An overview of proteomic and metabolomic technologies and their application to pregnancy research

Quantitative Proteomics and Metabolomics of Culture Medium from Single Human Embryo Reveal Embryo Quality-Related Multiomics Biomarkers

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Impacts of neonicotinoid insecticides on bumble bee energy metabolism are revealed under nectar starvation

Bumble bees are an important group of insects that provide essential pollination services as a consequence of their foraging behaviors. These pollination services are driven, in part, by energetic exchanges between flowering plants and individual bees. Thus, it is important to examine bumble bee energy metabolism and explore how it might be influenced by external stressors contributing to declines in global pollinator populations. Two stressors that are commonly encountered by bees are insecticides, such as the neonicotinoids, and nutritional stress, resulting from deficits in pollen and nectar availability. Our study uses a metabolomic approach to examine the effects of neonicotinoid insecticide exposure on bumble bee metabolism, both alone and in combination with nutritional stress. We hypothesized that exposure to imidacloprid disrupts bumble bee energy metabolism, leading to changes in key metabolites involved in central carbon metabolism. We tested this by exposing Bombus impatiens workers to imidacloprid according to one of three exposure paradigms designed to explore how chronic versus more acute (early or late) imidacloprid exposure influences energy metabolite levels, then also subjecting them to artificial nectar starvation. The strongest effects of imidacloprid were observed when bees also experienced nectar starvation, suggesting a combinatorial effect of neonicotinoids and nutritional stress on bumble bee energy metabolism. Overall, this study provides important insights into the mechanisms underlying the impact of neonicotinoid insecticides on pollinators, and underscores the need for further investigation into the complex interactions between environmental stressors and energy metabolism.

10. Role of high dimensional technology in preeclampsia (omics in preeclampsia)

Preeclampsia is a pregnancy-specific disease that has no known precise cause. Integrative biology approach based on multi-omics has been applied to identify upstream pathways and better understand the pathophysiology of preeclampsia. At DNA level, genomics and epigenomics studies have revealed numerous genetic variants associated with preeclampsia, including those involved in regulating blood pressure and immune response. Transcriptomics analyses have revealed altered expression of genes in preeclampsia, particularly those related to inflammation and angiogenesis. At protein level, proteomics studies have identified potential biomarkers for preeclampsia diagnosis and prediction in addition to revealing the main pathophysiological pathways involved in this disease. At metabolite level, metabolomics has highlighted altered lipid and amino acid metabolisms in preeclampsia. Finally, microbiomics studies have identified dysbiosis in the gut and vaginal microbiota in pregnant women with preeclampsia. Overall, omics technologies have improved our understanding of the complex molecular mechanisms underlying preeclampsia. However, further research is warranted to fully integrate and translate these omics findings into clinical practice.

Early Pregnancy Markers in the Serum of Ewes Identified via Proteomic and Metabolomic Analyses

The diagnosis of ewes' pregnancy status at an early stage is an efficient way to enhance the reproductive output of sheep and allow producers to optimize production and management. The techniques of proteomics and metabolomics have been widely used to detect regulatory factors in various physiological processes of animals. The aim of this study is to explore the differential metabolites and proteins in the serum of pregnant and non-pregnant ewes by proteomics and metabolomics. The serum of ewes at 21, 28 and 33 days after artificial insemination (AI) were collected. The pregnancy stratus of the ewes was finally determined through ultrasound examination and then the ewes were grouped as Pregnant (n = 21) or N on-pregnant (n = 9). First, the serum samples from pregnant or non-pregnant ewes at 21 days after AI were selected for metabolomic analysis. It was found that the level of nine metabolites were upregulated and 20 metabolites were downregulated in the pregnant animals (p < 0.05). None of these differential metabolomes are suitable as markers of pregnancy due to their small foldchange. Next, the proteomes of serum from pregnant or non-pregnant ewes were evaluated. At 21 days after AI, the presence of 321 proteins were detected, and we found that the level of three proteins were upregulated and 11 proteins were downregulated in the serum of pregnant ewes (p < 0.05). The levels of serum amyloid A (SAA), afamin (AFM), serpin family A member 6 (SERPINA6) and immunoglobulin-like domain-containing protein between pregnant and non-pregnant ewes at 21-, 28- and 33-days post-AI were also analyzed via enzyme-linked immunosorbent assay (ELISA). The levels of SAA and AFM were significantly higher in pregnant ewes than in non-pregnant ewes, and could be used as markers for early pregnancy detection. Overall, our results show that SAA and AFM are potential biomarkers to determine the early pregnancy status of ewes.

Hemolymph Metabolism Analysis of Honey Bee (Apis mellifera L.) Response to Different Bee Pollens

Pollen is essential to the development of honey bees. The nutrients in bee pollen vary greatly among plant species. Here, we analyzed the differences in the amino acid compositions of pear (Pyrus bretschneideri), rape (Brassica napus), and apricot (Armeniaca sibirica) pollens and investigated the variation in hemolymph metabolites and metabolic pathways through untargeted metabolomics in caged adult bees at days 7 and 14. The results showed that the levels of five essential amino acids (isoleucine, phenylalanine, lysine, methionine, and histidine) were the highest in pear pollen, and the levels of four amino acids (isoleucine: 50.75 ± 1.93 mg/kg, phenylalanine: 87.25 ± 2.66 mg/kg, methionine: 16.00 ± 0.71 mg/kg and histidine: 647.50 ± 24.80 mg/kg) were significantly higher in pear pollen than in the other two kinds of bee pollen (p < 0.05). The number of metabolites in bee hemolymph on day 14 (615) was significantly lower than that on day 7 (1466). The key metabolic pathways of bees, namely, “sphingolipid metabolism (p = 0.0091)”, “tryptophan metabolism (p = 0.0245)”, and “cysteine and methionine metabolism (p = 0.0277)”, were significantly affected on day 7. There was no meaningful pathway enrichment on day 14. In conclusion, pear pollen had higher nutritional value among the three bee pollens in terms of amino acid level, followed by rape and apricot pollen, and the difference in amino acid composition among bee pollens was reflected in the lipid and amino acid metabolism pathways of early adult honey bee hemolymph. This study provides new insights into the physiological and metabolic functions of different bee pollens in bees.

Integrative proteomics and metabolomics approach to elucidate metabolic dysfunction induced by silica nanoparticles in hepatocytes

Silica nanoparticles (SiNPs) are derived from manufactured materials and the natural environment, and they cause detrimental effects on human health via various exposure routes. The liver is proven to be a key target organ for SiNP toxicity; however, the mechanisms causing toxicity remain largely uncertain. Here, we investigated the effects of SiNPs on the metabolic spectrum in hepatocytes via integrative analyses of proteomics and metabolomics. First, a proteomic analysis was used to screen for critical proteins (including RPL3, HSP90AA1, SOD, PGK1, GOT1, and PNP), indicating that abnormal protein synthesis, protein misfolding, oxidative stress, and metabolic dysfunction may contribute to SiNP-induced hepatotoxicity. Next, metabolomic data demonstrated that SiNPs caused metabolic dysfunction by altering vital metabolites (including glucose, alanine, GSH, CTP, and ATP). Finally, a systematic bioinformatic analysis of protein-metabolite interactions showed that SiNPs disturbed glucose metabolism (glycolysis and pentose phosphate pathways, amino acid metabolism (alanine, aspartate, and glutamate), and ribonucleotide metabolism (purine and pyrimidine). These metabolic dysfunctions could exacerbate oxidative stress and lead to liver injury. Moreover, SOD, TKT, PGM1, GOT1, PNP, and NME2 may be key proteins for SiNP-induced hepatotoxicity. This study revealed the metabolic mechanisms underlying SiNP-induced hepatotoxicity and illustrated that integrative omics analyses can be a powerful approach for toxicity evaluations and risk assessments of nanoparticles.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Cow mastitis, with high incidence rate and complex cause of disease, is one of the main diseases that affect the development of dairy industry in the world. Clinical mastitis and subclinical mastitis caused by Staphylococcus aureus, Escherichia coli, Streptococcus, and other pathogens have a huge potential safety hazard to food safety and the rapid development of animal husbandry. The economic loss caused by cow mastitis is billions of dollars every year in the world. In recent years, the omics technology has been widely used in animal husbandry with the continuous breakthrough of sequencing technology and the continuous reduction of sequencing cost. For dairy cow mastitis, the traditional diagnostic technique, such as histopathological screening, somatic cell count, milk pH test, milk conductivity test, enzyme activity test, and infrared thermography, are difficult to fully and comprehensively clarify its pathogenesis due to their own limitations. Metabolomics technology is an important part of system biology, which can simultaneously analyze all low molecular weight metabolites such as amino acids, lipids, carbohydrates under the action of complex factors including internal and external environment and in a specific physiological period accurately and efficiently, and then clarify the related metabolic pathways. Metabolomics, as the most downstream of gene expression, can amplify the small changes of gene and protein expression at the level of metabolites, which can more fully reflect the cell function. The application of metabolomics technology in cow mastitis can analyze the hetero metabolites, identify the related biomarkers, and reveal the physiological and pathological changes of cow mammary gland, so as to provide valuable reference for the prediction, diagnosis, and treatment of mastitis. The research progress of metabolomics technology in cow mastitis in recent years was reviewed, in order to provide guidance for the development of cow health and dairy industry safety in this manuscript.

Identification of Indicators for Preterm Birth Using Retinoid Metabolites

Metabolites reflect the biochemical dynamics for the maintenance of pregnancy and parturition. UPLC-Q/TOF-MS and LC-MS/MS metabolomics were performed to identify and validate the plasma metabolomic signatures of preterm birth (PTB). We recruited pregnant women between 16 and 40 weeks 5 days gestational age at Ewha Womans Mokdong Hospital for a nested case-control study. In untargeted UPLC-Q/TOF-MS, score plots of partial least-squares discriminant analysis clearly separated the PTB group from the term birth (TB, n = 10; PTB, n = 11). Fifteen metabolites were significantly different between the two groups, as indicated by a variable importance in projection >1 and p < 0.05. Metabolic pathways involving retinol, linoleic acid, d-arginine, and d-ornithine were associated with PTB. Verification by LC-MS/MS focused on retinol metabolism (TB, n = 39; PTB, n = 20). Retinol levels were significantly reduced in PTB compared to TB, while retinal palmitate, all-trans-retinal, and 13-cis-retinoic acid (¹³cis-RA) significantly increased (p < 0.05). Retinol-binding protein levels were also elevated in PTB. Additionally, all-trans-retinal (AUC 0.808, 95% CI: 0.683–0.933) and ¹³cis-RA (AUC 0.826, 95% CI: 0.723–0.930) showed improved predictions for PTB-related retinol metabolites. This study suggests that retinoid metabolism improves the accuracy of PTB predictions and plays an important role in maintaining pregnancy and inducing early parturition.

Complement and coagulation cascades activation is the main pathophysiological pathway in early-onset severe preeclampsia revealed by maternal proteomics

Preeclampsia is a pregnancy-specific multisystem disorder and a leading cause of maternal and perinatal morbidity and mortality. The exact pathogenesis of this multifactorial disease remains poorly defined. We applied proteomics analysis on maternal blood samples collected from 14 singleton pregnancies with early-onset severe preeclampsia and 6 uncomplicated pregnancies to investigate the pathophysiological pathways involved in this specific subgroup of preeclampsia. Maternal blood was drawn at diagnosis for cases and at matched gestational age for controls. LC-MS/MS proteomics analysis was conducted, and data were analyzed by multivariate and univariate statistical approaches with the identification of differential pathways by exploring the global human protein-protein interaction network. The unsupervised multivariate analysis (the principal component analysis) showed a clear difference between preeclamptic and uncomplicated pregnancies. The supervised multivariate analysis using orthogonal partial least square discriminant analysis resulted in a model with goodness of fit (R2X = 0.99, p < 0.001) and a strong predictive ability (Q2Y = 0.8, p < 0.001). By univariate analysis, we found 17 proteins statistically different after 5% FDR correction (q-value < 0.05). Pathway enrichment analysis revealed 5 significantly enriched pathways whereby the activation of the complement and coagulation cascades was on top (p = 3.17e-07). To validate these results, we assessed the deposits of C5b-9 complement complex and on endothelial cells that were exposed to activated plasma from an independent set of 4 cases of early-onset severe preeclampsia and 4 uncomplicated pregnancies. C5b-9 and Von Willbrand factor deposits were significantly higher in early-onset severe preeclampsia. Future studies are warranted to investigate potential therapeutic targets for early-onset severe preeclampsia within the complement and coagulation pathway.

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Spontaneous preterm birth (sPTB) is the leading cause of infant morbidity and mortality worldwide. Deficiency of effective predict methods is an urgent problem that needs to be solved. Numbers of researchers spare no efforts to investigate differential indicators. To evaluate the value of the differential indicators, a prospective nested case-control study was carried out. Among an overall cohort of 1,050 pregnancies, 20 sPTB pregnancies, and 20 full-term pregnancies were enrolled in this study. Participants were followed-up until labor. The psychological profile was evaluated utilizing the Zung Self-Rating Depression Scale at 11-14 weeks. Stress-related biomarker-cortisol and metabolites were detected by Electrochemiluminescence Immunoassay (ECLIA) and Gas Chromatography-Mass Spectrometry (GC-MS) in serum samples during pregnancy, respectively. The expression level of cortisol was up-regulated in serum and the score of the Zung Self-Rating Depression Scale was significantly higher in the sPTB group when compared to the control group. Note that, 29 metabolomics were differentially expressed between the sPTB group and the control group. The scores of the Zung Self-Rating Depression Scale, the level of cortisol, Eicosane, methyltetradecanoate, and stearic acid in serum were selected to establish the model with lasso logistic regression. Validation of the model yielded an optimum corrected AUC value of 89.5%, 95% CI: 0.8006-0.9889 with a sensitivity of 100.0%, and specificity of 78.9%. In conclusion, this study establishes a prediction model of sPTB with five variables, which may predict sPTB more accurately and sensitively in the second trimester.

Maternal proteomic profiling reveals alterations in lipid metabolism in late-onset fetal growth restriction

Fetal growth restriction defined as the failure to achieve the fetal genetic growth potential is a major cause of perinatal morbidity and mortality. The role of maternal adaptations to placental insufficiency in this disorder is still not fully understood. We aimed to investigate the biological processes and protein–protein interactions involved in late-onset fetal growth restriction in particular. We applied 2D nano LC–MS/MS proteomics analysis on maternal blood samples collected at the time of delivery from 5 singleton pregnancies with late-onset fetal growth restriction and 5 uncomplicated pregnancies. Data were analyzed using R package “limma” and Ingenuity Pathway Analysis. 25 proteins showed significant changes in their relative abundance in late-onset fetal growth restriction (p value < 0.05). Direct protein–protein interactions network demonstrated that Neurogenic locus notch homolog protein 1 (NOTCH1) was the most significant putative upstream regulator of the observed profile. Gene ontology analysis of these proteins revealed the involvement of 14 canonical pathways. The most significant biological processes were efflux of cholesterol, efflux of phospholipids, adhesion of blood cells, fatty acid metabolism and dyslipidemia. Future studies are warranted to validate the potential role of the detected altered proteins as potential therapeutic targets in the late-onset form of fetal growth restriction.

Identifying biomarkers for predicting successful embryo implantation: applying single to multi-OMICs to improve reproductive outcomes

BACKGROUND

Successful embryo implantation is a complex process that requires the coordination of a series of events, involving both the embryo and the maternal endometrium. Key to this process is the intricate cascade of molecular mechanisms regulated by endocrine, paracrine and autocrine modulators of embryonic and maternal origin. Despite significant progress in ART, implantation failure still affects numerous infertile couples worldwide and fewer than 10% of embryos successfully implant. Improved selection of both the viable embryos and the optimal endometrial phenotype for transfer remains crucial to enhancing implantation chances. However, both classical morphological embryo selection and new strategies incorporated into clinical practice, such as embryonic genetic analysis, morphokinetics or ultrasound endometrial dating, remain insufficient to predict successful implantation. Additionally, no techniques are widely applied to analyse molecular signals involved in the embryo-uterine interaction. More reliable biological markers to predict embryo and uterine reproductive competence are needed to improve pregnancy outcomes. Recent years have seen a trend towards 'omics' methods, which enable the assessment of complete endometrial and embryonic molecular profiles during implantation. Omics have advanced our knowledge of the implantation process, identifying potential but rarely implemented biomarkers of successful implantation.

OBJECTIVE AND RATIONALE

Differences between the findings of published omics studies, and perhaps because embryonic and endometrial molecular signatures were often not investigated jointly, have prevented firm conclusions being reached. A timely review summarizing omics studies on the molecular determinants of human implantation in both the embryo and the endometrium will help facilitate integrative and reliable omics approaches to enhance ART outcomes.

SEARCH METHODS

In order to provide a comprehensive review of the literature published up to September 2019, Medline databases were searched using keywords pertaining to omics, including 'transcriptome', 'proteome', 'secretome', 'metabolome' and 'expression profiles', combined with terms related to implantation, such as 'endometrial receptivity', 'embryo viability' and 'embryo implantation'. No language restrictions were imposed. References from articles were also used for additional literature.

OUTCOMES

Here we provide a complete summary of the major achievements in human implantation research supplied by omics approaches, highlighting their potential to improve reproductive outcomes while fully elucidating the implantation mechanism. The review highlights the existence of discrepancies among the postulated biomarkers from studies on embryo viability or endometrial receptivity, even using the same omic analysis.

WIDER IMPLICATIONS

Despite the huge amount of biomarker information provided by omics, we still do not have enough evidence to link data from all omics with an implantation outcome. However, in the foreseeable future, application of minimally or non-invasive omics tools, together with a more integrative interpretation of uniformly collected data, will help to overcome the difficulties for clinical implementation of omics tools. Omics assays of the embryo and endometrium are being proposed or already being used as diagnostic tools for personalised single-embryo transfer in the most favourable endometrial environment, avoiding the risk of multiple pregnancies and ensuring better pregnancy rates.

The Metabolomic Underpinnings of Symptom Burden in Patients With Multiple Chronic Conditions

Over 25% of the adult population in the United States suffers from multiple chronic conditions, with numbers continuing to rise. Those with multiple chronic conditions often experience symptoms or symptom clusters that undermine their quality of life and ability to self-manage. Importantly, symptom severity in those with even the same multiple chronic conditions varies, suggesting that the mechanisms driving symptoms in patients with multiple chronic conditions are not fixed but may differ in ways that could make them amenable to targeted interventions. In this manuscript we describe at a metabolic level, the symptom experience of persons with multiple chronic conditions, including how symptoms may synergize or cluster across multiple chronic conditions to augment one's symptom burden. To guide this discussion, we consider the metabolites and metabolic pathways known to span multiple adverse health conditions and associate with severe symptoms of fatigue, depression, and anxiety and their cluster. We also describe how severe versus mild symptoms, and their associated metabolites and metabolic pathways, may vary, depending on the presence of covariates; two of which, sex as a biological variable and the contribution of gut microbiota dysbiosis, are discussed in additional detail. Intertwining metabolomics and symptom science into nursing research, offers the unique opportunity to better understand how the metabolites and metabolic pathways affected in those with multiple chronic conditions may initiate or exacerbate symptom presence within a given individual, ultimately allowing clinicians to develop targeted interventions to improve the health quality of patients their families.

Identification of Potential Biomarkers in the Cervicovaginal Fluid by Metabolic Profiling for Preterm Birth

During pregnancy, dysbiosis in the vaginal microbiota directly affects the metabolic profiles, which might impact preterm birth (PTB). In this study, we performed cervicovaginal fluid (CVF) metabolic profiling using nuclear magnetic resonance (NMR) spectroscopy and identified the metabolic markers for predicting PTB. In this nested case-control study, 43 South Korean pregnant women with PTB (n = 22), and term birth (TB; n = 21) were enrolled with their demographic profiles, and CVF samples were collected by vaginal swabs. The PTB group had two subgroups based on post-CVF sampling birth: PTB less than (PTB < 7 d) and more than 7 days (PTB ≥ 7 d). We observed significant differences in the gestational age at birth (GAB), cervical length (CL), and neonatal birth weight among the groups. The principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA) scatter plot showed the separation between the PTB < 7 d group, and the TB group. Out of 28 identified metabolites, acetone, ethanol, ethylene glycol, formate, glycolate, isopropanol, methanol, and trimethylamine N-oxide (TMAO) were significantly increased in the PTB group compared with the TB group. The ROC curve analysis revealed that the acetone, ethylene glycol, formate, glycolate, isopropanol, methanol, and TMAO had the best predictive values for PTB. Additionally, the correlation analysis of these metabolites showed a strong negative correlation with GAB and CL. These metabolites could be beneficial markers for the clinical application of PTB prediction.

Metabolomic Pathways Predicting Labor Dystocia by Maternal Body Mass Index

Objectives  The purpose of this study was to evaluate the metabolic pathways activated in the serum of African-American women during late pregnancy that predicted term labor dystocia. Study Design  Matched case-control study ( n  = 97; 48 cases of term labor dystocia and 49 normal labor progression controls) with selection based on body mass index (BMI) at hospital admission and maternal age. Late pregnancy serum samples were analyzed using ultra-high-resolution metabolomics. Differentially expressed metabolic features and pathways between cases experiencing term labor dystocia and normal labor controls were evaluated in the total sample, among women who were obese at the time of labor (BMI ≥ 30 kg/m2), and among women who were not obese. Results  Labor dystocia was predicted by different metabolic pathways in late pregnancy serum among obese (androgen/estrogen biosynthesis) versus nonobese African-American women (fatty acid activation, steroid hormone biosynthesis, bile acid biosynthesis, glycosphingolipid metabolism). After adjusting for maternal BMI and age in the total sample, labor dystocia was predicted by tryptophan metabolic pathways in addition to C21 steroid hormone, glycosphingolipid, and androgen/estrogen metabolism. Conclusion  Metabolic pathways consistent with lipotoxicity, steroid hormone production, and tryptophan metabolism in late pregnancy serum were significantly associated with term labor dystocia in African-American women.

Trace biomarkers associated with spontaneous preterm birth from the maternal serum metabolome of asymptomatic nulliparous women - parallel case-control studies from the SCOPE cohort

Prediction of spontaneous preterm birth (sPTB) in asymptomatic women remains a great challenge; accurate and reproducible screening tools are still not available in clinical practice. We aimed to investigate whether the maternal serum metabolome together with clinical factors could be used to identify asymptomatic women at risk of sPTB. We conducted two case-control studies using gas chromatography-mass spectrometry to analyse maternal serum samples collected at 15- and 20-weeks' gestation from 164 nulliparous women from Cork, and 157 from Auckland. Smoking and vaginal bleeding before 15 weeks were the only significant clinical predictors of sPTB for Auckland and Cork subsets, respectively. Decane, undecane, and dodecane were significantly associated with sPTB (FDR < 0.05) in the Cork subset. An odds ratio of 1.9 was associated with a one standard deviation increase in log (undecane) in a multiple logistic regression which also included vaginal bleeding as a predictor. In summary, elevated serum levels of the alkanes decane, undecane, and dodecane were associated with sPTB in asymptomatic nulliparous women from Cork, but not in the Auckland cohort. The association is not strong enough to be a useful clinical predictor, but suggests that further investigation of the association between oxidative stress processes and sPTB risk is warranted.

Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation

Sleep fragmentation (SF) commonly occurs in several pathologic conditions and is especially associated with impairments of hippocampus-dependent neurocognitive functions. Although the effects of SF on hippocampus in terms of protein or gene levels were examined in several studies, the impact of SF at the metabolite level has not been investigated. Thus, in this study, the differentially expressed large-scale metabolite profiles of hippocampus in a rat model of SF were investigated using untargeted metabolomics approaches. Forty-eight rats were divided into the following 4 groups: 4-day SF group, 4-day exercise control (EC) group, 15-day SF group, and 15-day EC group (n = 12, each). SF was accomplished by forced exercise using a walking wheel system with 30-s on/90-s off cycles, and EC condition was set at 10-min on/30-min off. The metabolite profiles of rat hippocampi in the SF and EC groups were analyzed using liquid chromatography/mass spectrometry. Multivariate analysis revealed distinctive metabolic profiles and marker signals between the SF and corresponding EC groups. Metabolic changes were significant only in the 15-day SF group. In the 15-day SF group, L-tryptophan, myristoylcarnitine, and palmitoylcarnitine were significantly increased, while adenosine monophosphate, hypoxanthine, L-glutamate, L-aspartate, L-methionine, and glycerophosphocholine were decreased compared to the EC group. The alanine, aspartate, and glutamate metabolism pathway was observed as the common key pathway in the 15-day SF groups. The results from this untargeted metabolomics study provide a perspective on metabolic impact of SF on the hippocampus.

Metabolomics-based biomarker discovery for bee health monitoring: A proof of concept study concerning nutritional stress in Bombus terrestris

Bee pollinators are exposed to multiple natural and anthropogenic stressors. Understanding the effects of a single stressor in the complex environmental context of antagonistic/synergistic interactions is critical to pollinator monitoring and may serve as early warning system before a pollination crisis. This study aimed to methodically improve the diagnosis of bee stressors using a simultaneous untargeted and targeted metabolomics-based approach. Analysis of 84 Bombus terrestris hemolymph samples found 8 metabolites retained as potential biomarkers that showed excellent discrimination for nutritional stress. In parallel, 8 significantly altered metabolites, as revealed by targeted profiling, were also assigned as candidate biomarkers. Furthermore, machine learning algorithms were applied to the above-described two biomarker sets, whereby the untargeted eight components showed the best classification performance with sensitivity and specificity up to 99% and 100%, respectively. Based on pathway and biochemistry analysis, we propose that gluconeogenesis contributed significantly to blood sugar stability in bumblebees maintained on a low carbohydrate diet. Taken together, this study demonstrates that metabolomics-based biomarker discovery holds promising potential for improving bee health monitoring and to identify stressor related to energy intake and other environmental stressors.

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.

Use of metabolomics for predicting spontaneous preterm birth in asymptomatic pregnant women: protocol for a systematic review and meta-analysis

INTRODUCTION

Preterm birth (PTB) is the leading cause of neonatal mortality and short- and long-term morbidity. The aetiology and pathophysiology of spontaneous PTB (sPTB) are still unclear, which makes the identification of reliable and accurate predictor markers more difficult, particularly for unscreened or asymptomatic women. Metabolomics biomarkers have been demonstrated to be potentially accurate biomarkers for many disorders with complex mechanisms such as PTB. Therefore, we aim to perform a systematic review of metabolomics markers associated with sPTB. Our research question is 'What is the performance of metabolomics for predicting spontaneous preterm birth in asymptomatic pregnant women?'

METHODS AND ANALYSIS

We will focus on studies assessing metabolomics techniques for predicting sPTB in asymptomatic pregnant women. We will conduct a comprehensive systematic review of the literature from the last 10 years. Only observational cohort and case-control studies will be included. Our search strategy will be carried out by two independent reviewers, who will scan title and abstract before carrying out a full review of the article. The scientific databases to be explored include PubMed, MedLine, ScieLo, EMBASE, LILACS, Web of Science, Scopus and others.

ETHICS AND DISSEMINATION

This systematic review protocol does not require ethical approval. We intend to disseminate our findings in scientific peer-reviewed journal, the Preterm SAMBA study open access website, specialists' conferences and to our funding agencies.

PROSPERO REGISTRATION NUMBER

CRD42018100172.

Normal pregnancy induced glucose metabolic stress in a longitudinal cohort of healthy women: Novel insights generated from a urine metabolomics study

During normal pregnancy, mothers face a unique physiological challenge in the adaptation of glucose metabolism in preparation for the metabolic stress presented by fetal development. However, the responsible mechanism remains elusive. The purpose of this study is to investigate the mechanism of the metabolic stress of glucose metabolism in pregnant women using metabolomics method.A Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometer-based untargeted metabolomics study was performed to investigate the dynamic urinary signature of the intermediates of glucose metabolism in a longitudinal cohort of 232 healthy pregnant women in their first, second, and third trimesters.Twelve glucose metabolic intermediates were screened out from hundreds of candidate metabolites using partial least squares discriminant analysis models. These 12 markers were mainly involved in the metabolic pathways of insulin resistance, glycolysis/gluconeogenesis, tricarboxylic acid cycle, nonabsorbable carbohydrate metabolism, and N-glycan biosynthesis. In particular, L-acetylcarnitine, a metabolite that is beneficial for the amelioration of insulin resistance, decreased in a time-dependent manner during normal pregnancy. Moreover, thiamine pyrophosphate, an intermediate product of glycolysis/gluconeogenesis, significantly increased in the second trimester, and argininosuccinic acid and oxalosuccinic acid, intermediates involved in the tricarboxylic acid cycle, significantly decreased in the third trimester, suggesting an increased glucose demand in the maternal body during fetal development.These findings provide novel insight into the normal pregnancy-induced elevation of insulin resistance and glycolysis/gluconeogenesis, as well as the observed reduction in the aerobic oxidation of glucose.

Historical and contemporary stable isotope tracer approaches to studying mammalian protein metabolism

Over a century ago, Frederick Soddy provided the first evidence for the existence of isotopes; elements that occupy the same position in the periodic table are essentially chemically identical but differ in mass due to a different number of neutrons within the atomic nucleus. Allied to the discovery of isotopes was the development of some of the first forms of mass spectrometers, driven forward by the Nobel laureates JJ Thomson and FW Aston, enabling the accurate separation, identification, and quantification of the relative abundance of these isotopes. As a result, within a few years, the number of known isotopes both stable and radioactive had greatly increased and there are now over 300 stable or radioisotopes presently known. Unknown at the time, however, was the potential utility of these isotopes within biological disciplines, it was soon discovered that these stable isotopes, particularly those of carbon (13 C), nitrogen (15 N), oxygen (18 O), and hydrogen (2 H) could be chemically introduced into organic compounds, such as fatty acids, amino acids, and sugars, and used to "trace" the metabolic fate of these compounds within biological systems. From this important breakthrough, the age of the isotope tracer was born. Over the following 80 yrs, stable isotopes would become a vital tool in not only the biological sciences, but also areas as diverse as forensics, geology, and art. This progress has been almost exclusively driven through the development of new and innovative mass spectrometry equipment from IRMS to GC-MS to LC-MS, which has allowed for the accurate quantitation of isotopic abundance within samples of complex matrices. This historical review details the development of stable isotope tracers as metabolic tools, with particular reference to their use in monitoring protein metabolism, highlighting the unique array of tools that are now available for the investigation of protein metabolism in vivo at a whole body down to a single protein level. Importantly, it will detail how this development has been closely aligned to the technological development within the area of mass spectrometry. Without the dedicated development provided by these mass spectrometrists over the past century, the use of stable isotope tracers within the field of protein metabolism would not be as widely applied as it is today, this relationship will no doubt continue to flourish in the future and stable isotope tracers will maintain their importance as a tool within the biological sciences for many years to come. © 2016 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc. Mass Spec Rev.

"Omics" in oral cancer: New approaches for biomarker discovery

OBJECTIVES

In this review paper, we explored the application of "omics" approaches in the study of oral cancer (OC). It will provide a better understanding of how "omics" approaches may lead to novel biomarker molecules or molecular signatures with potential value in clinical practice. A future direction of "omics"-driven research in OC is also discussed.

METHODS

Studies on "omics"-based approaches [genomics/proteomics/transcriptomics/metabolomics] were investigated for differentiating oral squamous cell carcinoma,oral sub-mucous fibrosis, oral leukoplakia, oral lichen planus, oral erythroplakia from normal cases. Electronic databases viz., PubMed, Springer, and Google Scholar were searched.

RESULTS

One eighty-one studies were included in this review. The review shows that the fields of genomics, transcriptomics, proteomics, and metabolomics-based marker identification have implemented advanced tools to screen early changes in DNA, RNA, protein, and metabolite expression in OC population.

CONCLUSIONS

It may be concluded that despite advances in OC therapy, symptomatic presentation occurs at an advanced stage, where various curative treatment options become very limited. A molecular level study is essential for detecting an OC biomarker at an early stage. Modern "Omics" strategies can potentially make a major contribution to meet this need.

Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA

Background

Spontaneous preterm birth is a complex syndrome with multiple pathways interactions determining its occurrence, including genetic, immunological, physiologic, biochemical and environmental factors. Despite great worldwide efforts in preterm birth prevention, there are no recent effective therapeutic strategies able to decrease spontaneous preterm birth rates or their consequent neonatal morbidity/mortality. The Preterm SAMBA study will associate metabolomics technologies to identify clinical and metabolite predictors for preterm birth. These innovative and unbiased techniques might be a strategic key to advance spontaneous preterm birth prediction.

Methods/design

Preterm SAMBA study consists of a discovery phase to identify biophysical and untargeted metabolomics from blood and hair samples associated with preterm birth, plus a validation phase to evaluate the performance of the predictive modelling. The first phase, a case–control study, will randomly select 100 women who had a spontaneous preterm birth (before 37 weeks) and 100 women who had term birth in the Cork Ireland and Auckland New Zealand cohorts within the SCOPE study, an international consortium aimed to identify potential metabolomic predictors using biophysical data and blood samples collected at 20 weeks of gestation. The validation phase will recruit 1150 Brazilian pregnant women from five participant centres and will collect blood and hair samples at 20 weeks of gestation to evaluate the performance of the algorithm model (sensitivity, specificity, predictive values and likelihood ratios) in predicting spontaneous preterm birth (before 34 weeks, with a secondary analysis of delivery before 37 weeks).

Discussion

The Preterm SAMBA study intends to step forward on preterm birth prediction using metabolomics techniques, and accurate protocols for sample collection among multi-ethnic populations. The use of metabolomics in medical science research is innovative and promises to provide solutions for disorders with multiple complex underlying determinants such as spontaneous preterm birth.

Identifying metabolite markers for preterm birth in cervicovaginal fluid by magnetic resonance spectroscopy

INTRODUCTION

Preterm birth (PTB) may be preceded by changes in the vaginal microflora and metabolite profiles.

OBJECTIVES

We sought to characterise the metabolite profile of cervicovaginal fluid (CVF) of pregnant women by 1H NMR spectroscopy, and assess their predictive value for PTB.

METHODS

A pair of high-vaginal swabs was obtained from pregnant women with no evidence of clinical infection and grouped as follows: asymptomatic low risk (ALR) women with no previous history of PTB, assessed at 20-22 gestational weeks, g.w., n = 83; asymptomatic high risk (AHR) women with a previous history of PTB, assessed at both 20-22 g.w., n = 71, and 26-28 g.w., n = 58; and women presenting with symptoms of preterm labor (PTL) (SYM), assessed at 24-36 g.w., n = 65. Vaginal secretions were dissolved in phosphate buffered saline and scanned with a 9.4 T NMR spectrometer.

RESULTS

Six metabolites (lactate, alanine, acetate, glutamine/glutamate, succinate and glucose) were analysed. In all study cohorts vaginal pH correlated with lactate integral (r = -0.62, p < 0.0001). Lactate integrals were higher in the term ALR compared to the AHR (20-22 g.w.) women (p = 0.003). Acetate integrals were higher in the preterm versus term women for the AHR (20-22 g.w.) (p = 0.048) and SYM (p = 0.003) groups; and was predictive of PTB < 37 g.w. (AUC 0.78; 95 % CI 0.61-0.95), and delivery within 2 weeks of the index assessment (AUC 0.84; 95 % CI 0.64-1) in the SYM women, whilst other metabolites were not.

CONCLUSION

High CVF acetate integral of women with symptoms of PTL appears predictive of preterm delivery, as well as delivery within 2 weeks of presentation.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium

BACKGROUND

'Omics' high-throughput analyses, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, are widely applied in human endometrial studies. Analysis of endometrial transcriptome patterns in physiological and pathophysiological conditions has been to date the most commonly applied 'omics' technique in human endometrium. As the technologies improve, proteomics holds the next big promise for this field. The 'omics' technologies have undoubtedly advanced our knowledge of human endometrium in relation to fertility and different diseases. Nevertheless, the challenges arising from the vast amount of data generated and the broad variation of 'omics' profiling according to different environments and stimuli make it difficult to assess the validity, reproducibility and interpretation of such 'omics' data. With the expansion of 'omics' analyses in the study of the endometrium, there is a growing need to develop guidelines for the design of studies, and the analysis and interpretation of 'omics' data.

METHODS

Systematic review of the literature in PubMed, and references from relevant articles were investigated up to March 2013.

RESULTS

The current review aims to provide guidelines for future 'omics' studies on human endometrium, together with a summary of the status and trends, promise and shortcomings in the high-throughput technologies. In addition, the approaches presented here can be adapted to other areas of high-throughput 'omics' studies.

CONCLUSION

A highly rigorous approach to future studies, based on the guidelines provided here, is a prerequisite for obtaining data on biological systems which can be shared among researchers worldwide and will ultimately be of clinical benefit.

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.

Metabolomics reveals metabolic alterations by intrauterine growth restriction in the fetal rabbit brain

Background

Intrauterine Growth Restriction (IUGR) due to placental insufficiency occurs in 5–10% of pregnancies and is a major risk factor for abnormal neurodevelopment. The perinatal diagnosis of IUGR related abnormal neurodevelopment represents a major challenge in fetal medicine. The development of clinical biomarkers is considered a promising approach, but requires the identification of biochemical/molecular alterations by IUGR in the fetal brain. This targeted metabolomics study in a rabbit IUGR model aimed to obtain mechanistic insight into the effects of IUGR on the fetal brain and identify metabolite candidates for biomarker development.

Methodology/Principal Findings

At gestation day 25, IUGR was induced in two New Zealand rabbits by 40–50% uteroplacental vessel ligation in one horn and the contralateral horn was used as control. At day 30, fetuses were delivered by Cesarian section, weighed and brains collected for metabolomics analysis. Results showed that IUGR fetuses had a significantly lower birth and brain weight compared to controls. Metabolomics analysis using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) and database matching identified 78 metabolites. Comparison of metabolite intensities using a t-test demonstrated that 18 metabolites were significantly different between control and IUGR brain tissue, including neurotransmitters/peptides, amino acids, fatty acids, energy metabolism intermediates and oxidative stress metabolites. Principle component and hierarchical cluster analysis showed cluster formations that clearly separated control from IUGR brain tissue samples, revealing the potential to develop predictive biomarkers. Moreover birth weight and metabolite intensity correlations indicated that the extent of alterations was dependent on the severity of IUGR.

Conclusions

IUGR leads to metabolic alterations in the fetal rabbit brain, involving neuronal viability, energy metabolism, amino acid levels, fatty acid profiles and oxidative stress mechanisms. Overall findings identified aspargine, ornithine, N-acetylaspartylglutamic acid, N-acetylaspartate and palmitoleic acid as potential metabolite candidates to develop clinical biomarkers for the perinatal diagnosis of IUGR related abnormal neurodevelopment.

Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes

This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8-20; V2: week 28±2; V3 10-16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using (1)H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.

Metabolomics in the developing human being

Metabolomics is based on the detailed analysis of metabolites and represents a unique chemical fingerprint of an organism. This approach allows assessing the dynamic behavior of biologic systems with multiple network interactions among individual components. The field of metabolic profiling has rapidly developed over the last decade, with successful applications in various research areas including toxicology, disease diagnosis and classification, pharmacology, and nutrition. This article provides a comprehensive account of existing data in the literature from animal and clinical studies on the use of metabolomics for improved understanding of medical conditions affecting the neonate and the developing human being.

A metabolomic approach identifies differences in maternal serum in third trimester pregnancies that end in poor perinatal outcome

Metabolomics offers a powerful holistic approach to examine the metabolite composition of biofluids to identify disruptions present in disease. We used ultra performance liquid chromatography-mass spectroscopy on the maternal serum obtained in the third trimester to address the hypothesis that pregnancies ending in poor outcomes (small for gestational age infant, preterm birth, or neonatal intensive care admission, n = 40) would have a different maternal serum metabolic profiles to matched healthy pregnancies (n = 40). Ninety-eight identified metabolic features differed between normal and poor pregnancy outcomes. Classes of metabolites perturbed included free fatty acids, glycerolipids, progesterone metabolites, sterol lipids, vitamin D metabolites, and sphingolipids; these highlight potential molecular mechanisms associated with pregnancy complications in the third trimester linked by placental dysfunction. In this clinical setting, metabolomics has the potential to describe differences in fetoplacental and maternal metabolites in pregnancies with poor pregnancy outcomes compared with controls.

Characterization of the gastrointestinal microbiota in health and inflammatory bowel disease

The enteric bacterial flora play a key role in maintaining health. Inflammatory bowel disease is associated with quantitative and qualitative alterations in the microbiota. Early characterization of the microbiota involved culture-dependent techniques. The advent of metagenomic techniques, however, allows for structural and functional characterization using culture-independent methods. Changes in diversity, together with quantitative alterations in specific bacterial species, have been identified. The functional significance of these changes, and their pathogenic role, remain to be elucidated.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

OBJECTIVE

Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the 'metabolic network' of cells (metabolome) is now possible by using a 'metabolomics' approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI).

STUDY DESIGN

Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second.

RESULTS

(1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide).

CONCLUSION

Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

PROTEINURIA IN PREECLAMPSIA: FROM BENCH TO BEDSIDE

Pre-eclampsia (PE) remains the leading cause of maternal and fetal mortality in the developed world and parts of the developing world. Morbidity and mortality from PE is increased in the developing world compared to the developed world, as availability and access to antenatal care and pathology services are limited.