Simultaneous exploration of nutrients and pollutants in human milk and their impact on preterm infant growth: An integrative cross-platform approach

Early nutritional management including fortified human breastmilk is currently recommended to fulfil the energy demands and counterbalance risks associated to preterm birth. However, little is known about the potential adverse effects of exposure to persistent organic pollutants (POPs) carried in human milk on preterm infant growth. We conducted a pilot study proving the application of an integrative analytical approach based on mass spectrometry (MS) coupled to advanced statistical models, favouring the comprehensive molecular profiling to support the identification of multiple biomarkers. We applied this workflow in the frame of a preterm infants' cohort to explore environmental determinants of growth. The combination of high resolution gas and liquid chromatography MS platforms generated a large molecular profile, including 102 pollutants and nutrients (targeted analysis) and 784 metabolites (non-targeted analysis). Data analysis consisted in a preliminary examination of associations between the signatures of POPs and the normalised growth of preterm infants, using multivariate linear regression adjusting for known confounding variables. A second analysis aimed to identify multidimensional biomarkers using a multiblock algorithm allowing the integration of multiple datasets in the growth model of preterm infants. The preliminary results did not suggest an impairment of preterm growth associated to the milk concentrations of POPs. The multiblock approach however revealed complex interrelated molecular networks of POPs, lipids, metabolites and amino acids in breastmilk associated to preterm infant growth, supporting the high potential of biomarkers exploration of this proposed workflow. Whereas the present study intended to identify simultaneously pollutant and nutrient exposure profiles associated to early preterm infant growth, this workflow may be easily adapted and applied to other matrices (e.g. serum) and research settings, favouring the functional exploration of environmental determinants of complex and multifactorial diseases.

Comprehensive Preterm Breast Milk Metabotype Associated with Optimal Infant Early Growth Pattern

Early nutrition impacts preterm infant early growth rate and brain development but can have long lasting effects as well. Although human milk is the gold standard for feeding new born full-term and preterm infants, little is known about the effects of its bioactive compounds on breastfed preterm infants' growth outcomes. This study aims to determine whether breast milk metabolome, glycome, lipidome, and free-amino acids profiles analyzed by liquid chromatography-mass spectrometry had any impact on the early growth pattern of preterm infants. The study population consisted of the top tercile-Z score change in their weight between birth and hospital discharge ("faster grow", n = 11) and lowest tercile ("slower grow", n = 15) from a cohort of 138 premature infants (27⁻34 weeks gestation). This holistic approach combined with stringent clustering or classification statistical methods aims to discriminate groups of milks phenotype and identify specific metabolites associated with early growth of preterm infants. Their predictive reliability as biomarkers of infant growth was assessed using multiple linear regression and taking into account confounding clinical factors. Breast-milk associated with fast growth contained more branched-chain and insulino-trophic amino acid, lacto-N-fucopentaose, choline, and hydroxybutyrate, pointing to the critical role of energy utilization, protein synthesis, oxidative status, and gut epithelial cell maturity in prematurity.

Further development of biomarkers in amyotrophic lateral sclerosis

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is an idiopathic neurodegenerative disease usually fatal in less than three years. Even if standard guidelines are available to diagnose ALS, the mean diagnosis delay is more than one year. In this context, biomarker discovery is a priority. Research has to focus on new diagnostic tools, based on combined explorations.

AREAS COVERED

In this review, we specifically focus on biology and imaging markers. We detail the innovative field of 'omics' approach and imaging and explain their limits to be useful in routine practice. We describe the most relevant biomarkers and suggest some perspectives for biomarker research. Expert commentary: The successive failures of clinical trials in ALS underline the need for new strategy based on innovative tools to stratify patients and to evaluate their responses to treatment. Biomarker data may be useful to improve the designs of clinical trials. Biomarkers are also needed to better investigate disease pathophysiology, to identify new therapeutic targets, and to improve the performance of clinical assessments for diagnosis and prognosis in the clinical setting. A consensus on the best management of neuroimaging and 'omics' methods is necessary and a systematic independent validation of findings may add robustness to future studies.

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Molecular effects of supraphysiological doses of doping agents on health

Supraphysiological doses of doping agents, such as T/DHT and GH/IGF-1, affect cellular pathways associated with apoptosis and inflammation.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

BACKGROUND

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients.

RESULTS

Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS.

CONCLUSION

DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).

Time window-dependent effect of perinatal maternal protein restriction on insulin sensitivity and energy substrate oxidation in adult male offspring

Epidemiological and experimental evidence suggests that a suboptimal environment during perinatal life programs offspring susceptibility to the development of metabolic syndrome and Type 2 diabetes. We hypothesized that the lasting impact of perinatal protein deprivation on mitochondrial fuel oxidation and insulin sensitivity would depend on the time window of exposure. To improve our understanding of underlying mechanisms, an integrative approach was used, combining the assessment of insulin sensitivity and untargeted mass spectrometry-based metabolomics in the offspring. A hyperinsulinemic-euglycemic clamp was performed in adult male rats born from dams fed a low-protein diet during gestation and/or lactation, and subsequently exposed to a Western diet (WD) for 10 wk. Metabolomics was combined with targeted acylcarnitine profiling and analysis of liver gene expression to identify markers of adaptation to WD that influence the phenotype outcome evaluated by body composition analysis. At adulthood, offspring of protein-restricted dams had impaired insulin secretion when fed a standard diet. Moreover, rats who demonstrated catch-up growth at weaning displayed higher gluconeogenesis and branched-chain amino acid catabolism, and lower fatty acid β-oxidation compared with control rats. Postweaning exposure of intrauterine growth restriction-born rats to a WD exacerbated incomplete fatty acid β-oxidation and excess fat deposition. Control offspring nursed by protein-restricted mothers showed peculiar low-fat accretion through adulthood and preserved insulin sensitivity even after WD-exposure. Altogether, our findings suggest a testable hypothesis about how maternal diet might influence metabolic outcomes (insulin sensitivity) in the next generation such as mitochondrial overload and/or substrate oxidation inflexibility dependent on the time window of perinatal dietary manipulation.

Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Biological and neuroimaging biomarkers for amyotrophic lateral sclerosis: 2013 and beyond

SUMMARY Amyotrophic lateral sclerosis is an idiopathic, incurable neurodegenerative disease that is fatal for most patients in less than 3 years from the time weakness first appears. Alongside identification of etiologies and stronger neuroprotective agents, the development of biomarkers is a main research priority. Since the original description, diagnosis and progression measurement in amyotrophic lateral sclerosis has been clinical. The time from symptom onset to diagnosis is usually more than a year, and clinical research studies utilize clinical end points that have low sensitivity. Few eligible patients and inefficient trials mean that just one or a few new therapies can be tested each year. Biological markers are needed not only to improve the sensitivity of clinical assessments, but also to better examine disease pathophysiology in vivo.

Metabolomics in cerebrospinal fluid of patients with amyotrophic lateral sclerosis: an untargeted approach via high-resolution mass spectrometry

Amyotrophic lateral sclerosis (ALS) is characterized by the absence of reliable diagnostic biomarkers. The aim of the study was to (i) devise an untargeted metabolomics methodology that reliably compares cerebrospinal fluid (CSF) from ALS patients and controls by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS); (ii) ascertain a metabolic signature of ALS by use of the LC-HRMS platform; (iii) identify metabolites for use as diagnostic or pathophysiologic markers. We developed a method to analyze CSF components by UPLC coupled with a Q-Exactive mass spectrometer that uses electrospray ionization. Metabolomic profiles were created from the CSF obtained at diagnosis from ALS patients and patients with other neurological conditions. We performed multivariate analyses (OPLS-DA) and univariate analyses to assess the contribution of individual metabolites as well as compounds identified in other studies. Sixty-six CSF samples from ALS patients and 128 from controls were analyzed. Metabolome analysis correctly predicted the diagnosis of ALS in more than 80% of cases. OPLS-DA identified four features that discriminated diagnostic group (p < 0.004). Our data demonstrate that untargeted metabolomics with LC-HRMS is a robust procedure to generate a specific metabolic profile for ALS from CSF and could be an important aid to the development of biomarkers for the disease.

Metabolomics as a potential new approach for investigating human reproductive disorders

Metabolomics has been emerging for several years as a global chemical phenotyping approach offering fascinating descriptive capabilities for addressing life complexity. It facilitates the understanding of the mechanisms of biological and biochemical processes in complex systems and promises new insights into specific research questions. The objective of this study was to use for the first time a metabolomic approach based on liquid chromatography high resolution mass spectrometry for characterizing an alteration of the testicular function, namely impaired semen quality. Metabolomic fingerprints were generated from serum samples collected from Danish young men presenting low, intermediate, or high sperm concentrations. Serum metabolic profiles were found to be significantly different among the three groups of volunteers. The developed methodology permitted to correlate the studied clinical parameter (i.e., sperm concentration) with the metabolite profiles generated. Peptides related to the Protein Complement C3f were identified as putative markers associated with this clinical parameter. The biological interpretation and further robustness linked to this observation remain to be further investigated, in particular to address the inter- and intraindividual variabilities.

Maternal and cord blood LC-HRMS metabolomics reveal alterations in energy and polyamine metabolism, and oxidative stress in very-low birth weight infants

To assess the global effect of preterm birth on fetal metabolism and maternal-fetal nutrient transfer, we used a mass spectrometric-based chemical phenotyping approach on cord blood obtained at the time of birth. We sampled umbilical venous, umbilical arterial, and maternal blood from mothers delivering very-low birth weight (VLBW, with a median gestational age and weight of 29 weeks, and 1210 g, respectively) premature or full-term (FT) neonates. In VLBW group, we observed a significant elevation in the levels and maternal-fetal gradients of butyryl-, isovaleryl-, hexanoyl- and octanoyl-carnitines, suggesting enhanced short- and medium chain fatty acid β-oxidation in human preterm feto-placental unit. The significant decrease in glutamine-glutamate in preterm arterial cord blood beside lower levels of amino acid precursors of Krebs cycle suggest increased glutamine utilization in the fast growing tissues of preterm fetus with a deregulation in placental glutamate-glutamine shuttling. Enhanced glutathione utilization is likely to account for the decrease in precursor amino acids (serine, betaine, glutamate and methionine) in arterial cord blood. An increase in both the circulating levels and maternal-fetal gradients of several polyamines in their acetylated form (diacetylspermine and acetylputrescine) suggests an enhanced polyamine metabolic cycling in extreme prematurity. Our metabolomics study allowed the identification of alterations in fetal energy, antioxidant defense, and polyamines and purines flux as a signature of premature birth.

A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS² as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.