Liquid Chromatography-mass Spectrometry-based Metabolomics Research Articles

Longitudinal analysis of fecal microbiome and metabolome during renal fibrotic progression in a unilateral ureteral obstruction animal model

Renal fibrosis is a major pathological process in the progression of various chronic kidney diseases to end-stage renal disease (ESRD). Growing evidence has suggested that gut microbiota dysbiosis is closely related to ESRD. However, the interplay between altered fecal microbiome and metabolome during the renal fibrotic process remains unclear. Herein, an integrated approach of 16S ribosomal DNA sequencing combined with an ultra-high performance liquid chromatography-mass spectrometry-based metabolomics platform was applied to investigate the dynamic changes of fecal microbiota and metabolites throughout renal fibrosis progression in a mouse model of unilateral ureteral obstruction (UUO). The composition of gut microbiota changed markedly before and after UUO surgery. UUO mice showed a decrease in short-chain fatty acids-producing genera, including Bacteroides, Prevotellaceae_UCG-001, Roseburia, and Lachnospiraceae_NK4A136_group, as well as an increase in the genera Parasutterella and Alistipes, which changed dynamically over time. Additionally, 41 differential metabolites, mainly involved in 12 metabolic pathways, including inositol phosphate metabolism, primary bile acid biosynthesis, biosynthesis of unsaturated fatty acids, taurine and hypotaurine metabolism, purine metabolism, were identified in the UUO mice before and after surgery. Four fecal metabolites, myo-inositol, dodecanoic acid, N-acetylputrescine, and anthranilic acid, were positively associated with the progression of renal fibrosis. Moreover, by using multi-omics analyses, we found the alteration in UUO-related gut microbiota was correlated with a change in fecal metabolites. Therefore, our results provide insights into disturbances of the microbiome–metabolome interface in the progression of UUO-related renal fibrosis.

Metabolic perturbations prior to hepatocellular carcinoma diagnosis: Findings from a prospective observational cohort study.

Hepatocellular carcinoma (HCC) development entails changes in liver metabolism. Current knowledge on metabolic perturbations in HCC is derived mostly from case-control designs, with sparse information from prospective cohorts. Our objective was to apply comprehensive metabolite profiling to detect metabolites whose serum concentrations are associated with HCC development, using biological samples from within the prospective European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (>520 000 participants), where we identified 129 HCC cases matched 1:1 to controls. We conducted high-resolution untargeted liquid chromatography-mass spectrometry-based metabolomics on serum samples collected at recruitment prior to cancer diagnosis. Multivariable conditional logistic regression was applied controlling for dietary habits, alcohol consumption, smoking, body size, hepatitis infection and liver dysfunction. Corrections for multiple comparisons were applied. Of 9206 molecular features detected, 220 discriminated HCC cases from controls. Detailed feature annotation revealed 92 metabolites associated with HCC risk, of which 14 were unambiguously identified using pure reference standards. Positive HCC-risk associations were observed for N1-acetylspermidine, isatin, p-hydroxyphenyllactic acid, tyrosine, sphingosine, l,l-cyclo(leucylprolyl), glycochenodeoxycholic acid, glycocholic acid and 7-methylguanine. Inverse risk associations were observed for retinol, dehydroepiandrosterone sulfate, glycerophosphocholine, γ-carboxyethyl hydroxychroman and creatine. Discernible differences for these metabolites were observed between cases and controls up to 10 years prior to diagnosis. Our observations highlight the diversity of metabolic perturbations involved in HCC development and replicate previous observations (metabolism of bile acids, amino acids and phospholipids) made in Asian and Scandinavian populations. These findings emphasize the role of metabolic pathways associated with steroid metabolism and immunity and specific dietary and environmental exposures in HCC development.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Liquid chromatography-mass spectrometry-based metabolomics analysis of flavonoids and anthraquinones in Fagopyrum tataricum L. Gaertn. (tartary buckwheat) seeds to trace morphological variations.

Polyphenols (flavonoids and anthraquinones) are one of the most important phytochemicals in Fagopyrum tataricum L. Gaertn. (tartary buckwheat). However, the relationship between the polyphenols of tartary buckwheat seeds and their morphological variations is unclear. We developed a liquid chromatography-mass spectrometry-based targeted metabolomics method to study the chemical profiles of 60 flavonoids and 11 anthraquinones in 40 seed cultivars (groats and hulls). Both flavonoids and anthraquinones were related to variations in seed color; the fold change from yellowish-brown to black seeds was 1.24-1.55 in groats and 0.26-0.76 in hulls. Only flavonoids contributed to significant differences in seed shape; the fold change from long to short seeds was 1.29-1.78 in groats and 1.39-1.44 in hulls. Some differential metabolites were identified at higher concentrations in hulls than in groats. This study provides new insights into differences in polyphenols among tartary buckwheat seeds with different color and shape.

Changes in Metabolites from Bovine Milk with β-Casein Variants Revealed by Metabolomics.

β-casein is a primary protein in milk, and its variants have been associated with changes in the protein content of bovine milk. However, there has been little research focused on the effects of β-casein variants on milk metabolites. In the present study, dairy cows producing milk with β-casein variant A1/A1 (A1), A2/A2 (A2), and their heterozygote A1/A2 (A12) were screened by a high-resolution melting method. Individual milk samples were then collected from each of the cows, and the milk metabolites were separated and analyzed using nuclear magnetic resonance spectroscopy- and liquid-chromatography mass spectrometry-based metabolomics techniques. Differences in metabolites among the variant groups were evaluated by multivariate statistical analysis. The relative abundances of methionine, proline, and α-lactose were the highest in β-casein variant A2 milk, whereas choline, glycine, citric acid, and cyclic adenosine monophosphate (cAMP) showed the highest abundances in variant A1 milk. Metabolic pathways analysis indicated that the differential metabolites between variants A1 and A2 were involved in pantothenate and coenzyme A biosynthesis, butanoate metabolism, and valine, leucine, and isoleucine biosynthesis. Our results reveal the differences in milk metabolites among the β-casein variants A1, A2, and the heterozygote. These findings, thus, provide novel insights into the effects of β-casein variants on milk metabolites, facilitating further research into the mechanism of the biosynthesis of milk components in the mammary gland and the potential physiological function of milk associated with β-casein variants.

Investigation of Early Death-Induced Changes in Rat Brain by Solid Phase Microextraction via Untargeted High Resolution Mass Spectrometry: In Vivo versus Postmortem Comparative Study.

Analysis of brain samples obtained postmortem remains a standard approach in neuroscience, despite often being suboptimal for inferring roles of small molecules in the pathophysiology of brain diseases. Sample collection and preservation further hinders conclusive interpretation of biomarker analysis in autopsy samples. We investigate purely death-induced changes affecting rat hippocampus in the first hour of postmortem interval (PMI) by means of untargeted liquid chromatography-mass spectrometry-based metabolomics. The unique possibility of sampling the same brain area of each animal both in vivo and postmortem was enabled by employing solid phase microextraction (SPME) probes. Four millimeter probes coated with mixed mode extraction phase were used to sample awake, freely roaming animals, with 2 more sampling events performed after death. Significant changes in brain neurochemistry were found to occur as soon as 30 min after death, further progressing with increasing PMI, evidenced by relative changes in levels of metabolites and lipids. These included species from several distinct groups, which can be classified as engaged in energy metabolism-related processes, signal transduction, neurotransmission, or inflammatory response. Additionally, we perform thorough analysis of interindividual variability in response to death, which provides insights into how this aspect can obscure conclusions drawn from an untargeted study at single metabolite and pathway level. The results suggest high demand for systematic studies examining the PMI time course with in vivo sampling as a starting point to eliminate artifacts in the form of neurochemical changes assumed to occur in vivo.

Current status of retention time prediction in metabolite identification.

Metabolite identification is a crucial step in nontargeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem mass spectrometry analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem mass spectrometry data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010.

Comprehensive Metabolomic and Lipidomic Analysis Reveals Metabolic Changes After Mindfulness Training

Mindfulness is a subtype of meditation. Increasing evidence suggests that trainers benefit mental and physical health improvement, but the mechanism and molecular changes of mindfulness have not been elucidated. We aim to find the metabolomic and lipidomic changes in serum after mindfulness trainings and to study how mindfulness affect body metabolism. We performed liquid chromatography-mass spectrometry-based metabolomics and lipidomics to systematically analyze the changed metabolites after mindfulness trainings. Participants (n = 27) who joined a 5-day intensive mindfulness training retreat and participants (n = 14) who joined a 5-day mindfulness training retreat in dark ambiance were recruited in the study. Serum samples of each trainer before and after mindfulness training were collected and analyzed. Statistical analysis and bioinformatics were carried out by MetaboAnalyst web service. Statistical analysis revealed that 22 metabolites were dysregulated in serum after mindfulness trainings. Pathway enrichment analysis showed that several metabolic pathways were significantly perturbed, such as arginine and proline metabolism, nitrogen metabolism, and histidine metabolism. Lipidomic analysis revealed 378 dysregulated lipids totally in two mindfulness trainings. Among these lipids, most of the lipid species showed trends of upregulation, such as Cer, PC, PE, LPC, LPE, and TAG, while a few lipid species showed trends of downregulation, such as SM, OxFA, and OxPC. Our study highlights a comprehensive view of metabolic and lipidomic changes related to mindfulness trainings, providing clues on influence of mindfulness meditation on body metabolism.

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.

Untargeted metabolomics based on liquid chromatography-mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning. An additional classifier and ranker are trained to provide adversarial regularization during the training of the AE model, latent representations are extracted by the encoder, and then the decoder reconstructs the data without batch effects. The NormAE method was tested on two real metabolomics data sets. After calibration by NormAE, the quality control samples (QCs) for both data sets gathered most closely in a PCA score plot (average distances decreased from 56.550 and 52.476 to 7.383 and 14.075, respectively) and obtained the highest average correlation coefficients (from 0.873 and 0.907 to 0.997 for both). Additionally, NormAE significantly improved biomarker discovery (median number of differential peaks increased from 322 and 466 to 1140 and 1622, respectively). NormAE was compared with four commonly used batch effect removal methods. The results demonstrated that using NormAE produces the best calibration results.

Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population-potential utility for innovative approach by predictive, preventive, and personalized medicine.

Suboptimal health status (SHS) is a subclinical stage of chronic diseases, and the identification of SHS provides an opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have reported the associations between metabolic signatures and early signs of chronic diseases. This study aimed to detect the metabolic biomarkers for the identification of SHS in a case-control study. SHS questionnaire-25 (SHSQ-25) was used in a population-based health survey to measure the SHS levels of participants. The liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls. After adjusting for the confounders, 24 significantly differential metabolites, such as sphingomyelin, sphingosine, sphinganine, progesterone, pregnanolone, and bilirubin, were identified as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an area under the receiver operating characteristic curve of 0.977. Plasma metabolites are valuable biomarkers for SHS identification, and meanwhile, SHSQ-25 can be used as an alternative health screening tool in the population-based health survey. SHS-related metabolic disturbances could be detected at the early onset of SHS, and SHS-related metabolites could create a window opportunity for PPPM of chronic diseases.

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism's biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Glycerol-3-phosphate is an FGF23 regulator derived from the injured kidney.

Fibroblast growth factor 23 (FGF23) is a bone-derived hormone that controls blood phosphate levels by increasing renal phosphate excretion and reducing 1,25-dihydroxyvitamin D3 [1,25(OH)2D] production. Disorders of FGF23 homeostasis are associated with significant morbidity and mortality, but a fundamental understanding of what regulates FGF23 production is lacking. Because the kidney is the major end organ of FGF23 action, we hypothesized that it releases a factor that regulates FGF23 synthesis. Using aptamer-based proteomics and liquid chromatography-mass spectrometry-based (LC-MS-based) metabolomics, we profiled more than 1600 molecules in renal venous plasma obtained from human subjects. Renal vein glycerol-3-phosphate (G-3-P) had the strongest correlation with circulating FGF23. In mice, exogenous G-3-P stimulated bone and bone marrow FGF23 production through local G-3-P acyltransferase-mediated (GPAT-mediated) lysophosphatidic acid (LPA) synthesis. Further, the stimulatory effect of G-3-P and LPA on FGF23 required LPA receptor 1 (LPAR1). Acute kidney injury (AKI), which increases FGF23 levels, rapidly increased circulating G-3-P in humans and mice, and the effect of AKI on FGF23 was abrogated by GPAT inhibition or Lpar1 deletion. Together, our findings establish a role for kidney-derived G-3-P in mineral metabolism and outline potential targets to modulate FGF23 production during kidney injury.

Liquid Chromatography-Mass Spectrometry-Based Plasma Metabolomics Study of the Effects of Moxibustion with Seed-Sized Moxa Cone on Hyperlipidemia.

Hyperlipidemia (HLP) is a disorder with disturbed lipid metabolism. HLP is a major risk factor in cardiovascular diseases, atherosclerosis, diabetes mellitus, and coronary heart disease. This study focuses on understanding the effects of moxibustion with a seed-sized moxa cone on HLP and the potential metabolic pathways associated with HLP. An automatic analyzer was used to measure the levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in healthy controls (HCs), HLP patients, and in patients before moxibustion with seed-sized moxa cone treatment (BMT) and after moxibustion treatment (AMT). Liquid chromatography-mass spectrometry and pathway analyses were performed using differential plasma metabolites derived from the HC, HLP, BMT, and AMT groups. Higher levels of TC, TG, and LDL-C and lower levels of HDL-C were detected in HLP patients than in HCs. The levels of TC and TG were significantly decreased in the AMT group compared to those of the BMT group. A total of 87 differential metabolites were identified from the HLP vs HC samples and 51 for the AMT vs BMT samples. Of these, 21 terms were shared. The differential metabolites in both the HLP vs HC and AMT vs BMT groups were significantly enriched in the glycerophospholipid and sphingolipid metabolism pathways. We suggest that moxibustion with seed-sized moxa cone treatment is effective against hyperlipidemia by altering the levels of TC and TG, which might be regulated by glycerophospholipid and sphingolipid metabolism.

Potential of temperature- and salinity-driven shifts in diatom compatible solute concentrations to impact biogeochemical cycling within sea ice

Sea-ice algae are an important source of primary production in polar regions, yet we have limited understanding of their responses to the seasonal cycling of temperature and salinity. Using a targeted liquid chromatography-mass spectrometry-based metabolomics approach, we found that axenic cultures of the Antarctic sea-ice diatom, Nitzschia lecointei, displayed large differences in their metabolomes when grown in a matrix of conditions that included temperatures of –1 and 4°C, and salinities of 32 and 41, despite relatively small changes in growth rate. Temperature exerted a greater effect than salinity on cellular metabolite pool sizes, though the N- or S-containing compatible solutes, 2, 3-dihydroxypropane-1-sulfonate (DHPS), glycine betaine (GBT), dimethylsulfoniopropionate (DMSP), and proline responded strongly to both temperature and salinity, suggesting complexity in their control. We saw the largest (> 4-fold) response to salinity for proline. DHPS, a rarely studied but potential compatible solute, had the highest intracellular concentrations among all compatible solutes of ~85 mM. When comparing the culture findings to natural Arctic sea-ice diatom communities, we found extensive overlap in metabolite profiles, highlighting the relevance of culture-based studies to probe environmental questions. Large changes in sea-ice diatom metabolomes and compatible solutes over a seasonal cycle could be significant components of biogeochemical cycling within sea ice.

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics.

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.

Proteinase K Combining Two-Step Liquid-Liquid Extraction for Plasma Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics To Discover the Potential Mechanism of Colorectal Adenoma.

LC-MS-based untargeted metabolomics have been proven to be an extremely promising technique to discover biomarkers and explore the mechanisms underlying diseases, which, however, relies heavily on sample pretreatment for metabolite extraction. In the present study, a systematic and pragmatic evaluation of eight protocols employing conventional metabolites extraction strategies, protein precipitation (PPT), and liquid-liquid extraction (LLE), with and without proteinase K (PK) incubation, was performed simultaneously, using human plasma and a mixture of 39 endogenous metabolite standards. These protocols were as follows: (1) PPT with methanol, (2) PPT with acetonitrile, (3) PPT with 2-propanol, (4) two-step LLE of CH2Cl2-MeOH, followed by MeOH-H2O, (5) PK incubation combining two-step LLE of CH2Cl2-MeOH followed by MeOH-H2O, (6) two-step LLE of CHCl3-MeOH, followed by MeOH-H2O, (7) PK incubation combining two-step LLE of CHCl3-MeOH, followed by MeOH-H2O, (8) PK incubation combining MeOH-EtOH PPT. The results suggested that two-step LLE produced broader metabolome coverage than protein precipitation, and the addition of proteinase K enhanced the extraction performance further. Taken together, PK incubation combining two-step LLE of CHCl3-MeOH, followed by MeOH-H2O, was determined to be the most suitable extraction method, because of its broad metabolome coverage, high reproducibility, and satisfactory recovery. Next, the developed optimal sample preparation method was applied successfully to profile the plasma metabolome of colorectal adenoma and uncover its potential mechanism for significant differential changes in linoleic acid and phospholipid metabolism.

Metabolomic Profiles for HBV Related Hepatocellular Carcinoma Including Alpha-Fetoproteins Positive and Negative Subtypes.

Background: Hepatocellular carcinoma (HCC) is very common globally prevalent cancer. Due to its poor clinical prognosis, increasing the diagnostic rate of HCC is urgently needed. Herein, we validate discovered metabolomic biomarkers to distinguish Hepatitis B virus (HBV)-related HCC, including alpha-fetoprotein (AFP) negative (AFP–) and positive (AFP+) individuals.Methods: We recruited 130 HCC subjects (independent case-control, randomized clinical cohorts) to our study. We separated the subjects randomly into two panels: (1) 58 individuals for the discovery panel; and (2) 72 individuals for the validation panel. For each panel, gender and age-matched hepatitis B group (HBG) and healthy group were included as controls. Plasma samples were collected for metabolic profiling by liquid chromatography—mass spectrometry—based metabolomics assays. We applied both non-targeted metabolomics analyses and targeted metabolomics analyses. Significantly changed metabolites (SCMs) were identified. The power of SCMs to discriminate HCC and HBG or healthy group was determined by receiver operating characteristic curve (ROC) analysis.Results: Ten SCMs were selected form the discovery panel, and further verified in the validation panel. ROC analyses indicated that 1 SCMs (LysoPC (24:0)) could discriminate HCC from HBG (AUC = 0.765). Further, 8 SCMs including (LysoPC (17:0), LysoPC (20:4(8Z,11Z,14Z,17Z)), LysoPC (22:0), LysoPC (24:0), PE (P-16:0/22:4(7Z,10Z,13Z,16Z)), SM (d18:1/22:1(13Z)), Creatinine, and L-Isoleucine) displayed a heightened ability to discriminate between HCC and healthy controls (AUC were more than 0.800). Most of these SCMs were important in lipid metabolism.Conclusions: LysoPC (24:0) could distinguished HCC from HBG, and 8 SCMs distinguished HCC from healthy controls. LysoPC and other metabolites have the potential to serve as non-invasive biomarkers for HBV related AFP– and AFP+ HCC.

Fish-oil supplementation in pregnancy, child metabolomics and asthma risk

BackgroundWe recently demonstrated that maternal dietary supplementation with fish oil-derived n-3 long-chain polyunsaturated fatty acids (n-3 LCPUFAs) during pregnancy reduces the risk of asthma in the offspring but the mechanisms involved are unknown. MethodsHere we investigated potential metabolic mechanisms using untargeted liquid chromatography-mass spectrometry-based metabolomics on 577 plasma samples collected at age 6 months in the offspring of mothers participating in the n-3 LCPUFA randomized controlled trial. First, associations between the n-3 LCPUFA supplementation groups and child metabolite levels were investigated using univariate regression models and data-driven partial least square discriminant analyses (PLS-DA). Second, we analyzed the association between the n-3 LCPUFA metabolomic profile and asthma development using Cox-regression. Third, we conducted mediation analyses to investigate whether the protective effect of n-3 LCPUFA on asthma was mediated via the metabolome. FindingsThe univariate analyses and the PLS-DA showed that maternal fish oil supplementation affected the child's metabolome, especially with lower levels of the n-6 LCPUFA pathway-related metabolites and saturated and monounsaturated long-chain fatty acids-containing compounds, lower levels of metabolites of the tryptophan pathway, and higher levels of metabolites in the tyrosine and glutamic acid pathway. This fish oil-related metabolic profile at age 6 months was significantly associated with a reduced risk of asthma by age 5 and the metabolic profile explained 24% of the observed asthma-protective effect in the mediation analysis. InterpretationSeveral of the observed pathways may be involved in the asthma-protective effect of maternal n-3 LCPUFA supplementation and act as mediators between the intervention and disease development. FundingCOPSAC is funded by private and public research funds all listed on www.copsac.com.

Comparative Metabolomics Reveals Key Pathways Associated With the Synergistic Killing of Colistin and Sulbactam Combination Against Multidrug-Resistant Acinetobacter baumannii.

Background: Polymyxins are a last-line class of antibiotics against multidrug-resistant Acinetobacter baumannii. However, polymyxin resistance can emerge with monotherapy, highlighting the need for synergistic combination therapies. Polymyxins in combination with β-lactams have shown remarkable synergy against multidrug-resistant A. baumannii. Methods: Liquid chromatography–mass spectrometry-based metabolomics was conducted to investigate the metabolic perturbations in an A. baumannii clinical isolate, AB090342, in response to colistin (1 mg/L), sulbactam (128 mg/L), and their combination at 1, 4, and 24 h. Metabolomics data were analyzed using univariate and multivariate statistics, and metabolites showing ≥2-fold changes were subjected to pathway analysis. Results: The synergistic activity of colistin–sulbactam combination was initially driven by colistin through perturbation of fatty acid and phospholipid levels at 1 h. Cell wall biosynthesis was perturbed by sulbactam alone and the combination over 24 h; this was demonstrated by the decreased levels of two important precursors, uridine diphosphate-N-acetylglucosamine and uridine diphosphate-N-acetylmuramate, together with perturbed lysine and amino sugar metabolism. Moreover, sulbactam alone and the combination significantly depleted nucleotide metabolism and the associated arginine biosynthesis, glutamate metabolism, and pentose phosphate pathway. Notably, the colistin–sulbactam combination decreased amino acid and nucleotide levels more dramatically at 4 h compared with both monotherapies. Conclusions: This is the first metabolomics study revealing the time-dependent synergistic activity of colistin and sulbactam against A. baumannii, which was largely driven by sulbactam through the inhibition of cell wall biosynthesis. Our mechanistic findings may help optimizing synergistic colistin combinations in patients.