The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome

The role of family history of preterm delivery in the individual risk of spontaneous preterm delivery: a case-control study

PURPOSE

To investigate the role of family history of preterm delivery (PTD) in the individual risk of spontaneous preterm delivery.

METHODS

A retrospective case-control study was conducted on 354 patients who delivered between 2018 and 2020. 177 women who delivered preterm were matched with 177 controls who had full-term delivery. A questionnaire was administered to investigate the family history of PTD of both the patient and her partner. Cases and controls were matched for the anamnestic risk factors for PTD.

RESULTS

Seventeen of 173 women (9.8%) in the PTD group reported being born preterm, compared to five of 169 women (2.9%) in the control group (p = 0.01), with an odds ratio (OR) of 3.57 (95% confidence interval, CI 1.29-9.92). Women who delivered preterm also reported more frequently having a sibling who was born preterm (12.4% vs. 4.2%, p = 0.01), with an OR of 3.18 (95% CI 1.31-7.7). No association was found between the partner's family history of premature delivery and the patient's risk of preterm delivery in the present pregnancy.

CONCLUSIONS

Pregnant patients who were born prematurely or who have siblings born preterm have an increased risk of preterm delivery in their own pregnancies. Assessment of female personal and family history of PTD should be used to identify women at risk of having a PTD in the present pregnancy.

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

Integrated bioinformatics analysis and experimental animal models identify a robust biomarker and its correlation with the immune microenvironment in pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.

Methods

Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and immune cells was substantiated.

Results

Using SVM, LASSO, and WGCNA, we identified seven hub genes, including (BPIFA1, HBA2, HBB, LOC441081, PI15, S100A9, and WIF1), of which only BPIFA1 remained stable in the external datasets and was validated in an MCT-induced rat model. Furthermore, the results of the functional enrichment analysis established a link between PAH and both metabolism and the immune system. Correlation assessment showed that BPIFA1 expression in the MCP-counter algorithm was negatively associated with various immune cell types, positively correlated with macrophages in the ssGSEA algorithm, and correlated with M1 and M2 macrophages in the CIBERSORT algorithm.

Conclusion

BPIFA1 serves as a modulator of PAH, with the potential to impact the immune microenvironment and disease progression, possibly through its regulatory influence on both M1 and M2 macrophages.

Boosting distributional copula regression

Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup, each parameter of the copula model, that is, the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression via model-based boosting, which is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high-dimensional data setting, that is, situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm for copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.

Multi-Omics Analysis of Lung Tissue Demonstrates Changes to Lipid Metabolism during Allergic Sensitization in Mice

Allergy and asthma pathogenesis are associated with the dysregulation of metabolic pathways. To understand the effects of allergen sensitization on metabolic pathways, we conducted a multi-omics study using BALB/cJ mice sensitized to house dust mite (HDM) extract or saline. Lung tissue was used to perform untargeted metabolomics and transcriptomics while both lung tissue and plasma were used for targeted lipidomics. Following statistical comparisons, an integrated pathway analysis was conducted. Histopathological changes demonstrated an allergic response in HDM-sensitized mice. Untargeted metabolomics showed 391 lung tissue compounds were significantly different between HDM and control mice (adjusted p < 0.05); with most compounds mapping to glycerophospholipid and sphingolipid pathways. Several lung oxylipins, including 14-HDHA, 8-HETE, 15-HETE, 6-keto-PGF1α, and PGE2 were significantly elevated in HDM-sensitized mice (p < 0.05). Global gene expression analysis showed upregulated calcium channel, G protein–signaling, and mTORC1 signaling pathways. Genes related to oxylipin metabolism such as Cox, Cyp450s, and cPla2 trended upwards. Joint analysis of metabolomics and transcriptomics supported a role for glycerophospholipid and sphingolipid metabolism following HDM sensitization. Collectively, our multi-omics results linked decreased glycerophospholipid and sphingolipid compounds and increased oxylipins with allergic sensitization; concurrent upregulation of associated gene pathways supports a role for bioactive lipids in the pathogenesis of allergy and asthma.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

The amniotic fluid proteome predicts imminent preterm delivery in asymptomatic women with a short cervix

Preterm birth, the leading cause of perinatal morbidity and mortality, is associated with increased risk of short- and long-term adverse outcomes. For women identified as at risk for preterm birth attributable to a sonographic short cervix, the determination of imminent delivery is crucial for patient management. The current study aimed to identify amniotic fluid (AF) proteins that could predict imminent delivery in asymptomatic patients with a short cervix. This retrospective cohort study included women enrolled between May 2002 and September 2015 who were diagnosed with a sonographic short cervix (< 25 mm) at 16–32 weeks of gestation. Amniocenteses were performed to exclude intra-amniotic infection; none of the women included had clinical signs of infection or labor at the time of amniocentesis. An aptamer-based multiplex platform was used to profile 1310 AF proteins, and the differential protein abundance between women who delivered within two weeks from amniocentesis, and those who did not, was determined. The analysis included adjustment for quantitative cervical length and control of the false-positive rate at 10%. The area under the receiver operating characteristic curve was calculated to determine whether protein abundance in combination with cervical length improved the prediction of imminent preterm delivery as compared to cervical length alone. Of the 1,310 proteins profiled in AF, 17 were differentially abundant in women destined to deliver within two weeks of amniocentesis independently of the cervical length (adjusted p-value < 0.10). The decreased abundance of SNAP25 and the increased abundance of GPI, PTPN11, OLR1, ENO1, GAPDH, CHI3L1, RETN, CSF3, LCN2, CXCL1, CXCL8, PGLYRP1, LDHB, IL6, MMP8, and PRTN3 were associated with an increased risk of imminent delivery (odds ratio > 1.5 for each). The sensitivity at a 10% false-positive rate for the prediction of imminent delivery by a quantitative cervical length alone was 38%, yet it increased to 79% when combined with the abundance of four AF proteins (CXCL8, SNAP25, PTPN11, and MMP8). Neutrophil-mediated immunity, neutrophil activation, granulocyte activation, myeloid leukocyte activation, and myeloid leukocyte-mediated immunity were biological processes impacted by protein dysregulation in women destined to deliver within two weeks of diagnosis. The combination of AF protein abundance and quantitative cervical length improves prediction of the timing of delivery compared to cervical length alone, among women with a sonographic short cervix.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Protein interaction networks define the genetic architecture of preterm birth

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein–protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

Identifying biomarkers for prenatal diagnosis of neural tube defects based on "omics"

Neural tube defects (NTDs) are the most severe birth defects and the main cause of newborn death; posing a great challenge to the affected children, families, and societies. Presently, the clinical diagnosis of NTDs mainly relies on ultrasound images combined with certain indices, such as alpha-fetoprotein levels in the maternal serum and amniotic fluid. Recently, the discovery of additional biomarkers in maternal tissue has presented new possibilities for prenatal diagnosis. Over the past 20 years, "omics" techniques have provided the premise for the study of biomarkers. This review summarizes recent advances in candidate biomarkers for the prenatal diagnosis of fetal NTDs based on omics techniques using maternal biological specimens of different origins, including amniotic fluid, blood, and urine, which may provide a foundation for the early prenatal diagnosis of NTDs.

Integration of Gut Microbiota and Metabolomics for Chinese Medicines Research: Opportunities and Challenges

Chinese medicines (CM) are gaining more attentions from all over the world. However, there are a large body of questions to be answered because of the chemical complexity of CM and intricate molecular reactions within human body. In recent years, gut microbiota and metabolomics have emerged as two cynosures in deciphering the mechanisms of how our body is functioning. Since gut microbiota and host is a closely interrelated system, paying attention only to gut microbiota or metabolites may omit the interplays among CM, gut microbiota, and hosts. To systemically study these interplays, a network understanding of CM components, gut microbiota, metabolites of gut microbiota, metabolites in human body is necessary. Although there are some obstacles impeding the application of this integrative approach, the potential areas for implementation of the integrative approach is vast. These areas include, but not limited to, elucidating the mechanisms of CM at system level, screening bioactive compounds in CM, and guiding quality control of CM.

Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study

PURPOSE

To determine whether previously established mRNA signatures are predictive of early preeclampsia when evaluated by maternal cellular transcriptome analysis in samples collected before clinical manifestation.

MATERIALS AND METHODS

We profiled gene expression at exon-level resolution in whole blood samples collected longitudinally from 49 women with normal pregnancy (controls) and 13 with early preeclampsia (delivery <34 weeks of gestation). After preprocessing and removal of gestational age-related trends in gene expression, data were converted into Z-scores based on the mean and standard deviation among controls for six gestational-age intervals. The average Z-scores of mRNAs in each previously established signature considered herein were compared between cases and controls at 9-11, 11-17, 17-22, 22-28, 28-32, and 32-34 weeks of gestation.Results: (1) Average expression of the 16-gene untargeted cellular mRNA signature was higher in women diagnosed with early preeclampsia at 32-34 weeks of gestation, yet more importantly, also prior to diagnosis at 28-32 weeks and 22-28 weeks of gestation, compared to controls (all, p < .05). (2) A combination of four genes from this signature, including a long non-protein coding RNA [H19 imprinted maternally expressed transcript (H19)], fibronectin 1 (FN1), tubulin beta-6 class V (TUBB6), and formyl peptide receptor 3 (FPR3) had a sensitivity of 0.85 (0.55-0.98) and a specificity of 0.92 (0.8-0.98) for prediction of early preeclampsia at 22-28 weeks of gestation. (3) H19, FN1, and TUBB6 were increased in women with early preeclampsia as early as 11-17 weeks of gestation (all, p < .05). (4) After diagnosis at 32-34 weeks, but also prior to diagnosis at 11-17 weeks, women destined to have early preeclampsia showed a coordinated increase in whole blood expression of several single-cell placental signatures, including the 20-gene signature of extravillous trophoblast (all, p < .05). (5) A combination of three mRNAs from the extravillous trophoblast signature (MMP11, SLC6A2, and IL18BP) predicted early preeclampsia at 11-17 weeks of gestation with a sensitivity of 0.83 (0.52-0.98) and specificity of 0.94 (0.79-0.99).

CONCLUSIONS

Circulating early transcriptomic markers for preeclampsia can be found either by untargeted profiling of the cellular transcriptome or by focusing on placental cell-specific mRNAs. The untargeted cellular mRNA signature was consistently increased in early preeclampsia after 22 weeks of gestation, and individual mRNAs of this signature were significantly increased as early as 11-17 weeks of gestation. Several single-cell placental signatures predicted future development of the disease at 11-17 weeks and were also increased in women already diagnosed at 32-34 weeks of gestation.

Association of maternal prenatal selenium concentration and preterm birth: a multicountry meta-analysis

BACKGROUND

Selenium (Se), an essential trace mineral, has been implicated in preterm birth (PTB). We aimed to determine the association of maternal Se concentrations during pregnancy with PTB risk and gestational duration in a large number of samples collected from diverse populations.

METHODS

Gestational duration data and maternal plasma or serum samples of 9946 singleton live births were obtained from 17 geographically diverse study cohorts. Maternal Se concentrations were determined by inductively coupled plasma mass spectrometry analysis. The associations between maternal Se with PTB and gestational duration were analysed using logistic and linear regressions. The results were then combined using fixed-effect and random-effect meta-analysis.

FINDINGS

In all study samples, the Se concentrations followed a normal distribution with a mean of 93.8 ng/mL (SD: 28.5 ng/mL) but varied substantially across different sites. The fixed-effect meta-analysis across the 17 cohorts showed that Se was significantly associated with PTB and gestational duration with effect size estimates of an OR=0.95 (95% CI: 0.9 to 1.00) for PTB and 0.66 days (95% CI: 0.38 to 0.94) longer gestation per 15 ng/mL increase in Se concentration. However, there was a substantial heterogeneity among study cohorts and the random-effect meta-analysis did not achieve statistical significance. The largest effect sizes were observed in UK (Liverpool) cohort, and most significant associations were observed in samples from Malawi.

INTERPRETATION

While our study observed statistically significant associations between maternal Se concentration and PTB at some sites, this did not generalise across the entire cohort. Whether population-specific factors explain the heterogeneity of our findings warrants further investigation. Further evidence is needed to understand the biologic pathways, clinical efficacy and safety, before changes to antenatal nutritional recommendations for Se supplementation are considered.

The Female Snark Is Still a Boojum: Looking toward the Future of Studying Female Reproductive Biology

Philosophical truths are hidden in Lewis Carroll's nonsense poems, such as "The hunting of the snark." When the poem is used as a scientific allegory, a snark stands for the pursuit of scientific truth, while a boojum is a spurious discovery. In the study of female biology, boojums have been the result of the use of cultural stereotypes to frame hypotheses and methodologies. Although female reproduction is key for the continuation of sexually reproducing species, not only have females been understudied in many regards, but also data have commonly been interpreted in the context of now-outdated social mores. Spurious discoveries, boojums, are the result. In this article, we highlight specific gaps in our knowledge of female reproductive biology and provide a jumping-off point for future research. We discuss the promise of emerging methodologies (e.g., micro-CT scanning, high-throughput sequencing, proteomics, big-data analysis, CRISPR-Cas9, and viral vector technology) that can yield insights into previously cryptic processes and features. For example, in mice, deoxyribonucleic acid sequencing via chromatin immunoprecipitation followed by sequencing is already unveiling how epigenetics lead to sex differences in brain development. Similarly, new explorations, including microbiome research, are rapidly debunking dogmas such as the notion of the "sterile womb." Finally, we highlight how understanding female reproductive biology is well suited to the National Science Foundation's big idea, "Predicting Rules of Life." Studies of female reproductive biology will enable scholars to (1) traverse levels of biological organization from reproductive proteins at the molecular level, through anatomical details of the ovum and female reproductive tract, into physiological aspects of whole-organism performance, leading to behaviors associated with mating and maternal care, and eventually reaching population structure and ecology; (2) discover generalizable rules such as the co-evolution of maternal-offspring phenotypes in gestation and lactation; and (3) predict the impacts of changes to reproductive timing when the reliability of environmental cues becomes unpredictable. Studies in these key areas relative to female reproduction are sure to further our understanding across a range of diverse taxa.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Neurosteroid involvement in threatened preterm labour

Introduction

The neurosteroid allopregnanolone modulates oxytocin expression in the brain, and its effects arise from its action on the GABAA receptor. Whether neurosteroid levels and the function of the GABAA receptor are involved in the risk of preterm labour in pregnant women is unknown.

Methods

Pregnant women with (n = 16) or without (n = 20) threatened preterm labour (TPL) in gestational week 33 + 6 days to 37 + 0 days were studied prospectively with procedures including foetal heart rate monitoring, vaginal examination, ultrasound examination and blood tests to determine allopregnanolone, progesterone and oxytocin levels. The GABAA receptor function in both groups was measured with a saccadic eye velocity test (SEVT).

Results

Plasma oxytocin levels were higher in the TPL group than in the control group (41.5 vs. 37.0 pmol/L, respectively, p = .021). Although the allopregnanolone and progesterone levels in both groups did not differ, there was a negative association between blood oxytocin and allopregnanolone (as predictor) levels in the TPL group (B: -3.2, 95% confidence interval (CI): -5.5 to -0.9, p = .012). As a predictor of TPL, progesterone was associated with cervix maturity (odds ratio: 1.02, 95% CI: 1.00-1.04, p = .038). SEVT showed that the women in both groups had similar GABAA receptor functions. In both groups, body mass index correlated with peak saccadic eye velocity (r = .34, p = .044) and negatively with allopregnanolone (r = -.41, p = .013).

Conclusions

Neurosteroid levels were unchanged in the peripheral blood of women with TPL, despite the increase in available oxytocin. Although the function of the GABAA receptor was unchanged in women with TPL, to ensure reliable results, saccadic eye velocity should be investigated during a challenge test with a GABAA receptor agonist.

Intra-Amniotic Infection and Sterile Intra-Amniotic Inflammation in Cervical Insufficiency with Prolapsed Fetal Membranes: Clinical Implications

INTRODUCTION

The aim of this study was to identify the rates of 2 phenotypes of intra-amniotic inflammation: intra-amniotic infection (with microbial invasion of the amniotic cavity [MIAC]) and sterile intra-amniotic inflammation (without MIAC), and their outcomes, among women with cervical insufficiency with prolapsed fetal membranes.

METHODS OF STUDY

This is a retrospective study of women admitted to the Department of Obstetrics and Gynecology, University Hospital Hradec Kralove between January 2014 and May 2020. Transabdominal amniocentesis to evaluate intra-amniotic inflammation (amniotic fluid interleukin-6) and MIAC (culturing and molecular biology methods) was performed as part of standard clinical management.

RESULTS

In total, 37 women with cervical insufficiency and prolapsed fetal membranes were included; 11% (4/37) and 43% (16/37) of them had intra-amniotic infection and sterile intra-amniotic inflammation, respectively. In women with intra-amniotic infection and sterile intra-amniotic inflammation, we noted shorter intervals between admission and delivery (both p < 0.0001), and lower gestational age at delivery (p < 0.0001 and p = 0.004) and percentiles of birth/abortion weight (p = 0.03 and p = 0.009, respectively) than in those without intra-amniotic inflammation.

CONCLUSIONS

Both phenotypes of intra-amniotic inflammation, with sterile intra-amniotic inflammation being more frequent, are associated with worse outcomes in pregnancies with cervical insufficiency with prolapsed fetal membranes.

Monochorionic twins with selective fetal growth restriction: insight from placental whole-transcriptome analysis

BACKGROUND

The underlying pathomechanism in placenta-related selective fetal growth restriction in monochorionic diamniotic twin pregnancy is not known.

OBJECTIVE

This study aimed to investigate any differences in placental transcriptomic profile between the selectively growth-restricted twins and the normally grown cotwins in monochorionic diamniotic twin pregnancies.

STUDY DESIGN

This was a prospective study of monochorionic diamniotic twin pregnancies complicated by selective fetal growth restriction. Placental biopsy specimens were obtained from the subjects in the delivery suite. The placental transcriptome of the selectively growth-restricted twin was compared with that of the normally grown cotwin. This study was divided into 2 stages: (1) gene discovery phase in which placental tissues from 5 monochorionic diamniotic twin pregnancies complicated by selective fetal growth restriction plus 2 control twin pregnancies underwent transcriptome profiling, and transcriptome profiling was carried out using whole-genome RNA sequencing; and (2) validation phase in which placental tissues from 13 monochorionic diamniotic twin pregnancies with selective fetal growth restriction underwent RNA and protein validation. RNA and protein expression levels of candidate genes were determined using quantitative real-time polymerase chain reaction and immunohistochemistry staining.

RESULTS

A total of 1429 transcripts were differentially expressed in the placentae of selectively growth-restricted twin pairs, where 610 were up-regulated and 819 were down-regulated. Endoplasmic reticulum lectin and mannose 6-phosphate receptor were consistently differentially up-regulated in all placentae of selectively growth-restricted twins. Quantitative real-time polymerase chain reaction and immunohistochemistry staining were used to validate the results (P<.05).

CONCLUSION

The expression of endoplasmic reticulum lectin and mannose 6-phosphate receptor, which are important for angiogenesis and fetal growth, was significantly increased in the placentae of selectively growth-restricted twin of a monochorionic twin pair.

Lipidomic-Based Advances in Diagnosis and Modulation of Immune Response to Cancer

While immunotherapies for diverse types of cancer are effective in many cases, relapse is still a lingering problem. Like tumor cells, activated immune cells have an anabolic metabolic profile, relying on glycolysis and the increased uptake and synthesis of fatty acids. In contrast, immature antigen-presenting cells, as well as anergic and exhausted T-cells have a catabolic metabolic profile that uses oxidative phosphorylation to provide energy for cellular processes. One goal for enhancing current immunotherapies is to identify metabolic pathways supporting the immune response to tumor antigens. A robust cell expansion and an active modulation via immune checkpoints and cytokine release are required for effective immunity. Lipids, as one of the main components of the cell membrane, are the key regulators of cell signaling and proliferation. Therefore, lipid metabolism reprogramming may impact proliferation and generate dysfunctional immune cells promoting tumor growth. Based on lipid-driven signatures, the discrimination between responsiveness and tolerance to tumor cells will support the development of accurate biomarkers and the identification of potential therapeutic targets. These findings may improve existing immunotherapies and ultimately prevent immune escape in patients for whom existing treatments have failed.

Mid-trimester amniotic fluid proteome's association with spontaneous preterm delivery and gestational duration

BACKGROUND

Amniotic fluid is clinically accessible via amniocentesis and its protein composition may correspond to birth timing. Early changes in the amniotic fluid proteome could therefore be associated with the subsequent development of spontaneous preterm delivery.

OBJECTIVE

The main objective of this study was to perform unbiased proteomics analysis of the association between mid-trimester amniotic fluid proteome and spontaneous preterm delivery and gestational duration, respectively. A secondary objective was to validate and replicate the findings by enzyme-linked immunosorbent assay using a second independent cohort.

METHODS

Women undergoing a mid-trimester genetic amniocentesis at Sahlgrenska University Hospital/Östra between September 2008 and September 2011 were enrolled in this study, designed in three analytical stages; 1) an unbiased proteomic discovery phase using LC-MS analysis of 22 women with subsequent spontaneous preterm delivery (cases) and 37 women who delivered at term (controls), 2) a validation phase of proteins of interest identified in stage 1, and 3) a replication phase of the proteins that passed validation using a second independent cohort consisting of 20 cases and 40 matched controls.

RESULTS

Nine proteins were nominally significantly associated with both spontaneous preterm delivery and gestational duration, after adjustment for gestational age at sampling, but none of the proteins were significant after correction for multiple testing. Several of these proteins have previously been described as being associated with spontaneous PTD etiology and six of them were thus validated using enzyme linked immunosorbent assay. Two of the proteins passed validation; Neutrophil gelatinase-associated lipocalin and plasminogen activator inhibitor 1, but the results could not be replicated in a second cohort.

CONCLUSIONS

Neutrophil gelatinase-associated lipocalin and Plasminogen activator inhibitor 1 are potential biomarkers of spontaneous preterm delivery and gestational duration but the findings could not be replicated. The negative findings are supported by the fact that none of the nine proteins from the exploratory phase were significant after correction for multiple testing.

Transcriptomic analysis reveals the key regulators and molecular mechanisms underlying myometrial activation during equine placentitis†

The key event in placentitis-induced preterm labor is myometrial activation with the subsequent initiation of labor. However, the molecular mechanisms underlying myometrial activation are not fully understood in the mares. Therefore, the equine myometrial transcriptome was characterized during placentitis (290.0 ± 1.52 days of GA, n = 5) and the prepartum period (330 days of GA, n = 3) in comparison to normal pregnant mares (289.8 ± 2.18 days of GA, n = 4). Transcriptome analysis identified 596 and 290 DEGs in the myometrium during placentitis and the prepartum period, respectively, with 138 DEGs in common. The placentitis DEGs included eight genes (MMP1, MMP8, S100A9, S100A8, PI3, APOBEC3Z1B, RETN, and CXCL2) that are exclusively expressed in the inflamed myometrium. Pathway analysis elucidated that inflammatory signaling, Toll-like receptor signaling, and apoptosis pathways dominate myometrial activation during placentitis. The prepartum myometrium was associated with overexpression of inflammatory signaling, oxidative stress, and 5-hydroxytryptamine degradation. Gene ontology enrichment analysis identified several chemoattractant factors in the myometrium during placentitis and prepartum period, including CCL2, CXCL1, CXCL3, and CXCL6 in common. Upstream regulator analysis revealed 19 potential upstream regulators in placentitis dataset including transcription regulators (E2F1, FOXM1, HIF1A, JUNB, NFKB1A, and STAT1), transmembrane receptors (FAS, ICAM1, SELP, TLR2, and TYROBP), growth factors (HGF and TGFB3), enzymes (PTGS2 and PRKCP), and others (S100A8, S100A9, CD44, and C5AR1). Additionally, three upstream regulators (STAT3, EGR1, and F2R) were identified in the prepartum dataset. These findings revealed the key regulators and pathways underlying myometrial activation during placentitis, which aid in understanding the disease and facilitate the development of efficacious therapies.

Phytotoxicity induced by engineered nanomaterials as explored by metabolomics: Perspectives and challenges

Given the wide applications of engineered nanomaterials (ENMs) in various fields, the ecotoxicology of ENMs has attracted much attention. The traditional plant physiological activity (e.g., reactive oxygen species and antioxidant enzymes) are limited in that they probe one specific process of nanotoxicity, which may result in the loss of understanding of other important biological reactions. Metabolites, which are downstream of gene and protein expression, are directly related to biological phenomena. Metabolomics is an easily performed and efficient tool for solving the aforementioned problems because it involves the comprehensive exploration of metabolic profiles. To understand the roles of metabolomics in phytotoxicity, the analytical methods for metabolomics should be organized and discussed. Moreover, the dominant metabolites and metabolic pathways are similar in different plants, which determines the universal applicability of metabolomics analysis. The analysis of regulated metabolism will globally and scientifically help determine the ecotoxicology that is induced by ENMs. In the past several years, great developments in nanotoxicology have been achieved using metabolomics. However, many knowledge gaps remain, such as the relationships between biological responses that are induced by ENMs and the regulation of metabolism (e.g., carbohydrate, energy, amino acid, lipid and secondary metabolism). The phytotoxicity that is induced by ENMs has been explored by metabolomics, which is still in its infancy. The detrimental and defence mechanisms of plants in their response to ENMs at the level of metabolomics also deserve much attention. In addition, owing to the regulation of metabolism in plants by ENMs affected by multiple factors, it is meaningful to uniformly identify the key influencing factor.

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

BACKGROUND

In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized.

OBJECTIVE

To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation.

STUDY DESIGN

This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential abundance was inferred based on false discovery adjusted p-values (FDR) from Mann-Whitney-Wilcoxon U tests <0.1 and a minimum median abundance ratio (fold change) of 1.5. Alternatively, metabolic data were quantile normalized to remove sample-to-sample differences in the overall metabolite abundance (adjusted analysis).

RESULTS

Overall, 637 small molecules met the inclusion criteria and were tested for association with pregnancy; 44% (281/637) of small molecules had significantly different abundance, of which 81% (229/281) were less abundant in pregnant than in non-pregnant women. Eight percent (14/169) of the metabolites that remained significant in the adjusted analysis also changed as a function of gestational age. A pathway analysis revealed enrichment in steroid metabolites related to sex hormones, caffeine metabolites, lysolipids, dipeptides, and polypeptide bradykinin derivatives (all, FDR < 0.1).

CONCLUSIONS

This high-throughput mass spectrometry study identified: 1) differences between pregnant vs. non-pregnant women in the abundance of 44% of the profiled plasma metabolites, including known and novel molecules and pathways; and 2) specific metabolites that changed with gestational age.

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.

Predicting the likelihood of bronchopulmonary dysplasia in premature neonates

Introduction: Bronchopulmonary dysplasia (BPD) is the most common serious pulmonary morbidity in premature infants. Despite ongoing advances in neonatal care, the incidence of BPD has not improved. A potential explanation for this phenomenon is the limited ability for accurate early prediction of the risk of BPD. BPD continues to represent a therapeutic challenge and no single effective therapy exists for this condition. Areas covered: Here, we review risk factors of BPD derived from clinical data, biological fluid biomarkers, respiratory management data, and scientific advancements using 'omics' technologies, and their ability to predict the pathogenesis of BPD in preterm neonates. Risk factors and biomarkers were identified via literature search with a focus on the last 5 years of data. Expert opinion: The most accurate predictive tools utilize risk factors that encompass a variety of categories. Numerous predictive models have been proposed but suffer from a lack of adequate validation. An ideal model should include multiple, easily measurable variables validated across a heterogeneous population. In addition to evaluating recent BPD prediction models, we suggest approaches to enhance future models.

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

Neurodevelopmental disorders represent a challenging biological and medical problem due to their genetic and phenotypic complexity. In many cases, we lack the comprehensive understanding of disease mechanisms necessary for targeted therapeutic development. One key component that could improve both mechanistic understanding and clinical trial design is reliable molecular biomarkers. Presently, no objective biological markers exist to evaluate most neurodevelopmental disorders. Here, we discuss how systems biology and "omic" approaches can address the mechanistic and biomarker limitations in these afflictions. We present heuristic principles for testing the potential of systems biology to identify mechanisms and biomarkers of disease in the example of Rett syndrome, a neurodevelopmental disorder caused by a well-defined monogenic defect in methyl-CpG-binding protein 2 (MECP2). We propose that such an approach can not only aid in monitoring clinical disease severity but also provide a measure of target engagement in clinical trials. By deepening our understanding of the "big picture" of systems biology, this approach could even help generate hypotheses for drug development programs, hopefully resulting in new treatments for these devastating conditions.

Proteomic identification of membrane-associated placental protein 4 (MP4) as perlecan and characterization of its placental expression in normal and pathologic pregnancies

Background

More than 50 human placental proteins were isolated and physico-chemically characterized in the 70–80s by Hans Bohn and co-workers. Many of these proteins turned to have important role in placental functions and diagnostic significance in pregnancy complications. Among these proteins was membrane-associated placental protein 4 (MP4), for which identity or function has not been identified yet. Our aim was to analyze the sequence and placental expression of this protein in normal and complicated pregnancies including miscarriage, preeclampsia and HELLP syndrome.

Methods

Lyophilized MP4 protein and frozen healthy placental tissue were analyzed using HPLC-MS/MS. Placental tissue samples were obtained from women with elective termination of pregnancy (first trimester controls, n = 31), early pregnancy loss (EPL) (n = 13), early preeclampsia without HELLP syndrome (n = 7) and with HELLP syndrome (n = 8), late preeclampsia (n = 8), third trimester early controls (n = 5) and third trimester late controls (n = 9). Tissue microarrays were constructed from paraffin-embedded placentas (n = 81). Slides were immunostained with monoclonal perlecan antibody and evaluated using light microscopy and virtual microscopy. Perlecan was also analyzed for its expression in placentas from normal pregnancies using microarray data.

Results

Mass spectrometry-based proteomics of MP4 resulted in the identification of basement membrane-specific heparan sulfate proteoglycan core protein also known as perlecan. Immunohistochemistry showed cytoplasmic perlecan localization in syncytiotrophoblast and cytotrophoblasts of the villi. Perlecan immunoscore decreased with gestational age in the placenta. Perlecan immunoscores were higher in EPL compared to controls. Perlecan immunoscores were higher in early preeclampsia without and with HELLP syndrome and lower in late preeclampsia than in respective controls. Among patients with preeclampsia, placental perlecan expression positively correlated with maternal vascular malperfusion and negatively correlated with placental weight.

Conclusion

Our findings suggest that an increased placental perlecan expression may be associated with hypoxic ischaemic injury of the placenta in miscarriages and in early preeclampsia with or without HELLP syndrome.

The prediction of early preeclampsia: Results from a longitudinal proteomics study

OBJECTIVES

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

STUDY DESIGN

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

RESULTS

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

CONCLUSION

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Comparative transcriptomic analysis of human placentae at term and preterm delivery

Preterm birth affects 1 out of every 10 infants in the United States, resulting in substantial neonatal morbidity and mortality. Currently, there are few predictive markers and few treatment options to prevent preterm birth. A healthy, functioning placenta is essential to positive pregnancy outcomes. Previous studies have suggested that placental pathology may play a role in preterm birth etiology. Therefore, we tested the hypothesis that preterm placentae may exhibit unique transcriptomic signatures compared to term samples reflective of their abnormal biology leading to this adverse outcome. We aggregated publicly available placental villous microarray data to generate a preterm and term sample dataset (n = 133, 55 preterm placentae and 78 normal term placentae). We identified differentially expressed genes using the linear regression for microarray (LIMMA) package and identified perturbations in known biological networks using Differential Rank Conservation (DIRAC). We identified 129 significantly differentially expressed genes between term and preterm placenta with 96 genes upregulated and 33 genes downregulated (P-value <0.05). Significant changes in gene expression in molecular networks related to Tumor Protein 53 and phosphatidylinositol signaling were identified using DIRAC. We have aggregated a uniformly normalized transcriptomic dataset and have identified novel and established genes and pathways associated with developmental regulation of the placenta and potential preterm birth pathology. These analyses provide a community resource to integrate with other high-dimensional datasets for additional insights in normal placental development and its disruption.

History of the establishment of the Preterm Birth international collaborative (PREBIC)

INTRODUCTION

The primary aim of PREBIC is to assess the underlying mechanisms and developing strategies for preterm birth (PTB) prevention.

MATERIALS AND METHODS

We used concept mapping and logic models to track goals. This paper reviews our progress over 13 years using working group activities, research developments, guest speakers, and publications.

RESULTS

Using interactions between genetics, environment, and behaviors we identified complex interactions between biological systems. PREBIC determined that epidemiology and biomarkers should be an initial focus. In 2005, we initiated presentations by young investigators, yearly satellite meetings, working groups including nutrition and inflammation, assessment of clinical trials, and accepted an invitation by the WHO to begin yearly meetings in Geneva.

DISCUSSION

PREBIC used epidemiology to identify PTB factors and complex pathways. Candidate genes are associated with the environment, behavior (stress), obesity, inflammation and insulin resistance. Epigenetic changes and production of proteins can be used as biomarkers to define risk. Subsequently, we found risk factors for PTB that were also associated with the risk of cardiovascular disease (CVD) of the mother. Tanz et al. (2017) found that a history of PTB is independently predictive of CVD later in life and suggested that a modest proportion of PTB-CVD association was accounted by CVD risk factors, many of which have been identified in this paper.

CONCLUSION

Our findings support a relationship between genes, environment, behaviors and risk of CVD in women. The next several years must assess which factors are modifiable early in life and before pregnancy to prevent PTB.

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.

[Preterm Birth Screening: What Does Really Make Sense?]

Preterm Birth Screening: What Does Really Make Sense? Abstract. Spontaneous preterm birth is a syndrome triggered by multiple mechanisms. In view of the pathophysiological heterogeneity of preterm birth, a single biomarker cannot show the required high negative and positive predictive values. From a clinical point of view, anamnesis, sonographic measurement of cervical length, and placental alpha-microglobulin-1 (PAMG-1) testing from cervico-vaginal secretion are established. Further prospective, large-scale longitudinal studies must validate the combined use of new biomarkers.

Is maternal serum relaxin associated with preterm delivery in Chinese pregnant women? A meta-analysis

Objective: A meta-analysis was performed to study the relationship between serum relaxin and preterm delivery in women with singleton pregnancies without estrogen stimulation. Methods: Cohort and case-control studies were identified through searching databases (PubMed, Embase, Ovid, CBM, Wan fang, VIP, and CNKI). We carried out a continuous variable meta-analysis. The outcome was preterm delivery (gestation age <37 weeks). Results: Fifteen studies were included, involving 1607 women with a singleton pregnancy. The pooled standard mean deviation (SMD) of 15 studies was 0.559 (95%CI: 0.002-1.196) and the heterogeneity was 96.6%. To reduce the heterogeneity, we chose random effects model and made subgroup analysis according to gestational age at sample testing (<18 weeks and ≥18 weeks) and race of included pregnant women. The pooled SMD of gestational age at sample testing ≥18 weeks and Chinese were 1.19 (95%CI: 0.63-1.75) and 1.61 (95%CI: 0.82-2.41) and the heterogeneity values (measured by I²) were 93.5% and 76.5%, respectively. Conclusions: Elevated maternal serum relaxin of later than 18 weeks of gestational age is associated with singleton preterm birth in Chinese women. It might be an important information to prevent singleton preterm delivery in Chinese women. What's already known about this topic? Previous reports reveal that there is a relationship between elevated maternal serum relaxin and preterm birth. However, the included articles contained twin pregnancies and estrogen stimulation, which obviously resulted in higher relaxin concentrations. What does this study add?

Metabolomics as a clinical testing method for the diagnosis of vaginal dysbiosis

Microbes play an important role in vaginal health, with lactobacilli a particularly abundant species. When dysbiosis occurs, the tools to determine whether it is a condition such as bacterial vaginosis, and whether it warrants antibiotic treatment, are currently suboptimal. We propose that standardization and implementation of an affordable metabolomics-based diagnostic technique could reduce instances of false positives, stress associated with misdiagnosis, and potentially save time and money. Basing diagnosis on the detection of pH elevated above 4.5 and specific polyamines could provide a better method to assist a physician determine whether treatment is warranted.

Preliminary analysis of the protein profile in saliva during physiological term and preterm delivery

From a clinical point of view, easily obtainable and useful markers of a particular pathological status are required for appropriate diagnosis and treatment. Analysis of the proteomic profile of saliva may allow for the selection of potential marker of preterm delivery in humans. Saliva samples were collected from 12 patients diagnosed with threatened preterm delivery and 10 controls with uncomplicated pregnancies at the same gestational age. Samples were analysed using 2D electrophoresis. Based on statistical analysis, spots of interest were selected and collected from gels. Subsequently, spots were decoloured and proteins were identified by mass spectrometry using the matrix assisted laser desorption ionization-time of flight technique. The results of identification were compared with the Swiss-Prot database. A total of 1,393 spots were detected in the present study with 59 significantly different between control and preterm samples. Increased intensity of staining of 32 spots was observed in the premature delivery group compared with control patients and 27 spots were stained more intensely in the control group compared with the premature delivery group. A total of nine spots, which were significantly different between examined samples were identified and three of them exhibited increased intensity of staining in premature delivery group compared with controls, including dedicator of cytokinesis protein 1, metallothionein-2, guanylyl cyclase-activating protein 1. The six remaining spots included, epithelial-stromal interaction protein 1, serum albumin, tyrosine-tRNA ligase, cytoplasmic, protein chibby homolog 3, leukemia inhibitory factor receptor and adenosylhomocysteinase 3, and exhibited increased intensity of staining in healthy controls compared with premature delivery group. Further studies with an increased number of patients and identification of the complete protein profile are required to confirm the results of the present study and applicability of saliva as a source of disease biomarkers.

Optimising the collection of female genital tract fluid for cytokine analysis in pregnant women

Introduction

To better understand the immunology of pregnancy, study of female genital tract fluid (FGF) is desirable. However the optimum method of collection of FGF in pregnant women for immunological methods, specifically cytokine measurement, is unknown.

Methods

A prospective study of HIV-uninfected pregnant women comparing two methods of FGF collection: polyvinyl acetal sponge collection of cervical fluid (CF) and menstrual cup collection of cervicovaginal fluid (CVF). Samples were collected at 3 time points across the second and third trimesters: 14–21, 22–25 and 26–31 weeks. Multiplex chemi-luminescent assays were used to measure: IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13 and TNF-α. Optimal methodology for cytokine normalisation (sample weight, volume and total protein) was explored.

Results

All cytokines were measurable in both fluid types. IL-1β, IL-8 and IL-6 were detected at the highest concentrations (ranking order CF > CVF > plasma). CVF collection was simpler, provided the largest volume of sample (median 0.5 g) with the potential for undiluted usage, and allowed for self-insertion. CF cytokine concentrations were intrinsically associated with sample weight and protein concentration however CVF cytokines were independent of these.

Conclusion

Both methods of collection are robust for measurement of FGF cytokines during pregnancy. We recommend CVF collection using a menstrual cup as a viable option in pregnant women for high dimensional biological techniques.

•

PVA sponges and MCs are robust methods for measuring FGF cytokines in pregnancy.

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MCs enable collection of large undiluted CVF volumes for high dimensional assays.

•

CVF (not CF) cytokine concentrations are largely independent of sample weight or protein.

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Self-insertion and short collection time of MCs is an attractive option to women.

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Cytokine concentrations are higher in sponge samples reflecting the CF immune site.

Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor

BACKGROUND

Preterm birth is the leading cause of newborn death worldwide, and is associated with significant cognitive and physiological challenges in later life. There is a pressing need to define the mechanisms that initiate spontaneous preterm labor, and for development of novel clinical biomarkers to identify high-risk pregnancies. Most preterm birth studies utilize fetal tissues, and there is limited understanding of the transcriptional changes that occur in mothers undergoing spontaneous preterm labor. Earlier work revealed that a specific population of maternal peripheral leukocytes (macrophages/monocytes) play an active role in the initiation of labor. Thus, we hypothesized that there are dynamic gene expression changes in maternal blood leukocytes during preterm labor.

OBJECTIVE

Using next-generation sequencing we aim to characterize the transcriptome in whole blood leukocytes and peripheral monocytes of women undergoing spontaneous preterm labor compared to healthy pregnant women who subsequently delivered at full term.

STUDY DESIGN

RNA sequencing was performed in both whole blood and peripheral monocytes from women who underwent preterm labor (24-34 weeks of gestation, N = 20) matched for gestational age to healthy pregnant controls (N = 30). All participants were a part of the Ontario Birth Study cohort (Toronto, Ontario, Canada).

RESULTS

We identified significant differences in expression of 262 genes in peripheral monocytes and 184 genes in whole blood of women who were in active spontaneous preterm labor compared to pregnant women of the same gestational age not undergoing labor, with 43 of these genes differentially expressed in both whole blood and peripheral monocytes. ADAMTS2 expression was significantly increased in women actively undergoing spontaneous preterm labor, which we validated through digital droplet reverse transcriptase polymerase chain reaction. Intriguingly, we have also identified a number of gene sets including signaling by stem cell factor-KIT, nucleotide metabolism, and trans-Golgi network vesicle budding, which exhibited changes in relative gene expression that was predictive of preterm labor status in both maternal whole blood and peripheral monocytes.

CONCLUSION

This study is the first to investigate changes in both whole blood leukocytes and peripheral monocytes of women actively undergoing spontaneous preterm labor through robust transcript measurements from RNA sequencing. Our unique study design overcame confounding based on gestational age by collecting blood samples from women matched by gestational age, allowing us to study transcriptomic changes directly related to the active preterm parturition. We performed RNA profiling using whole genome sequencing, which is highly sensitive and allowed us to identify subtle changes in specific genes. ADAMTS2 expression emerged as a marker of prematurity within peripheral blood leukocytes, an accessible tissue that plays a functional role in signaling during the onset of labor. We identified changes in relative gene expression in a number of gene sets related to signaling in monocytes and whole blood of women undergoing spontaneous preterm labor compared to controls. These genes and pathways may help identify potential targets for the development of novel drugs for preterm birth prevention.

Beyond genomics: understanding exposotypes through metabolomics

BACKGROUND

Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

MAIN TEXT

Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.

CONCLUSIONS

Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Translational Systems Pharmacology Studies in Pregnant Women

Pregnancy involves rapid physiological adaptation and complex interplay between mother and fetus. New analytic technologies provide large amounts of genomic, proteomic, and metabolomics data. The integration of these data through bioinformatics, statistical, and systems pharmacology techniques can improve our understanding of the mechanisms of normal maternal physiologic changes and fetal development. New insights into the mechanisms of pregnancy‐related disorders, such as preterm birth (PTB), may lead to the development of new therapeutic interventions and novel biomarkers.

Urinary metabolomic analysis to identify preterm neonates exposed to histological chorioamnionitis: A pilot study

OBJECTIVE

Chorioamnionitis is a leading cause of preterm birth worldwide, with higher incidence at lower gestational ages. An early and reliable diagnosis of histological chorioamnionitis (HCA) in preterm infants may be helpful in guiding postnatal management, especially the administration of prophylactic antibiotics to prevent early-onset sepsis. The main aim of this study was to investigate metabolomic analysis of urines collected in the first 24 hours of life as diagnostic tool of HCA.

METHODS

Gestational age-, birth weight-, delivery mode- and sex- matched (1:2) preterm neonates (< 35 weeks' gestation) born to mothers with or without HCA were enrolled from an observational study. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis was performed on urine samples non-invasively collected in the first 24 hours of life. Univariate analysis, partial least square discriminant analysis (PLS-DA) and its associated variable importance in projection (VIP) score were performed. The most affected metabolic pathways were examined by Metabolite Sets Enrichment Analysis (MSEA).

RESULTS

Fifteen cases (mean GA 30.2 ± 3.8 weeks, mean BW 1415 ± 471.9 grams) and 30 controls (mean GA 30.2 ± 2.9 weeks, mean BW 1426 ± 569.8 grams) were enrolled. Following univariate analysis, 29 metabolites had a significantly different concentration between cases and controls. The supervised PLS-DA model confirmed a separation between the two groups. Only gluconic acid, an oxidation product of glucose, was higher in cases than in controls. All other VIP metabolites were more abundant in the control group. Glutamate metabolism, mitochondrial electron transport chain, citric acid cycle, galactose metabolism, and fructose and mannose degradation metabolism were the most significantly altered pathways (P < 0.01).

CONCLUSIONS

For the first time, urinary metabolomics was able to discriminate neonates born to mothers with and without HCA. The identification of specifically altered metabolic pathways may be helpful in understanding metabolic derangement following chorioamnionitis.

Probing for Sparse and Fast Variable Selection with Model-Based Boosting

We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model. This allows variable selection in a single fit of the model without requiring further parameter tuning. We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.

The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study

BACKGROUND

Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.

METHODS

A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.

RESULTS

1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.

CONCLUSIONS

Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Characteristic Changes in Decidual Gene Expression Signature in Spontaneous Term Parturition

Background

The decidua has been implicated in the “terminal pathway” of human term parturition, which is characterized by the activation of pro-inflammatory pathways in gestational tissues. However, the transcriptomic changes in the decidua leading to terminal pathway activation have not been systematically explored. This study aimed to compare the decidual expression of developmental signaling and inflammation-related genes before and after spontaneous term labor in order to reveal their involvement in this process.

Methods

Chorioamniotic membranes were obtained from normal pregnant women who delivered at term with spontaneous labor (TIL, n = 14) or without labor (TNL, n = 15). Decidual cells were isolated from snap-frozen chorioamniotic membranes with laser microdissection. The expression of 46 genes involved in decidual development, sex steroid and prostaglandin signaling, as well as pro- and anti-inflammatory pathways, was analyzed using high-throughput quantitative real-time polymerase chain reaction (qRT-PCR). Chorioamniotic membrane sections were immunostained and then semi-quantified for five proteins, and immunoassays for three chemokines were performed on maternal plasma samples.

Results

The genes with the highest expression in the decidua at term gestation included insulin-like growth factor-binding protein 1 (IGFBP1), galectin-1 (LGALS1), and progestogen-associated endometrial protein (PAEP); the expression of estrogen receptor 1 (ESR1), homeobox A11 (HOXA11), interleukin 1β (IL1B), IL8, progesterone receptor membrane component 2 (PGRMC2), and prostaglandin E synthase (PTGES) was higher in TIL than in TNL cases; the expression of chemokine C-C motif ligand 2 (CCL2), CCL5, LGALS1, LGALS3, and PAEP was lower in TIL than in TNL cases; immunostaining confirmed qRT-PCR data for IL-8, CCL2, galectin-1, galectin-3, and PAEP; and no correlations between the decidual gene expression and the maternal plasma protein concentrations of CCL2, CCL5, and IL-8 were found.

Conclusions

Our data suggests that with the initiation of parturition, the decidual expression of anti-inflammatory mediators decreases, while the expression of pro-inflammatory mediators and steroid receptors increases. This shift may affect downstream signaling pathways that can lead to parturition.

Renal injury in neonates: use of "omics" for developing precision medicine in neonatology

Preterm birth is associated with increased risks of morbidity and mortality along with increased healthcare costs. Advances in medicine have enhanced survival for preterm infants but the overall incidence of major morbidities has changed very little. Abnormal renal development is an important consequence of premature birth. Acute kidney injury (AKI) in the neonatal period is multifactorial and may increase lifetime risk of chronic kidney disease.Traditional biomarkers in newborns suffer from considerable confounders, limiting their use for early identification of AKI. There is a need to develop novel biomarkers that can identify, in real time, the evolution of renal dysfunction in an early diagnostic, monitoring and prognostic fashion. Use of "omics", particularly metabolomics, may provide valuable information regarding functional pathways underlying AKI and prediction of clinical outcomes.The emerging knowledge generated by the application of "omics" (genomics, proteomics, metabolomics) in neonatology provides new insights that can help to identify markers of early diagnosis, disease progression, and identify new therapeutic targets. Additionally, omics will have major implications in the field of personalized healthcare in the future. Here, we will review the current knowledge of different omics technologies in neonatal-perinatal medicine including biomarker discovery, defining as yet unrecognized biologic therapeutic targets, and linking of omics to relevant standard indices and long-term outcomes.

Metabolomics and Its Application in the Development of Discovering Biomarkers for Osteoporosis Research

Osteoporosis is a progressive skeletal disorder characterized by low bone mass and increased risk of fracture in later life. The incidence and costs associated with treating osteoporosis cause heavy socio-economic burden. Currently, the diagnosis of osteoporosis mainly depends on bone mineral density and bone turnover markers. However, these indexes are not sensitive and accurate enough to reflect the osteoporosis progression. Metabolomics offers the potential for a holistic approach for clinical diagnoses and treatment, as well as understanding of the pathological mechanism of osteoporosis. In this review, we firstly describe the study subjects of osteoporosis and bio-sample preparation procedures for different analytic purposes, followed by illustrating the biomarkers with potentially predictive, diagnosis and pharmaceutical values when applied in osteoporosis research. Then, we summarize the published metabolic pathways related to osteoporosis. Furthermore, we discuss the importance of chronological data and combination of multi-omics in fully understanding osteoporosis. The application of metabolomics in osteoporosis could provide researchers the opportunity to gain new insight into the metabolic profiling and pathophysiological mechanisms. However, there is still much to be done to validate the potential biomarkers responsible for the progression of osteoporosis and there are still many details needed to be further elucidated.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

OBJECTIVE

Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.

METHODS

In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.

RESULTS

To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.

CONCLUSION

Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.

SIGNIFICANCE

Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.