Metabolomics approach of infant formula for the evaluation of contamination and degradation using hydrophilic interaction liquid chromatography coupled with mass spectrometry

3-Chloropropane-1,2-diol exposure adversely influenced the bio-accessibility signatures of digested infant foods by suppressing the destabilization of α-lactalbumin and d-aspartate oxidase in a dose-dependent manner

The potential mechanisms about the health risks of endogenous 3-MCPD remain elusive. Here, we researched the influences of 3-MCPD on the metabolic landscape of digested goat infant formulas via integrative UHPLC-Q-Orbitrap HRMS-MS/MS-based peptidomics and metabolomics (%RSDs ≤ 7.35 %, LOQ 2.99-58.77 μg kg^-1). Digested goat infant formulas under 3-MCPD-interference caused metabolic perturbation by down-regulating levels of peptides VGINYWLAHK (5.98-0.72 mg kg^-1) and HLMCLSWQ (3.25-0.72 mg kg^-1) pertained to health-promoting bioactive components, and accelerated the down-regulation of non-essential amino acids (AAs, l-tyrosine 0.88-0.39 mg kg^-1, glutamic acid 8.83-0.88 μg kg^-1, and d-aspartic acid 2.93-0.43 μg kg^-1), semi-essential AA (l-arginine 13.06-8.12 μg kg^-1) and essential AAs (l-phenylalanine 0.49-0.05 mg kg^-1) that provide nutritional value. Peptidomics and metabolomics interactions elucidated that 3-MCPD altered the stability of α-lactalbumin and d-aspartate oxidase in a dose-dependent manner, and affected the flavor perception of goat infant formulas, leading to a decline of nutritional value of goat infant formulas.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL-1) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg-1 and 0.3~2.1 µg kg-1 in lettuce and maize matrices, respectively.

Nontargeted screening method for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry

Nontargeted screening of both veterinary drugs and their metabolites is important for comprehensive safety evaluation of animal-derived foods. In this study, a novel nontargeted screening strategy was developed for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry. First, an in-house database of mass spectra including 3,710 veterinary drugs and their metabolites was constructed. Second, fragmentation characteristics of parent drugs and their metabolites in mass spectrometry were investigated and summarized. Then, a nontargeted screening procedure was established based on fragmentation characteristics to screen unknown parent drugs and their metabolites. Finally, the strategy was applied to 33 egg samples, and four veterinary drugs and three drug metabolites were determined and identified. These results showed that the developed strategy can realize suspect and nontargeted screening of veterinary drugs and their metabolites, and can also be applied to other animal-derived foods.

Metabolomics-based non-targeted screening analysis of 34 PPCPs in bovine and piscine muscles

The metabolomics-based analytical strategy has showed superiority on the non-targeted screening of contaminants, especially for unknown and unexpected (U&U) contaminants in the field of food safety, but data analysis is often the bottleneck of the strategy. In this study, a novel metabolomics-based analytical method via searching for marker compounds was developed on the basis of ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) results to accurately, rapidly and comprehensively achieve the non-targeted screening of 34 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked in bovine and piscine muscle matrices. Three concentration groups (20, 50 and 100 ng mL^-1) were intentionally designed to simulate the control and experimental groups for the discovery of marker compounds, for which multivariate and univariate analyses were adopted. In multivariate analysis, each concentration group was fully separated from the other two groups in PCA and OPLS-DA plots, laying a foundation to distinguish marker compounds among groups. The S-plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify marker compounds, which were further verified by pairwise t-test and fold change judgement in univariate analysis. The results indicate that 34 PPCPs spiked in two muscle matrices were all identified as marker compounds, proving the validity and practicability of this novel metabolomics-based non-targeted screening method, which will exhibit great superiority and broad application prospects, especially in the face of massive PPCPs and various animal matrices in the field of food safety control. In addition, the limits of detection (LODs) for 34 PPCPs were calculated to be 0.2-2.6 μg kg^-1 and 0.3-2.1 μg kg^-1 in bovine and piscine muscle matrices, respectively.

Metabolomics-based screening analysis of PPCPs in water pretreated with five different SPE columns

The selection of solid phase extraction (SPE) columns in the pretreatment process plays a decisive role in the screening and quantification of pharmaceutical and personal care products (PPCPs). As growing PPCPs have frequently been detected in the aquatic environment, it is a burdensome task through one-by-one recovery comparison to judge which column presents relatively ideal pretreatment results for PPCPs. In view of this, we developed a novel metabolomics-based screening method based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) results to accurately, rapidly and comprehensively choose a suitable column from 5 different kinds to handle 64 PPCPs in two water environments (50 μg L^-1/pH ≅ 7.0/pure water and 1 μg L^-1/pH ≅ 7.0/reservoir water) through seeking 'biomarkers', for which multivariate and univariate analyses were adopted. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) play a crucial role in multivariate analysis, and the pairwise t-test and fold change judgement in univariate analysis. Each column group was fully separated from the other 4 groups in PCA and OPLS-DA plots, laying a foundation to distinguish 'biomarkers' between groups. The S-Plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify 'biomarkers', which were further verified by a pairwise t-test and fold change judgement. Eventually, the 64 PPCPs as 'biomarkers' were divided into 5 groups, which correspond to 5 column groups, consistent with the findings of traditional PPCP recovery comparison, proving the validity of the metabolomics-based screening method. This novel method will exhibit greater superiority in choosing suitable SPE columns to handle a growing and larger number of PPCPs in water environments and beyond.

Metabolomic Insights into the Effects of Breast Milk Versus Formula Milk Feeding in Infants

PURPOSE OF REVIEW

This review summarizes the latest scientific evidence for the presence of metabolomic differences between infants fed breast milk (I-BM) and infants fed formula milk (I-FM).

RECENT FINDINGS

Across the studies included in this review, a total of 261 metabolites were analyzed, of which 151 metabolites were reported as significantly associated with infant feeding modality (BM versus FM). However, taken as a whole, the relevant literature was notable both for methodological limitations, such as small sample sizes, and heterogeneity between the studies. This may be why many associations between infant metabolite profile and feeding modality have not replicated across studies. To our knowledge, this is the first review to integrate the available literature on metabolomic differences between I-BM versus I-FM. This narrative review synthesized the data across studies and identified those metabolites which show the most robust associations with infant feeding modality. Methodological limitations of the current studies are identified, followed by recommendations for how to address these in future studies.

Assessment of Anticancer Drug Effects on Pancreatic Cancer Cells under Glucose-Depleted Conditions Using Intracellular and Extracellular Amino Acid Metabolomics

Previously, we developed a method to evaluate states of cells treated with anticancer drugs via the comprehensive analysis of amino acids, termed amino acid metabolomics. In the present study, we evaluated the effects of the anticancer drugs, gemcitabine hydrochloride and pyrvinium pamoate, on the proliferation of a pancreatic cancer cell line (PANC-1) under hypoglycemic conditions using amino acid metabolomics. Intracellular and extracellular amino acid profiles of PANC-1 were determined by hydrophilic interaction chromatography-tandem mass spectrometry with simple pretreatment. Changes to the drugs' anticancer effects resulting from glucose starvation conditions were presented in score plots obtained from principal component analyses. In particular, the analysis of intracellular amino acids was found to be the superior approach because the results allowed a clearer assessment of the cell state. Further, orthogonal partial least squares discriminant analysis was performed to search for amino acid candidates that discriminate with anticancer drug-treated PANC-1 cells. We identified several amino acids that might be able to distinguish the drug-treated group from the control group. These results might provide a better understanding of the mechanisms underlying cell responses such as drug resistance or austerity. The present study is the first to evaluate the efficacy of anticancer drugs under glucose starvation based on the analysis of the variation of extracellular and intracellular amino acid profiles in vitro.

MS-Based Analytical Techniques: Advances in Spray-Based Methods and EI-LC-MS Applications

Mass spectrometry is the most powerful technique for the detection and identification of organic compounds. It can provide molecular weight information and a wealth of structural details that give a unique fingerprint for each analyte. Due to these characteristics, mass spectrometry-based analytical methods are showing an increasing interest in the scientific community, especially in food safety, environmental, and forensic investigation areas where the simultaneous detection of targeted and nontargeted compounds represents a key factor. In addition, safety risks can be identified at the early stage through online and real-time analytical methodologies. In this context, several efforts have been made to achieve analytical instrumentation able to perform real-time analysis in the native environment of samples and to generate highly informative spectra. This review article provides a survey of some instrumental innovations and their applications with particular attention to spray-based MS methods and food analysis issues. The survey will attempt to cover the state of the art from 2012 up to 2017.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.

Principal component analysis of molecularly based signals from infant formula contaminations using LC-MS and NMR in foodomics

BACKGROUND

The challenge in developing analytical assessment of unexpected excess contaminations in infant formula has been the most significant project to address the widespread issue of food safety and security. Foodomics based on metabolomics techniques provides powerful tools for the detection of tampering cases with intentional contaminations. However, the safety and risk assessments of infant formula to reveal not only the targeted presence of toxic chemicals, but also molecular changes involving unexpected contaminations, have not been reported. In this study, a huge amount of raw molecularly based signals from infant formula was analysed using reversed phase and hydrophilic interaction chromatography with time-of-flight MS (LC-MS) and (1) H nuclear magnetic resonance (NMR) and then processed by a principal component analysis (PCA).

RESULTS

PCA plots visualised signature trends in the complex signal-data batches from each excess contamination of detectable chemicals by LC-MS and NMR. These trends in the different batches from a portion of excess chemical contaminations such as pesticides, melamine and heavy metals and out-of-date products can be visualised from spectrally discriminated infant formula samples.

CONCLUSION

PCA plots provide possible attempts to maximise the covariance between the stable lot-to-lot uniformity and excess exogenous contaminations and/or degradation to discriminate against the molecularly based signals from infant formulas. © 2015 Society of Chemical Industry.

The anti-atherosclerotic effect of tanshinol borneol ester using fecal metabolomics based on liquid chromatography-mass spectrometry

Tanshinol borneol ester (DBZ) is a novel experimental compound that consists of two chemical structural units from danshensu and borneol. It exhibits efficacious anti-ischemic and anti-atherosclerosis activities in rats. A fecal metabolomics based on Liquid Chromatography-Mass Spectrometry combined with clinical histopathology and blood lipid estimation was employed to assess the efficacy and the metabolic changes caused by administration of DBZ in atherosclerotic rats. There were the typical pathological features of atherosclerosis and significantly increased levels of TC, TG and LDL-C in the atherosclerotic rat group. Nevertheless, atherosclerotic rats administered both DBZ (at a dose of 40 mg kg(-1)) and simvastatin (at a dose of 20 mg kg(-1)) showed good therapeutic effects. The results of the metabolomics studies showed that 55 differential metabolites such as sebacic acid, enterodiol, nonanedioic acid, dodecanedioic acid, cholic acid, 13(S)-HPODE, deoxycholic acid, some phosphatidylglycerol and phosphatidic acids were found, indicating that abnormal metabolism occurred in the pathways of fatty acid oxidation, linoleic acid metabolism, bile acid biosynthesis and glycerophospholipid metabolism in atherosclerotic rats. Compared to those in the model group, the contents of 41 differential metabolites showed a tendency to recover to a healthy level after DBZ administration. Metabolomics studies suggested that DBZ exhibited good treatment efficacy against atherosclerosis by adjusting disturbed metabolic pathways related to atherosclerosis. This study could provide an experimental basis for DBZ's application to act as a candidate drug with anti-atherosclerosis activity.

Metabolite Profile of Cervicovaginal Fluids from Early Pregnancy Is Not Predictive of Spontaneous Preterm Birth

In our study, we used a mass spectrometry-based metabolomic approach to search for biomarkers that may act as early indicators of spontaneous preterm birth (sPTB). Samples were selected as a nested case-control study from the Screening for Pregnancy Endpoints (SCOPE) biobank in Auckland, New Zealand. Cervicovaginal swabs were collected at 20 weeks from women who were originally assessed as being at low risk of sPTB. Samples were analysed using gas chromatography-mass spectrometry (GC-MS). Despite the low amount of biomass (16–23 mg), 112 compounds were detected. Statistical analysis showed no significant correlations with sPTB. Comparison of reported infection and plasma inflammatory markers from early pregnancy showed two inflammatory markers were correlated with reported infection, but no correlation with any compounds in the metabolite profile was observed. We hypothesise that the lack of biomarkers of sPTB in the cervicovaginal fluid metabolome is simply because it lacks such markers in early pregnancy. We propose alternative biofluids be investigated for markers of sPTB. Our results lead us to call for greater scrutiny of previously published metabolomic data relating to biomarkers of sPTB in cervicovaginal fluids, as the use of small, high risk, or late pregnancy cohorts may identify metabolite biomarkers that are irrelevant for predicting risk in normal populations.