Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective

Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico

Although phthalate exposure has been linked with multiple adverse pregnancy outcomes, their underlying biological mechanisms are not fully understood. We examined associations between biomarkers of phthalate exposures and metabolic alterations using untargeted metabolomics in 99 pregnant women and 86 newborns [mean (SD) gestational age = 39.5 (1.5) weeks] in the PROTECT cohort. Maternal urinary phthalate metabolites were quantified using isotope dilution high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), while metabolic profiles in maternal and cord blood plasma were characterized via reversed-phase LC-MS. Multivariable linear regression was used in metabolome-wide association studies (MWAS) to identify individual metabolic features associated with elevated phthalate levels, while clustering and correlation network analyses were used to discern the interconnectedness of biologically relevant features. In the MWAS adjusted for maternal age and prepregnancy BMI, we observed significant associations between specific phthalates, namely, di(2-ethylhexyl) phthalate (DEHP) and mono(3-carboxypropyl) phthalate (MCPP), and 34 maternal plasma metabolic features. These associations predominantly included upregulation of fatty acids, amino acids, purines, or their derivatives and downregulation of ceramides and sphingomyelins. In contrast, fewer significant associations were observed with metabolic features in cord blood. Correlation network analysis highlighted the overlap of features associated with phthalates and those identified as differentiating markers for preterm birth in a previous study. Overall, our findings underscore the complex impact of phthalate exposures on maternal and fetal metabolism, highlighting metabolomics as a tool for understanding associated biological processes. Future research should focus on expanding the sample size, exploring the effects of phthalate mixtures, and validating identified metabolic features in larger, more diverse populations.

Differential impact of perfluorooctanoic acid and fluorotelomer ethoxylates on placental metabolism in mice

Poly- and perfluoroalkyl substances (PFAS) are a group of compounds with uses in industry and many consumer products. Concerns about the potential health effects of these compounds resulted in regulation by the Stockholm Convention on the use of three of the most common PFAS, including perfluorooctanoic acid (PFOA). Thousands of PFAS remain in production that are unregulated and for which their toxicity is unknown. Our group recently identified a new class of PFAS, fluorotelomer ethoxylates (FTEOs), in indoor dust and industrial wastewater. In this study, we investigated the effect of PFAS on placental metabolism by exposing healthy, pregnant CD-1 mice to PFOA or FTEOs at one of three concentrations (0 ng/L (controls), 5 ng/L, 100 ng/L) (n = 7-8/group). While PFOA is banned and PFOA concentrations in human blood are decreasing, we hypothesize that FTEOs will cause adverse pregnancy outcomes similar to PFOA, the compounds they were meant to replace. Placental tissue samples were collected at embryonic day 17.5 and 1H solid-state magic angle spinning nuclear magnetic resonance spectroscopy was used to determine the relative concentration of placental metabolites (n = 18-20/group). At the highest concentration, the relative concentrations of glucose and threonine were increased and the relative concentration of creatine was decreased in the PFOA-exposed placentas compared to controls (p < 0.05). In contrast, the relative concentrations of asparagine and lysine were decreased and the relative concentration of creatine was increased in the FTEOs-exposed placentas compared to controls (p < 0.05). Partial least squares - discriminant analysis showed the FTEOs-exposed and control groups were significantly separated (p < 0.005) and pathway analysis found four biochemical pathways were perturbed following PFOA exposure, while one pathway was altered following FTEOs exposure. Maternal exposure to PFOA and FTEOs had a significant impact on the placental metabolome, with the effect depending on the pollutant. This work motivates further studies to determine exposure levels and evaluate associations with adverse outcomes in human pregnancies.

Metabolomic analysis of the human placenta reveals perturbations in amino acids, purine metabolites, and small organic acids in spontaneous preterm birth

Spontaneous preterm delivery presents one of the most complex challenges in obstetrics and is a leading cause of perinatal morbidity and mortality. Although it is a common endpoint for multiple pathological processes, the mechanisms governing the etiological complexity of spontaneous preterm birth and the placental responses are poorly understood. This study examined placental tissues collected between May 2019 and May 2022 from a well-defined cohort of women who experienced spontaneous preterm birth (n = 72) and healthy full-term deliveries (n = 30). Placental metabolomic profiling of polar metabolites was performed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) analysis. The resulting data were analyzed using multi- and univariate statistical methods followed by unsupervised clustering. A comprehensive metabolomic evaluation of the placenta revealed that spontaneous preterm birth was associated with significant changes in the levels of 34 polar metabolites involved in intracellular energy metabolism and biochemical activity, including amino acids, purine metabolites, and small organic acids. We found that neither the preterm delivery phenotype nor the inflammatory response explain the reported differential placental metabolome. However, unsupervised clustering revealed two molecular subtypes of placentas from spontaneous preterm pregnancies exhibiting differential enrichment of clinical parameters. We also identified differences between early and late preterm samples, suggesting distinct placental functions in early spontaneous preterm delivery. Altogether, we present evidence that spontaneous preterm birth is associated with significant changes in the level of placental polar metabolites. Dysregulation of the placental metabolome may underpin important (patho)physiological mechanisms involved in preterm birth etiology and long-term neonatal outcomes.

The Role of Genetics in Preterm Birth

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, affects approximately 11% of live births and is the leading cause of death in children under 5 years. PTB is a complex disease with multiple risk factors including genetic variation. Much research has aimed to establish the biological mechanisms underlying PTB often through identification of genetic markers for PTB risk. The objective of this review is to present a comprehensive and updated summary of the published data relating to the field of PTB genetics. A literature search in PubMed was conducted and English studies related to PTB genetics were included. Genetic studies have identified genes within inflammatory, immunological, tissue remodeling, endocrine, metabolic, and vascular pathways that may be involved in PTB. However, a substantial proportion of published data have been largely inconclusive and multiple studies had limited power to detect associations. On the contrary, a few large hypothesis-free approaches have identified and replicated multiple novel variants associated with PTB in different cohorts. Overall, attempts to predict PTB using single "-omics" datasets including genomic, transcriptomic, and epigenomic biomarkers have been mostly unsuccessful and have failed to translate to the clinical setting. Integration of data from multiple "-omics" datasets has yielded the most promising results.

Identifying placental metabolic biomarkers of preterm birth using nuclear magnetic resonance of intact tissue samples

INTRODUCTION

Our understanding of the etiology of preterm birth (PTB) is incomplete; however, recent evidence has found a strong association between placental dysfunction and PTB. Altered placental metabolism may precede placental dysfunction and therefore the study of placental metabolic profiles could identify early biomarkers of PTB. In this study, we evaluated the placental metabolome in PTB in intact tissue samples using nuclear magnetic resonance (NMR) and spectral editing.

METHODS

Placental tissue samples were collected from nine term pregnancies and nine preterm pregnancies (<37 weeks' gestation). 1H NMR experiments on unprocessed tissue samples were performed using a high field magnet (500 MHz spectrometer) and a comprehensive multiphase NMR probe. The relative concentrations of 23 metabolites were corrected for gestational age and compared between groups.

RESULTS

The relative concentration of valine, glutamate and creatine were significantly decreased while alanine, choline and glucose were elevated in placentas from PTB pregnancies compared to controls (p < 0.05). Multivariate analysis using principal component analysis showed the PTB and control groups were significantly separated (p < 0.0001) and pathway analysis identified perturbations in the glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis and valine, leucine and isoleucine biosynthesis pathways.

CONCLUSION

PTB is associated with significant alterations in placental metabolism. This study helps improve our understanding of the etiology of PTB. It also highlights the potential for small molecule metabolites to serve as placental metabolic biomarkers to aid in the prediction and diagnosis of PTB. The results can be translated to clinical use via in utero magnetic resonance spectroscopy.

Roles of the Microbiota of the Female Reproductive Tract in Gynecological and Reproductive Health

The microbiome of the female reproductive tract defies the convention that high biodiversity is a hallmark of an optimal ecosystem. Although not universally true, a homogeneous vaginal microbiome composed of species of Lactobacillus is generally associated with health, whereas vaginal microbiomes consisting of other taxa are generally associated with dysbiosis and a higher risk of disease. The past decade has seen a rapid advancement in our understanding of these unique biosystems. Of particular interest, substantial effort has been devoted to deciphering how members of the microbiome of the female reproductive tract impact pregnancy, with a focus on adverse outcomes, including but not limited to preterm birth. Herein, we review recent research efforts that are revealing the mechanisms by which these microorganisms of the female reproductive tract influence gynecologic and reproductive health of the female reproductive tract.

Metabolomics: The Key to Unraveling the Role of the Microbiome in Visceral Pain Neurotransmission

Inflammatory bowel disease (IBD), comprising Crohn's disease and Ulcerative colitis, is a relapsing and remitting disease of the gastrointestinal tract, presenting with chronic inflammation, ulceration, gastrointestinal bleeding, and abdominal pain. Up to 80% of patients suffering from IBD experience acute pain, which dissipates when the underlying inflammation and tissue damage resolves. However, despite achieving endoscopic remission with no signs of ongoing intestinal inflammation or damage, 30-50% of IBD patients in remission experience chronic abdominal pain, suggesting altered sensory neuronal processing in this disorder. Furthermore, effective treatment for chronic pain is limited such that 5-25% of IBD outpatients are treated with narcotics, with associated morbidity and mortality. IBD patients commonly present with substantial alterations to the microbial community structure within the gastrointestinal tract, known as dysbiosis. The same is also true in irritable bowel syndrome (IBS), a chronic disorder characterized by altered bowel habits and abdominal pain, in the absence of inflammation. An emerging body of literature suggests that the gut microbiome plays an important role in visceral hypersensitivity. Specific microbial metabolites have an intimate relationship with host receptors that are highly expressed on host cell and neurons, suggesting that microbial metabolites play a key role in visceral hypersensitivity. In this review, we will discuss the techniques used to analysis the metabolome, current potential metabolite targets for visceral hypersensitivity, and discuss the current literature that evaluates the role of the post-inflammatory microbiota and metabolites in visceral hypersensitivity.

Prediction of gestational age using urinary metabolites in term and preterm pregnancies

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

System pharmacology analysis to decipher the effect and mechanism of active ingredients combination from herb couple on rheumatoid arthritis in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional herb couple Angelicae pubescentis radix (APR) and Notopterygii rhizoma et radix (NRR), composition of two traditional Chinese medicinal herbs, has been used clinically in China for the treatment of rheumatoid arthritis (RA) over years. APR and NRR contain coumarins and phenolic acids, which have been reported to have analgesic and anti-inflammatory activities.

AIM OF THE STUDY

The active ingredients combination (AIC) and potential therapeutic mechanism of APR and NRR (AN) herb couple remain unclear. Therefore, the present study aimed to identify the AIC and elucidate the underlying mechanism of AIC on RA.

MATERIALS AND METHODS

Firstly, a novel strategy of in vitro experiments, computational analysis, UPLC-QTOF-MS and UPLC-QQQ-MS was established to confirm the optimum ratio of AN herb couple samples and identified the AIC. Then, the anti-arthritis effects of the optimal herb couple and AIC were studied with Collagen II induced rheumatoid arthritis (CIA) rats in vivo. Finally, an integrated model of network pharmacology, metabolomics, gut microbiota analysis and biological techniques were applied to clarify the underlying mechanism through a comprehensive perspective.

RESULTS

AN7:3 herb couple was regarded as the optimal ratio of AN herbal samples, and AIC was screened as osthole, columbianadin, notopterol, isoimperatorin, psoralen, xanthotoxin, bergapten, nodakenin and bergaptol respectively. Additionally, AIC exerted similar therapeutic effects as AN 7:3 in CIA rats. Moreover, AIC ameliorated RA might via regulating MAPK signaling pathway, altering metabolic disorders and gut microbiome involved autoimmunity.

CONCLUSIONS

our findings provided scientific evidence to support that AIC of AN herb couple could be used as a prebiotic agent for RA. Importantly, this research provided a systematic and feasible strategy to optimize the proportion of medicinal materials and screen AIC from multi-component traditional Chinese herb couples or Chinese medicine formulae. Moreover, it provided a comprehensive perspective to discover AIC, clarify the overall effects and understand the mechanisms for natural products through the perspective of database and multi-omics integration.

Comparative diagnosis of premature rupture of membrane by nitrazine test, urea, and creatinine estimation

OBJECTIVE

The study compared the reliability of nitrazine, urea, and creatinine in the diagnosis of premature rupture of membrane (PROM). Nitrazine strip measures pH levels while urea and creatinine are produced mainly in amniotic fluid and not in the maternal vagina.

METHODS

Sixty-four pregnant women with demonstrable passage or pooling of liquor par vaginum on speculum examination and 64 with no liquor drainage at 28-42 weeks gestation were studied. Vaginal fluid aspirates from both groups of patient were tested for urea, creatinine, and pH levels using nitrazine strip. Receiver Operating Characteristic (ROC) curves were plotted to determine cutoff values for urea and creatinine. Statistical analysis was done using SPSS version 23.0 and setting statistical significance at P ≤ 0.05.

RESULTS

Nitrazine test showed high level of specificity (100%) but a sensitivity of 87.5%. Predictive ability of urea at >12.7 mg/dl obtained as cutoff value from the ROC curve showed a sensitivity of 19.64% and specificity of 94.44% while for creatinine the cutoff value was at >0.8 mg/dl with sensitivity of 48.21% and specificity of 65.28%.

CONCLUSION

Nitrazine strip was the most reliable of the three in the diagnosis of PROM. This was followed by creatinine and then urea. In view of the gaps in sensitivity and accuracy and the importance of precise diagnosis to prevent maternal and fetal complications, there is still the need to find other affordable, more sensitive and more accurate biochemical marker/s that will help in diagnosing PROM especially in difficult cases.

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.

Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine

Introduction:

Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions.

Methods:

This paper first uses GC-MS metabolomics to detect changes in the urine components of the poisoned group and control rats to form a metabolic dataset, and then uses the SVM classification algorithm in machine learning to train the hydrogen sulfide poisoning training dataset to obtain a classification recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen sulfide gas as the control group. The treated urine samples were tested using a GC-MS.

Results:

The method locates the optimal parameters of SVM, which improves the accuracy of SVM classification to 100%. This paper uses the information to gain an attribute evaluation method to screen out the top 6 biomarkers that contribute to the predicted category (Glycerol, -Hydroxybutyric acid, arabinofuranose, Pentitol, L-Tyrosine, L-Proline).

Conclusion:

The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis of hydrogen sulfide poisoning.

Associations of prenatal metabolomics profiles with early childhood growth trajectories and obesity risk in African Americans: the CANDLE study

OBJECTIVE

Prenatal metabolomics profiles, providing measures of in utero nutritional and environmental exposures, may improve the prediction of childhood outcomes. We aimed to identify prenatal plasma metabolites associated with early childhood body mass index (BMI) trajectories and overweight/obesity risk in offspring.

METHODS

This study included 450 African American mother-child pairs from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood Study. An untargeted metabolomics analysis was performed on the mothers' plasma samples collected during the second trimester. The children's BMI-z-score trajectories from birth to age 4 [rising-high- (9.8%), moderate- (68.2%), and low-BMI (22.0%)] and overweight/obesity status at age 4 were the main outcomes. The least absolute shrinkage and selection operator (LASSO) was used to select the prenatal metabolites associated with childhood outcomes.

RESULTS

The mothers were 24.5 years old on average at recruitment, 76.4% having education less than 12 years and 80.0% with Medicaid or Medicare. In LASSO, seven and five prenatal metabolites were associated with the BMI-z-score trajectories and overweight/obese at age 4, respectively. These metabolites are mainly from/relevant to the pathways of steroid biosynthesis, amino acid metabolism, vitamin B complex, and xenobiotics metabolism (e.g., caffeine and nicotine). The odds ratios (95% CI) associated with a one SD increase in the prenatal metabolite risk scores (MRSs) constructed from the LASSO-selected metabolites were 2.97 (1.95-4.54) and 2.03 (1.54-2.67) for children being in the rising-high-BMI trajectory group and overweight/obesity at age 4, respectively. The MRSs significantly improved the risk prediction for childhood outcomes beyond traditional prenatal risk factors. The increase (95% CI) in the area under the receiver operating characteristic curves were 0.10 (0.03-0.18) and 0.07 (0.02-0.12) for the rising-high-BMI trajectory (P = 0.005) and overweight/obesity at age 4 (P = 0.007), respectively.

CONCLUSIONS

Prenatal metabolomics profiles advanced prediction of early childhood growth trajectories and obesity risk in offspring.

Comparison of dried blood spot and plasma sampling for untargeted metabolomics

INTRODUCTION

Untargeted metabolomics holds significant promise for biomarker detection and development. In resource-limited settings, a dried blood spot (DBS)-based platform would offer significant advantages over plasma-based approaches that require a cold supply chain.

OBJECTIVES

The primary goal of this study was to compare the ability of DBS- and plasma-based assays to characterize maternal metabolites. Utility of the two assays was also assessed in the context of a case-control predictive model in pregnant women living with HIV.

METHODS

Untargeted metabolomics was performed on archived paired maternal plasma and DBS from n = 79 women enrolled in a large clinical trial.

RESULTS

A total of 984 named biochemicals were detected across both plasma and DBS samples, of which 627 (63.7%), 260 (26.4%), and 97 (9.9%) were detected in both plasma and DBS, plasma alone, and DBS alone, respectively. Variation attributable to study individual (R² = 0.54, p < 0.001) exceeded that of the sample type (R² = 0.21, p < 0.001), suggesting that both plasma and DBS were capable of differentiating individual metabolomic profiles. Log-transformed metabolite abundances were strongly correlated (mean Spearman rho = 0.51) but showed low agreement (mean intraclass correlation of 0.15). However, following standardization, DBS and plasma metabolite profiles were strongly concordant (mean intraclass correlation of 0.52). Random forests classification models for cases versus controls identified distinct feature sets with comparable performance in plasma and DBS (86.5% versus 91.2% mean accuracy, respectively).

CONCLUSION

Maternal plasma and DBS samples yield distinct metabolite profiles highly predictive of the individual subject. In our case study, classification models showed similar performance albeit with distinct feature sets. Appropriate normalization and standardization methods are critical to leverage data from both sample types. Ultimately, the choice of sample type will likely depend on the compounds of interest as well as logistical demands.

Gestational age-dependent development of the neonatal metabolome

BACKGROUND

Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome.

METHODS

Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age.

RESULTS

A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine.

CONCLUSIONS

Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders.

IMPACT

A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.

Sex Affects Human Premature Neonates' Blood Metabolome According to Gestational Age, Parenteral Nutrition, and Caffeine Treatment

Prematurity is the leading cause of neonatal deaths and high economic costs; it depends on numerous biological and social factors, and is highly prevalent in males. Several factors can affect the metabolome of premature infants. Accordingly, the aim of the present study was to analyze the role played by gestational age (GA), parenteral nutrition (PN), and caffeine treatment in sex-related differences of blood metabolome of premature neonates through a MS/MS-based targeted metabolomic approach for the detection of amino acids and acylcarnitines in dried blood spots. GA affected the blood metabolome of premature neonates: male and female very premature infants (VPI) diverged in amino acids but not in acylcarnitines, whereas the opposite was observed in moderate or late preterm infants (MLPI). Moreover, an important reduction of metabolites was observed in female VPI fed with PN, suggesting that PN might not satisfy an infant's nutritional needs. Caffeine showed the highest significant impact on metabolite levels of male MLPI. This study proves the presence of a sex-dependent metabolome in premature infants, which is affected by GA and pharmacological treatment (e.g., caffeine). Furthermore, it describes an integrated relationship among several features of physiology and health.

Untargeted analysis of first trimester serum to reveal biomarkers of pregnancy complications: a case-control discovery phase study

Understanding of causal biology and predictive biomarkers are lacking for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB). First-trimester serum specimens from 51 cases of HDP, including 18 cases of pre-eclampsia (PE) and 33 cases of gestational hypertension (GH); 53 cases of PTB; and 109 controls were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository. Metabotyping was conducted using liquid chromatography high resolution mass spectroscopy and nuclear magnetic resonance spectroscopy. Multivariable logistic regression was used to identify signals that differed between groups after controlling for confounders. Signals important to predicting HDP and PTB were matched to an in-house physical standards library and public databases. Pathway analysis was conducted using GeneGo MetaCore. Over 400 signals for endogenous and exogenous metabolites that differentiated cases and controls were identified or annotated, and models that included these signals produced substantial improvements in predictive power beyond models that only included known risk factors. Perturbations of the aminoacyl-tRNA biosynthesis, L-threonine, and renal secretion of organic electrolytes pathways were associated with both HDP and PTB, while pathways related to cholesterol transport and metabolism were associated with HDP. This untargeted metabolomics analysis identified signals and common pathways associated with pregnancy complications.

Global metabolomic profiling reveals hepatic biosignatures that reflect the unique metabolic needs of late-term mother and fetus

OBJECTIVE

Gestational disorders including preeclampsia, growth restriction and diabetes are characterized, in part, by altered metabolic interactions between mother and fetus. Understanding their functional relevance requires metabolic characterization under normotypic conditions.

METHODS

We performed untargeted metabolomics on livers of pregnant, late-term C57Bl/6J mice (N = 9 dams) and their fetuses (pooling 4 fetuses/litter), using UPLC-MS/MS.

RESULTS

Multivariate analysis of 730 hepatic metabolites revealed that maternal and fetal metabolite profiles were highly compartmentalized, and were significantly more similar within fetuses (ρ_average = 0.81), or within dams (ρ_average = 0.79), than within each maternal-fetal dyad (ρ_average = - 0.76), suggesting that fetal hepatic metabolism is under distinct and equally tight metabolic control compared with its respective dam. The metabolite profiles were consistent with known differences in maternal-fetal metabolism. The reduced fetal glucose reflected its limited capacity for gluconeogenesis and dependence upon maternal plasma glucose pools. The fetal decreases in essential amino acids and elevations in their alpha-keto acid carnitine conjugates reflects their importance as secondary fuel sources to meet fetal energy demands. Whereas, contrasting elevations in fetal serine, glycine, aspartate, and glutamate reflects their contributions to endogenous nucleotide synthesis and fetal growth. Finally, the elevated maternal hepatic lipids and glycerol were consistent with a catabolic state that spares glucose to meet competing maternal-fetal energy demands.

CONCLUSIONS

The metabolite profile of the late-term mouse dam and fetus is consistent with prior, non-rodent analyses utilizing plasma and urine. These data position mouse as a suitable model for mechanistic investigation into how maternal-fetal metabolism adapts (or not) to gestational stressors.

Supportive techniques to investigate in vitro culture and cryopreservation efficiencies of equine ovarian tissue: A review

During the reproductive lifespan of a female, only a limited quantity of oocytes are naturally ovulated; therefore, the mammalian ovary possesses a substantial population of preantral follicles available to be handled and explored in vitro. Hence, the manipulation of preantral follicles enclosed in ovarian tissue aims to recover a considerable population of oocytes of high-value animals for potential application in profitable assisted reproductive technologies (ARTs). For this purpose, the technique of preantral follicle in vitro culture (IVC) has been the most common research tool, achieving extraordinary results with offspring production in the mouse model. Although promising outcomes have been generated in livestock animals after IVC of preantral follicles, the quantity and quality of embryo production with those oocytes are still poor. In recent years, the mare has become an additional model for IVC studies due to remarkable similarities with women and livestock animals regarding in vivo and in vitro ovarian folliculogenesis. For a successful IVC system, several factors should be carefully considered to provide an optimum culture environment able to support the viability and growth of preantral follicles enclosed in ovarian tissue. The cryopreservation of the ovarian tissue is another important in vitro manipulation technique that has been used to preserve the reproductive potential in humans and, in the future, may be used in highly valuable domestic animals or endangered species. Several improvements in cryopreservation protocols are necessary to support the utilization of ovarian tissue of different species in follow-up ARTs (e.g., ovarian fragment transplantation). This review aims to provide an update on the most current advances regarding supportive in vitro techniques used in equids to evaluate and manipulate preantral follicles and ovarian tissue, as well as methodological approaches used during IVC and cryopreservation techniques.

Metabolomic profiles of mid-trimester amniotic fluid are not associated with subsequent spontaneous preterm delivery or gestational duration at delivery

INTRODUCTION

Spontaneous preterm delivery (<37 gestational weeks) has a multifactorial etiology with still incompletely identified pathways. Amniotic fluid is a biofluid with great potential for insights into the feto-maternal milieu. It is rich in metabolites, and metabolic consequences of inflammation is yet researched only to a limited extent. Metabolomic profiling provides opportunities to identify potential biomarkers of inflammatory conditioned pregnancy complications such as spontaneous preterm delivery.

OBJECTIVE

The aim of this study was to perform metabolomic profiling of amniotic fluid from uncomplicated singleton pregnancies in the mid-trimester to identify potential biomarkers associated with spontaneous preterm delivery and gestational duration at delivery. A secondary aim was to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers of spontaneous preterm delivery in asymptomatic women.

METHOD

A nested case-control study was performed within a larger cohort study of asymptomatic pregnant women undergoing mid-trimester genetic amniocentesis at 14-19 gestational weeks in Gothenburg, Sweden. Medical records were used to obtain clinical data and delivery outcome variables. Amniotic fluid samples from women with a subsequent spontaneous preterm delivery (n = 37) were matched with amniotic fluid samples from women with a subsequent spontaneous delivery at term (n = 37). Amniotic fluid samples underwent untargeted metabolomic analyses using liquid chromatography-mass spectrometry. Multivariate random forest analyses were used for data processing. A secondary targeted analysis was performed, aiming to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers in women with a subsequent spontaneous preterm delivery.

RESULTS

Multivariate analysis did not distinguish the samples from women with a subsequent spontaneous preterm delivery from those with a subsequent term delivery. Neither was the metabolic profile associated with gestational duration at delivery. Potential metabolic biomarker candidates were identified from four publications by two different research groups relating mid-trimester amniotic fluid metabolomes to spontaneous PTD, of which fifteen markers were included in the secondary analysis. None of these were replicated.

CONCLUSIONS

Metabolomic profiles of early mid-trimester amniotic fluid were not associated with spontaneous preterm delivery or gestational duration at delivery in this cohort.

Abnormal metabolism of gut microbiota reveals the possible molecular mechanism of nephropathy induced by hyperuricemia

The progression of hyperuricemia disease is often accompanied by damage to renal function. However, there are few studies on hyperuricemia nephropathy, especially its association with intestinal flora. This study combines metabolomics and gut microbiota diversity analysis to explore metabolic changes using a rat model as well as the changes in intestinal flora composition. The results showed that amino acid metabolism was disturbed with serine, glutamate and glutamine being downregulated whilst glycine, hydroxyproline and alanine being upregulated. The combined glycine, serine and glutamate could predict hyperuricemia nephropathy with an area under the curve of 1.00. Imbalanced intestinal flora was also observed. Flavobacterium, Myroides, Corynebacterium, Alcaligenaceae, Oligella and other conditional pathogens increased significantly in the model group, while Blautia and Roseburia, the short-chain fatty acid producing bacteria, declined greatly. At phylum, family and genus levels, disordered nitrogen circulation in gut microbiota was detected. In the model group, the uric acid decomposition pathway was enhanced with reinforced urea liver-intestine circulation. The results implied that the intestinal flora play a vital role in the pathogenesis of hyperuricemia nephropathy. Hence, modulation of gut microbiota or targeting at metabolic enzymes, i.e., urease, could assist the treatment and prevention of this disease.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.