A proteomic clock of human pregnancy

Solving the Puzzle of Preterm Birth

Solving the puzzle of preterm birth has been challenging and will require novel integrative solutions as preterm birth likely arises from many etiologies. It has been demonstrated that many sociodemographic and psychological determinants of preterm birth relate to its complex biology. It is this understanding that has enabled the development of a novel preventative strategy, which integrates the omics profile (genome, epigenome, transcriptome, proteome, metabolome, microbiome) with sociodemographic, environmental, and psychological determinants of individual pregnant people to solve the puzzle of preterm birth.

Predicting Preterm Birth Using Proteomics

The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Background

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA).

Methods

Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed.

Findings

Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively.

Interpretation

We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation.

Funding

The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study

BACKGROUND

Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.

METHODS

Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist).

RESULTS

We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes.

CONCLUSIONS

Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing

Labor is a complex physiological process requiring a well-orchestrated dialogue between the mother and fetus. However, the cellular contributions and communications that facilitate maternal-fetal cross-talk in labor have not been fully elucidated. Here, single-cell RNA sequencing (scRNA-seq) was applied to decipher maternal-fetal signaling in the human placenta during term labor. First, a single-cell atlas of the human placenta was established, demonstrating that maternal and fetal cell types underwent changes in transcriptomic activity during labor. Cell types most affected by labor were fetal stromal and maternal decidual cells in the chorioamniotic membranes (CAMs) and maternal and fetal myeloid cells in the placenta. Cell-cell interaction analyses showed that CAM and placental cell types participated in labor-driven maternal and fetal signaling, including the collagen, C-X-C motif ligand (CXCL), tumor necrosis factor (TNF), galectin, and interleukin-6 (IL-6) pathways. Integration of scRNA-seq data with publicly available bulk transcriptomic data showed that placenta-derived scRNA-seq signatures could be monitored in the maternal circulation throughout gestation and in labor. Moreover, comparative analysis revealed that placenta-derived signatures in term labor were mirrored by those in spontaneous preterm labor and birth. Furthermore, we demonstrated that early in gestation, labor-specific, placenta-derived signatures could be detected in the circulation of women destined to undergo spontaneous preterm birth, with either intact or prelabor ruptured membranes. Collectively, our findings provide insight into the maternal-fetal cross-talk of human parturition and suggest that placenta-derived single-cell signatures can aid in the development of noninvasive biomarkers for the prediction of preterm birth.

The PRotective Effect of Maternal Immunisation on preTerm birth: characterising the Underlying mechanisms and Role in newborn immune function: the PREMITUR study protocol

Maternal immunisation, a low cost and high efficacy intervention is recommended for its pathogen specific protection. Evidence suggests that maternal immunisation has another significant impact: reduction of preterm birth (PTB), the single greatest cause of childhood morbidity and mortality globally. Our overarching question is: how does maternal immunisation modify the immune system in pregnant women and/or their newborn to reduce adverse pregnancy outcomes and enhance the newborn infant's capacity to protect itself from infectious diseases during early childhood? To answer this question we are conducting a multi-site, prospective observational cohort study collecting maternal and infant biological samples at defined time points during pregnancy and post-partum from nulliparous women. We aim to enrol 400 women and determine the immune trajectory in pregnancy and the impact of maternal immunisation (including influenza, pertussis and/or COVID-19 vaccines) on this trajectory. The results are expected to identify areas that can be targeted for future intervention studies.

Evaluation of T-cell subsets in pregnant women infected with SARS-CoV-2

OBJECTIVE

Immune responses to SARS-CoV-2 are the main cause of tissue damage in coronavirus disease 2019. However, the pathophysiological mechanism of the disease has not been fully elucidated. The aim of this study was to examine T cell subsets of pregnant women infected with SARS-CoV-2 and evaluate the relationship between the possible differences in trimesters and clinical findings of the disease.

MATERIALS AND METHODS

Fifty-six pregnant patients with SARS-CoV-2 and 61 healthy pregnant controls were included in the study. T cell subsets were analyzed by flow cytometry.

RESULTS

The CD3+ total T cell (p = 0.006 and p = 0.027) of pregnant patients infected with SARS-CoV-2 in second and third trimesters was found to be lower than in the control group. CD3+CD4+ helper T cell (p = 0.035), Treg (p = 0.001), and Treg/Th17 ratio (p = 0.001) were found to be lower in the third trimester patients infected with SARS-CoV-2 than in the controls. Significant decreases were observed only in the Treg (p = 0.001) and Treg/Th17 ratio (p = 0.001) in the first trimester patients infected with SARS-CoV-2 compared to the controls. When trimesters were compared in terms of T subsets, no difference was found (p > 0.05).

CONCLUSION

The CD3+ total T cell (p = 0.001), CD3+CD4+ helper T cell (p = 0.011), Treg (p = 0.001), and Treg/Th17 ratio (p = 0.001) were found to be lower in pregnant women infected with SARS-CoV-2. This difference was associated with the development of pneumonia but not with adverse pregnancy outcomes.

The transition to motherhood: linking hormones, brain and behaviour

We are witnessing a stark increase in scientific interest in the neurobiological processes associated with pregnancy and maternity. Convergent evidence suggests that around the time of labour, first-time mothers experience a specific pattern of neuroanatomical changes that are associated with maternal behaviour. Here we provide an overview of the human neurobiological adaptations of motherhood, focusing on the interplay between pregnancy-related steroid and peptide hormones, and neuroplasticity in the brain. We discuss which brain plasticity mechanisms might underlie the structural changes detected by MRI, which hormonal systems are likely to contribute to such neuroanatomical changes and how these brain mechanisms may be linked to maternal behaviour. This Review offers an overarching framework that can serve as a roadmap for future investigations.

An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction

Despite advances in automated liquid handling and microfluidics, preparing samples for RNA sequencing at scale generally requires expensive equipment, which is beyond the reach of many academic laboratories. Manual sample preparation remains a slow, expensive and error-prone process. Here, we describe a low-cost, semi-automated pipeline to extract cell-free RNA using one of two commercially available, inexpensive and open-source robotic systems: the Opentrons OT1.0 or OT2.0. Like many RNA isolation protocols, ours can be decomposed into three subparts: RNA extraction, DNA digestion and RNA cleaning and concentration. RT-qPCR data using a synthetic spike-in confirms comparable RNA quality to the gold standard, manual sample processing. The semi-automated pipeline also shows improvement in sample throughput (+12×), time spent (-11×), cost (-3×) and biohazardous waste produced (-4×) compared with its manual counterpart. This protocol enables cell-free RNA extraction from 96 samples simultaneously in 4.5 h; in practice, this dramatically improves the time to results, as we recently demonstrated. Importantly, any laboratory already has most of the parts required (manual pipette and corresponding tips and kits for RNA isolation, cleaning and concentration) to build a semi-automated sample processing pipeline of their own and would only need to purchase or three-dimensionally print a few extra parts (US$5.5 K-12 K in total). This pipeline is also generalizable for many nucleic acid extraction applications, thereby increasing the scale of studies, which can be performed in small research laboratories.

Intestinal permeability and peripheral immune cell composition are altered by pregnancy and adiposity at mid- and late-gestation in the mouse

It is clear that the gastrointestinal tract influences metabolism and immune function. Most studies to date have used male test subjects, with a focus on effects of obesity and dietary challenges. Despite significant physiological maternal adaptations that occur across gestation, relatively few studies have examined pregnancy-related gut function. Moreover, it remains unknown how pregnancy and diet can interact to alter intestinal barrier function. In this study, we investigated the impacts of pregnancy and adiposity on maternal intestinal epithelium morphology, in vivo intestinal permeability, and peripheral blood immunophenotype, using control (CTL) and high-fat (HF) fed non-pregnant female mice and pregnant mice at mid- (embryonic day (E)14.5) and late (E18.5) gestation. We found that small intestine length increased between non-pregnant mice and dams at late-gestation, but ileum villus length, and ileum and colon crypt depths and goblet cell numbers remained similar. Compared to CTL-fed mice, HF-fed mice had reduced small intestine length, ileum crypt depth and villus length. Goblet cell numbers were only consistently reduced in HF-fed non-pregnant mice. Pregnancy increased in vivo gut permeability, with a greater effect at mid- versus late-gestation. Non-pregnant HF-fed mice had greater gut permeability, and permeability was also increased in HF-fed pregnant dams at mid but not late-gestation. The impaired maternal gut barrier in HF-fed dams at mid-gestation coincided with changes in maternal blood and bone marrow immune cell composition, including an expansion of circulating inflammatory Ly6Chigh monocytes. In summary, pregnancy has temporal effects on maternal intestinal structure and barrier function, and on peripheral immunophenotype, which are further modified by HF diet-induced maternal adiposity. Maternal adaptations in pregnancy are thus vulnerable to excess maternal adiposity, which may both affect maternal and child health.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine

BACKGROUND

Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures.

OBJECTIVE

This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome.

STUDY DESIGN

Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins.

RESULTS

A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10^-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10^-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10^-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10^-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins.

CONCLUSION

Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Linking Electronic Health Records With Wearable Technology From the All of Us Research Program

OBJECTIVE

To evaluate the feasibility of using electronic health records (EHRs) and wearable data to describe patterns of longitudinal change in day-level heart rate before, during, and after pregnancy and how these patterns differ by age and body mass index.

DESIGN

Descriptive secondary analysis feasibility study using data from the National Institutes of Health All of Us Research Program.

SETTING

United States.

PARTICIPANTS

Women (N = 89) who had a birth or length of gestation code in the EHR and at least 60 days of Fitbit heart rate data during pregnancy.

METHODS

We estimated pregnancy-related episodes using EHR codes. Time consisted of five 3-month periods: before pregnancy, first trimester, second trimester, third trimester, and after birth. We analyzed data using descriptive statistics and locally estimated scatterplot smoothing.

RESULTS

An average of 330 days (SD = 112) of Fitbit heart rate data (29,392 days) were available from participants. During pregnancy, distinct peaks in heart rate occurred during the first trimester (6% increase) and third trimester (15% increase).

CONCLUSION

Future researchers can examine whether longitudinal timing and patterns of heart rate from wearable devices could be leveraged to detect health problems early in pregnancy.

The amniotic fluid proteome changes with term labor and informs biomarker discovery in maternal plasma

The intra-uterine components of labor, namely, myometrial contractility, cervical ripening, and decidua/membrane activation, have been extensively characterized and involve a local pro-inflammatory milieu of cellular and soluble immune mediators. Targeted profiling has demonstrated that such processes extend to the intra-amniotic space, yet unbiased analyses of the proteome of human amniotic fluid during labor are lacking. Herein, we utilized an aptamer-based platform to characterize 1,310 amniotic fluid proteins and found that the proteome undergoes substantial changes with term labor (251 proteins with differential abundance, q < 0.1, and fold change > 1.25). Proteins with increased abundance in labor are enriched for immune and inflammatory processes, consistent with prior reports of labor-associated changes in the intra-uterine space. By integrating the amniotic fluid proteome with previously generated placental-derived single-cell RNA-seq data, we demonstrated the labor-driven upregulation of signatures corresponding to stromal-3 and decidual cells. We also determined that changes in amniotic fluid protein abundance are reflected in the maternal plasma proteome. Collectively, these findings provide novel insights into the amniotic fluid proteome in term labor and support its potential use as a source of biomarkers to distinguish between true and false labor by using maternal blood samples.

Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease

Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

Pregnancy imparts distinct systemic adaptive immune function

PROBLEM

Pregnancy represents a state of systemic immune activation that is primarily driven by alterations in circulating innate immune cells. Recent studies have suggested that cellular adaptive immune components, T cells and B cells, also undergo changes throughout gestation. However, the phenotypes and functions of such adaptive immune cells are poorly understood. Herein, we utilized high-dimensional flow cytometry and functional assays to characterize T-cell and B-cell responses in pregnant and non-pregnant women.

METHODS

Peripheral blood mononuclear cells from pregnant (n = 20) and non-pregnant (n = 25) women were used for phenotyping of T-cell and B-cell subsets. T-cell proliferation and B-cell activation were assessed by flow cytometry after in vitro stimulation, and lymphocyte cytotoxicity was evaluated by using a cell-based assay. Statistical comparisons were performed with linear mixed-effects models.

RESULTS

Pregnancy was associated with modestly enhanced basal activation of peripheral CD4+ T cells. Both CD4+ and CD8+ T cells from pregnant women showed increased activation-induced proliferation; yet, a reduced proportion of these cells expressed activation markers compared to non-pregnant women. There were no differences in peripheral lymphocyte cytotoxicity between study groups. A greater proportion of B cells from pregnant women displayed memory-like and activated phenotypes, and such cells exhibited higher activation following stimulation.

CONCLUSION

Maternal circulating T cells and B cells display distinct responses during pregnancy. The former may reflect the unique capacity of T cells to respond to potential threats without undergoing aberrant activation, thereby preventing systemic inflammatory responses that can lead to adverse perinatal consequences.

Placenta-derived proteins across gestation in healthy pregnancies-a novel approach to assess placental function?

BACKGROUND

Placenta-derived proteins in the systemic maternal circulation are suggested as potential biomarkers for placental function. However, the identity and longitudinal patterns of such proteins are largely unknown due to the inaccessibility of the human placenta and limitations in assay technologies. We aimed to identify proteins derived from and taken up by the placenta in the maternal circulation. Furthermore, we aimed to describe the longitudinal patterns across gestation of placenta-derived proteins as well as identify placenta-derived proteins that can serve as reference curves for placental function.

METHODS

We analyzed proteins in plasma samples collected in two cohorts using the Somalogic 5000-plex platform. Antecubital vein samples were collected at three time points (gestational weeks 14-16, 22-24, and 30-32) across gestation in 70 healthy pregnancies in the longitudinal STORK cohort. In the cross sectional 4-vessel cohort, blood samples were collected simultaneously from the maternal antecubital vein (AV), radial artery (RA), and uterine vein (UV) during cesarean section in 75 healthy pregnancies. Placenta-derived proteins and proteins taken up by the placenta were identified using venoarterial differences (UV-RA). Placenta-derived proteins were defined as placenta-specific by comparison to the venoarterial difference in the antecubital vein-radial artery (AV-RA). These proteins were described longitudinally based on the STORK cohort samples using a linear mixed effects model per protein. Using a machine learning algorithm, we identified placenta-derived proteins that could predict gestational age, meaning that they closely tracked gestation, and were potential read-outs of placental function.

RESULTS

Among the nearly 5000 measured proteins, we identified 256 placenta-derived proteins and 101 proteins taken up by the placenta (FDR < 0.05). Among the 256 placenta-derived proteins released to maternal circulation, 101 proteins were defined as placenta-specific. These proteins formed two clusters with distinct developmental patterns across gestation. We identified five placenta-derived proteins that closely tracked gestational age when measured in the systemic maternal circulation, termed a "placental proteomic clock."

CONCLUSIONS

Together, these data may serve as a first step towards a reference for the healthy placenta-derived proteome that can be measured in the systemic maternal circulation and potentially serve as biomarkers of placental function. The "placental proteomic clock" represents a novel concept that warrants further investigation. Deviations in the proteomic pattern across gestation of such proteomic clock proteins may serve as an indication of placental dysfunction.

Longitudinal Proteomic Analysis of Plasma across Healthy Pregnancies Reveals Indicators of Gestational Age

Longitudinal changes in the blood proteome during gestation relate to fetal development and maternal homeostasis. Charting the maternal blood proteome in normal pregnancies is critical for establishing a baseline reference when assessing complications and disease. Using mass spectrometry-based shotgun proteomics, we surveyed the maternal plasma proteome across uncomplicated pregnancies. Results indicate a significant rise in proteins that govern placentation and are vital to the development and health of the fetus. Importantly, we uncovered proteome signatures that strongly correlated with gestational age. Fold increases and correlations between the plasma concentrations of ADAM12 (ρ = 0.973), PSG1 (ρ = 0.936), and/or CSH1/2 (ρ = 0.928) with gestational age were validated with ELISA. Proteomic and validation analyses demonstrate that the maternal plasma concentration of ADAM12, either independently or in combination with either PSG1 or CSH1/2, correlates with gestational age within ±8 days throughout pregnancy. These findings suggest that the gestational age in healthy pregnancies may be determined by referencing the concentration of select plasma proteins.

Stress and Its Consequences-Biological Strain

Understanding the role of stress in pregnancy and its consequences is important, particularly given documented associations between maternal stress and preterm birth and other pathological outcomes. Physical and psychological stressors can elicit the same biological responses, known as biological strain. Chronic stressors, like poverty and racism (race-based discriminatory treatment), may create a legacy or trajectory of biological strain that no amount of coping can relieve in the absence of larger-scale socio-behavioral or societal changes. An integrative approach that takes into consideration simultaneously social and biological determinants of stress may provide the best insights into the risk of preterm birth. The most successful computational approaches and the most predictive machine-learning models are likely to be those that combine information about the stressors and the biological strain (for example, as measured by different omics) experienced during pregnancy.

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

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

Single cell RNA sequencing reveals immunological rewiring at the maternal-fetal interface following asymptomatic/mild SARS-CoV-2 infection

While severe coronavirus 2019 (COVID-19) is associated with immune activation at the maternal-fetal interface, responses to asymptomatic/mild severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy remain unknown. Here, we assess immunological adaptations in blood and term decidua in response to asymptomatic/mild disease in pregnant women. We report attenuated antigen presentation and type I interferon (IFN) signaling pathways, loss of tissue-resident decidual macrophages, and upregulated cytokine/chemokine signaling in monocyte-derived decidual macrophages. Furthermore, we describe increased frequencies of activated tissue-resident T cells and decreased abundance of regulatory T cells with infection while frequencies of cytotoxic CD4/CD8 T cells are increased in the blood. In contrast to decidual macrophages, type I IFN signaling is higher in decidual T cells. Finally, infection leads to a narrowing of T cell receptor diversity in both blood and decidua. Collectively, these observations indicate that asymptomatic/mild COVID-19 during pregnancy results in remodeling of the immunological landscape of the maternal-fetal interface, with a potential for long-term adverse outcomes for the offspring.

Monocytes and macrophages in pregnancy: The good, the bad, and the ugly

A successful human pregnancy requires precisely timed adaptations by the maternal immune system to support fetal growth while simultaneously protecting mother and fetus against microbial challenges. The first trimester of pregnancy is characterized by a robust increase in innate immune activity that promotes successful implantation of the blastocyst and placental development. Moreover, early pregnancy is also a state of increased vulnerability to vertically transmitted pathogens notably, human immunodeficiency virus (HIV), Zika virus (ZIKV), SARS-CoV-2, and Listeria monocytogenes. As gestation progresses, the second trimester is marked by the establishment of an immunosuppressive environment that promotes fetal tolerance and growth while preventing preterm birth, spontaneous abortion, and other gestational complications. Finally, the period leading up to labor and parturition is characterized by the reinstatement of an inflammatory milieu triggering childbirth. These dynamic waves of carefully orchestrated changes have been dubbed the "immune clock of pregnancy." Monocytes in maternal circulation and tissue-resident macrophages at the maternal-fetal interface play a critical role in this delicate balance. This review will summarize the current data describing the longitudinal changes in the phenotype and function of monocyte and macrophage populations in healthy and complicated pregnancies.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

A single-cell atlas of the myometrium in human parturition

Parturition is a well-orchestrated process characterized by increased uterine contractility, cervical ripening, and activation of the chorioamniotic membranes; yet, the transition from a quiescent to a contractile myometrium heralds the onset of labor. However, the cellular underpinnings of human parturition in the uterine tissues are still poorly understood. Herein, we performed a comprehensive study of the human myometrium during spontaneous term labor using single-cell RNA sequencing (scRNA-Seq). First, we established a single-cell atlas of the human myometrium and unraveled the cell type–specific transcriptomic activity modulated during labor. Major cell types included distinct subsets of smooth muscle cells, monocytes/macrophages, stromal cells, and endothelial cells, all of which communicated and participated in immune (e.g., inflammation) and nonimmune (e.g., contraction) processes associated with labor. Furthermore, integrating scRNA-Seq and microarray data with deconvolution of bulk gene expression highlighted the contribution of smooth muscle cells to labor-associated contractility and inflammatory processes. Last, myometrium-derived single-cell signatures can be quantified in the maternal whole-blood transcriptome throughout pregnancy and are enriched in women in labor, providing a potential means of noninvasively monitoring pregnancy and its complications. Together, our findings provide insights into the contributions of specific myometrial cell types to the biological processes that take place during term parturition.

Physiology, pathology and the biomolecular corona: the confounding factors in nanomedicine design

The biomolecular corona that forms on nanomedicines in different physiological and pathological environments confers a new biological identity. How the recipient biological system's state can potentially affect nanomedicine corona formation, and how this can be modulated, remains obscure. With this perspective, this review summarizes the current knowledge about the content of biological fluids in various compartments and how they can be affected by pathological states, thus impacting biomolecular corona formation. The content of representative biological fluids is explored, and the urgency of integrating corona formation, as an essential component of nanomedicine designs for effective cargo delivery, is highlighted.

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background:

High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:

We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype.

Method:

Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge.

Results :

Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:

OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

Early-pregnancy prediction of risk for pre-eclampsia using maternal blood leptin/ceramide ratio: discovery and confirmation

OBJECTIVE

This study aimed to develop a blood test for the prediction of pre-eclampsia (PE) early in gestation. We hypothesised that the longitudinal measurements of circulating adipokines and sphingolipids in maternal serum over the course of pregnancy could identify novel prognostic biomarkers that are predictive of impending event of PE early in gestation.

STUDY DESIGN

Retrospective discovery and longitudinal confirmation.

SETTING

Maternity units from two US hospitals.

PARTICIPANTS

Six previously published studies of placental tissue (78 PE and 95 non-PE) were compiled for genomic discovery, maternal sera from 15 women (7 non-PE and 8 PE) enrolled at ProMedDx were used for sphingolipidomic discovery, and maternal sera from 40 women (20 non-PE and 20 PE) enrolled at Stanford University were used for longitudinal observation.

OUTCOME MEASURES

Biomarker candidates from discovery were longitudinally confirmed and compared in parallel to the ratio of placental growth factor (PlGF) and soluble fms-like tyrosine kinase (sFlt-1) using the same cohort. The datasets were generated by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays.

RESULTS

Our discovery integrating genomic and sphingolipidomic analysis identified leptin (Lep) and ceramide (Cer) (d18:1/25:0) as novel biomarkers for early gestational assessment of PE. Our longitudinal observation revealed a marked elevation of Lep/Cer (d18:1/25:0) ratio in maternal serum at a median of 23 weeks' gestation among women with impending PE as compared with women with uncomplicated pregnancy. The Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting impending event of PE with superior sensitivity (85% vs 20%) and area under curve (0.92 vs 0.52) from 5 to 25 weeks of gestation.

CONCLUSIONS

Our study demonstrated the longitudinal measurement of maternal Lep/Cer (d18:1/25:0) ratio allows the non-invasive assessment of PE to identify pregnancy at high risk in early gestation, outperforming the established sFlt-1/PlGF ratio test.

Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease

The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.

Cytokine Patterns in Maternal Serum From First Trimester to Term and Beyond

Pregnancy implies delicate immunological balance between two individuals, with constant changes and adaptions in response to maternal capacity and fetal demands. We performed cytokine profiling of 1149 longitudinal serum samples from 707 pregnant women to map immunological changes from first trimester to term and beyond. The serum levels of 22 cytokines and C-reactive protein (CRP) followed diverse but characteristic trajectories throughout pregnancy, consistent with staged immunological adaptions. Eotaxin showed a particularly robust decrease throughout pregnancy. A strong surge in cytokine levels developed when pregnancies progressed beyond term and the increase was amplified as labor approached. Maternal obesity, smoking and pregnancies with large fetuses showed sustained increase in distinct cytokines throughout pregnancy. Multiparous women had increased cytokine levels in the first trimester compared to nulliparous women with higher cytokine levels in the third trimester. Fetal sex affected first trimester cytokine levels with increased levels in pregnancies with a female fetus. These findings unravel important immunological dynamics of pregnancy, demonstrate how both maternal and fetal factors influence maternal systemic cytokines, and serve as a comprehensive reference for cytokine profiles in normal pregnancies.

A reduction in voluntary physical activity in early pregnancy in mice is mediated by prolactin

As part of the maternal adaptations to pregnancy, mice show a rapid, profound reduction in voluntary running wheel activity (RWA) as soon as pregnancy is achieved. Here, we evaluate the hypothesis that prolactin, one of the first hormones to change secretion pattern following mating, is involved in driving this suppression of physical activity levels during pregnancy. We show that prolactin can acutely suppress RWA in non-pregnant female mice, and that conditional deletion of prolactin receptors (Prlr) from either most forebrain neurons or from GABA neurons prevented the early pregnancy-induced suppression of RWA. Deletion of Prlr specifically from the medial preoptic area, a brain region associated with multiple homeostatic and behavioral roles including parental behavior, completely abolished the early pregnancy-induced suppression of RWA. As pregnancy progresses, prolactin action continues to contribute to the further suppression of RWA, although it is not the only factor involved. Our data demonstrate a key role for prolactin in suppressing voluntary physical activity during early pregnancy, highlighting a novel biological basis for reduced physical activity in pregnancy.

Deep immune profiling of the maternal-fetal interface with mild SARS-CoV-2 infection

Pregnant women are an at-risk group for severe COVID-19, though the majority experience mild/asymptomatic disease. Although severe COVID-19 has been shown to be associated with immune activation at the maternal-fetal interface even in the absence of active viral replication, the immune response to asymptomatic/mild COVID-19 remains unknown. Here, we assessed immunological adaptations in both blood and term decidua from 9 SARS-exposed pregnant women with asymptomatic/mild disease and 15 pregnant SARS-naive women. In addition to selective loss of tissue-resident decidual macrophages, we report attenuation of antigen presentation and type I IFN signaling but upregulation of inflammatory cytokines and chemokines in blood monocyte derived decidual macrophages. On the other hand, infection was associated with remodeling of the T cell compartment with increased frequencies of activated CD69+ tissue-resident T cells and decreased abundance of Tregs. Interestingly, frequencies of cytotoxic CD4 and CD8 T cells increased only in the blood, while CD8 effector memory T cells were expanded in the decidua. In contrast to decidual macrophages, signatures of type I IFN signaling were increased in decidual T cells. Finally, T cell receptor diversity was significantly reduced with infection in both compartments, albeit to a much greater extent in the blood. The resulting aberrant immune activation in the placenta, even with asymptomatic disease may alter the exquisitely sensitive developing fetal immune system, leading to long-term adverse outcomes for offspring.

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

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

Development and validation of a risk score for predicting postoperative delirium after major abdominal surgery by incorporating preoperative risk factors and surgical Apgar score

STUDY OBJECTIVE

To develop and validate a simple delirium-predicting scoring system in patients undergoing major abdominal surgery by incorporating preoperative risk factors and intraoperative surgical Apgar score (SAS).

DESIGN

Observational retrospective cohort study.

SETTING

A tertiary general hospital in China.

PATIENTS

1055 patients who received major abdominal surgery from January 2015 to December 2019.

MEASUREMENTS

We collected data on preoperative and intraoperative variables, and postoperative delirium. A risk scoring system for postoperative delirium in patients after major open abdominal surgery was developed and validated based on traditional logistic regression model. The elastic net algorithm was further developed and evaluated.

MAIN RESULTS

The incidence of postoperative delirium was 17.8% (188/1055) in these patients. They were randomly divided into the development (n = 713) and validation (n = 342) cohorts. Both the logistic regression model and the elastic net regression model identified that advanced age, arrythmia, hypoalbuminemia, coagulation dysfunction, mental illness or cognitive impairments and low surgical Apgar score are related with increased risk of postoperative delirium. The elastic net algorithm has an area under the receiver operating characteristic curve (AUROC) of 0.842 and 0.822 in the development and validation cohorts, respectively. A prognostic score was calculated using the following formula: Prognostic score = Age classification (0 to 3 points) + arrythmia + 2 * hypoalbuminemia + 2 * coagulation dysfunction + 4 * mental illness or cognitive impairments + (10-surgical Apgar score). The 22-point risk scoring system had good discrimination and calibration with an AUROC of 0.823 and 0.834, and a non-significant Hosmer-Lemeshow test P = 0.317 and P = 0.853 in the development and validation cohorts, respectively. The bootstrapping internal verification method (R = 1000) yielded a C-index of 0.822 (95% CI: 0.759-0.857).

CONCLUSION

The prognostic scoring system, which used both preoperative risk factors and surgical Apgar score, serves as a good first step toward a clinically useful predictive model for postoperative delirium in patients undergoing major open abdominal surgery.

Deficiency in CD4 T Cells Leads to Enhanced Postpartum Internal Carotid Artery Vasoconstriction in Mice: The Role of Nitric Oxide

The risk of postpartum (PP) stroke is increased in complicated pregnancies. Deficiency in CD4 T cell subsets is associated with preeclampsia and may contribute to PP vascular disease, including internal carotid artery (ICA) stenosis and stroke. We hypothesized that CD4 T cell deficiency in pregnancy would result in ICA dysregulation, including enhanced ICA vasoconstriction. We characterized the function, mechanical behavior, and structure of ICAs from C57BL/6 (WT) and CD4 deficient (CD4KO) mice, and assessed the role of NO in the control of ICA function at pre-conception and PP. WT and CD4KO mice were housed under pathogen-free conditions, mated to same-strain males, and allowed to litter or left virgin. At 3 days or 4 weeks PP, mice were euthanized. The responses to phenylephrine (PE), high K⁺ and acetylcholine (ACh) were assessed in pressurized ICAs before and after NOS inhibition. Passive lumen diameters were measured at 3–140 mmHg. eNOS and iNOS expression as well as the presence of T cells were evaluated by immunohistochemistry. Constriction of WT ICAs to PE was not modified PP. In contrast, responses to PE were significantly increased in ICAs from PP as compared to virgin CD4KO mice. Constriction to high K⁺ was not enhanced PP. ICAs from WT and CD4KO mice were equally sensitive to ACh with a significant rightward shift of dose-response curves after L-NNA treatment. NOS inhibition enhanced PE constriction of ICAs from WT virgin and PP mice. Although a similar effect was detected in ICAs of virgin CD4KO mice, no such changes were observed in vessels from PP CD4KO mice. Passive arterial distensibility at physiological levels of pressure was not modified at PP. ICA diameters were significantly increased in PP with no change in vascular wall thickness. Comparison of eNOS expression in virgin, 3 days and 4 weeks PP revealed a reduced expression in ICA from CD4 KO vs. WT PP vessels which reached significance at 4 weeks PP. iNos expression was similar and decreased over the PP period in vessels from WT and CD4KO mice. Dysregulation of the CD4 T cell population in pregnancy may make ICA vulnerable to vasospasm due to decreased NO-dependent control of ICA constriction. This may lead to cerebral hypoperfusion and increase the risk of maternal PP stroke.

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).

Functional and genomic adaptations of blood monocytes to pregravid obesity during pregnancy

Pregravid obesity is associated with several adverse maternal health outcomes, such as increased risk of infection, suggesting an altered immunological state. However, the mechanisms by which obesity disrupts the pregnancy “immune clock” are still unknown. Here, we profiled circulating immune mediators, immune cell subset frequencies, and peripheral immune responses during the first and third trimesters of pregnancy in lean and obese mothers. While both Th1 and Th2 cytokines were elevated with pregnancy regardless of BMI, obese subjects had dysregulated myeloid factors in circulation at term. Pregnancy in lean subjects was associated with enhanced monocyte activation, augmented chromatin accessibility at inflammatory loci, and heightened responses to LPS. Pregravid obesity disrupted this trajectory, resulting in a lack of transcriptional, epigenetic, and metabolic changes strongly suggesting a skewing toward innate immune tolerance. These findings provide novel insight into the increased susceptibility to infections in women with obesity during pregnancy and following cesarean delivery.

•

Pregnancy is associated with activation and enhanced responses of monocytes

•

Heightened monocyte response is associated with epigenetic adaptions

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Pregravid obesity leads to a state akin to LPS tolerance in monocytes

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Pregravid obesity is associated with a lack of epigenetic and metabolic plasticity

Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies

Background:

Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care.

Objective:

The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes.

Study design:

Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE.

Results:

The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences.

Conclusions:

Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

Data-Driven Modeling of Pregnancy-Related Complications

A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

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

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

Predicting Acute Pain After Surgery: A Multivariate Analysis

OBJECTIVES

To identify perioperative practice patterns that predictably impact postoperative pain.

BACKGROUND

Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society.

METHODS

The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery.

RESULTS

The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance.

CONCLUSION

The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables.

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US

OBJECTIVES

The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.

STUDY DESIGN

A retrospective cohort study.

SETTING

Two medical centres from the USA.

PARTICIPANTS

Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.

OUTCOME MEASURES

Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.

RESULTS

A model to determine gestational age was developed (R²=0.98) and validated (R²=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R²=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.

CONCLUSIONS

In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries

IMPORTANCE

Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.

OBJECTIVE

To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.

EXPOSURES

Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.

MAIN OUTCOMES AND MEASURES

The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.

RESULTS

Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.

CONCLUSIONS AND RELEVANCE

This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Regulation of maternal-fetal metabolic communication

Pregnancy may be the most nutritionally sensitive stage in the life cycle, and improved metabolic health during gestation and early postnatal life can reduce the risk of chronic disease in adulthood. Successful pregnancy requires coordinated metabolic, hormonal, and immunological communication. In this review, maternal-fetal metabolic communication is defined as the bidirectional communication of nutritional status and metabolic demand by various modes including circulating metabolites, endocrine molecules, and other secreted factors. Emphasis is placed on metabolites as a means of maternal-fetal communication by synthesizing findings from studies in humans, non-human primates, domestic animals, rabbits, and rodents. In this review, fetal, placental, and maternal metabolic adaptations are discussed in turn. (1) Fetal macronutrient needs are summarized in terms of the physiological adaptations in place to ensure their proper allocation. (2) Placental metabolite transport and maternal physiological adaptations during gestation, including changes in energy budget, are also discussed. (3) Maternal nutrient limitation and metabolic disorders of pregnancy serve as case studies of the dynamic nature of maternal-fetal metabolic communication. The review concludes with a summary of recent research efforts to identify metabolites, endocrine molecules, and other secreted factors that mediate this communication, with particular emphasis on serum/plasma metabolomics in humans, non-human primates, and rodents. A better understanding of maternal-fetal metabolic communication in health and disease may reveal novel biomarkers and therapeutic targets for metabolic disorders of pregnancy.

Longitudinal plasma inflammatory proteome profiling during pregnancy in the Born into Life study

The maternal immune system is going through considerable changes during pregnancy. However, little is known about the determinants of the inflammatory proteome and its relation to pregnancy stages. Our aim was to investigate the plasma inflammatory proteome before, during and after pregnancy. In addition we wanted to test whether maternal and child outcomes were associated with the proteome. A cohort of 94 healthy women, enrolled in a longitudinal study with assessments at up to five time points around pregnancy, ninety-two inflammatory proteins were analysed in plasma with a multiplex Proximity Extension Assay. First, principal components analysis were applied and thereafter regression modelling while correcting for multiple testing. We found profound shifts in the overall inflammatory proteome associated with pregnancy stage after multiple testing (p < .001). Moreover, maternal body mass index (BMI) was associated with inflammatory proteome primarily driven by VEGFA, CCL3 and CSF-1 (p < .05). The levels of most inflammatory proteins changed substantially during pregnancy and some of these were related to biological processes such as regulation of immune response. Maternal BMI was significantly associated with higher levels of three inflammation proteins calling for more research in the interplay between pregnancy, inflammation and BMI.

Protein Concentrations of Thrombospondin-1, MIP-1β, and S100A8 Suggest the Reflection of a Pregnancy Clock in Mid-Trimester Amniotic Fluid

The development of immunoassays enables more sophisticated studies of the associations between protein concentrations and pregnancy outcomes, allowing early biomarker identification that can improve neonatal outcomes. The aim of this study was to explore associations between selected mid-trimester amniotic fluid proteins and (1) overall gestational duration and (2) spontaneous preterm delivery. A prospective cohort study, including women undergoing mid-trimester transabdominal genetic amniocentesis, was performed in Gothenburg, Sweden, 2008-2016 (n = 1072). A panel of 27 proteins related to inflammation was analyzed using Meso-Scale multiplex technology. Concentrations were adjusted for gestational age at sampling, experimental factors, year of sampling, and covariates (maternal age at sampling, parity (nulliparous/multiparous), smoking at first prenatal visit, and in vitro fertilization). Cox regression analysis of the entire cohort was performed to explore possible associations between protein concentrations and gestational duration. This was followed by Cox regression analysis censored at 259 days or longer, to investigate whether associations were detectable in women with spontaneous preterm delivery (n = 47). Finally, linear regression models were performed to analyze associations between protein concentrations and gestational duration in women with spontaneous onset of labor at term (n = 784). HMG-1, IGFBP-1, IL-18, MIP-1α, MIP-1β, S100A8, and thrombospondin-1 were significantly associated with gestational duration at term, but not preterm. Increased concentrations of thrombospondin-1, MIP-1β, and S100A8, respectively, were significantly associated with decreased gestational duration after the Holm-Bonferroni correction in women with spontaneous onset of labor at term. This adds to the concept of a pregnancy clock, where our findings suggest that such a clock is also reflected in the amniotic fluid at early mid-trimester, but further research is needed to confirm this.

Abnormal angiogenesis of placenta in progranulin‑deficient mice

Progranulin (PGRN) is a secreted growth factor involved in pleiotropic functions, particularly angiogenesis. A distinctly different placental expression of PGRN has been reported between normal pregnancies and pregnancies with complications, such as pre‑eclampsia or fetal growth restriction. However, the role of PGRN in placental vascular development remains to be elucidated. In the present study, PGRN‑knockout mice (PGRN‑/‑) were used to investigate the role of PGRN in the development of placental blood vessels and placental formation. Placental weights and pup body weights were significantly lower in the PGRN‑/‑ mice compared with the wild‑type mice. Reduced labyrinthine layer areas and aberrant vascularization were also observed via hematoxylin and eosin staining of PGRN‑/‑ mice at embryonic day 14.5 (E14.5) and E17.5. In addition, the morphological data obtained via immunohistochemistry, immunofluorescence staining and western blotting demonstrated decreased expression levels of the blood vessel markers α‑smooth muscle actin and CD31 in PGRN‑/‑ placentas. Furthermore, vasodilator endothelial nitric oxide synthase was reduced in the PGRN‑/‑ placenta. These results indicated that PGRN serves an essential role in the normal angiogenesis of the placental labyrinth in mice.

Multiomic immune clockworks of pregnancy

Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.