Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair

Identification of Plasma Biomarkers in Drug-Naïve Schizophrenia Using Targeted Metabolomics

OBJECTIVE

Schizophrenia (SCZ) is a severe psychiatric disorder with unknown etiology and lacking specific biomarkers. Herein, we aimed to explore plasma biomarkers relevant to SCZ using targeted metabolomics.

METHODS

Sixty drug-naïve SCZ patients and 36 healthy controls were recruited. Psychotic symptoms were assessed using the Positive and Negative Syndrome Scale. We analyzed the levels of 271 metabolites in plasma samples from all subjects using targeted metabolomics, and identified metabolites that differed significantly between the two groups. Then we evaluated the diagnostic power of the metabolites based on receiver operating characteristic curves, and explored metabolites associated with the psychotic symptoms in SCZ patients.

RESULTS

Twenty-six metabolites showed significant differences between SCZ patients and healthy controls. Among them, 12 metabolites were phosphatidylcholines and cortisol, ceramide (d18:1/22:0), acetylcarnitine, and γ-aminobutyric acid, which could significantly distinguish SCZ from healthy controls with the area under the curve (AUC) above 0.7. Further, a panel consisting of the above 4 metabolites had an excellent performance with an AUC of 0.867. In SCZ patients, phosphatidylcholines were positively related with positive symptoms, and cholic acid was positively associated with negative symptoms.

CONCLUSION

Our study provides insights into the metabolite alterations associated with SCZ and potential biomarkers for its diagnosis and symptom severity assessment.

Consequences of the exposome to gestational diabetes mellitus

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including those from the environment, diet, behaviour, and endogenous processes. The exposome concept and the 2030 Agenda for the Sustainable Development Goals (SDGs) from the United Nations are the basis for understanding the aetiology and consequences of non-communicable diseases, including gestational diabetes mellitus (GDM). Pregnancy may be developed in an environment with adverse factors part of the immediate internal medium for fetus development and the external medium to which the pregnant woman is exposed. The placenta is the interface between maternal and fetal compartments and acts as a protective barrier or easing agent to transfer exposome from mother to fetus. Under and over-nutrition in utero, exposure to adverse environmental pollutants such as heavy metals, endocrine-disrupting chemicals, pesticides, drugs, pharmaceuticals, lifestyle, air pollutants, and tobacco smoke plays a determinant role in the development of GDM. This phenomenon is worsened by metabolic stress postnatally, such as obesity which increases the risk of GDM and other diseases. Clinical risk factors for GDM development include its aetiology. It is proposed that knowledge-based interventions to change the potential interdependent ecto-exposome and endo-exposome could avoid the occurrence and consequences of GDM.

Extraction strategies for tackling complete hair metabolome using LC-HRMS-based analysis

Over recent years, metabolomics has been featured as the state-of-the-art technology that successfully opens the paths to understanding biological mechanisms and facilitating biomarker discovery. However, the inherent dynamic and sensitive nature of the metabolome have been challenging the accuracy of capturing the timepoints of interest while using biofluids such as urine and blood. Hair has thus emerged as a valuable analytical specimen for the long-term and retrospective determinations. Unfortunately, notwithstanding the apparent interest on global hair metabolomics, very few studies have engaged in the optimisation of the extraction strategy. In this study, we systemically investigated the extraction procedures for hair metabolome using a single factor experimental design. Three pH values (acidic, neutral, and basic) in aqueous solution, six extraction solvents (methanol, acetonitrile, acetone, phosphate-buffered saline, deionised water, and dichloromethane), different compositions of selected solvent mixtures and their sequential extraction, and a series of extraction times (15, 45, 60, 120, 240, and 480 min) were evaluated. The ideal condition for hair extraction is ultrasonic-assisted extraction with methanol:phosphate-buffered saline 50:50 (v/v) under +55 °C for 240 min. This strategy may secure the true composition of the metabolome, maximise the signal abundance, and guarantee a high coverage of wide-range metabolites in a straightforward approach. The optimised extraction strategy was then coupled with structure annotation tools for hair metabolome profiling. After a single RPLC-HRMS run, hair metabolite identification was achieved as the annotations of 171 probable structures and 853 tentative structures as well as the assignments of 414 unequivocal molecular formulae. In conclusion, we established an efficient extraction strategy for untargeted hair metabolomics, which the method is deliverable to any analytical laboratories and the sample can be directly profiled by means of a conventional RPLC-HRMS gradient.

Towards Best Practice in Hair Metabolomic Studies: Systematic Investigation on the Impact of Hair Length and Color

Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.

MULTIOMIC PATTERNS IN BODY FLUIDS: TECHNOLOGICAL CHALLENGE WITH A GREAT POTENTIAL TO IMPLEMENT THE ADVANCED PARADIGM OF 3P MEDICINE

Liquid biopsy (LB) is defined as a sample of any of body fluids (blood, saliva, tear fluid, urine, sweat, amniotic, cerebrospinal and pleural fluids, cervicovaginal secretion, and wound efflux, amongst others), which can be ex vivo analysed to detect and quantity the target(s) of interest. LB represents diagnostic approach relevant for organ-specific changes and systemic health conditions including both manifested diseases and their prestages such as suboptimal health. Further, experts emphasise that DNA-based analysis alone does not provide sufficient information for optimal diagnostics and effective treatments. Consequently, of great scientific and clinical utility are molecular patterns detected by hybrid technologies such as metabolomic tools and molecular imaging. Future proposed strategies utilise multiomic pillars (generally genome, tanscriptome, proteome, metabolome, epigenome, radiome, and microbiome), system-biological approach, and multivariable algorithms for diagnostic, prognostic, and therapeutic purposes. Current article analyses pros and cons of the mass spectrometry-based technologies, provides eminent examples of a success story "from discovery to clinical application," and demonstrates a "road-map" for the technology-driven paradigm change from reactive to predictive, preventive and personalised medical services as the medicine of the future benefiting the patient and healthcare at large. © 2019 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Metabolic infrastructure of pregnant women with methylenetetrahydrofolate reductase polymorphisms: A metabolomic analysis

The aim of this study was to demonstrate the altered metabolic infrastructure of pregnant women with methylenetetrahydrofolate reductase (MTHFR) polymorphisms at first trimester and during delivery. Eight singleton pregnant women with MTHFR polymorphisms were compared with 10 normal pregnant women. Maternal blood samples were obtained twice during their pregnancy period (between the 11th and 14th gestational weeks and during delivery). Metabolomic analysis was performed using GC-MS. The GC-MS based metabolomic profile helped identify 95 metabolites in the plasma samples. In the MTHFR group, the levels of 1-monohexadecanoylglycerol, pyrophosphate, benzoin, and linoleic acid significantly decreased (P ˂ 0.05 for all), whereas the levels of glyceric acid, l-tryptophan, l-alanine, l-proline, norvaline, l-threonine, and myo-inositol significantly increased (P ˂ 0.01 for the first two metabolites, P ˂ 0.05 for the others) at 11-14 gestational weeks. Conversely, the levels of benzoin, 1-monohexadecanoylglycerol, pyruvic acid, l-proline, phosphoric acid, epsilon-caprolactam, and pipecolic acid significantly decreased in the MTHFR group, whereas metabolites such as hexadecanoic acid and 2-hydroxybutyric acid increased significantly in the study group during delivery. An impaired energy metabolism pathway, vitamin B complex disorders, tendency for metabolic acidosis (oxidative stress), and the need for cell/tissue support seem prevalent in pregnancies with MTHFR polymorphisms.

Metabolites and metabolic pathways associated with glucocorticoid resistance in pregnant African-American women

Glucocorticoid resistance (GR) is associated with exposure to chronic stress and an increased risk of metabolic and inflammatory disorders in both animal and human populations. Studies on ethnic disparities highlight the African-American (AA) population as having a high propensity to both GR and chronic stress exposure. Glucocorticoids and inflammation play a very important role in pregnancy outcome and fetal development. To date, however, the metabolites and metabolic pathways associated with GR during pregnancy have not been identified, obscuring the mechanisms by which adverse health consequences arise, and thus impeding targeted therapeutic intervention. The objective of this study was to perform untargeted high-resolution metabolomics (HRM) profiling on 273 pregnant AA women, to identify metabolites and metabolic pathways associated with GR during the first trimester of pregnancy and to evaluate their cross-sectional association with birth outcomes and psychosocial variables related to chronic stress exposure. For this study, GR was determined by the concentration of dexamethasone required for 50% inhibition (Dex IC50) of the cytokine tumor-necrosis factor alpha (TNF-alpha) release in vitro in response to a standard dose of lipopolysaccharide. The results for Metabolome-Wide Association Studies (MWAS) and pathway enrichment analysis for serum metabolic associations with Dex IC50, showed energy (nicotinamide and TCA cycle), amino acid, and glycosphingolipid metabolism as top altered pathways. Bioinformatic analysis showed that GR, as indicated by elevated Dex IC50 in the pregnant women, was associated with increased inflammatory metabolites, oxidative stress related metabolites, increased demand for functional amino acids to support growth and development, and disruption in energy-related metabolites. If confirmed in future studies, targeting these physiologically significant metabolites and metabolic pathways may lead to future assessment and intervention strategies to prevent inflammatory and metabolic complications observed in pregnant populations.

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GR is associated with chronic stress and is a risk factor for adverse health outcomes, especially among African Americans.

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Metabolites and metabolic pathways associated with GR relate to energy production, amino acid metabolism, and inflammation.

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Findings provide a foundation for future studies investigating risk factors in this health disparity population.