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

Optimized ultrasound neuromodulation for non-invasive control of behavior and physiology

Focused ultrasound can non-invasively modulate neural activity, but whether effective stimulation parameters generalize across brain regions and cell types remains unknown. We used focused ultrasound coupled with fiber photometry to identify optimal neuromodulation parameters for four different arousal centers of the brain in an effort to yield overt changes in behavior. Applying coordinate descent, we found that optimal parameters for excitation or inhibition are highly distinct, the effects of which are generally conserved across brain regions and cell types. Optimized stimulations induced clear, target-specific behavioral effects, whereas non-optimized protocols of equivalent energy resulted in substantially less or no change in behavior. These outcomes were independent of auditory confounds and, contrary to expectation, accompanied by a cyclooxygenase-dependent and prolonged reduction in local blood flow and temperature with brain-region-specific scaling. These findings demonstrate that carefully tuned and targeted ultrasound can exhibit powerful effects on complex behavior and physiology.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

The mark of success: The role of vaccine-induced skin scar formation for BCG and smallpox vaccine-associated clinical benefits

Skin scar formation following Bacille Calmette-Guérin (BCG) or smallpox (Vaccinia) vaccination is an established marker of successful vaccination and 'vaccine take'. Potent pathogen-specific (tuberculosis; smallpox) and pathogen-agnostic (protection from diseases unrelated to the intentionally targeted pathogen) effects of BCG and smallpox vaccines hold significant translational potential. Yet despite their use for centuries, how scar formation occurs and how local skin-based events relate to systemic effects that allow these two vaccines to deliver powerful health promoting effects has not yet been determined. We review here what is known about the events occurring in the skin and place this knowledge in the context of the overall impact of these two vaccines on human health with a particular focus on maternal-child health.

Novel biomarkers for prediction of atonic postpartum hemorrhage among 'low-risk' women in labor

Background

Postpartum hemorrhage (PPH) is the primary cause of maternal mortality globally, with uterine atony being the predominant contributing factor. However, accurate prediction of PPH in the general population remains challenging due to a lack of reliable biomarkers.

Methods

Using retrospective cohort data, we quantified 48 cytokines in plasma samples from 40 women diagnosed with PPH caused by uterine atony. We also analyzed previously reported hemogram and coagulation parameters related to inflammatory response. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to develop predictive models. Established models were further evaluated and temporally validated in a prospective cohort.

Results

Fourteen factors showed significant differences between the two groups, among which IL2Rα, IL9, MIP1β, TNFβ, CTACK, prenatal Hb, Lymph%, PLR, and LnSII were selected by LASSO to construct predictive model A. Further, by logistic regression, model B was constructed using prenatal Hb, PLR, IL2Rα, and IL9. The area under the curve (AUC) values of model A in the training set, internal validation set, and temporal validation set were 0.846 (0.757-0.934), 0.846 (0.749-0.930), and 0.875 (0.789-0.961), respectively. And the corresponding AUC values for model B were 0.805 (0.709-0.901), 0.805 (0.701-0.894), and 0.901 (0.824-0.979). Decision curve analysis results showed that both nomograms had a high net benefit for predicting atonic PPH.

Conclusion

We identified novel biomarkers and developed predictive models for atonic PPH in women undergoing "low-risk" vaginal delivery, providing immunological insights for further exploration of the mechanism underlying atonic PPH.

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.

The vaginal immunoproteome for the prediction of spontaneous preterm birth: A retrospective longitudinal study

Background

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.

Methods

Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.

Results

Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.

Conclusions

The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.

Funding

This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.

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.

Elevated Plasma Levels of Arginines During Labor Among Women with Spontaneous Preterm Birth: A Prospective Cohort Study

PROBLEM

Preterm birth (PTB) is a leading cause of infant mortality and morbidity. The pathogenesis of PTB is complex and involves many factors, including socioeconomy, inflammation and infection. Asymmetric dimethylarginine, ADMA and symmetric dimethylarginine, SDMA are involved in labor as inhibitors of nitric oxide, a known relaxant of the uterine smooth muscles. Arginines are scarcely studied in relation to PTB and we aimed to investigate arginines (ADMA, SDMA and L-arginine) in women with spontaneous PTB and term birth.

METHODS OF THE STUDY

The study was based on data from the population-based, prospective cohort BASIC study conducted in Uppsala County, Sweden, between September 2009 and November 2018. Arginines were analyzed by Ultra-High Performance Liquid Chromatography using plasma samples taken at the onset of labor from women with spontaneous PTB (n = 34) and term birth (n = 45). We also analyzed the inflammation markers CRP, TNF-R1 and TNF-R2 and GDF-15.

RESULTS

Women with spontaneous PTB had higher plasma levels of ADMA (p < 0.001), and L-Arginine (p = 0.03). In addition, inflammation marker, TNF-R1 (p = 0.01) was higher in spontaneous PTB compared to term birth. Further, in spontaneous PTB, no significant correlations could be observed when comparing levels of arginines with inflammation markers, except ADMA versus CRP.

CONCLUSIONS

These findings provide novel evidence for the potential involvement of arginines in the pathogenesis of spontaneous PTB and it seems that arginine levels at labor vary independently of several inflammatory markers. Further research is warranted to investigate the potential of arginines as therapeutic targets in the prevention and management of spontaneous PTB.

Predicting Spontaneous Preterm Birth Using the Immunome

Throughout pregnancy, the maternal peripheral circulation contains valuable information reflecting pregnancy progression, detectable as tightly regulated immune dynamics. Local immune processes at the maternal-fetal interface and other reproductive and non-reproductive tissues are likely to be the pacemakers for this peripheral immune "clock." This cellular immune status of pregnancy can be leveraged for the early risk assessment and prediction of spontaneous preterm birth (sPTB). Systems immunology approaches to sPTB subtypes and cross-tissue (local and peripheral) interactions, as well as integration of multiple biological data modalities promise to improve our understanding of preterm birth pathobiology and identify potential clinically actionable biomarkers.

Computational Approaches for Connecting Maternal Stress to Preterm Birth

Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors. Finally, we summarize the challenges of computational methods, highlighting potential future research directions within this field.

Computational Approaches for Predicting Preterm Birth and Newborn Outcomes

Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.

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.

Using Machine Learning to Construct the Blood-Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

Permeabilities of various trace elements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood-follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19-4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076-0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998-1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

Proteomic studies of VEGFR2 in human placentas reveal protein associations with preeclampsia, diabetes, gravidity, and labor

VEGFR2 (Vascular endothelial growth factor receptor 2) is a central regulator of placental angiogenesis. The study of the VEGFR2 proteome of chorionic villi at term revealed its partners MDMX (Double minute 4 protein) and PICALM (Phosphatidylinositol-binding clathrin assembly protein). Subsequently, the oxytocin receptor (OT-R) and vasopressin V1aR receptor were detected in MDMX and PICALM immunoprecipitations. Immunogold electron microscopy showed VEGFR2 on endothelial cell (EC) nuclei, mitochondria, and Hofbauer cells (HC), tissue-resident macrophages of the placenta. MDMX, PICALM, and V1aR were located on EC plasma membranes, nuclei, and HC nuclei. Unexpectedly, PICALM and OT-R were detected on EC projections into the fetal lumen and OT-R on 20-150 nm clusters therein, prompting the hypothesis that placental exosomes transport OT-R to the fetus and across the blood-brain barrier. Insights on gestational complications were gained by univariable and multivariable regression analyses associating preeclampsia with lower MDMX protein levels in membrane extracts of chorionic villi, and lower MDMX, PICALM, OT-R, and V1aR with spontaneous vaginal deliveries compared to cesarean deliveries before the onset of labor. We found select associations between higher MDMX, PICALM, OT-R protein levels and either gravidity, diabetes, BMI, maternal age, or neonatal weight, and correlations only between PICALM-OT-R (p < 2.7 × 10-8), PICALM-V1aR (p < 0.006), and OT-R-V1aR (p < 0.001). These results offer for exploration new partnerships in metabolic networks, tissue-resident immunity, and labor, notably for HC that predominantly express MDMX.

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

An integrated Bayesian framework for multi-omics prediction and classification

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

Deep Learning Model Using Continuous Skin Temperature Data Predicts Labor Onset

Background

Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.

Methods

We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a daily estimation of days until labor onset.

Results

Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until labor onset. During cross-validation AE-LSTM average error (true - predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor.

Conclusion

Continuous skin temperature reflects progression toward labor and hormonal status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor

Preterm birth (PTB) is the primary cause of neonatal mortality and long-term disabilities. The unknown mechanism behind PTB makes diagnosis difficult, yet early detection is necessary for controlling and averting related consequences. The primary focus of this work is to provide an overview of the known risk factors associated with preterm labor and the conventional and advanced procedures for early detection of PTB, including multi-omics and artificial intelligence/machine learning (AI/ML)- based approaches. It also discusses the principles of detecting various proteomic biomarkers based on lateral flow immunoassay and microfluidic chips, along with the commercially available point-of-care testing (POCT) devices and associated challenges. After briefing the therapeutic and preventive measures of PTB, this review summarizes with an outlook.

Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment

Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e-05) and maternal intake of fish (p = 4.1e-03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

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.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Association of the key immunological and hemodynamic determinants with cervix ripening in pregnant women

OBJECTIVE

Aim: To investigate a correlation between cervical ripening, the immunological features and the hemodynamic characteristics of the cervix during the preparation for vaginal labor.

PATIENTS AND METHODS

Materials and Methods: We examined 75 pregnant women at different gestational age. General clinical and immunological studies were conducted in order to check serum concentration of cytokines IL-6, IL-1β, and TNF-α. Ultrasound and Doppler study were used to determine resistance index and systolic-diastolic ratio of blood flow in the common uterine artery as well as the descending and ascending parts and cervical stromal arteries.

RESULTS

Results: Pregnant women with high cervical ripening score had high concentrations of the major proinflammatory cytokines (IL-1β, IL-6, and TNF-α). Analysis of the of the cervical blood flow indicators of the studied groups showed significant differences in the indices of vascular resistance in the vessels that feed the cervix. Our data showed a significant correlation between the cervix ripening and both the serum levels of the studied cytokines and the level of peripheral vascular resistance indices in the common uterine arteries of the cervix, and the blood flow indices in the cervical stromal vessels.

CONCLUSION

Conclusions: Our study shows that the process of preparing the woman's body for labor is associated with immunological adjustment and increased hemodynamics of the cervix. We report that cervical ripening is associated with the immunological components and hemodynamic parameters of the cervix at late-stage pregnancy. Measuring cervix ripening and the accompanied changes in cytokine levels and hemodynamic parameters will form a more accurate assessment of birth preparedness and labor complications.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at-risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

Multi-omics in stress and health research: study designs that will drive the field forward

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals' stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Host-microbiome interactions in distinct subsets of preterm labor and birth

Preterm birth, the leading cause of perinatal morbidity, often follows premature labor, a syndrome whose prevention remains a challenge. To better understand the relationship between premature labor and host-microbiome interactions, we conducted a mechanistic investigation using three preterm birth models. We report that intra-amniotic delivery of LPS triggers inflammatory responses in the amniotic cavity and cervico-vaginal microenvironment, causing vaginal microbiome changes and signs of active labor. Intra-amniotic IL-1α delivery causes a moderate inflammatory response in the amniotic cavity but increasing inflammation in the cervico-vaginal space, leading to vaginal microbiome disruption and signs of active labor. Conversely, progesterone action blockade by RU-486 triggers local immune responses accompanying signs of active labor without altering the vaginal microbiome. Preterm labor facilitates ascension of cervico-vaginal bacteria into the amniotic cavity, regardless of stimulus. This study provides compelling mechanistic insights into the dynamic host-microbiome interactions within the cervico-vaginal microenvironment that accompany premature labor and birth.

Nutrient regulation of development and cell fate decisions

Diet contributes to health at all stages of life, from embryonic development to old age. Nutrients, including vitamins, amino acids, lipids and sugars, have instructive roles in directing cell fate and function, maintaining stem cell populations, tissue homeostasis and alleviating the consequences of aging. This Review highlights recent findings that illuminate how common diets and specific nutrients impact cell fate decisions in healthy and disease contexts. We also draw attention to new models, technologies and resources that help to address outstanding questions in this emerging field and may lead to dietary approaches that promote healthy development and improve disease treatments.

Plasma levels of arginines at term pregnancy in relation to mode of onset of labor and mode of childbirth

PROBLEM

The exact biochemical mechanisms that initiate labor are not yet fully understood. Nitric oxide is a potent relaxant of uterine smooth muscles until labor starts, and its precursor is L-arginine. Asymmetric (ADMA) and symmetric (SDMA) dimethylarginines, are potent NO-inhibitors. However, arginines (dimethylarginines and L-arginine) are scarcely studied in relation to labor and childbirth. We aimed to investigate arginines in women with spontaneous (SLVB) and induced (ILVB) term labor with vaginal birth and in women undergoing elective caesarean section (ECS).

METHOD OF STUDY

Women at gestational week 16-18 were recruited to the population-based prospective cohort study BASIC at the Uppsala University Hospital, Sweden. Plasma samples taken at start of labor were analyzed for arginines, from SLVB (n = 45), ILVB (n = 45), and ECS (n = 45), using Ultra-High Performance Liquid Chromatography. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U-test.

RESULTS

Women with SLVB and ILVB had higher levels of ADMA (p < .0001), SDMA (p < .05) and lower L-arginines (p < .01), L-arginine/ADMA (p < .0001), and L-arginine/SDMA (p < .01, respectively <.001) compared to ECS. However, ILVB had higher ADMA (p < .0001) and lower L-arginine (p < .01), L-arginine/ADMA (p < .0001), and L-arginine/SDMA (p < .01) compared to SLVB. Results are adjusted for gestational length at birth and cervical dilatation at sampling.

CONCLUSION

Our novel findings of higher levels of dimethylarginines in term vaginal births compared to ECS give insights into the biochemical mechanisms of labor. These findings might also serve as a basis for further studies of arginines in complicated pregnancies and labor.

Siglecs family used by pathogens for immune escape may engaged in immune tolerance in pregnancy

The Siglecs family is a group of type I sialic acid-binding immunoglobulin-like receptors that regulate cellular signaling by recognizing sialic acid epitopes. Siglecs are predominantly expressed on the surface of leukocytes, where they play a crucial role in regulating immune activity. Pathogens can exploit inhibitory Siglecs by utilizing their sialic acid components to promote invasion or suppress immune functions, facilitating immune evasion. The establishing of an immune-balanced maternal-fetal interface microenvironment is essential for a successful pregnancy. Dysfunctional immune cells may lead to adverse pregnancy outcomes. Siglecs are important for inducing a phenotypic switch in leukocytes at the maternal-fetal interface toward a less toxic and more tolerant phenotype. Recent discoveries regarding Siglecs in the reproductive system have drawn further attention to their potential roles in reproduction. In this review, we primarily discuss the latest advances in understanding the impact of Siglecs as immune regulators on infections and pregnancy.

Predicting labor onset relative to the estimated date of delivery using smart ring physiological data

The transition from pregnancy into parturition is physiologically directed by maternal, fetal and placental tissues. We hypothesize that these processes may be reflected in maternal physiological metrics. We enrolled pregnant participants in the third-trimester (n = 118) to study continuously worn smart ring devices monitoring heart rate, heart rate variability, skin temperature, sleep and physical activity from negative temperature coefficient, 3-D accelerometer and infrared photoplethysmography sensors. Weekly surveys assessed labor symptoms, pain, fatigue and mood. We estimated the association between each metric, gestational age, and the likelihood of a participant's labor beginning prior to (versus after) the clinical estimated delivery date (EDD) of 40.0 weeks with mixed effects regression. A boosted random forest was trained on the physiological metrics to predict pregnancies that naturally passed the EDD versus undergoing onset of labor prior to the EDD. Here we report that many raw sleep, activity, pain, fatigue and labor symptom metrics are correlated with gestational age. As gestational age advances, pregnant individuals have lower resting heart rate 0.357 beats/minute/week, 0.84 higher heart rate variability (milliseconds) and shorter durations of physical activity and sleep. Further, random forest predictions determine pregnancies that would pass the EDD with accuracy of 0.71 (area under the receiver operating curve). Self-reported symptoms of labor correlate with increased gestational age and not with the timing of labor (relative to EDD) or onset of spontaneous labor. The use of maternal smart ring-derived physiological data in the third-trimester may improve prediction of the natural duration of pregnancy relative to the EDD.

Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities

Prenatal screening using sequencing of circulating cell-free DNA has transformed obstetric care over the past decade and significantly reduced the number of invasive diagnostic procedures like amniocentesis for genetic disorders. Nonetheless, emergency care remains the only option for complications like preeclampsia and preterm birth, two of the most prevalent obstetrical syndromes. Advances in noninvasive prenatal testing expand the scope of precision medicine in obstetric care. In this review, we discuss advances, challenges, and possibilities toward the goal of providing proactive, personalized prenatal care. The highlighted advances focus mainly on cell-free nucleic acids; however, we also review research that uses signals from metabolomics, proteomics, intact cells, and the microbiome. We discuss ethical challenges in providing care. Finally, we look to future possibilities, including redefining disease taxonomy and moving from biomarker correlation to biological causation.

Abrupt perturbation and delayed recovery of the vaginal ecosystem following childbirth

The vaginal ecosystem is closely tied to human health and reproductive outcomes, yet its dynamics in the wake of childbirth remain poorly characterized. Here, we profile the vaginal microbiota and cytokine milieu of participants sampled longitudinally throughout pregnancy and for at least one year postpartum. We show that delivery, regardless of mode, is associated with a vaginal pro-inflammatory cytokine response and the loss of Lactobacillus dominance. By contrast, neither the progression of gestation nor the approach of labor strongly altered the vaginal ecosystem. At 9.5-months postpartum-the latest timepoint at which cytokines were assessed-elevated inflammation coincided with vaginal bacterial communities that had remained perturbed (highly diverse) from the time of delivery. Time-to-event analysis indicated a one-year postpartum probability of transitioning to Lactobacillus dominance of 49.4%. As diversity and inflammation declined during the postpartum period, dominance by L. crispatus, the quintessential health-associated commensal, failed to return: its prevalence before, immediately after, and one year after delivery was 41%, 4%, and 9%, respectively. Revisiting our pre-delivery data, we found that a prior live birth was associated with a lower odds of L. crispatus dominance in pregnant participants-an outcome modestly tempered by a longer ( > 18-month) interpregnancy interval. Our results suggest that reproductive history and childbirth in particular remodel the vaginal ecosystem and that the timing and degree of recovery from delivery may help determine the subsequent health of the woman and of future pregnancies.

Increased late preterm birth risk and altered uterine blood flow upon exposure to heat stress

BACKGROUND

Climate change, in particular the exposure to heat, impacts on human health and can trigger diseases. Pregnant people are considered a vulnerable group given the physiological changes during pregnancy and the potentially long-lasting consequences for the offspring. Evidence published to date on higher risk of pregnancy complications upon heat stress exposure are from geographical areas with high ambient temperatures. Studies from geographic regions with temperate climates are sparse; however, these areas are critical since individuals may be less equipped to adapt to heat stress. This study addresses a significant gap in knowledge due to the temperature increase documented globally.

METHODS

Birth data of singleton pregnancies (n = 42,905) from a tertiary care centre in Hamburg, Germany, between 1999 and 2021 were retrospectively obtained and matched with climate data from the warmer season (March to September) provided by the adjacent federal meteorological station of the German National Meteorological Service to calculate the relative risk of heat-associated preterm birth. Heat events were defined by ascending temperature percentiles in combination with humidity over exposure periods of up to 5 days. Further, ultrasound data documented in a longitudinal prospective pregnancy cohort study (n = 612) since 2012 were used to identify pathophysiological causes of heat-induced preterm birth.

FINDINGS

Both extreme heat and prolonged periods of heat exposure increased the relative risk of preterm birth (RR: 1.59; 95% CI: 1.01-2.43; p = 0.045; RR: 1.20; 95% CI: 1.02-1.40; p = 0.025). We identified a critical period of heat exposure during gestational ages 34-37 weeks that resulted in increased risk of late preterm birth (RR: 1.67; 95% CI: 1.14-1.43; p = 0.009). Pregnancies with a female fetus were more prone to heat stress-associated preterm birth. We found heat exposure was associated with altered vascular resistance within the uterine artery.

INTERPRETATION

Heat stress caused by high ambient temperatures increases the risk of preterm birth in a geographical region with temperate climate. Prenatal routine care should be revised in such regions to provide active surveillance for women at risk.

FUNDING

Found in acknowledgements.

Prematurely delivering mothers show reductions of lachnospiraceae in their gut microbiomes

BACKGROUND

Preterm birth is the leading cause of perinatal morbidity and mortality. Despite evidence shows that imbalances in the maternal microbiome associates to the risk of preterm birth, the mechanisms underlying the association between a perturbed microbiota and preterm birth remain poorly understood.

METHOD

Applying shotgun metagenomic analysis on 80 gut microbiotas of 43 mothers, we analyzed the taxonomic composition and metabolic function in gut microbial communities between preterm and term mothers.

RESULTS

Gut microbiome of mothers delivering prematurely showed decreased alpha diversity and underwent significant reorganization, especially during pregnancy. SFCA-producing microbiomes, particularly species of Lachnospiraceae, Ruminococcaceae, and Eubacteriaceae, were significantly depleted in preterm mothers. Lachnospiraceae and its species were the main bacteria contributing to species' differences and metabolic pathways.

CONCLUSION

Gut microbiome of mothers delivering prematurely has altered and demonstrates the reduction of Lachnospiraceae.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

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.

Single cell profiling at the maternal-fetal interface reveals a deficiency of PD-L1+ non-immune cells in human spontaneous preterm labor

The mechanisms that underlie the timing of labor in humans are largely unknown. In most pregnancies, labor is initiated at term (≥ 37 weeks gestation), but in a signifiicant number of women spontaneous labor occurs preterm and is associated with increased perinatal mortality and morbidity. The objective of this study was to characterize the cells at the maternal-fetal interface (MFI) in term and preterm pregnancies in both the laboring and non-laboring state in Black women, who have among the highest preterm birth rates in the U.S. Using mass cytometry to obtain high-dimensional single-cell resolution, we identified 31 cell populations at the MFI, including 25 immune cell types and six non-immune cell types. Among the immune cells, maternal PD1⁺ CD8 T cell subsets were less abundant in term laboring compared to term non-laboring women. Among the non-immune cells, PD-L1⁺ maternal (stromal) and fetal (extravillous trophoblast) cells were less abundant in preterm laboring compared to term laboring women. Consistent with these observations, the expression of CD274, the gene encoding PD-L1, was significantly depressed and less responsive to fetal signaling molecules in cultured mesenchymal stromal cells from the decidua of preterm compared to term women. Overall, these results suggest that the PD1/PD-L1 pathway at the MFI may perturb the delicate balance between immune tolerance and rejection and contribute to the onset of spontaneous preterm labor.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

Maternal vaccination to prevent adverse pregnancy outcomes: An underutilized molecular immunological intervention?

Adverse pregnancy outcomes including maternal mortality, stillbirth, preterm birth, intrauterine growth restriction cause millions of deaths each year. More effective interventions are urgently needed. Maternal immunization could be one such intervention protecting the mother and newborn from infection through its pathogen-specific effects. However, many adverse pregnancy outcomes are not directly linked to the infectious pathogens targeted by existing maternal vaccines but rather are linked to pathological inflammation unfolding during pregnancy. The underlying pathogenesis driving such unfavourable outcomes have only partially been elucidated but appear to relate to altered immune regulation by innate as well as adaptive immune responses, ultimately leading to aberrant maternal immune activation. Maternal immunization, like all immunization, impacts the immune system beyond pathogen-specific immunity. This raises the possibility that maternal vaccination could potentially be utilised as a pathogen-agnostic immune modulatory intervention to redirect abnormal immune trajectories towards a more favourable phenotype providing pregnancy protection. In this review we describe the epidemiological evidence surrounding this hypothesis, along with the mechanistic plausibility and present a possible path forward to accelerate addressing the urgent need of adverse pregnancy outcomes.

Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles

BACKGROUND

Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking.

METHODS

Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein-protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB.

RESULTS

We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB.

CONCLUSIONS

In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4

Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.

Circumvention of luteolysis reveals parturition pathways in mice dependent upon innate type 2 immunity

Although mice normally enter labor when their ovaries stop producing progesterone (luteolysis), parturition can also be triggered in this species through uterus-intrinsic pathways potentially analogous to the ones that trigger parturition in humans. Such pathways, however, remain largely undefined in both species. Here, we report that mice deficient in innate type 2 immunity experienced profound parturition delays when manipulated endocrinologically to circumvent luteolysis, thus obliging them to enter labor through uterus-intrinsic pathways. We found that these pathways were in part driven by the alarmin IL-33 produced by uterine interstitial fibroblasts. We also implicated important roles for uterine group 2 innate lymphoid cells, which demonstrated IL-33-dependent activation prior to labor onset, and eosinophils, which displayed evidence of elevated turnover in the prepartum uterus. These findings reveal a role for innate type 2 immunity in controlling the timing of labor onset through a cascade potentially relevant to human parturition.

Postoperative systemic inflammatory dysregulation and corticosteroids: a narrative review

In some patients, the inflammatory-immune response to surgical injury progresses to a harmful, dysregulated state. We posit that postoperative systemic inflammatory dysregulation forms part of a pathophysiological response to surgical injury that places patients at increased risk of complications and subsequently prolongs hospital stay. In this narrative review, we have outlined the evolution, measurement and prediction of postoperative systemic inflammatory dysregulation, distinguishing it from a healthy and self-limiting host response. We reviewed the actions of glucocorticoids and the potential for heterogeneous responses to peri-operative corticosteroid supplementation. We have then appraised the evidence highlighting the safety of corticosteroid supplementation, and the potential benefits of high/repeated doses to reduce the risks of major complications and death. Finally, we addressed how clinical trials in the future should target patients at higher risk of peri-operative inflammatory complications, whereby corticosteroid regimes should be tailored to modify not only the a priori risk, but also further adjusted in response to markers of an evolving pathophysiological response.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Setting a stage: Inflammation during preeclampsia and postpartum

Preeclampsia (PE) is a leading cause of maternal and fetal mortality worldwide. The immune system plays a critical role in normal pregnancy progression; however, inappropriate inflammatory responses have been consistently linked with PE pathophysiology. This inflammatory phenotype consists of activation of the innate immune system, adaptive immune system, and increased inflammatory mediators in circulation. Moreover, recent studies have shown that the inflammatory profile seen in PE persists into the postpartum period. This manuscript aims to highlight recent advances in research relating to inflammation in PE as well as the inflammation that persists postpartum in women after a PE pregnancy. With the advent of the COVID-19 pandemic, there has been an increase in obstetric disorders associated with COVID-19 infection during pregnancy. This manuscript also aims to shed light on the relationship between COVID-19 infection during pregnancy and the increased incidence of PE in these women.

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.

Antinuclear antibodies positivity in women in reproductive age: From infertility to adverse obstetrical outcomes - A meta-analysis

This systematic review and meta-analysis were designed to identify possible correlations between isolated serum antinuclear antibody (ANA) and (i) infertility in the context of in-vitro fertilization (IVF), (ii) idiopathic recurrent pregnancy losses (RPL), and (iii) second/ third trimester pregnancy complications. We performed a systematic review and meta-analysis of the literature in PubMed Library database from inception to March 2022 following PRISMA guidelines. Our pooled results showed a lower pregnancy rate among ANA-positive women undergoing IVF/ICSI compared to ANA-negative women undergoing the same procedures (279/908 versus 1136/2347, random effect, odds ratio -OR- 0.50, 95% confidence interval -CI- 0.38-0.67, p 0.00001, I² = 58%). We also reported a higher miscarriage rate among ANA-positive compared to ANA-negative women (48/223 versus 109/999, random effect, OR: 3.25 95% CI: 1.57-6.76, p = 0.002, I² = 61%) and a lower implantation rate (320/1489 versus 1437/4205, random effect, OR: 0.51, 95% CI: 0.36-0.72, p = 0.0001, I² = 78%). Regarding RPL, pooled results demonstrated a higher prevalence of ANA-positivity in RPL women compared to controls (698/2947 versus 240/3145, random effect, OR: 3.22, 95% CI: 2.12-4.88, p 0.00001, I² 77%), either using > 2 or > 3 pregnancy losses threshold for defining RPL. Heterogeneity of reporting outcome did not allow a quantitative analysis and led to no clear demonstration of an effect of serum ANA on the incidence of stillbirth, preeclampsia and hypertensive disorders. In conclusion, the unfavorable effect of serum ANA was observed in women following IVF. Similarly, ANA were associated with the risk of RPL, while data were unconclusive in terms of late pregnancy complications.

Microbiome epidemiology and association studies in human health

Studies of the human microbiome share both technical and conceptual similarities with genome-wide association studies and genetic epidemiology. However, the microbiome has many features that differ from genomes, such as its temporal and spatial variability, highly distinct genetic architecture and person-to-person variation. Moreover, there are various potential mechanisms by which distinct aspects of the human microbiome can relate to health outcomes. Recent advances, including next-generation sequencing and the proliferation of multi-omic data types, have enabled the exploration of the mechanisms that connect microbial communities to human health. Here, we review the ways in which features of the microbiome at various body sites can influence health outcomes, and we describe emerging opportunities and future directions for advanced microbiome epidemiology.