSAT1: phosphoserine aminotransferase 1; PSP: phosphoserine phosphatase; ROS: reactive oxygen species; Ser: serine; SHMT: serine hydroxymethyltransferase; TCA: tricarboxylic acid. Red indicates metabolites positively associated with pro-inflammory cytokines; blue indicates metabolism indicators positively associated with pro-inflammory cytokines
Introduction Pro-inflammatory cytokines are responsible for initiating an effective defense against exogenous pathogens, and their regulation has a vital role in maintaining physiological homeostasis. The involvement of pro-inflammatory cytokines in pathological conditions have been explored in great detail, however, studies investigating metabolic pathways associated with these cytokines under normal homeostatic conditions are scarce. Objectives The aim of the current study was to identify metabolites and metabolic pathways associated with circulating pro-inflammatory cytokines under homeostatic conditions using a metabolomics approach. Methods The study participants (n = 133) were derived from the Newfoundland Osteoarthritis Study (NFOAS) and the Complex Diseases in the Newfoundland population: Environment and Genetics (CODING) study. Plasma concentrations of cytokines including tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and macrophage migration inhibitory factor (MIF) were assessed by enzyme-linked immunosorbent assay. Targeted metabolomic profiling on fasting plasma samples was performed using Biocrates MxP® Quant 500 kit which measures a total of 630 metabolites. Associations between natural log-transformed metabolite concentrations and metabolite sums/ratios and cytokine levels were assessed using linear regression with adjustment for age, sex, body mass index (BMI), and osteoarthritis status. Results Seven metabolites and 11 metabolite sums/ratios were found to be significantly associated with TNF-α, IL-1β, and MIF (all p ≤ 5.13 × 10− 5) after controlling multiple testing with Bonferroni method, indicating the association between glutathione (GSH), polyamine, and lysophosphatidylcholine (lysoPC) synthesis pathways and these pro-inflammatory cytokines. Conclusion GSH, polyamine, and lysoPC synthesis pathways were positively associated with circulating TNF-α, IL-1β, and MIF levels under homeostatic conditions.
 
Changes in circulating (A) total fatty acid, (B) LPS-binding protein (LBP), (C) serum amyloid A (SAA), and (D) cortisol concentrations, and (E) white blood cells counts in late-lactation Holstein dairy cows experiencing hyperlipidemia and administered an intravenous saline (CON; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or a lipopolysaccharide (LPS; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) bolus. Data at h 0 were utilized as covariates in the model
The heatmap represents generalized log-transformed and auto-scaled data to showcase global changes in plasma lysophosphatidycholine (LPC) concentrations across time (hour) as high (red) or low (blue) in late-lactation Holstein dairy cows experiencing hyperlipidemia and administered an intravenous saline (CON; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or a lipopolysaccharide (LPS; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) bolus. Lysophosphatidylcholines denoted in bold were significantly modified by treatment (P < 0.05), whereas LPC that are italicized tended to be modified by treatment (P < 0.10). Data at h 0 were utilized as covariates in the model
Changes in plasma lysophosphatidylcholine concentrations in late-lactation Holstein dairy cows experiencing hyperlipidemia and administered an intravenous saline (CON; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or a lipopolysaccharide (LPS; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) bolus. Closed circles represent cows administered saline (CON). Open circles represent cows administered a lipopolysaccharide (LPS) bolus. The arrow at h 16 indicates end of feed restriction and lipid infusion. Differences at each time point was performed and marked with ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document} if P value < 0.05. Data at h 0 were utilized as covariates in the model
Changes in plasma phosphatidylcholine (PC) to phosphatidylethanolamine (PE) ratios in late-lactation Holstein dairy cows experiencing hyperlipidemia and administered an intravenous saline (CON; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or a lipopolysaccharide (LPS; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) bolus. Plots for PC:PE (A) −14:0p/20:4, (B) −18:0p/20:4, (C) −33:2p, and (D) −38:4p. Closed circles represent cows administered saline (CON). Open circles represent cows administered a lipopolysaccharide (LPS) bolus. The arrow at h 16 indicates end of feed restriction and lipid infusion. The letter p refers to vinyl ether bonds. Differences at each time point was performed and marked with ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document} if P value < 0.05. Data at h 0 were utilized as covariates in the model
Changes in plasma ceramide concentrations in late-lactation Holstein dairy cows experiencing hyperlipidemia and administered an intravenous saline (CON; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or a lipopolysaccharide (LPS; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) bolus. Plots of plasma (A) ceramide-d18:0/24:0 and (B) CerG2-d18:1/16:0. CerG2, dihexosyl ceramide. Closed circles represent cows administered saline (CON). Open circles represent cows administered a lipopolysaccharide (LPS) bolus. The d designation refers to 1,3-dihydroxy long chain base of sphingolipids. The arrow at h 16 indicates end of feed restriction and lipid infusion. Differences at each time point was performed and marked with ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document} if P value < 0.05. Data at h 0 were utilized as covariates in the model
Introduction The effects of lipopolysaccharides (i.e., endotoxin; LPS) on metabolism are poorly defined in lactating dairy cattle experiencing hyperlipidemia. Objectives Our objective was to explore the effects of acute intravenous LPS administration on metabolism in late-lactation Holstein cows experiencing hyperlipidemia induced by intravenous triglyceride infusion and feed restriction. Methods Ten non-pregnant lactating Holstein cows (273 ± 35 d in milk) were administered a single bolus of saline (3 mL of saline; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5) or LPS (0.375 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}g of LPS/kg of body weight; n =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=$$\end{document} 5). Simultaneously, cows were intravenously infused a triglyceride emulsion and feed restricted for 16 h to induce hyperlipidemia in an attempt to model the periparturient period. Blood was sampled at routine intervals. Changes in circulating total fatty acid concentrations and inflammatory parameters were measured. Plasma samples were analyzed using untargeted lipidomics and metabolomics. Results Endotoxin increased circulating serum amyloid A, LPS-binding protein, and cortisol concentrations. Endotoxin administration decreased plasma lysophosphatidylcholine (LPC) concentrations and increased select plasma ceramide concentrations. These outcomes suggest modulation of the immune response and insulin action. Lipopolysaccharide decreased the ratio of phosphatidylcholine to phosphatidylethanomanine, which potentially indicate a decrease in the hepatic activation of phosphatidylethanolamine N-methyltransferase and triglyceride export. Endotoxin administration also increased plasma concentrations of pyruvic and lactic acids, and decreased plasma citric acid concentrations, which implicate the upregulation of glycolysis and downregulation of the citric acid cycle (i.e., the Warburg effect), potentially in leukocytes. Conclusion Acute intravenous LPS administration decreased circulating LPC concentrations, modified ceramide and glycerophospholipid concentrations, and influenced intermediary metabolism in dairy cows experiencing hyperlipidemia.
 
Schematic representation of the experimental procedures performed to evaluate the biological control agents against G. smithogilvyi. A Non-volatile compounds assay (nVOCs). See Sect. 2.3, B Volatile compounds (VOCs) assay. See Sect. 2.4, C Dual culture assay. See Sect. 2.5, D Extraction and analysis of metabolites through LC–MS. See Sect. 2.7
Effect of volatile organic compounds (VOCs) and non-volatile compounds (nVOCs) on mycelial growth of G. smithogilvyi isolates B15 and F1N1. A Shows the effect of VOCs (Left) and nVOCs (right) on the radial growth of the isolates at each concentration tested (centre). B Representation of the effect of nVOCs secreted by the highest three BCAs concentrations on the mycelial growth of both isolates. Plates were incubated at 23 °C in the dark for six days. Means ± SEM labelled with the same letter are not significantly different to the control according to Dunnett’s test at p = 0.05
Effect of the biological control agents TRI and SUP on the growth of G. smithogilvyi isolates B15 and F1N1 evaluated in a dual culture assay. A Inhibition of growth of G. smithogilvyi due to exposure to TRI and SUP at 6 and 8 days in the dual culture assay compared with controls. Note for the BCA present in TRI overgrowing G. smithogilvyi colony and the halo of inhibition displayed under the SUP treatment. B Growth area of the isolates measured after 6 and 8 days. Means ± SEM labelled with different letters are significantly different to the control according to Dunnett’s test at p = 0.05
Analysis of the non-volatile compounds (nVOCs) profile of biological control agents SUP, D25 and TRI for the positive (+) and negative (−) ionisation modes. A, B Heat map analysis, C, D principal component analysis (PCA) and E, F UpSet plots for the positive (+) and (−) modes respectively. Heat maps display the normalized compounds abundance in a colour code: low level (green) and high level (red). Both heat maps were clustered with Person’s distance function, and the median was used as the linkage method. UpSet plots show the distribution of nVOCs unique to each BCA (first three bars) and shred between two or three BCAs (last four bars) (Color figure online)
Disc diffusion assay of BCAs methanolic crude extracts (500 mg/mL) and effect on G. smithogilvyi conidia germination and mycelial growth. A A representative plate displaying the zone of inhibition of each crude extract compared to the control treatment. B Size of the zone of inhibition for each BCA. Means ± SEM labelled with different letters are significantly different to the control according to Dunnett’s test at p = 0.05. (*) Represents means with a value of zero
Introduction Chestnut rot caused by the fungus Gnomoniopsis smithogilvyi is a disease present in the world’s major chestnut growing regions. The disease is considered a significant threat to the global production of nuts from the sweet chestnut (Castanea sativa). Conventional fungicides provide some control, but little is known about the potential of biological control agents (BCAs) as alternatives to manage the disease. Objective Evaluate whether formulated BCAs and their secreted metabolites inhibit the in vitro growth of G. smithogilvyi. Methods The antifungal potential of BCAs was assessed against the pathogen through an inverted plate assay for volatile compounds (VOCs), a diffusion assay for non-volatile compounds (nVOCs) and in dual culture. Methanolic extracts of nVOCs from the solid medium were further evaluated for their effect on conidia germination and were screened through an LC–MS-based approach for antifungal metabolites. Results Isolates of Trichoderma spp., derived from the BCAs, significantly suppressed the pathogen through the production of VOCs and nVOCs. The BCA from which Bacillus subtilis was isolated was more effective in growth inhibition through the production of nVOCs. The LC–MS based metabolomics on the nVOCs derived from the BCAs showed the presence of several antifungal compounds. Conclusion The results show that G. smithogilvyi can be effectively controlled by the BCAs tested and that their use may provide a more ecological alternative for managing chestnut rot. The in vitro analysis should now be expanded to the field to assess the effectiveness of these alternatives for chestnut rot management.
 
IntroductionWork-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.Objectives The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.Methods PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.ResultsMost of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.Conclusion Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.
 
Selectivity ratio plots of regression models using HOMA-IR as outcome and the 26 lipoprotein features as explanatory variables
Variance plot showing the influence of covariates on outcome and explanatory variables
Selectivity ratio plot for HOMA-IR with adjusted variables included as explanatory variables
Introduction Comprehensive lipoprotein profiling using proton nuclear magnetic resonance (NMR) spectroscopy of serum represents an alternative to the homeostatic model assessment of insulin resistance (HOMA-IR). Both adiposity and physical (in)activity associate to insulin resistance, but quantification of the influence of these two lifestyle related factors on the association pattern of HOMA-IR to lipoproteins suffers from lack of appropriate methods to handle multicollinear covariates. Objectives We aimed at (i) developing an approach for assessment and adjustment of the influence of multicollinear and even linear dependent covariates on regression models, and (ii) to use this approach to examine the influence of adiposity and physical activity on the association pattern between HOMA-IR and the lipoprotein profile. Methods For 841 children, lipoprotein profiles were obtained from serum proton NMR and physical activity (PA) intensity profiles from accelerometry. Adiposity was measured as body mass index, the ratio of waist circumference to height, and skinfold thickness. Target projections were used to assess and isolate the influence of adiposity and PA on the association pattern of HOMA-IR to the lipoproteins. Results Adiposity explained just over 50% of the association pattern of HOMA-IR to the lipoproteins with strongest influence on high-density lipoprotein features. The influence of PA was mainly attributed to a strong inverse association between adiposity and moderate and high-intensity physical activity. Conclusion The presented covariate projection approach to obtain net association patterns, made it possible to quantify and interpret the influence of adiposity and physical (in)activity on the association pattern of HOMA-IR to the lipoprotein features.
 
IntroductionSolitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates.Objective We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics.Methods In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics.Results and conclusionGC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.
 
One possible QC scheme for the use of QC samples in the analytical section of manuscripts describing untargeted mass spectrometry based metabolic profiling. Clearly a similar one could easily be constructed that covers other methodologies such as e.g., NMR spectroscopy etc.
Information that should be documented in manuscripts to show the steps that have been taken to ensure the robustness of the analytical stages of a metabolic phenotyping experiment and its resulting data (see also Table OR3)
The various cumulative levels of analytical reporting for QC samples are depicted in a hierarchy of value and effort. Each layer builds upon lower layers. A The samples that support interlaboratory comparability have the highest value and can be reported in the minimal sense (a qualitative description of the samples in the study) and in a Best Reporting Practice sense where QC metrics are also reported. B Long-term intra-laboratory QC samples represent ongoing efforts in the reporting laboratory to present consistent results across their various projects, and these can also be reported in a Minimal or Best Reporting Practice sense. C Individual project comparability during the analytical phase of the project can be demonstrated by Intra-study QC samples and reported in a minimal or as best reporting practice. D Instrument QC sample reporting demonstrates fitness-for-purpose of the instrument at the time of the project and represents the foundation upon which the other layers rest
Background Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. Objective The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. Key reporting practices Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: “minimal” and “best reporting practice” levels.
 
Global metabolomics profile analysis. A, B PCA score plot of 30 hepatolithiasis patients (red dots) and 20 healthy controls (green dots) based on the metabolomics data in positive ion mode and negative ion mode. QC samples (blue dots) clustered together tightly in both modes, indicating great QC repeatability and analysis system stability. C, D OPLS-DA score plots of 30 hepatolithiasis patients (red dots) and 20 healthy controls (blue dots) based on serum spectral data of positive ion mode and negative ion mode. X axis and Y axis represent contributions of persons to the first two principal components (PC1 and PC2). Each dot represents the serum metabolomic profile of a single sample. E, F Cross-validation plot with a permutation test repeated 200 times in positive ion mode and negative ion mode. The intercepts of(G): R2 = (0.0, 0.75) and Q2 = (0.0,− 0.46),(H):R2 = (0.0, 0.69) and Q2 = (0.0,− 0.49) suggest that the OPLS-DA model is not overfitting. Class A represents hepatolithiasis serum; Class B represents healthy control serum
Volcano plots of the differential metabolites. A, B show the results of pairwise comparisons of metabolites in each hepatolithiasis serum relative to healthy controls in positive ion mode and negative ion mode. The vertical dashed lines indicate the threshold for the twofold abundance difference. The horizontal dashed line indicates the p = 0.05 threshold. Between-group comparisons were performed using Student’s t test. Metabolites with significant changes are presented in red (upregulated) or green (downregulated). Class A represents hepatolithiasis serum; Class B represents healthy control serum
Heat map of the differential metabolites. A, B Heat map visualization of differential metabolites in hepatolithiasis serum relative to healthy controls in positive ion mode and negative ion mode. The color scale (right) illustrates the relative expression levels of metabolites across all samples: red color represents an expression levels above mean, blue color represents expression lower than the mean. Class A represents hepatolithiasis serum; Class B represents healthy control serum
Classification analyses of metabolites in hepatolithiasis serum. A, B Metabolites in positive ion mode and negative ion mode were annotated by the secondary classification in HMDB. The abscissa represents the number of metabolites, and the ordinate represents the annotated HMDB classification. C, D Metabolites in positive ion mode and negative ion mode were annotated by the 8 major lipid classification. The abscissa represents the number of metabolites, and the ordinate represents the annotated lipid classification. E, F Metabolites in positive ion mode and negative ion mode were annotated by each secondary classification under the primary classification of pathway. The abscissa represents the number of metabolites, and the ordinate represents the annotated KEGG pathway. G, H Pathway enrichment analysis of significantly elevated metabolites according to the KEGG pathway in positive ion mode and negative ion mode. All matched pathways are plotted according to P-values from pathway enrichment analysis and pathway impact values from pathway topology analysis. Color gradient and circle size indicate the significance of the pathway ranked by P-values (yellow: higher P-values and red: lower P-values) and pathway impact values (the larger the circle the higher the impact score). Class A represents hepatolithiasis serum; Class B represents healthy control serum
29 over-expressed metabolites with significantly difference according to the stratification analysis of the degree of cholestasis in hepatolithiasis. Data are expressed as mean ± standard deviation, statistical significance is determined by one-way analysis of variance. **p < 0.01; ***p < 0.001; ****p < 0.0001. Low level group (total bile acid < 10 μmol/L, n = 20); Middle level group (total bile acid: 10–40 μmol/L, n = 5); High level group (total bile acid > 40 μmol/L, n = 5)
Background & aimsA metabolomic study of hepatolithiasis has yet to be performed. The purpose of the present study was to characterize the metabolite profile and identify potential biomarkers of hepatolithiasis using a metabolomic approach.Methods We comprehensively analyzed the serum metabolites from 30 patients with hepatolithiasis and 20 healthy individuals using ultra-high performance liquid chromatography-tandem mass spectrometry operated in negative and positive ionization modes. Statistical analyses were performed using univariate (Student’s t-test) and multivariate (orthogonal partial least-squares discriminant analysis) statistics and R language. Receiver operator characteristic (ROC) curve analysis was performed to identify potential predictors of hepatolithiasis.ResultsWe identified 277 metabolites that were significantly different between hepatolithiasis serum group and healthy control serum group. These metabolites were principally lipids and lipid-like molecules and amino acid metabolites. The steroid hormone biosynthesis pathway was enriched in hepatolithiasis serum group. In all specific metabolites, 75 metabolites were over-expressed in hepatolithiasis serum group. The AUC values for 60 metabolites exceeded 0.70, 4 metabolites including 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) exceeded 0.90.Conclusions We have identified serum metabolites that are associated with hepatolithiasis for the first time. 60 potential metabolic biomarkers were identified, 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) may have the potential clinical utility in hepatolithiasis.
 
Renal sections stained with Pizzolato stain for detecting CaOx crystals
Circulating concentrations of creatinine, SDMA, and urea at the end of life (EOL) of cats with healthy kidneys (Con), renal disease, or CaOx stones
Introduction There is a significant incidence of cats with renal disease (RD) and calcium oxalate (CaOx) kidney uroliths in domesticated cats. Foods which aid in the management of these diseases may be enhanced through understanding the underlying metabolomic changes. Objective Assess the metabolomic profile with a view to identifying metabolomic targets which could aid in the management of renal disease and CaOx uroliths. Method This is a retrospective investigation of 42 cats: 19 healthy kidney controls, 11 with RD, and 12 that formed CaOx nephroliths. Cats were evaluated as adults (2 through 7 years) and at the end of life for plasma metabolomics, body composition, and markers of renal dysfunction. Kidney sections were assessed by Pizzolato stain at the end of life for detection of CaOx crystals. CaOx stone presence was also assessed by analysis of stones removed from the kidney at the end of life. Results There were 791 metabolites identified with 91 having significant (p < 0.05, q < 0.1) changes between groups. Many changes in metabolite concentrations could be explained by the loss of renal function being most acute in the cats with RD while the cats with CaOx stones were intermediate between control and RD (e.g., urea, creatinine, pseudouridine, dimethylarginines). However, the concentrations of some metabolites differentiated RD from CaOx stone forming cats. These were either increased in the RD cats (e.g., cystathionine, dodecanedioate, 3-(3-amino-3-carboxypropyl) uridine, 5-methyl-2′-deoxycytidine) or comparatively increased in the CaOx stone forming cats (phenylpyruvate, 4-hydroxyphenylpyruvate, alpha-ketobutyrate, retinal). Conclusions The metabolomic changes show specific metabolites which respond generally to both renal diseases while the metabolomic profile still differentiates cats with RD and cats with CaOx uroliths.
 
Introduction The leptin signaling pathway plays an important role as a key regulator of glucose homeostasis, metabolism control and systemic inflammatory responses. However, the metabolic effects of leptin on infectious diseases, for example tuberculosis (TB), are still little known. Objectives In this study, we aim to investigate the role of leptin on metabolism in the absence and presence of mycobacterial infection in zebrafish larvae and mice. Methods Metabolites in entire zebrafish larvae and the blood of mice were studied using high-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and mass spectrometry, respectively. For transcriptome studies of zebrafish larvae, deep RNA sequencing was used. Results The results show that leptin mutation leads to a similar metabolic syndrome as caused by mycobacterial infection in the two species, characterized by the decrease of 11 amine metabolites. In both species, this metabolic syndrome was not aggravated further when the leptin mutant was infected by mycobacteria. Therefore, we conclude that leptin and mycobacterial infection are both impacting metabolism non-synergistically. In addition, we studied the transcriptomes of lepb ibl54 mutant zebrafish larvae and wild type (WT) siblings after mycobacterial infection. These studies showed that mycobacteria induced a very distinct transcriptome signature in the lepb ibl54 mutant zebrafish compared to WT sibling control larvae. Furthermore, lepb ibl55 Tg ( pck1:luc1 ) zebrafish line was constructed and confirmed this difference in transcriptional responses. Conclusions Leptin mutation and TB lead non-synergistically to a similar metabolic syndrome. Moreover, different transcriptomic responses in the lepb ibl54 mutant and TB can lead to the similar metabolic end states.
 
Introduction Nowadays,the mechanical ventilation (MV) aims to rest the respiratory muscles while providing adequate gas exchange, and it has been a part of basic life support during general anesthesia as well as in critically ill patients with and without respiratory failure. However, MV itself has the potential to cause or worsen lung injury, which is also known as ventilator-induced lung injury (VILI). Thus, the early diagnosis of VILI is of great importance for the prevention and treatment of VILI. Objective This study aimed to investigate the metabolomes in the lung and plasma of mice receiving mechanical ventilation (MV). Methods Healthy mice were randomly assigned into control group; (2) high volume tidal (HV) group (30 ml/kg); (3) low volume tidal (LV) group (6 ml/kg). After ventilation for 4 h, mice were sacrificed and the lung tissue and plasma were collected. The lung and plasma were processed for the metabolomics analysis. We also performed histopathological examination on the lung tissue. Results We detected moderate inflammatory damage with alveolar septal thickening in the HV group compared with the normal and LV groups.The metabolomics analysis results showed MV altered the metabolism which was characterized by the dysregulation of γ-amino butyric acid (GABA) system and urea cycle (desregulations in plasma and lung guanidinosuccinic acid, argininosuccinic acid, succinic acid semialdehyde and lung GABA ), Disturbance of citric acid cycle (CAC) (increased plasma glutamine and lung phosphoenol pyruvate) and redox imbalance (desregulations in plasma and/or lung ascorbic acid, chenodeoxycholic acid, uric acid, oleic acid, stearidonic acid, palmitoleic acid and docosahexaenoic acid). Moreover, the lung and plasma metabolomes were also significantly different between LV and HV groups. Conclusions Some lung and plasma metabolites related to the GABA system and urea cycle, citric acid cycle and redox balance were significantly altered, and they may be employed for the evaluation of VILI and serve as targets in the treatment of VILI.
 
Multiblock PLS-DA scores plot from the metabolomics A and proteomics B data, and the most significant metabolites C and proteins D from the partial correlation analysis. Multiblock sPLS-DA model performance was assessed using ‘perf’ function from ‘mixOmics’ package, obtaining an AUC = 0.95 for the metabolomics block and AUC = 0.97 for the proteomics block
Main confounding factors in the proteomics and metabolomics datasets. Metabolomics data demonstrates that (case–control (CC) > medication types > BMI > sex > age) has influence on the data variance, whilst proteomics data suggests (case–control (CC) > medication types > BMI > sex > age) has influence on the data variance
Partial correlation network analysis presenting the interactions between metabolites (M), proteins (P) and demographic data
Beanplots of the selected metabolites in the partial correlation analysis and their distribution in the sample groups
Metabolomics molecular signature panel built using ridge-logistic regression model [BD vs. HC] adjusted for age, sex and BMI
Introduction Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities. Objectives This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease. Methods Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites. Results Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914). Conclusion From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.
 
The spectral binning approach for post-acquisition processing of FIE-HRMS metabolomic fingerprinting data. The blue and red arrows denote the actions applied to 0.01 and 0.00001 amu binned data respectively
An example FIE-HRMS ion chromatogram. The grey area shows plug flow region that is extracted for spectral binning. The red dashed line shows the 50% level of the maximum ion count, above which scans are selected for processing
The average bin purity at incremental binning width (amu) across the example biological sample matrices for each ionisation mode. Bin purity was averaged across all bins from the 10 technical injections of each matrix. The error bars show the standard error of the mean
Error of accurate m/z matched to the predicted ionisation products in the standards mix plotted against a their detected abundance and b the calculated 0.01 amu bin purity
Introduction Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis. Objectives Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data. Methods A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively. Results The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB. Conclusion Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.
 
a Tuning plot and b shrinkage plot of elastic net regression for metabolites differentiating osteoporosis
Importance plot of metabolites differentiating osteoporosis. a women; b men; c both
Introduction and objectives Amino acids are the most frequently reported metabolites associated with low bone mineral density (BMD) in metabolomics studies. We aimed to evaluate the association between amino acid metabolic profile and bone indices in the elderly population. Methods 400 individuals were randomly selected from 2384 elderly men and women over 60 years participating in the second stage of the Bushehr elderly health (BEH) program, a population-based prospective cohort study that is being conducted in Bushehr, a southern province of Iran. Frozen plasma samples were used to measure 29 amino acid and derivatives metabolites using the UPLC-MS/MS-based targeted metabolomics platform. We conducted Elastic net regression analysis to detect the metabolites associated with BMD of different sites and lumbar spine trabecular bone score, and also to examine the ability of the measured metabolites to differentiate osteoporosis. Results We adjusted the analysis for possible confounders (age, BMI, diabetes, smoking, physical activity, vitamin D level, and sex). Valine, leucine, isoleucine, and alanine in women and tryptophan in men were the most important amino acids inversely associated with osteoporosis (OR range from 0.77 to 0.89). Sarcosine, followed by tyrosine, asparagine, alpha aminobutyric acid, and ADMA in women and glutamine in men and when both women and men were considered together were the most discriminating amino acids detected in individuals with osteoporosis (OR range from 1.15 to 1.31). Conclusion We found several amino acid metabolites associated with possible bone status in elderly individuals. Further studies are required to evaluate the utility of these metabolites as clinical biomarkers for osteoporosis prediction and their effect on bone health as dietary supplements.
 
The global population is aging. Preserving function and independence of our aging population is paramount. A key component to maintaining independence is the preservation of cognitive function. Metabolomics can be used to identify biomarkers of cognition before noticeable deterioration. Our study investigated the plasma metabolome of 332 community-living New Zealanders between 65 and 74 years of age, using gas chromatography-mass spectrometry. Six cognitive domains were assessed. Of the 123 metabolites identified using an in-house mass spectral libraries of standards, nervonic acid had a significant, inverse association with the attention domain (P-value = 1.52E − 4 ; FDR = 0.019), after adjusting for covariates (apolipoprotein E -ε4 genotype, sex, body fat percentage (standardised by sex), age, education, deprivation index, physical activity, metabolic syndrome, polypharmacy, smoking status, and alcohol intake) and multiple testing. Attention is defined as the ability to concentrate on selected aspects of the environment while ignoring other stimuli. This is the first study to identify nervonic acid as a potential biomarker of attention in older adults. Future research should confirm this association in a longitudinal study.
 
Identifying significant metabolic pathways related to gastric cancer occurrence using nontargeted metabolite screening in the discovery set. A OPLS-DA score plots were obtained by comparison between the control (n = 52) and gastric cancer occurrence (n = 50) groups. B Eighteen putative metabolite sets differed between two groups obtained by enrichment analysis
Comparisons of each metabolite between two groups in the validation set. Mean ± standard error (SE). P-values were derived from an independent t-test adjusted for age, sex, and smoking status for comparison between the control group and the gastric cancer occurrence group. §Tested following logarithmic transformation. Relative peak area (y-axis) was calculated by target metabolite peak area/IS peak area
ROC curves for prediction of GC. Prediction models in the total subjects of validation set (n = 87). The blue line is a GC prediction model consist of age, sex, GGT. The red line is a prediction model added metabolites, associated with GC development (age, sex, GGT, l-carnitine, and citric acid). The green line is a reference line
Correlations among the clinical traits, biochemical traits, and metabolites for the subjects in the validation set. r Pearson’s correlation coefficients adjusted for age, sex, and smoking status. Red represents a positive correlation, and blue represents a negative correlation. †p < 0.05, ††p < 0.01, ‡p < 0.001. A Control (n = 43) group. B Gastric cancer occurrence (n = 44) group
Introduction Monitoring metabolic biomarkers could be utilized as an effective tool for the early detection of gastric cancer (GC) risk. Objective We aimed to discover predictive serum biomarkers for GC and investigate biomarker-related metabolism. Methods Subjects were randomly selected from the Korean Cancer Prevention Study-II cohort and matched by age and sex. We analyzed baseline serum samples of 160 subjects (discovery set; control and GC occurrence group, 80 each) via nontargeted screening. Identified putative biomarkers were validated in baseline serum samples of 140 subjects (validation set; control and GC occurrence group, 70 each) using targeted metabolites analysis. Results The final analysis was conducted on the discovery set (control, n = 52 vs. GC occurrence, n = 50) and the validation set (control, n = 43 vs. GC occurrence, n = 44) applying exclusion conditions. Eighteen putative metabolite sets differed between two groups found on nontargeted metabolic screening. We focused on fatty acid-related energy metabolism. In targeted analysis, levels of decanoyl-l-carnitine (p = 0.019), l-carnitine (p = 0.033), and citric acid (p = 0.025) were significantly lower in the GC occurrence group, even after adjusting for age, sex, and smoking status. Additionally, l-carnitine and citric acid were confirmed to have an independently significant relationship to GC development. Notably, alkaline phosphatase showed a significant correlation with these two biomarkers. Conclusion Changes in serum l-carnitine and citric acid levels that may result from alterations of fatty-acid-related energy metabolism are expected to be valuable biomarkers for the early diagnosis of GC risk.
 
Partial Least Squares Discriminant Analysis of A homogenate and B cell lysate samples, comparing TB infected (inf) in green and negative badgers (Cntrl) in red. The shaded circles show 95% CI for each group (N = 12)
The major sources of variation in homogenates and cell lysates from TB infected and negative badgers. Heatmap of all t-test significant m/z values with an AUC of 1 from FIE-MS on the A homogenate and B cell lysate samples. Hierarchical clustering can be seen with clear separation of the TB positive and TB negative samples (N = 12). The significant m/z from C homogenates and D cell lysates were assessed by pathway enrichment analysis. Y-axis:-log p-values from pathway enrichment analysis. X-axis: pathway impact values from pathway topology analysis. The node colour and radius is based on its p-value and pathway impact values, respectively
Heatmap of all t-test significant m/z values with an AUC of 1 from mummichog algorithm identification of metabolites by FIE-MS on the homogenate samples. ierarchical clustering can be seen with clear separation of the TB positive and TB negative samples (N = 12)
Heatmap of all t-test significant m/z values with an AUC of 1 from mummichog algorithm identification of metabolites by FIE-MS on the cell lysate samples. Hierarchical clustering can be seen with clear separation of the TB positive and TB negative samples (N = 12)
Introduction Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB) in cattle, represents a major disease burden to UK cattle farming, with considerable costs associated with its control. The European badger (Meles meles) is a known wildlife reservoir for bTB and better knowledge of the epidemiology of bTB through testing wildlife is required for disease control. Current tests available for the diagnosis of bTB in badgers are limited by cost, processing time or sensitivities. Materials and Methods We assessed the ability of flow infusion electrospray—high-resolution mass spectrometry (FIE-HRMS) to determine potential differences between infected and non-infected badgers based on thoracic blood samples obtained from badgers found dead in Wales. Thoracic blood samples were autoclaved for handling in a containment level 2 (CL2) hazard laboratory. Results Here we show the major differences associated with with M. bovis infection were changes to folate, pyrimidine, histidine, glycerophospholipid and phosphonate metabolism. Conclusions Our studies have indicated differences in the metabolomic signature of badgers found dead in relation to their infection status, suggesting metabolomics could hold potential for developing novel diagnostics for bTB in badgers. As well as highlighting a potential way to handle samples containing a highly pathogenic agent at CL2 for metabolomics studies.
 
Introduction Obesity occurs partly due to consumption of a high-fat, high-sugar and low fiber diet and is associated with an altered gut microbiome. Prebiotic supplementation can reverse obesity and beneficially alter the gut microbiome, evidenced by previous studies in rodents. However, the role of the small intestinal metabolome in obese and prebiotic supplemented rodents has never been investigated. Objectives To investigate and compare the small intestinal metabolome of healthy and obese rats, as well as obese rats supplemented with the prebiotic oligofructose (OFS). Methods Untargeted metabolomics was performed on small intestinal contents of healthy chow-fed, high fat diet-induced obese, and obese rats supplemented with oligofructose using UPLC-MS/MS. Quantification of enterohepatic bile acids was performed with UPLC-MS to determine specific effects of obesity and fiber supplementation on the bile acid pool composition. Results The small intestinal metabolome of obese rats was distinct from healthy rats. OFS supplementation did not significantly alter the small intestinal metabolome but did alter levels of several metabolites compared to obese rats, including bile acid metabolites, amino acid metabolites, and metabolites related to the gut microbiota. Further, obese rats had lower total bile acids and increased taurine-conjugated bile acid species in enterohepatic circulation; this effect was reversed with OFS supplementation in high fat-feeding. Conclusion Obesity is associated with a distinct small intestinal metabolome, and OFS supplementation reverses some metabolite levels that were altered in obese rats. Future research into the effects of specific metabolites identified in this study will provide deeper insight into the mechanism of fiber supplementation on improved body weight.
 
MRM chromatograms of the 47 acylcarnitines detected in the serum pool
Influence of the organic mobile phase composition on the ionization of the predominant acylcarnitines, represented by MeOH concentration in phase B
Comparison of the quantitative signal for predominant acylcarnitines provided by the proposed method and protein precipitation using different organic solvents (IPA, MeOH and ACN)
Acylcarnitines (ACs) are metabolites involved in fatty acid β-oxidation and organic acid metabolism. Metabolic disorders associated to these two processes can be evaluated by determining the complete profile of ACs. In this research, we present an overall strategy for identification, confirmation, and quantitative determination of acylcarnitines in human serum. By this strategy we identified the presence of 47 ACs from C2 to C24 with detection of the unsaturation degree by application of a data-independent acquisition (DIA) liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. Complementary, quantitative determination of ACs is based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC–MS/MS in data-dependent acquisition (DDA) to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL–1 level, the intra-day and between-day variability were below 15–20%, respectively; and the accuracy, expressed as bias, was always within ± 25%. The proposed method was tested with 40 human volunteers to determine the relative concentration of ACs in serum and identify predominant forms. Significant differences were detected by comparing the ACs profile of obese versus non-obese individuals.
 
Diagram of machine learning workflow. Baser learners are trained on the internal validation set using fivefold cross validation with 10 resampling iterations on each feature subset. Feature selection is employed by base learner variable importance. After all base learners are trained and evaluated, a stacked ensemble model is evaluated after filtering base learners which did not achieve an AUROCTRAIN of 0.7 or greater across all feature subsets in the internal validation data. The ensemble model is then evaluated on all feature subsets using an ensemble method of feature selection (Eq. 1). The classification model performance of all base-learners and meta-learners is evaluated across the feature subsets on the external validation data. EVTREE = tree models from genetic algorithms. RF random forest, NNET neural network (single layer), MLP multi-layer perceptron, NSC nearest shrunken centroids, NB naïve Bayes, BGLM boosted general linear model, KNN k-nearest neighbors, SVM support vector machine, SPLS sparse partial least squares, BLR boosted logistic regression, RLR regularized logistic regression, NNFE neural network with feature extraction, WKNN weighted k-nearest neighbors, MANN model averaged neural network, RRF regularized random forest, BGAM boosted generalized additive model. ORFSVM oblique random forest with SVM as splitting model, SVMPoly support vector machine with polynomial kernel
Maximum AUROC obtained from feature selection after external test set validation of all base learner models and stacked ensemble meta learners for Overall Survival and Progression Free Survival. Patients were stratified into “long” and “short” survival groups for classification by the prediction models. Base learners which achieved a max AUROCTRAIN of 0.7 or above in the internal validation data (gray bars, top row) were selected for the stacked ensemble models (black bars, middle row). ROC curves of optimal stacked ensemble meta learners with repeated internal cross-validation (gray) and external validation (black) for prediction of “long” and “short” OS and PFS are shown for each case (bottom row)
Relative abundance of metabolites identified as significant for “short” versus “long” OS and PFS by unpaired T-test assuming equal variance or Wilcoxon rank sum test, depending on normality of the data. Each box represents 1st and 3rd quartiles. Bands within represent the median and x is the mean. Ends of whiskers are maximum and minimum, with points outside being outliers. “Long” survival groups are in green and “Short” is in yellow (*p ≤ 0.1, **p ≤ 0.05). Color figure online
Quantitative enrichment analysis. Enriched metabolic pathways were found with MetaboAnalyst 5.0 using KEGG pathway database for OS and PFS (Color figure online). KEGG database was accessed June 2022
Key metabolic biomarkers identified by ensemble feature selection (EFS), where the top 25 metabolites are shown.
Introduction While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. Objectives Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. Methods Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting “short” versus “long” survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. Results Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. Conclusion This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.
 
Forest plot of measurands associated (p < 0.0036) with high serum fructosamine. The odds ratio represents the strength of the measurand association to high serum fructosamine, values under 1 indicating an inverse relationship, and values over 1 indicating a direct relationship. Due to data scaling to median absolute deviation (MAD) units, a one-unit increase in the odds ratio corresponds to one MAD of the measurand. In quadratic measurands (SQ), an increased odds ratio corresponds to the squared value of the MAD scaled data. N cases = 79, n controls = 25. CI confidence interval, BCAA branched-chain amino acids, HDL high-density lipoprotein, LDL low-density lipoprotein, VLDL very low-density lipoprotein, SFA saturated fatty acids, Omega6 omega-6 fatty acids, L- large, XL- very large, S- small
Forest plot of measurands associated (p < 0.0036) with low T4 concentration. The odds ratio represents the strength of the measurand association to low T4 concentrations, values under 1 indicating an inverse relationship, and values over 1 indicating a direct relationship. Due to data scaling to median absolute deviation (MAD) units, a one-unit increase in the odds ratio corresponds to one MAD of the measurand. In quadratic measurands (SQ), an increased odds ratio corresponds to the squared value of the MAD scaled data. N cases = 47, n controls = 25. CI confidence interval, VLDL very low-density lipoprotein, SFA saturated fatty acids, HDL high-density lipoprotein, L- large. S- small, LDL low-density lipoprotein, XL- very large
Introduction Metabolomics studies in canine endocrine abnormalities are sparse and basic information on these abnormalities must be generated. Objectives To characterize the metabolic changes associated with elevated fructosamine, reflecting poor glycemic control, and low thyroxine, a thyroid hormone controlling metabolism. Methods Leftovers of clinical serum samples; 25 controls, 79 high fructosamine, and 47 low thyroxine, were analyzed using ¹H NMR and differences were evaluated using Firth logistic regression. Results Both high fructosamine and low thyroxine were associated with changes in concentrations of multiple metabolites, including glycoprotein acetyls and lipids. Conclusion These findings suggest promising makers for further research and clinical validation.
 
E. coli growth profiles (a), succinic production (b) and glycerol in the culture medium (c) of M4 mutant, M4-∆gnd and M4-∆iclR strains in mineral M9 medium (Soto-Varela, 2021) with glycerol and 2 g/L of bicarbonate over a 72 h experiment. Data points are average ± standard deviation with n = 6–9. The asterisk (*) indicates there is statistically significant differences between M4-∆gnd an M4 mutant strains and this symbol (‡) indicates the M4-∆iclR values are statistically significant differences respect to M4 mutant values by statistics Student’s t-test with P value < 0.05
PCA scores plot of the GC–MS data (relative peak areas) from M4 mutant, M4-∆gnd and M4-∆iclR strains at 24 and 48 h after inoculation (n = 3). For plotting PC1 (x-axis) and PC2 (y-axis) were used with of 29.1% and 11.9 of total explained variance (TEV) respectively. QC means quality control samples as described in material and methods section
Map of central carbon pathway of glycolysis, TCA cycle, gluconeogenesis, glycerol assimilation, Pentose Phosphate Pathway (PPP), and several aminoacids pathways, was constructed following EcoCyc Database information including plots with the relativized metabolite values of M4∆gnd and M4-∆iclR strains respect to the M4 values at 24 and 48 h after inoculation with three replicates. In green bars are represented the M4-∆gnd respect to M4 values at 24 h (dark green bars) and at 48 h (light green bars). In grey bars are represented the M4-∆iclR respect to M4 values at 24 h (dark grey bars) and at 48 h (light grey bars) (n = 3). Absence of bars in the graph indicates that there are non-statistically significant differences between the M4-∆gnd and M4-∆iclR mutants respect to that obtained in M4 strain. The asterisks (*) indicate there is statistically significant differences between M4-∆gnd an M4-∆iclR. The red cross (X) indicates the blocking of the pathways by the gene deletion in M4 (1), M4-∆gnd (2), M4-∆iclR (3), M4-∆gnd∆mtlD and M4-∆iclR∆mtlD (4) and M4-∆iclR∆otsA (5) mutant strains. The abbreviations for the intermediate metabolites used in this figure are described as follows: Glyc, glycerol; DHA, dihydroxyacetone; Glyc-3P, glycerol 3-Phosphate; DHAP, dihydroxyacetone phosphate; GAP, glyceraldehyde 3-phosphate; 1,3 BPG, 1,3-bisphosphoglycerate; 3PG, 3-phospho-D-glycerate; 2PG, 2-phosphoglycerate; PEP, phosphoenolpyruvate; Pyr, pyruvate; Ac-CoA, acetyl CoA; Cit, citrate; Aco, aconitate; Isoc, isocitrate; αKG, α-ketoglutarate or 2-oxoglutarate; Suc-CoA, succinyl-CoA; Suc, succinate; Fum, fumarate; Mal, malate; OAA, oxaloacetate; Glyox, glyoxylate; Glyc, glycolate; IclR, isocitrate lyase repressor protein; Glu, glutamate; Gln, glutamine; Act-P, Acetyl phosphate; Ac, acetate; Ala, alanine; 3-Met-2-Ox, 3-methyl-2-oxobutanoate; 4-Met,2-Ox, 4-methyl-2-oxopentanoate; Val, valine; Leu, leucine; Ser, serine; MetGlyox, methylglyoxal; Lac, lactate; FBF, fructose 1,6-bisphosphate; F6P, fructose 6-phosphate; G6P, glucose 6-P; Tre, trehalose; G1P, glucose 1-phosphate; Gal, galactose; Rham, rhamnose; Mnl, mannitol; Man-6P, mannose 6-phosphate; Fru, fructose; 6-PGL, 6-phosphoglucono-1,5-lactone; 6PGC, gluconate 6-phosphate; Ru-5P, ribulose 5-phosphate; Xu-5P, xylulose 5-phosphate; R-5P, ribose 5-phosphate; Sed-7P, sedoheptulose 7-phosphate; E4P, erythrose 4-phophate; Shik, shikimate; Cho, chorismate; 3-Phe-Pyr, 3-Phenylpyruvic acid; Phe, phenylalanine; 2K-3DGP, 2-keto-3-deoxy-6-phospho gluconate. In red are indicated the metabolites that are shown twice in the diagram
Heatmap performed with metabolites signals average data with n = 3 obtained from GC–MS analysis which are involved N-metabolism of M4-∆gnd and M4-∆iclR strains at 24 and 48 h after inoculation. The software used was Heatmapper with Spearman rank correlation of distance measurement method and the clustering method of average linkage
E. coli profiles of succinic acid (a and b), remaining glycerol in the culture medium (c and d) and g Cell Dried Weight (CDW)/L (e and f) of: M4-∆gnd and M4-∆gnd∆mtlD mutant strains (a, c and e); M4-∆iclR, M4-∆iclR∆mtlD and M4-∆iclR∆otsA mutant strains (b, d and e). All of the strains were cultured in M9 medium with glycerol and 4 g/L of bicarbonate over a 48 h experiment. Data points are average ± standard deviation with n = 4–9. Asterisks denote statistically significant differences between the group’s averages using Student’s t- test with P value < 0.05; the comparison of M4-∆gnd vs M4-∆gnd∆mtlD mutant strains (a) and M4-∆iclR vs M4-∆iclR∆mtlD (b) (*); and the comparison of M4-∆iclR vs M4-∆iclR∆otsA (b and f) (**). The ANOVA showed that the variances between the groups were homogenous using Levene’s test with P value < 0.05
Introduction Glycerol is a byproduct from the biodiesel industry that can be biotransformed by Escherichia coli to high added-value products such as succinate under aerobic conditions. The main genetic engineering strategies to achieve this aim involve the mutation of succinate dehydrogenase (sdhA) gene and also those responsible for acetate synthesis including acetate kinase, phosphate acetyl transferase and pyruvate oxidase encoded by ackA, pta and pox genes respectively in the ΔsdhAΔack-ptaΔpox (M4) mutant. Other genetic manipulations to rewire the metabolism toward succinate consist on the activation of the glyoxylate shunt or blockage the pentose phosphate pathway (PPP) by deletion of isocitrate lyase repressor (iclR) or gluconate dehydrogenase (gnd) genes on M4-ΔiclR and M4-Δgnd mutants respectively. Objective To deeply understand the effect of the blocking of the pentose phosphate pathway (PPP) or the activation of the glyoxylate shunt, metabolite profiles were analyzed on M4-Δgnd, M4-ΔiclR and M4 mutants. Methods Metabolomics was performed by FT-IR and GC–MS for metabolite fingerprinting and HPLC for quantification of succinate and glycerol. Results Most of the 65 identified metabolites showed lower relative levels in the M4-ΔiclR and M4-Δgnd mutants than those of the M4. However, fructose 1,6-biphosphate, trehalose, isovaleric acid and mannitol relative concentrations were increased in M4-ΔiclR and M4-Δgnd mutants. To further improve succinate production, the synthesis of mannitol was suppressed by deletion of mannitol dehydrogenase (mtlD) on M4-ΔgndΔmtlD mutant that increase ~ 20% respect to M4-Δgnd. Conclusion Metabolomics can serve as a holistic tool to identify bottlenecks in metabolic pathways by a non-rational design. Genetic manipulation to release these restrictions could increase the production of succinate.
 
Introduction: Data-dependent acquisition (DDA) is the most commonly used MS/MS scan method for lipidomics analysis on orbitrap-based instrument. However, MS instrument associated software decide the top N precursors for fragmentation, resulting in stochasticity of precursor selection and compromised consistency and reproducibility. We introduce a novel workflow using biologically relevant lipids to construct inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow. Objectives: To ensure consistent coverage of biologically relevant lipids in LC-MS/MS-based lipidomics analysis. Methods: Biologically relevant ion list was constructed based on LIPID MAPS and lipidome atlas in MS-DIAL 4. Lipids were extracted from mouse tissues and used to assess different MS/MS scan workflow (DDA, BRI-DIA, and hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer. Results: DDA resulted in more MS/MS events, but the total number of unique lipids identified by three methods (DDA, BRI-DIA, and hybrid MS/MS scan mode) is comparable (580 unique lipids across 44 lipid subclasses in mouse liver). Major cardiolipin molecular species were identified by data generated using BRI-DIA and hybrid methods and allowed calculation of cardiolipin compositions, while identification of the most abundant cardiolipin CL72:8 was missing in data generated using DDA method, leading to wrong calculation of cardiolipin composition. Conclusion: The method of using inclusion list comprised of biologically relevant lipids in DIA MS/MS scan is as efficient as traditional DDA method in profiling lipids, but offers better consistency of lipid identification, compared to DDA method. This study was performed using Orbitrap Exploris 480, and we will further evaluate this workflow on other platforms, and if verified by future work, this biologically relevant ion fragmentation workflow could be routinely used in many studies to improve MS/MS identification capacities.
 
Top-cited authors
Thomas Hankemeier
  • Leiden University
David Scott Wishart
  • University of Alberta
Royston Goodacre
  • University of Liverpool
Oliver Fiehn
  • University of California, Davis
Johan Westerhuis
  • University of Amsterdam