Applications of metabolomics to study the pathophysiology of adverse pregnancy outcomes

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Morillon, Aude-Claire
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University College Cork
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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 , Pregnancy complications , Fetal growth restriction , Small for gestational age , Pre-eclampsia , Mass spectrometry , Liquid chromatography coupled to mass spectrometry , Clinical metabolomics , Discovery metabolomics , Targeted metabolomics , Lipidomics
Morillon, A-C. 2020. Applications of metabolomics to study the pathophysiology of adverse pregnancy outcomes. PhD Thesis, University College Cork.
© 2020, Aude-Claire Morillon.