Biomarker identification and pathway analysis of preeclampsia based on serum metabolomics

Developmental programming: Impact of preconceptional and gestational exposure to a real-life environmental chemical mixture on maternal steroid, cytokine and oxidative stress milieus in sheep

BACKGROUND

Gestational exposure to environmental chemicals (ECs) is associated with adverse, sex-specific offspring health effects of global concern. As the maternal steroid, cytokine and oxidative stress milieus can have critical effects on pregnancy outcomes and the programming of diseases in offspring, it is important to study the impact of real-life EC exposure, i.e., chronic low levels of mixtures of ECs on these milieus. Sheep exposed to biosolids, derived from human waste, is an impactful model representing the ECs humans are exposed to in real-life. Offspring of sheep grazed on biosolids-treated pasture are characterized by reproductive and metabolic disruptions.

OBJECTIVE

To determine if biosolids exposure disrupts the maternal steroid, cytokine and oxidative stress milieus, in a fetal sex-specific manner.

METHODS

Ewes were maintained before mating and through gestation on pastures fertilized with biosolids (BTP), or inorganic fertilizer (Control). From maternal plasma collected mid-gestation, 19 steroids, 14 cytokines, 6 oxidative stress markers were quantified. Unpaired t-test and ANOVA were used to test for differences between control and BTP groups (n = 15/group) and between groups based on fetal sex, respectively. Correlation between the different markers was assessed by Spearman correlation.

RESULTS

Concentrations of the mineralocorticoids - deoxycorticosterone, corticosterone, the glucocorticoids - deoxycortisol, cortisol, cortisone, the sex steroids - androstenedione, dehydroepiandrosterone, 16-OH-progesterone and reactive oxygen metabolites were higher in the BTP ewes compared to Controls, while the proinflammatory cytokines IL-1β and IL-17A and anti-inflammatory IL-36RA were decreased in the BTP group. BTP ewes with a female fetus had lower levels of IP-10.

DISCUSSION

These findings suggest that pre-conceptional and gestational exposure to ECs in biosolids increases steroids, reactive oxygen metabolites and disrupts cytokines in maternal circulation, likely contributors to the aberrant phenotypic outcomes seen in offspring of BTP sheep - a translationally relevant precocial model.

Fibrinogen to albumin ratio, a novel serum indicator for evaluating the severity of preeclampsia: A single-center retrospective study

Preeclampsia (PE) is a disorder that affects approximately 5% to 10% of pregnant women. Timely and accurate identification of PE and assessment of its severity are crucial. Therefore, it is necessary to develop predictive indicators which are easily measured in routine antenatal examinations to enable the early detection of PE and assess its severity. We designed a single-center retrospective study in our daily work to assess whether the serum levels of fibrinogen to albumin ratio (FAR), fibrinogen (Fib), albumin (ALB), prothrombin time, calcium (Ca), activated partial thrombin time, creatinine (Cr), D-dimer(D-D), platelet, white blood cell, neutrophil, and lymphocyte counts could help in assessing PE and evaluating its severity. Our findings showed that the serum levels of FAR, Cr, Fib, and D-D were significantly higher in the severe preeclampsia group (sPE) compared with the control and mild preeclampsia groups, whereas the levels of ALB and Ca were significantly lower in sPE patients. In addition, no differences were found between the control and PE groups in terms of prothrombin time, activated partial thrombin time, platelet, white blood cell, neutrophils, and lymphocytes counts. Furthermore, FAR is a novel and better indicator for evaluating the severity of PE, which has not been reported before. And it is an independent risk factor for the development of sPE. In conclusion, the serum levels of FAR, Cr, D-D and Fib were positively correlated with PE, whereas ALB and Ca were negatively correlated with PE severity, which might be valuable in evaluating the severity of PE. FAR proved to be a feasible diagnostic marker for sPE with sensitivity and specificity comparable to those of ALB and Fib.

Monoisotopic silver nanoparticles-based mass spectrometry imaging of human bladder cancer tissue: Biomarker discovery

PURPOSE

Bladder cancer (BC) is the 10th most common form of cancer worldwide and the 2nd most common cancer of the urinary tract after prostate cancer, taking into account both incidence and prevalence.

MATERIALS/METHODS

Tissues from patients with BC and also tissue extracts were analyzed by laser desorption/ionization mass spectrometry imaging (LDI-MSI) with monoisotopic silver-109 nanoparticles-enhanced target (¹⁰⁹AgNPET).

RESULTS

Univariate and multivariate statistical analyses revealed 10 metabolites that differentiated between tumor and normal tissues from six patients with diagnosed BC. Selected metabolites are discussed in detail in relation to their mass spectrometry (MS) imaging results. The pathway analysis enabled us to link these compounds with 17 metabolic pathways.

CONCLUSIONS

According to receiver operating characteristic (ROC) analysis of biomarkers, 10 known metabolites were identified as the new potential biomarkers with areas under the curve (AUC) higher than >0.99. In both univariate and multivariate analysis, it was predicted that these compounds could serve as useful discriminators of cancerous versus normal tissue in patients diagnosed with BC.

Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

Metabolomic Markers for Predicting Preeclampsia in the First Trimester of Pregnancy: A Retrospective Study

We sought to identify the characteristic metabolite profile of blood plasma samples obtained from patients with preeclampsia. Direct high-resolution mass spectrometry was used to analyze samples from 79 pregnant women, 34 of whom had preeclampsia. We performed a comparative analysis of the metabolite profiles and found that they differed between pregnant women with and without preeclampsia. Lipids and sugars were identified as components of the metabolite profile that are likely to be associated with the development of preeclampsia. While PE was established only in the third trimester, a set of metabolites specific for the third trimester, including 2-(acetylamino)-1,5-anhydro-2-deoxy-4-O-b-D-galactopyranosyl-D-arabino-Hex-1-enitol, N-Acetyl-D-glucosaminyldiphosphodolichol, Cer(d18:0/20:0), and allolithocholic acid, was already traced in the first trimester. These components are also likely involved in lipid metabolism disorders and the development of oxidative stress.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

Identification of Biomarkers for Preeclampsia Based on Metabolomics

Background

Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE.

Methods

This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.

Results

Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE.

Conclusion

This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.

Metabolomics Applied to Cord Serum in Preeclampsia Newborns: Implications for Neonatal Outcomes

Preeclampsia (PE) is one of the leading causes of maternal and perinatal morbidity and mortality. However, it is still uncertain how PE affects neonate metabolism. We conducted an untargeted metabolomics analysis of cord blood to explore the metabolic changes in PE neonates. Umbilical cord serum samples from neonates with preeclampsia (n = 29) and non-preeclampsia (non-PE) (n = 32) pregnancies were analyzed using the UHPLC-QE-MS metabolomic platform. Different metabolites were screened, and pathway analysis was conducted. A subgroup analysis was performed among PE neonates to compare the metabolome between appropriate-for-gestational-age infants (n = 21) and small-for-gestational-age (SGA) infants (n = 8). A total of 159 different metabolites were detected in PE and non-PE neonates. Creatinine, N4-acetylcytidine, sphingomyelin (D18:1/16:0), pseudouridine, uric acid, and indolelactic acid were the most significant differential metabolites in the cord serum of PE neonates. Differential metabolite levels were elevated in PE neonates and were involved in the following metabolic pathways: glycine, serine, and threonine metabolism; sphingolipid, glyoxylate, and dicarboxylate metabolism; and arginine biosynthesis. In PE neonates, SGA neonates showed increased levels of hexacosanoyl carnitine and decreased abundance of 3-hydroxybutyric acid and 3-sulfinoalanine. Taurine-related metabolism and ketone body-related pathways were mainly affected. Based on the UHPLC-QE-MS metabolomics analysis, we identified the metabolic profiles of PE and SGA neonates. The abundance of metabolites related to certain amino acid, sphingolipid, and energy metabolism increased in the umbilical cord serum of PE neonates.

Identifying potential metabolic tissue biomarkers for papillary thyroid cancer in different iodine nutrient regions

PURPOSE

To investigate the applicability of metabolomics to select thyroid cancer-associated biomarkers and discover the effects of iodine on metabolic changes in thyroid cancer.

METHODS

In this study, a total of 33 papillary thyroid cancer (PTC) patients from areas with iodine excess and 32 PTC patients from areas with adequate iodine were recruited, and their cancerous tissue and paracancerous tissue were collected. These specimens were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS) in conjunction with multivariate statistical analysis.

RESULTS

Good separations were obtained for PTC tissue vs. paracancerous tissue, and 15 metabolites, including L-octanoylcarnitine, N-arachidonoylglycine, and others were found to be disturbed in PTC tissue. Moreover, the metabolic profile presented considerable separation between PTC tissue from different iodine areas, and 15 metabolomic biomarkers were found to be differentially expressed. Among them, 10 metabolites, including arachidonoylcarnitine and LysoPCs, were related to thyroid cancer and excess iodine. These biomarkers play a role in arachidonic acid metabolism pathways and others. In addition, biomarkers such as 3,5-tetradecadiencarnitine and oxidized glutathione were significantly correlated with thyroid function, and biomarkers such as L-octanoylcarnitine and arachidonic acid were significantly correlated with the clinical characteristics of PTC.

CONCLUSIONS

Distinct differences in metabolic profiles were found to exist between PTCs from areas with different levels of iodine nutrition. The identified biomarkers have significant potential for diagnosing PTC and investigating its underlying mechanisms.

Raman-based metabonomics unravels metabolic changes related to a first-line tenofovir-based treatment in a small cohort of South African HIV-infected patients

In addition to immunological disorders, human immunodeficiency virus (HIV) also causes metabolic abnormalities. Though successful in viral suppression and immune restoration, continued use of antiretroviral therapy (ART) has also been linked to the development of several metabolic ailments. Currently, the only clinical markers used to manage and monitor the development of HIV-induced metabolic disorders, disease progression as well as observing individual's response to antiviral treatment are CD4 count, viral loads and several other single variable colometric assays. Despite the common use of these clinical markers, these markers remain unreliable and limited in the ability to monitor the development of metabolic disorders as well as monitor treatment response. Given these limitations, it is imperative to discover and develop reliable biological markers for overall HIV disease management. Here, Raman spectroscopy was used to profile metabolic changes in the plasma of 22 HIV⁺ receiving a first-line tenofovir-based combination antiretroviral therapy compared to their 8 HIV⁺ ART^- and 10 HIV^- counterparts. Multivariate statistical analysis was performed in order to classify the samples into their respective groups and to identify significantly altered metabolites between the control and experimental groups. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) discriminant analysis identified significant differences (p < 0.05) in 9 different metabolites. Alterations were identified in spectral regions associated with glucose (1124 cm^-1), lipids/phospholipids (1116 cm^-1, 1098 cm^-1, 1077 cm^-1), proteins (1120 cm^-1), nucleic acids (1081 cm^-1) and phenylalanine (1103 cm^-1). Pathway analysis also revealed 3 significantly altered pathways. This study presented the reproducible nature of Raman spectroscopy in distinguishing between HIV-infected (treated and untreated) and uninfected blood plasma and allowed for the detection and identification of treatment induced metabolite changes. The results obtained in the study may, therefore, give insights into understanding the metabolic effect of HIV therapy.

MicroRNA-155 is upregulated in the placentas of patients with preeclampsia and affects trophoblast apoptosis by targeting SHH/GLi1/BCL2

The pathogenesis of preeclampsia (PE) is complicated and multiple risk factors have been associated with its occurrence. Still, the underlying molecular mechanisms involved in PE remain elusive. Aberrant apoptosis and insufficient invasion of trophoblasts have been observed and are considered vital pathological features in PE. Herein, we found that miR-155 can specifically degrade the mRNA of the Hedgehog ligand sonic hedgehog (SHH), using dual luciferase reporter assays. Quantitative real-time PCR found that administering miR-155 mimics or inhibitors could significantly decrease or increase the expression of SHH in the trophoblasts, respectively. The transcription levels of miR-155 in the placenta were higher in patients with PE compared to the levels in healthy pregnant women, as shown by quantitative real-time PCR. Serum levels of miR-155 could predict the diagnosis of PE by receiver operating characteristic curve analysis and diagnosis evaluation tests. A significant increase in apoptosis was observed after administering miR-155 in HTR8/SVneo cells cultured ex vivo, accompanied by reduced proliferation. Mechanistically, transcriptional activity and expression of GLi1 were also inhibited under treatment of miR-155, and could be recovered after supplying additional recombinant human SHH to primary trophoblasts from patients, as determined by luciferase activity assays and western blotting. We further found that inhibiting miR-155 increased the production of SHH and improved the phenotype in primary trophoblasts from patients with PE. Our data show that miR-155 regulates apoptosis of trophoblasts in PE, which has potential value for predicting PE risk and might be deemed as a therapeutic target for treating PE.

Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia

Early identification of patients at risk of developing preeclampsia (PE) would allow providers to tailor their prenatal management and adopt preventive strategies, such as low-dose aspirin. Nevertheless, no mid-trimester biomarkers have as yet been proven useful for prediction of PE. This study investigates the ability of metabolomic biomarkers in mid-trimester maternal plasma to predict PE. A case–control study was conducted including 33 pregnant women with mid-trimester maternal plasma (gestational age [GA], 16–24 weeks) who subsequently developed PE and 66 GA-matched controls with normal outcomes (mid-trimester cohort). Plasma samples were comprehensively profiled for primary metabolic and lipidomic signatures based on gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). A potential biomarker panel was computed based on binary logistic regression and evaluated using receiver operating characteristic (ROC) analysis. To evaluate whether this panel can be also used in late pregnancy, a retrospective cohort study was conducted using plasma collected from women who delivered in the late preterm period because of PE (n = 13) or other causes (n = 21) (at-delivery cohort). Metabolomic biomarkers were compared according to the indication for delivery. Performance of the metabolomic panel to identify patients with PE was compared also to a commonly used standard, the plasma soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio. In the mid-trimester cohort, a total of 329 metabolites were identified and semi-quantified in maternal plasma using GC-TOF MS and LC-Orbitrap-MS. Binary logistic regression analysis proposed a mid-trimester biomarker panel for the prediction of PE with five metabolites (SM C28:1, SM C30:1, LysoPC C19:0, LysoPE C20:0, propane-1,3-diol). This metabolomic model predicted PE better than PlGF (AUC [95% CI]: 0.868 [0.844–0.891] vs 0.604 [0.485–0.723]) and sFlt-1/PlGF ratio. Analysis of plasma from the at-delivery cohort confirmed the ability of this biomarker panel to distinguish PE from non-PE, with comparable discrimination power to that of the sFlt-1/PlGF ratio. In conclusion, an integrative metabolomic biomarker panel in mid-trimester maternal plasma can accurately predict the development of PE and showed good discriminatory power in patients with PE at delivery.

miR-132 serves as a diagnostic biomarker in gestational diabetes mellitus and its regulatory effect on trophoblast cell viability

BACKGROUND

Gestational diabetes mellitus (GDM) leads to poor pregnancy outcomes. Strategies that improve trophoblast cell function are important methods for GDM treatment. This study aimed to investigate the expression and diagnostic potential of microRNA-132 (miR-132) in GDM patients, and further analyzed the effects of miR-132 on HTR-8/SVneo cell proliferation.

METHODS

Quantitative real-time PCR was applied to estimate the expression of miR-132. A receiver operating characteristics curve (ROC) analysis was performed to evaluate the diagnostic value of serum miR-132 in GDM patients. In vitro regulation of miR-132 in trophoblast cell HTR-8/SVneo was achieved by cell transfection, and the effects of miR-132 on cell proliferation were assessed using CCK-8 assay.

RESULTS

Expression of miR-132 was decreased in serum and placenta tissues in GDM patients compared with the healthy women. A negative correlation was found between the serum miR-132 levels and fasting blood glucose of the GDM patients. A ROC curve shown the serum miR-132 had considerable diagnostic accuracy with an area under the curve (AUC) of 0.898. High glucose (HG) treatment induced an inhibition in HTR-8/SVneo cell proliferation and the expression of miR-132. The overexpression of miR-132 in HTR-8/SVneo cells could markedly rescued the HG - induced suppressed cell proliferation.

CONCLUSION

All the data of this study revealed the reduced expression of miR-132 in serum and placenta tissues of GDM, and serum miR-132 serves a candidate biomarker in the diagnosis of GDM. miR-132 may act a protective role against GDM via enhancing the trophoblast cell proliferation.

An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia

Preeclampsia (PE) is a disorder of pregnancy that is characterised by hypertension and a significant amount of proteinuria beginning after 20 weeks of pregnancy. It is closely associated with high maternal morbidity, mortality, maternal organ dysfunction or foetal growth restriction. Therefore, it is necessary to identify early and novel diagnostic biomarkers of PE. In the present study, we performed a multi-step integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of PE. With the help of gene expression profiles of the Gene Expression Omnibus (GEO) dataset GSE60438, a total of 268 dysregulated genes were identified including 131 up- and 137 down-regulated differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs suggested that DEGs were significantly enriched in disease-related biological processes (BPs) such as hormone activity, immune response, steroid hormone biosynthesis, metabolic pathways, and other signalling pathways. Using the STRING database, we established a protein–protein interaction (PPI) network based on the above DEGs. Module analysis and identification of hub genes were performed to screen a total of 17 significant hub genes. The support vector machines (SVMs) model was used to predict the potential application of biomarkers in PE diagnosis with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.958 in the training set and 0.834 in the test set, suggesting that this risk classifier has good discrimination between PE patients and control samples. Our results demonstrated that these 17 differentially expressed hub genes can be used as potential biomarkers for diagnosis of PE.

Second trimester inflammatory and metabolic markers in women delivering preterm with and without preeclampsia

OBJECTIVE

Inflammatory and metabolic pathways are implicated in preterm birth and preeclampsia. However, studies rarely compare second trimester inflammatory and metabolic markers between women who deliver preterm with and without preeclampsia.

STUDY DESIGN

A sample of 129 women (43 with preeclampsia) with preterm delivery was obtained from an existing population-based birth cohort. Banked second trimester serum samples were assayed for 267 inflammatory and metabolic markers. Backwards-stepwise logistic regression models were used to calculate odds ratios.

RESULTS

Higher 5-α-pregnan-3β,20α-diol disulfate, and lower 1-linoleoylglycerophosphoethanolamine and octadecanedioate, predicted increased odds of preeclampsia.

CONCLUSIONS

Among women with preterm births, those who developed preeclampsia differed with respect metabolic markers. These findings point to potential etiologic underpinnings for preeclampsia as a precursor to preterm birth.

Metabolomics profiling of methamphetamine addicted human serum and three rat brain areas

Methamphetamine (METH) has already been a serious problem all over the world. The identification of related biomarkers and pathways is helpful to evaluate the degree of METH addiction, develop appropriate treatment during abstinence, and explore the mechanism. Here, it is the first time to perform metabolomics profiling of METH addicted human serum and three regions of METH-induced conditioned place preference (CPP) rat brain by using UHPLC-MS/MS and matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI), respectively. Untargeted metabolomics analysis demonstrated clear differences between METH abusers and the healthy control by finding 35 distinct expressed metabolites in serum, including 5 TCA intermediates, 17 amino acids and 13 other biomolecules, 15 of which were newly identified following METH exposure. By using MALDI-MSI, the relative quantification and distribution of 14 metabolites were investigated in the nucleus accumbens (NAc), dorsal hippocampus (dHPC) and ventral hippocampus (vHPC) of CPP rat brain. Taken together, METH addiction could influence energy metabolism, amino acids metabolism, and phospholipids metabolism. A multi-parameter model consisting of these related metabolites can be established as a METH addiction biomarker in the future. The mapping of phospholipids provided new insights into the mechanism of METH addiction. Notably, the trend of metabolite changes in NAc and dHPC was almost the same, while it was opposite between dHPC and vHPC. It seems that NAc and dHPC were the two regions more susceptible to METH administration in the brain. And dHPC and vHPC play different roles in METH addiction proved by metabolites mapping.

To explore the mechanism of METH addiction, the metabolomics profiling of METH addicted human serum and rat brain were performed using UHPLC-MS/MS and MALDI-MSI, respectively. 35 differentially expressed metabolites were identified in the serum.

Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction

Preeclampsia currently remains one of the leading causes of death and severe maternal morbidity. Although its prevalence is still underestimated in some places due to underreporting, preeclampsia is a disease that health professionals need to know how to deal with and take action. For this reason, the studies about the theme remain along with the advances in their understanding that often implies improvement and change of concepts and conducts. The complexity of its etiology is a challenge and requires further studies for its full understanding. Apparently, poor adaptation of the maternal organism to the conceptus, marked by the nonoccurrence of changes in the uterine spiral arteries, determines a series of systemic repercussions that compound the various forms of preeclampsia presentation. In recent years, the use of acetylsalicylic acid to prevent cases of early onset of the disease has been consolidated and, alongside, studies have advanced the development of accessible and effective methods of identifying women at risk of preeclampsia. The aim of this review is to discuss updates on the occurrence, concept, pathophysiology, repercussion, prevention, and prediction of preeclampsia.

Application of metabolomics to preeclampsia diagnosis

Preeclampsia is a multifactorial disorder defined by hypertension and increased urinary protein excretion during pregnancy. It is a significant cause of maternal and neonatal deaths worldwide. Despite various research efforts to clarify pathogenies of preeclampsia and predict this disease before beginning of symptoms, the pathogenesis of preeclampsia is unclear. Early prediction and diagnosis of women at risk of preeclampsia has not markedly improved. Therefore, the objective of this study was to perform a review on metabolomic articles assessing predictive and diagnostic biomarkers of preeclampsia. Four electronic databases including PubMed/Medline, Web of Science, Sciencedirect, and Scopus were searched to identify studies of preeclampsia in humans using metabolomics from inception to March 2018. Twenty-one articles in a variety of biological specimens and analytical platforms were included in the present review. Metabolite profiles may assist in the diagnosis of preeclampsia and discrimination of its subtypes. Lipids and their related metabolites were the most generally detected metabolites. Although metabolomic biomarkers of preeclampsia are not routinely used, this review suggests that metabolomics has the potential to be developed into a clinical tool for preeclampsia diagnosis and could contribute to an improved understanding of disease mechanisms.

ABBREVIATIONS

PE: preeclampsia; sFlt-1: soluble FMS-like tyrosine kinase-1; PlGF: placental growth factor; GC-MS: gas chromatography-mass spectrometry; LC-MS: liquid chromatography-mass spectrometry; NMR: nuclear magnetic resonance spectroscopy; HMDB: human metabolome database; RCT: randomized control trial; e-PE: early-onset PE; l-PE: late-onset PE; PLS-DA: partial least-squares-discriminant analysis; CRL: crown-rump length; UtPI: uterine artery Doppler pulsatility index; BMI: body mass index; MAP: mean arterial pressure; OS: oxidative stress; PAPPA: plasma protein A; FTIR: Fourier transform infrared; BCAA: branched chain amino acids; Arg: arginine; NO: nitric oxide.

Biofluid Metabolomics in Preterm Birth Research

This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.

Applications of Metabolomics in the Study and Management of Preeclampsia; A Review of the Literature

Introduction

Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.

Methods

PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.

Results

Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.

Conclusion

Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.