Recent Developments along the Analytical Process for Metabolomics Workflows

Sample pre-treatment procedures for the omics analysis of human gut microbiota: Turning points, tips and tricks for gene sequencing and metabolomics

The connection between gut microbiota and human health is becoming increasingly relevant and the number of groups working in this field is constantly growing. In this context, from high-throughput gene sequencing to metabolomics analysis, the omics technologies have contributed enormously to unveil the secret crosstalk between us and our microbes. All the omics technologies produce a great amount of information, and processing this information is time-consuming and expensive. For this reason, a correct experimental design and a careful pre-analytical planning are crucial. To study the human gut microbiota, faeces are the sample of choice. Faecal material is complex, and procedures for collecting and preserving faeces are not well-established. Furthermore, increasing evidence suggests that multiple confounding factors, such as antibiotics consumption, mode of delivery, diet, aging and several diseases and disorders can alter the composition and functionality of the microbiota. This review is focused on the discussion of critical general issues during the pre-analytical planning, from patient handling to faeces sampling, including collection procedures, transport, storage conditions and possible pre-treatments, which are critical for a successful research in omics with a special attention to metabolomics and gene sequencing. We also point out that the adoption of standard operating procedures in the field is needed to guarantee accuracy and reproducibility of results.

Metabolomics analysis of microbiota-gut-brain axis in neurodegenerative and psychiatric diseases

Gut microbiota represents a complex physiological ecosystem that influences the host health. Alterations in the microbiome metabolism affect the body homeostasis and they have been associated with the development of different human neurodegenerative and neuropsychiatric disorders, such as Alzheimer's disease, autism spectrum disorder, bipolar disorder, depression, Huntington's disease, Parkinson's disease, posttraumatic stress disorder and schizophrenia. The development of these complex diseases is influenced by various factors, including genetic predisposition and environmental triggers. Gut microbiota has recently emerged as an important actor in their physiopathology that has been shown to play a role in inflammation, oxidative stress, and gut permeability. Therefore, targeting the metabolites that are produced by or associated with the gut microbiota may help us understand how imbalance in the gut-brain axis affects human health. This review offers a comprehensive overview of the literature on this matter, offering the readers an insight in the state-of-art metabolic measurements of the gut-brain axis in various brain-related diseases.

Insulin Resistance in Obese Children: What Can Metabolomics and Adipokine Modelling Contribute?

The evolution of obesity and its resulting comorbidities differs depending upon the age of the subject. The dramatic rise in childhood obesity has resulted in specific needs in defining obesity-associated entities with this disease. Indeed, even the definition of obesity differs for pediatric patients from that employed in adults. Regardless of age, one of the earliest metabolic complications observed in obesity involves perturbations in glucose metabolism that can eventually lead to type 2 diabetes. In children, the incidence of type 2 diabetes is infrequent compared to that observed in adults, even with the same degree of obesity. In contrast, insulin resistance is reported to be frequently observed in children and adolescents with obesity. As this condition can be prerequisite to further metabolic complications, identification of biological markers as predictive risk factors would be of tremendous clinical utility. Analysis of obesity-induced modifications of the adipokine profile has been one classic approach in the identification of biomarkers. Recent studies emphasize the utility of metabolomics in the analysis of metabolic characteristics in children with obesity with or without insulin resistance. These studies have been performed with targeted or untargeted approaches, employing different methodologies. This review summarizes some of the advances in this field while emphasizing the importance of the different techniques employed.

A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.

Early-life exposure to widespread environmental toxicants and maternal-fetal health risk: A focus on metabolomic biomarkers

Prenatal exposure to widespread environmental toxicants is detrimental to maternal health and fetal development. The effects of environmental toxicants on maternal and fetal metabolic profile changes have not yet been summarized. This systematic review aims to summarize the current studies exploring the association between prenatal exposure to environmental toxicants and metabolic profile alterations in mother and fetus. We searched the MEDLINE (PubMed) electronic database for relevant literature conducted up to September 18, 2019 with some key terms. From the initial 155 articles, 15 articles met the inclusion and exclusion criteria, and consist of highly heterogeneous research methods. Seven studies assessed the effects of multiple environmental pollutants (metals, organic pollutants, nicotine, air pollutants) on the maternal urine and blood metabolomic profile; five studies evaluated the effects of arsenic, polychlorinated biphenyls (PCBs), nicotine, and ambient fine particulate matter (PM_2.5) on the cord blood metabolomic profile; and one study assessed the effects of smoking exposure on the amniotic fluid metabolomic profile. The alteration of metabolic pathways in these studies mainly involve energy metabolism, hormone metabolism, oxidative stress and inflammation. No population study investigated the association between environmental toxicants and placental metabolomics. This systematic review provides evidence that prenatal exposure to a variety of environmental pollutants can affect maternal and fetal metabolomic characteristics. Integration of environmental toxicant exposure and metabolomics data in maternal-fetal samples is helpful to understand the interaction between toxicants and metabolites, so as to reveal the pathogenesis of fetal disease or diseases of fetal origin.

Oxidized lipids in the metabolic profiling of neuroendocrine tumors - Analytical challenges and biological implications

Untargeted metabolomics can be a great tool for exploring new scientific areas; however, wrong metabolite annotation questions the credibility and puts the success of the entire research at risk. Therefore, an effort should be made to improve the quality and robustness of the annotation despite of the challenges, especially when final identification with standards is not possible. Through non-targeted analysis of human plasma samples, from a large cancer cohort study using RP-LC-ESI-QTOF-MS/MS, we have resolved MS/MS annotation through spectral matching, directed to hydroxyeicosatetraenoic acids (HETEs) and, MS/MS structural elucidation for newly annotated oxidized lyso-phosphatidylcholines (oxLPCs). The annotation of unknowns is supported with structural information from fragmentation spectra as well as the fragmentation mechanisms involved, necessarily including data from both polarity modes and different collision energies. In this work, we present evidences that various oxidation products show significant differences between cancer patients and control individuals and we establish a workflow to help identify such modifications. We report here the upregulation of HETEs and oxLPCs in patients with neuroendocrine tumors (NETs). To our knowledge, this is the first attempt to determine HETEs in NETs and one of very few studies where oxLPCs are annotated. The obtained results provide an important insight regarding lipid oxidation in NETs, although their physiological functions still have to be established and require further research.

A Non-Targeted Capillary Electrophoresis-Mass Spectrometry Strategy to Study Metabolic Differences in an In vitro Model of High-Glucose Induced Changes in Human Proximal Tubular HK-2 Cells

Diabetic nephropathy is characterized by the chronic loss of kidney function due to high glucose renal levels. HK-2 proximal tubular cells are good candidates to study this disease. The aim of this work was to study an in vitro model of high glucose-induced metabolic alterations in HK-2 cells to contribute to the pathogenesis of this diabetic complication. An untargeted metabolomics strategy based on CE-MS was developed to find metabolites affected under high glucose conditions. Intracellular and extracellular fluids from HK-2 cells treated with 25 mM glucose (high glucose group), with 5.5 mM glucose (normal glucose group), and with 5.5 mM glucose and 19.5 mM mannitol (osmotic control group) were analyzed. The main changes induced by high glucose were found in the extracellular medium where increased levels of four amino acids were detected. Three of them (alanine, proline, and glutamic acid) were exported from HK-2 cells to the extracellular medium. Other affected metabolites include Amadori products and cysteine, which are more likely cause and consequence, respectively, of the oxidative stress induced by high glucose in HK-2 cells. The developed CE-MS platform provides valuable insight into high glucose-induced metabolic alterations in proximal tubular cells and allows identifying discriminative molecules of diabetic nephropathy.

Metabolomics strategies to discover new biomarkers associated to severe allergic phenotypes

In the last decades have emerged new technological platforms that allow evaluation of genes, transcripts, proteins, or metabolites of a living being, so-called omics sciences. More importantly, new technics for their integration have provided access to a complete set of information of the current conditions and features of a specific biological sample in a precise moment. Thus, omic sciences are now considered an essential tool for patient stratification in base to their severity, to understand disease progression and to identify new biomarkers. Severe patients, that are out of control, provide an excellent model to understand disease evolution and to identify new intervention and biomarkers strategies. Here we discuss the use of metabolomics to understand severity in allergic diseases in a strategy that opens new insights as well as identify new biological systems relevant for allergy progression. Metabolomics strategies are based in parallel evaluation of different allergy severity models by mean of untargeted analysis that allows the identification of potential biomarkers. Overlapping of different biomarkers in multiple models, provides information of general as well as specific biological systems involved in each model. Later a selected panel of biomarkers will be used in a target method to explore the diagnosis potential to stratify allergic patients.