Applications of metabolomics to study the pathophysiology of adverse pregnancy outcomes

Background: Clinical metabolomics is a growing field of research aiming to use metabolomic techniques to gain further knowledge into diseases, the use of biomarkers to predict their onset, or the effect of a potential therapeutic agent on the metabolome. Adverse pregnancy outcomes, such as small for gestation age (or fetal growth restriction), spontaneous preterm birth, and pre-eclampsia, lead to high maternal and fetal mortality and morbidity rates. However, despite research efforts to date, their pathophysiology remains poorly understood. Aim: The aims of this thesis was to determine the accuracy of metabolomics to predict small for gestation age (SGA) babies, to explore the metabolic pathways involved in the pathophysiology of SGA and spontaneous preterm birth (sPTB), to identify potential predictive biomarkers of sPTB, and investigate the use of a potential therapeutic agent in an animal model of pre-eclampsia. Methods: Firstly, a systematic review was undertaken to examine the predictive accuracy of metabolomics for the prediction of small for gestational age babies. The original search was conducted in February 2018 and the results are presented in Chapter 2. Secondly, we investigated the metabolic pathways involved in the pathophysiology of small for gestation age (SGA) using untargeted ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UPLC-Q-TOF-MS). Plasma (Cork) and urine (Cork, Auckland) samples were collected at 20 weeks of gestation from pregnant women participating in the SCreening fOr Pregnancy Endpoints (SCOPE) study, an international study that recruited 5,628 nulliparous women, with a singleton low-risk pregnancy. Cases were women with SGA (customised birthweight ≤ 10th centile) matched to controls who had uncomplicated pregnancies, according to age (±5 years), body mass index (BMI, ±3.5 kg/m2), and ethnicity. All samples were analysed in untargeted positive and negative ion modes, using UPLC-Q-TOF-MS. Data were processed, features were ranked based on p-values from empirical Bayes analysis adjusted for multiple testing, and significant features (adjusted p-values <0.05 were searched for identification (HMDB, LipidMaps)). Thirdly, we aimed to decipher the lipidomics pathways involved in pathophysiology of spontaneous preterm birth (sPTB). Our analysis focused on plasma samples from SCOPE in Cork, collected at 20 weeks of gestation. Samples were profiled using semi-targeted liquid chromatography-mass spectrometry lipidomics, and lipids significantly altered between sPTB (n=16) and Control (n=32) groups were identified using empirical Bayes testing, adjusting for multiple comparisons. Significantly altered lipids (adj. p-values <0.05) were database searched for identifications (HMDB, LipidMaps). Fourthly, in Chapter 5, we performed a discovery lipidomics experiment to determine potential biomarkers of sPTB, in plasma samples taken at 15 weeks of gestation in women who participated in SCOPE in Cork and Auckland. Selected participants were women who has sPTB before 34 weeks of gestation (n=16 from Cork, and n=23 from Auckland), matched to women who had an uncomplicated pregnancy (n=39) according to age (±5 years) and BMI (± 3 kg/m2). Lipidomics analysis was performed using UPLC-Q-TOF-MS. Statistical analysis using empirical Bayes, adjusted for multiple testing was used to create a list of potential biomarkers. Five potential biomarkers were selected for validation based on statistical analysis, and their identification was validated using standard mix and UPLC coupled to triple quadrupole mass spectrometer (TQ-MS) analyses. Their prediction potential was tested using samples taken at 15 and 20 weeks of gestation from women from SCOPE Cork who had sPTB before 37 weeks of gestation (n=54) matched to women who had an uncomplicated pregnancy (controls, n=108). In addition, plasma collected at time of delivery (ToD) was also analysed for six cases and their 12 matching controls. Cases were matched to controls according to age (±5 years) and BMI (± 3 kg/m2). Samples were analysed using UPLC-TQ-MS, and statistical analysis was performed using independent T tests on normalised data. In addition, independent T tests were performed to determine if the levels of each target were significantly different between cases and controls at each time point (15 or 20 weeks). We defined significance as p-value <0.05. Finally, in chapter 6 we performed metabolomics analysis of plasma from experiments examining L-Ergothioneine treatment in the Reduced Uterine Perfusion Pressure (RUPP) rat model of pre-eclampsia. The effect of L-Ergothioneine (ET) treatment was explored using in vivo treatment in rats: Sham control (SC, n=5), RUPP control (RC, n=5), Sham + ET (ST, n=5), RUPP + ET (RT, n=5). Metabolic profiles of plasma samples were obtained using UPLC-Q-TOF-MS, and statistical analysis of the data was performed on normalised data, using independent T tests adjusted with false discovery rate (FDR) to compare RC to SC, RT to RC and RT to ST. Metabolites significantly altered (FDR <0.05) were putatively identified through database search (HMDB). Results: The systematic review presented in Chapter 2 examining the predictive accuracy of metabolomics for small for gestational age babies showed that to date no combination of metabolites are able to predict small for gestational age accurately. However, the review revealed the potential of investigating lipids pathways, their involvement in the pathophysiology of small for gestational age, and their high predictive potential. The metabolomic studies performed on urine samples and reported in Chapter 3, showed lower levels of 4 metabolites of interest (sulfolithocholic acid, estriol-16-Glucuronide, Neuromedin N (1-4), and 4-Hydroxybenzaldehyde) in Cork were associated with SGA at 20 weeks of gestation, but not in Auckland samples. These urinary metabolites are associated with detoxification, nutrient transport and absorption pathways. The lipidomics analysis performed on plasma samples showed that higher levels of several glycerophospholipids (3 phosphatidylethanolamines, 5 phosphatidylserines, 3 phosphatidylcholines, 1 lyso phosphatidylcholine, 1 phosphatidylglycerophosphate, 1 lyso phosphatidylglycerophosphate, 2 phosphatidylinositols, 2 phosphatidylglycerophosphates, and 3 phosphatidylglycerols) in at 20 weeks of gestation were associated with the development of SGA in the Cork participants of the SCOPE pregnancy cohort. Chapter 4 demonstrated that twenty-six lipids showed lower levels in sPTB compared to controls (adjusted p <0.05), including 20 glycerophospholipids (12 phosphatidylcholines, 7 phosphatidylethanolamines, 1 phosphatidylinositol) and 6 sphingolipids (2 ceramides and 4 sphingomyelines). In addition, a diaglyceride, DG (34:4), was detected in higher levels in sPTB compared to controls. In Chapter 4, we reported that reduced levels of phospholipids (glycerophospholipids and sphingolipids) are associated with the pathophysiology of sPTB. In the UPLC-Q-TOF-MS discovery phase of the study presented in Chapter 5, a list of 120 potential lipid biomarkers were reported. Most were tentatively identified as glycerophospholipids and detected in lower levels in sPTB. From this list of features, 5 potential biomarkers predictive of sPTB were selected and used in a targeted UPLC-TQ-MS analysis. The results obtained showed that two of the targets showed significant differences between cases and controls and over time (between 15 and 20 weeks of gestation), PC (15:0/22:6) and TG (18:3/18:2/18:3). In Chapter 6, using untargeted UPLC-Q-TOF-MS, we tested the effect of L-Ergothioneine (ET) as a potential therapeutic agent for the treatment of pre-eclampsia in the RUPP rat model. We reported significantly higher levels of L-palmitoylcarnitine, fatty acyl substrate involved in beta-oxidation in the mitochondria, in RUPP rats compared to Sham rats. When comparing plasma metabolic profiles of RUPP + ET rats to RUPP rats, we reported 10 metabolites associated with inflammation significantly altered (FDR <0.05, e.g. 20-COOH-leukotriene E4). Glutamylcysteine, a metabolite associated with oxidative stress, was detected at significantly higher levels (FDR <0.05) when comparing RUPP + ET rats to RUPP rats, and RUPP + ET rats to Sham + ET rats. These results show that the therapeutic properties of L-Ergothioneine might be related to mitochondrial function preservation, by attenuating inflammatory response evident in pre-eclampsia in addition to increasing antioxidant levels. Conclusions: Overall, these results show that glycerophospholipids appear to play a key role in the pathophysiology of SGA and sPTB, and dysregulated glycerophospholipids are potential makers of adverse pregnancy outcomes. Further research is needed to understand their precise associations, whether they are a cause or effect of SGA and sPTB, as well as to validate their potential as predictive biomarkers in independent pregnancy cohorts. In addition, we have shown that the use of L-Ergothioneine for the treatment of pre-eclampsia in the RUPP rat model reduces the oxidative stress induced by pre-eclampsia, via amino acid and glycerophospholipids metabolism pathways. Future work should focus on a testing L-Ergothioneine as a treatment for pre-eclampsia in a clinical trial. This thesis has demonstrated the potential for metabolomics to help understand the pathophysiology of adverse pregnancy outcomes and has explored its use in assessing biological pathways, predictive biomarkers and potential therapeutic pharmacological interventions. To date results are limited with significant further validation required

Metabolomics for predicting fetal growth restriction: protocol for a systematic review and meta-analysis

Introduction Fetal growth restriction (FGR) is a relevant research and clinical concern since it is related to higher risks of adverse outcomes at any period of life. Current predictive tools in pregnancy (clinical factors, ultrasound scan, placenta-related biomarkers) fail to identify the true growth-restricted fetus. However, technologies based on metabolomics have generated interesting findings and seem promising. In this systematic review, we will address diagnostic accuracy of metabolomics analyses in predicting FGR.Methods and analysis Our primary outcome is small for gestational age infant, as a surrogate for FGR, defined as birth weight below the 10th centile by customised or population-based curves for gestational age. A detailed systematic literature search will be carried in electronic databases and conference abstracts, using the keywords ‘fetal growth retardation’, ‘metabolomics’, ‘pregnancy’ and ‘screening’ (and their variations). We will include original peer-reviewed articles published from 1998 to 2018, involving pregnancies of fetuses without congenital malformations; sample collection must have been performed before clinical recognition of growth impairment. If additional information is required, authors will be contacted. Reviews, case reports, cross-sectional studies, non-human research and commentaries papers will be excluded. Sample characteristics and the diagnostic accuracy data will be retrieved and analysed. If data allows, we will perform a meta-analysis.Ethics and dissemination As this is a systematic review, no ethical approval is necessary. This protocol will be publicised in our institutional websites and results will be submitted for publication in a peer-reviewed journal.PROSPERO registration number CRD42018089985

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: A systematic review

Introduction: To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality. Objective: To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition. Data sources: Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies. Study eligibility criteria: Cohort or nested case–control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile—as a surrogate for fetal growth restriction—by population-based or customised charts. Study appraisal and synthesis methods: Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary. Results: A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.Conclusions and implications: Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings. PROSPERO registration number CRD42018089985

Glycerophospholipid and detoxification pathways associated with small for gestation age pathophysiology: discovery metabolomics analysis in the SCOPE cohort

Introduction: Small for gestational age (SGA) may be associated with neonatal morbidity and mortality. Our understanding of the molecular pathways implicated is poor. Objectives: Our aim was to determine the metabolic pathways involved in the pathophysiology of SGA and examine their variation between maternal biofluid samples. Methods: Plasma (Cork) and urine (Cork, Auckland) samples were collected at 20 weeks’ gestation from nulliparous low-risk pregnant women participating in the SCOPE study. Women who delivered an SGA infant (birthweight < 10th percentile) were matched to controls (uncomplicated pregnancies). Metabolomics (urine) and lipidomics (plasma) analyses were performed using ultra performance liquid chromatography-mass spectrometry. Features were ranked based on FDR adjusted p-values from empirical Bayes analysis, and significant features putatively identified. Results: Lipidomics plasma analysis revealed that 22 out of the 33 significantly altered lipids annotated were glycerophospholipids; all were detected in higher levels in SGA. Metabolomic analysis identified reduced expression of metabolites associated with detoxification (D-Glucuronic acid, Estriol-16-glucuronide), nutrient absorption and transport (Sulfolithocholic acid) pathways. Conclusions: This study suggests higher levels of glycerophospholipids, and lower levels of specific urine metabolites are implicated in the pathophysiology of SGA. Further research is needed to confirm these findings in independent samples

Systemic perturbations in amine and kynurenine metabolism associated with acute SARS-CoV-2 infection and inflammatory cytokine responses

We performed quantitative metabolic phenotyping of blood plasma in parallel with cytokine/chemokine analysis from participants who were either SARS-CoV-2 (+) (n = 10) or SARS-CoV-2 (-) (n = 49). SARS-CoV-2 positivity was associated with a unique metabolic phenotype and demonstrated a complex systemic response to infection, including severe perturbations in amino acid and kynurenine metabolic pathways. Nine metabolites were elevated in plasma and strongly associated with infection (quinolinic acid, glutamic acid, nicotinic acid, aspartic acid, neopterin, kynurenine, phenylalanine, 3-hydroxykynurenine, and taurine; p \u3c 0.05), while four metabolites were lower in infection (tryptophan, histidine, indole-3-acetic acid, and citrulline; p \u3c 0.05). This signature supports a systemic metabolic phenoconversion following infection, indicating possible neurotoxicity and neurological disruption (elevations of 3-hydroxykynurenine and quinolinic acid) and liver dysfunction (reduction in Fischer’s ratio and elevation of taurine). Finally, we report correlations between the key metabolite changes observed in the disease with concentrations of proinflammatory cytokines and chemokines showing strong immunometabolic disorder in response to SARS-CoV-2 infection

Integrative Molecular Structure Elucidation and Construction of an Extended Metabolic Pathway Associated with an Ancient Innate Immune Response in COVID-19 Patients

We present compelling evidence for the existence of an extended innate viperin-dependent pathway, which provides crucial evidence for an adaptive response to viral agents, such as SARS-CoV-2. We show the in vivo biosynthesis of a family of novel endogenous cytosine metabolites with potential antiviral activities. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy revealed a characteristic spin-system motif, indicating the presence of an extended panel of urinary metabolites during the acute viral replication phase. Mass spectrometry additionally enabled the characterization and quantification of the most abundant serum metabolites, showing the potential diagnostic value of the compounds for viral infections. In total, we unveiled ten nucleoside (cytosine- and uracil-based) analogue structures, eight of which were previously unknown in humans allowing us to propose a new extended viperin pathway for the innate production of antiviral compounds. The molecular structures of the nucleoside analogues and their correlation with an array of serum cytokines, including IFN-α2, IFN-γ, and IL-10, suggest an association with the viperin enzyme contributing to an ancient endogenous innate immune defense mechanism against viral infection