Mode of delivery predicts postpartum maternal leukocyte telomere length.

BACKGROUND: Recent studies have suggested that pregnancy accelerates biologic aging, yet little is known about how biomarkers of aging are affected by events during the peripartum period. Given that immune shifts are known to occur following surgery, we explored the relation between mode of delivery and postpartum maternal leukocyte telomere length (LTL), a marker of biologic aging. STUDY DESIGN: Postpartum maternal blood samples were obtained from a prospective cohort of term, singleton livebirths without hypertensive disorders or peripartum infections between 2012 and 2018. The primary outcome was postpartum LTLs from one blood sample drawn between postpartum week 1 and up to 6 months postpartum, measured from thawed frozen peripheral blood mononuclear cells using quantitative PCR in basepairs (bp). Multivariable linear regression models compared LTLs between vaginal versus cesarean births, adjusting for age, body mass index, and nulliparity as potential confounders. Analyses were conducted in two mutually exclusive groups: those with LTL measured postpartum week 1 and those measured up to 6 months postpartum. Secondarily, we compared multiomics by mode of delivery using machine-learning methods to evaluate whether other biologic changes occurred following cesarean. These included transcriptomics, metabolomics, microbiomics, immunomics, and proteomics (serum and plasma). RESULTS: Of 67 included people, 50 (74.6 %) had vaginal and 17 (25.4 %) had cesarean births. LTLs were significantly shorter after cesarean in postpartum week 1 (5755.2 bp cesarean versus 6267.8 bp vaginal, p = 0.01) as well as in the later draws (5586.6 versus 5945.6 bp, p = 0.04). After adjusting for confounders, these differences persisted in both week 1 (adjusted beta -496.1, 95 % confidence interval [CI] -891.1, -101.1, p = 0.01) and beyond (adjusted beta -396.8; 95 % CI -727.2, -66.4. p = 0.02). Among the 15 participants who also had complete postpartum multiomics data available, there were predictive signatures of vaginal versus cesarean births in transcriptomics (cell-free [cf]RNA), metabolomics, microbiomics, and proteomics that did not persist after false discovery correction. CONCLUSION: Maternal LTLs in postpartum week 1 were nearly 500 bp shorter following cesarean. This difference persisted several weeks postpartum, even though other markers of inflammation had normalized. Mode of delivery should be considered in any analyses of postpartum LTLs and further investigation into this phenomenon is warranted.

Association of pregnancy complications and postpartum maternal leukocyte telomeres in two diverse cohorts: a nested case-control study.

BACKGROUND: Biologic strain such as oxidative stress has been associated with short leukocyte telomere length (LTL), as well as with preeclampsia and spontaneous preterm birth, yet little is known about their relationships with each other. We investigated associations of postpartum maternal LTL with preeclampsia and spontaneous preterm birth. METHODS: This pilot nested case control study included independent cohorts of pregnant people with singleton gestations from two academic institutions: Cohort 1 (hereafter referred to as Suburban) were enrolled prior to 20 weeks' gestation between 2012 and 2018; and Cohort 2 (hereafter referred to as Urban) were enrolled at delivery between 2000 and 2012. Spontaneous preterm birth or preeclampsia were the selected pregnancy complications and served as cases. Cases were compared with controls from each study cohort of uncomplicated term births. Blood was collected between postpartum day 1 and up to 6 months postpartum and samples were frozen, then simultaneously thawed for analysis. Postpartum LTL was the primary outcome, measured using quantitative polymerase chain reaction (PCR) and compared using linear multivariable regression models adjusting for maternal age. Secondary analyses were done stratified by mode of delivery and self-reported level of stress during pregnancy. RESULTS: 156 people were included; 66 from the Suburban Cohort and 90 from the Urban Cohort. The Suburban Cohort was predominantly White, Hispanic, higher income and the Urban Cohort was predominantly Black, Haitian, and lower income. We found a trend towards shorter LTLs among people with preeclampsia in the Urban Cohort (6517 versus 6913 bp, p = 0.07), but not in the Suburban Cohort. There were no significant differences in LTLs among people with spontaneous preterm birth compared to term controls in the Suburban Cohort (6044 versus 6144 bp, p = 0.64) or in the Urban Cohort (6717 versus 6913, p = 0.37). No differences were noted by mode of delivery. When stratifying by stress levels in the Urban Cohort, preeclampsia was associated with shorter postpartum LTLs in people with moderate stress levels (p = 0.02). CONCLUSION: Our exploratory results compare postpartum maternal LTLs between cases with preeclampsia or spontaneous preterm birth and controls in two distinct cohorts. These pilot data contribute to emerging literature on LTLs in pregnancy.

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.

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.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Deficiency of the Heterogeneous Nuclear Ribonucleoprotein U locus leads to delayed hindbrain neurogenesis.

Genetic variants affecting Heterogeneous Nuclear Ribonucleoprotein U (HNRNPU) have been identified in several neurodevelopmental disorders (NDDs). HNRNPU is widely expressed in the human brain and shows the highest postnatal expression in the cerebellum. Recent studies have investigated the role of HNRNPU in cerebral cortical development, but the effects of HNRNPU deficiency on cerebellar development remain unknown. Here, we describe the molecular and cellular outcomes of HNRNPU locus deficiency during in vitro neural differentiation of patient-derived and isogenic neuroepithelial stem cells with a hindbrain profile. We demonstrate that HNRNPU deficiency leads to chromatin remodeling of A/B compartments, and transcriptional rewiring, partly by impacting exon inclusion during mRNA processing. Genomic regions affected by the chromatin restructuring and host genes of exon usage differences show a strong enrichment for genes implicated in epilepsies, intellectual disability, and autism. Lastly, we show that at the cellular level HNRNPU downregulation leads to an increased fraction of neural progenitors in the maturing neuronal population. We conclude that the HNRNPU locus is involved in delayed commitment of neural progenitors to differentiate in cell types with hindbrain profile.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Cross-species comparative analysis of single presynapses.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Combined proteomics and CRISPR‒Cas9 screens in PDX identify ADAM10 as essential for leukemia in vivo.

BACKGROUND: Acute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells. METHODS: To identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice. Candidates were functionally screened by establishing a comprehensive CRISPR‒Cas9 pipeline in PDX models in vivo. RESULTS: A disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) was identified as an essential vulnerability required for the survival and growth of different types of acute leukemias in vivo, and reconstitution assays in PDX models confirmed the relevance of its sheddase activity. Of translational importance, molecular or pharmacological targeting of ADAM10 reduced PDX leukemia burden, cell homing to the murine bone marrow and stem cell frequency, and increased leukemia response to conventional chemotherapy in vivo. CONCLUSIONS: These findings identify ADAM10 as an attractive therapeutic target for the future treatment of acute leukemias.

Endothermic physiology of extinct megatooth sharks.

The evolution of the extinct megatooth shark, Otodus megalodon, and its close phylogenetic relatives remains enigmatic. A central question persists regarding the thermophysiological origins of these large predatory sharks through geologic time, including whether O. megalodon was ectothermic or endothermic (including regional endothermy), and whether its thermophysiology could help to explain the iconic shark's gigantism and eventual demise during the Pliocene. To address these uncertainties, we present unique geochemical evidence for thermoregulation in O. megalodon from both clumped isotope paleothermometry and phosphate oxygen isotopes. Our results show that O. megalodon had an overall warmer body temperature compared with its ambient environment and other coexisting shark species, providing quantitative and experimental support for recent biophysical modeling studies that suggest endothermy was one of the key drivers for gigantism in O. megalodon and other lamniform sharks. The gigantic body size with high metabolic costs of having high body temperatures may have contributed to the vulnerability of Otodus species to extinction when compared to other sympatric sharks that survived the Pliocene epoch.

Screening autism-associated environmental factors in differentiating human neural progenitors with fractional factorial design-based transcriptomics.

Research continues to identify genetic variation, environmental exposures, and their mixtures underlying different diseases and conditions. There is a need for screening methods to understand the molecular outcomes of such factors. Here, we investigate a highly efficient and multiplexable, fractional factorial experimental design (FFED) to study six environmental factors (lead, valproic acid, bisphenol A, ethanol, fluoxetine hydrochloride and zinc deficiency) and four human induced pluripotent stem cell line derived differentiating human neural progenitors. We showcase the FFED coupled with RNA-sequencing to identify the effects of low-grade exposures to these environmental factors and analyse the results in the context of autism spectrum disorder (ASD). We performed this after 5-day exposures on differentiating human neural progenitors accompanied by a layered analytical approach and detected several convergent and divergent, gene and pathway level responses. We revealed significant upregulation of pathways related to synaptic function and lipid metabolism following lead and fluoxetine exposure, respectively. Moreover, fluoxetine exposure elevated several fatty acids when validated using mass spectrometry-based metabolomics. Our study demonstrates that the FFED can be used for multiplexed transcriptomic analyses to detect relevant pathway-level changes in human neural development caused by low-grade environmental risk factors. Future studies will require multiple cell lines with different genetic backgrounds for characterising the effects of environmental exposures in ASD.

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.

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.

Ribonucleoprotein (RNP)-Mediated Targeted Mutagenesis in Barley (Hordeum vulgare L.).

The crop species barley is a genetic model for the small grain temperate cereals. Thanks to the availability of whole genome sequence and the development of customizable endonucleases, site-directed genome modification has recently revolutionized genetic engineering. Several platforms have been established in plants, with the most flexible one offered by the clustered regularly interspaced short palindromic repeats (CRISPR) technology. In this protocol, commercially available synthetic guide RNAs (gRNAs), Cas enzymes, or custom-generated reagents are used for targeted mutagenesis in barley. The protocol has been successfully used with immature embryo explants to generate site-specific mutations in regenerants. As the double-strand break-inducing reagents are customizable and can be efficiently delivered, pre-assembled ribonucleoprotein (RNP) complexes allow efficient generation of genome-modified plants.

Ribonucleoprotein (RNP)-Mediated Allele Replacement in Barley (Hordeum vulgare L.) Leaves.

Varietal differences within a species with agronomic importance are often based on minor changes in the genomic sequence. For example, fungus-resistant and fungus-susceptible wheat varieties may vary in only one amino acid. The situation is similar with the reporter genes Gfp and Yfp where two base pairs cause a shift in the emission spectrum from green to yellow. Methods of targeted double-strand break induction now allow this exchange precisely with the simultaneous transfer of the desired repair template. However, these changes rarely lead to a selective advantage that can be used in generating such mutant plants. The protocol presented here allows a corresponding allele replacement at the cellular level using ribonucleoprotein complexes in combination with an appropriate repair template. The efficiencies achieved are comparable to other methods with direct DNA transfer or integration of the corresponding building blocks in the host genome. They are in the range of 35 percent, considering one allele in a diploid organism as barley and using Cas9 RNP complexes.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Prediction of neuropathologic lesions from clinical data.

INTRODUCTION: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. METHODS: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. RESULTS: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. DISCUSSION: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Comparative Life Cycle Assessment of Chemical and Biocatalytic 2'3'-Cyclic GMP-AMP Synthesis.

Life cycle assessments (LCAs) can provide insights into the environmental impact of production processes. In this study, a comparative LCA was performed for the synthesis of 2'3'-cyclic GMP-AMP (2'3'-cGAMP) in an early development stage. The cyclic dinucleotide (CDN) is of interest for pharmaceutical applications such as cancer immunotherapy. CDNs can be synthesized either by enzymes or chemical catalysis. It is not known which of the routes is more sustainable as both routes have their advantages and disadvantages, such as a poor yield for the chemical synthesis and low titers for the biocatalytic synthesis. The synthesis routes were compared for the production of 200 g 2'3'-cGAMP based on laboratory data to assess the environmental impacts. The biocatalytic synthesis turned out to be superior to the chemical synthesis in all considered categories by at least one magnitude, for example, a global warming potential of 3055.6 kg CO2 equiv. for the enzymatic route and 56454.0 kg CO2 equiv. for the chemical synthesis, which is 18 times higher. This study demonstrates the value of assessment at an early development stage, when the choice between different routes is still possible.