Metabolomics application in maternal-fetal medicine

Metabolomics in maternal-fetal medicine is still an "embryonic" science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future

Pregnancy risk stratification using DESI-MS profiling of vaginal mucosa

Preterm birth is the leading cause of childhood mortality. Despite decades of research, the pathophysiology of spontaneous preterm birth (SPTB) remains poorly understood. Prevention strategies are limited by our inability to reliably predict women at risk and stratify depending on underlying aetiology. There is an established association between ascending vaginal infection and SPTB. More recently, highly diverse vaginal bacterial communities deplete of Lactobacillus species have been associated with SPTB. However, not all pregnant women with such community structures deliver preterm, highlighting the importance of individual host response. Medical swabs are routinely used for microbiological screening with culture-based techniques. However, these are time-consuming, have a narrow focus for specific microbes and provide no information regarding host response. We hypothesised that metabolic profiling of cervico-vaginal mucosa (CVM) may offer the ability to assess interactions between the vaginal microbiota and the pregnant host that are useful for prediction and stratification of SPTB risk. To address this hypothesis, we developed a technique using DESI-MS that enabled rapid acquisition of metabolic information directly from vaginal swabs. In Chapter 3, method optimisation is described and its capacity to detect variations in the CVM associated with physiological changes in the host (e.g. pregnancy) and disruptions in bacterial community compositions during pregnancy (e.g. bacterial vaginosis) are presented. The DESI-MS swab profiling approach was then used to characterise and compare CVM metabolic profiles associated with SPTB risk (Chapter 4). These results showed that the CVM metabolome associated with subsequent SPTB was highly variable, reflecting the heterogeneity of SPTB aetiology. In support of this, DESI-MS more effectively discriminated samples with differing severity of SPTB (early vs late) and phenotypes (SPTL and PPROM). In Chapter 5, DESI-MS profiling of CVM was shown to facilitate prediction of PPROM as well as enable its robust diagnosis. DESI-MS also had capacity to characterise microbial compositions following PPROM suggesting its potential to assist in directed treatment strategies based on underlying aetiology. This thesis highlights the predictive and therapeutic potential of DESI-MS in pregnancy.Open Acces

Identification of first trimester maternal serum markers predictive of spontaneous preterm birth

Preterm birth is the leading cause of perinatal morbidity and mortality worldwide. Despite considerable efforts, prediction and prevention of preterm birth continues to remain a challenge for obstetricians globally. Early identification of pregnancies at highest risk of preterm birth may enable the implementation of therapeutic strategies aiming to prevent preterm birth and/or the morbidities associated with early delivery. Screening for spontaneous preterm birth is made more complex due to the heterogeneity of this condition, which has a variety of underlying aetiologies and risk factors. Even though spontaneous preterm birth is caused by several aetiologies, there appears to be a final common pathway leading to the onset of labour. There may therefore be value either in developing screening tools that screen for multiple aetiological pathways or alternatively that identify common features that develop before women become symptomatic with the onset of spontaneous labour. Paper I reviews recent research findings related to first trimester prediction and prevention of adverse pregnancy outcomes. This thesis reports a body of work related to the development of a predictive test for spontaneous preterm labour. Paper II is focussed on the challenges of using current proteomic strategies to identify and quantify novel protein markers of disease in serum. I carried out various optimisation strategies to resolve protein species in maternal serum using a refined top-down two-dimensional gel electrophoresis method coupled with mass spectrometry. In addition to this, a process of deep imaging using third separation gel electrophoresis was adapted to effectively resolve protein species and isoforms that would not be recognised by traditional proteomic techniques as they would be masked by co-migrating protein species of higher abundance. These techniques were applied in Paper III where they were used to identify protein species and post translationally modified proteoforms (phosphorylation and glycosylation) in first trimester maternal serum banked from cohorts of women who delivered spontaneously before 37 weeks’ gestation. These findings were compared to serum collected from a cohort of women who delivered at term (≥ 37 weeks’ gestation). Paper IV utilised a western blot approach to determine serum concentrations of a select group of candidate protein species and proteoforms that were significantly altered in Paper II in a larger cohort of women that had delivered after spontaneous preterm labour (37 weeks) controls. A variant of Vitamin D-binding protein was found to be significantly decreased in women who delivered < 37 weeks spontaneously. This work has shown that there is evidence of change in protein abundance as early as 11-13 weeks of gestation in women who continue on to deliver preterm after the spontaneous onset of labour. Further work is needed to determine the strength of these findings in predicting risk of preterm birth. Further work should also examine how novel biomarkers can be combined with established screening tools in larger diverse patient cohorts to validate their potential use as candidates for prediction of spontaneous preterm birth

A comprehensive integrative approach to investigate factors associated with preterm birth, related perinatal outcomes and its prediction using metabolomic markers

Orientador: José Guilherme CecattiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: Introdução: O parto prematuro é uma das principais causas de morbimortalidade perinatal, neonatal e de crianças até 5 anos de idade e suas causas e fisiopatologia ainda são pouco conhecidas. Identificar quais são as mulheres de maior risco e desenvolver modelos de predição é ainda um grande desafio, potencialmente impactando nas medidas preventivas. Objetivo: Desenvolver uma abordagem abrangente com diferentes estudos e produtos relacionados aos fatores clínicos e epidemiológicos associados ao parto prematuro, seus preditores metabolômicos e respectivos desfechos perinatais. Métodos: Diferentes projetos de pesquisa e métodos foram utilizados, incluindo: duas análises secundárias de um estudo multicêntrico de corte transversal avaliando a associação do índice de massa corpórea (IMC), o ganho de peso gestacional por semana e fenótipos maternos com a ocorrência de prematuridade e desfechos maternos e perinatais; uma revisão narrativa sobre ciência ômica aplicada na área de saúde materna e perinatal, com enfoque na metabolômica; uma revisão sistemática e seu respectivo artigo de protocolo sobre a performance da metabolômica em predizer prematuridade espontânea em mulheres assintomáticas; dois artigos abordando o desenvolvimento do método e dos procedimentos técnicos para um estudo multicêntrico prospectivo para investigar parto prematuro; um estudo caso-controle aninhado a uma coorte multicêntrica internacional para identificar preditores clínicos e metabolômicos para prematuridade espontânea; dois artigos originais abordando a incidência, fatores clínicos e epidemiológicos e os desfechos maternos e perinatais associados ao parto prematuro em uma coorte multicêntrica no Brasil com gestantes nulíparas de baixo risco. Resultados: Nas análises secundárias do EMIP, observou-se que independente do IMC inicial, quanto maior o ganho de peso materno, maior a probabilidade para todos os subtipos de prematuridade, exceto para prematuridade espontânea em mulheres com IMC normal ou sobrepeso. Foram identificados três clusters de mulheres com parto prematuro, sendo um caracterizado principalmente por mulheres sem nenhuma das condições de risco, o segundo por mulheres com várias condições (cluster misto) e o terceiro por mulheres que tiveram pré-eclâmpsia, eclâmpsia, síndrome HELLP e/ou restrição de crescimento fetal. A revisão narrativa aborda os métodos e o embasamento teórico das ciências ômicas, como a genômica, transcriptômica, proteômica e metabolômica, dando enfoque especial à aplicação dessa última técnica na área de saúde materna e perinatal. A identificação e validação de marcadores pode auxiliar na predição e também no entendimento da fisiopatologia de doenças complexas como a prematuridade. A técnica de metabolômica identificou mais de 140 metabólitos nas amostras de soro de gestantes nulíparas e três destes foram significativamente associados com parto prematuro espontâneo nas amostras de Cork, Irlanda. Modelos preditores usando marcadores clínicos e metabolômicos mostraram uma área sob a curva ROC de 0,73 e 0,85 para parto prematuro abaixo de 37 e 34 semanas, respectivamente. Conclusão: O ganho de peso gestacional, um fator modificável, mostrou diferentes associações com a probabilidade de parto prematuro, a depender do IMC inicial. Possíveis investigações de risco e de prevenção devem considerar essa evidência. A utilização de critérios clínicos no rastreamento e predição do parto prematuro ainda mostra limitações. A análise por cluster, por exemplo, mostrou que um número considerável não possui nenhuma das condições pré-definidas como potencialmente associadas ao parto prematuro. A aplicação de estudos da ciência Ômica parece ser uma abordagem adequada para a identificação da etiologia e de marcadores para predição de complicações maternas e perinatais, embora ainda necessitem de sucessivas validações e evidência de reprodutibilidade. O desenvolvimento, implementação e coordenação de um estudo multicêntrico para estudar preditores e fatores associados ao parto prematuro requer recursos humanos qualificados, infraestrutura para pesquisa adequada, comprometimento institucional e envolvimento de agências de fomento e desenvolvimento de pesquisa. O modelo preditor para parto prematuro espontâneo em mulheres nulíparas mostra resultados de boa performance, entretanto requer futuras validações antes de qualquer uso clínico. É provável que os metabólitos que compõem o modelo não sejam identificados da mesma forma em outras populaçõesAbstract: Introduction: Preterm birth is the leading cause of perinatal, neonatal and under-5 year¿s morbidity and mortality. Identifying women at higher risk and developing prediction models remains a great challenge, potentially affecting preventive interventions. Objectives: To develop a comprehensive approach including diverse study designs to investigate clinical and epidemiological risk factors associated with preterm birth, its metabolomics predictors and respective perinatal outcomes. Methods: Different projects and methods were applied in this thesis, including: two secondary analysis of a multicentre cross-sectional with a nested case-control study addressing the association of maternal body mass index (BMI), gestational weight gain per week and phenotypes with the occurrence of preterm birth and maternal and perinatal outcomes; an integrative review about omics sciences applied to maternal and perinatal health, focusing on metabolomics; a systematic review and respective protocol investigating the performance of metabolomics to predict spontaneous preterm birth (sPTB) in asymptomatic women; two articles describing the methods, clinical protocol, technical procedures for the development and implementation of a multicentre prospective cohort study to investigate preterm birth and other maternal and perinatal complications; a nested case-control from a multicentre international cohort to identify clinical and metabolomics predictors for sPTB; two articles addressing incidence, clinical and epidemiological risk factors and maternal and perinatal outcomes associated with sPTB in a Brazilian multicentre cohort of low-risk nulliparous pregnant women. Results: According to the EMIP secondary analyses, the greater the rate of weight gain, the higher the predicted probability for all preterm birth subtypes regardless the initial BMI, except in normal BMI or overweight women and sPTB. Three clusters of women with preterm birth were identified; cluster one of women without any pre-defined conditions, cluster two with mixed conditions and cluster three with women who had preeclampsia, eclampsia, HELLP syndrome and/or fetal growth restriction. Maternal and perinatal outcomes did not differ between clusters. An integrative review addressed Omis Science's methods and theoretical background, as genomics, transcriptomics, proteomics and metabolomics, focusing on the application on maternal and perinatal health. Metabolomics approach has been applied to better understand the pathophysiology and to identify and validate predictors for complex diseases as preterm birth. Metabolomics technique identified more than 140 metabolites in serum samples of nulliparous pregnant women and three of them were significantly associated with sPTB in samples from Cork, Ireland. Predictive models associating metabolites and clinical markers showed an area under ROC curve of 0.73 and 0.85 for sPTB below 37 and 34 weeks, respectively. Conclusion: Gestational weight gain, a modifiable factor, showed to have different associations with the predicted probability for preterm birth, depending on the initial BMI. The use of clinical criteria in the screening of preterm birth still shows limited performance. Cluster analysis, for instance, showed that a substantial number of women does not present the predefined potential conditions associated with preterm birth. Omics science studies might be a reasonable approach to investigate the aetiology and predictive markers for maternal and perinatal complications. Metabolomic studies addressing the prediction for sPTB, preeclampsia, gestational diabetes mellitus and fetal growth restriction show promising findings, although they still require repeated validations and reproducibility. The development, implementation and management of a multicenter study to investigate factors associated with sPTB requires qualified human resources, adequate infrastructure, institutional commitment and the involvement of funding and research agencies. The predictive model for sPTB in nulliparous women showed a good performance, although further validation is required before clinical application. Possibly, reproducibility of the predictive model is limited, once metabolites comprising the model were only identified in one of the subsetDoutoradoSaúde Materna e PerinatalDoutor em Ciências da SaúdeCAPE

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10−40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10−7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and “omics” sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent “boom” in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies

Metabolomics applied to maternal and perinatal health : a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and “omics” sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent “boom” in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.74CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informação88881.134095/2016-01; 8881.134512/2016-0

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Development of novel mass spectrometric methods for point-of-care mucosal diagnostics

Human mucosal surfaces act as key interfaces between microbiota and host. As such, mucosal sampling using medical swabs is performed for diagnostic purposes that most commonly rely upon subsequent microscopy, culture or molecular-based assays. These approaches are limited in providing information on host response, which is a critical facet of pathology. In this thesis, I sought to test the hypothesis that both presence of specific microbes as well as their interactions with the human host are reflected in the mucosal metabolome and that this information could be exploited for mucosal diagnostic applications. The study aimed to develop a method for rapid, direct metabolic profiling from swabs using desorption electrospray ionisation mass spectrometry (DESI-MS). Method optimisation was conducted to elucidate optimal instrumental and geometrical conditions essential for the swab analysis. The application of the method for mucosal diagnostics was then assessed by characterising the metabolic profile of multiple bodysites (oral, nasal and vaginal mucosa), vaginal mucosa during two different physiological states (non-pregnant vs pregnant) and to detect a pathological state (bacterial vaginosis). Correlation of DESI-MS vaginal metabolic profiles with matched vaginal microbiota composition (VMC) characterised by 16S rRNA-based metataxonomics during pregnancy enabled to robustly predict a Lactobacillus dominant from depleted state but also major vaginal community states types (CST). The predictive performance of DESI-MS based models was comparable to “gold standard” LC-MS based models. Additionally, bacterial metabolite markers predictive of specific microbial genera were identified through matching to a spectral database constructed using pure cultures of commensal and pathogenic microbes often observed in the vaginal microbiome. In summary, DESI-MS has the potential to revolutionise the current way of mucosal based diagnostic by reducing significantly the time-demand needed for the characterisation of VMC, drug or inflammatory response to only few minutes and therefore could enable a faster decision making on patient’s treatment.Open Acces