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.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women.

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women's Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2-0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring.

Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950-Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231-Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.

Altered Cardiac Energetics and Mitochondrial Dysfunction in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant advances in elucidating sarcomeric structure-function relationships, there is still much to be learned about the mechanisms that link altered cardiac energetics to HCM phenotypes. In this work, we test the hypothesis that changes in cardiac energetics represent a common pathophysiologic pathway in HCM. METHODS: We performed a comprehensive multiomics profile of the molecular (transcripts, metabolites, and complex lipids), ultrastructural, and functional components of HCM energetics using myocardial samples from 27 HCM patients and 13 normal controls (donor hearts). RESULTS: Integrated omics analysis revealed alterations in a wide array of biochemical pathways with major dysregulation in fatty acid metabolism, reduction of acylcarnitines, and accumulation of free fatty acids. HCM hearts showed evidence of global energetic decompensation manifested by a decrease in high energy phosphate metabolites (ATP, ADP, and phosphocreatine) and a reduction in mitochondrial genes involved in creatine kinase and ATP synthesis. Accompanying these metabolic derangements, electron microscopy showed an increased fraction of severely damaged mitochondria with reduced cristae density, coinciding with reduced citrate synthase activity and mitochondrial oxidative respiration. These mitochondrial abnormalities were associated with elevated reactive oxygen species and reduced antioxidant defenses. However, despite significant mitochondrial injury, HCM hearts failed to upregulate mitophagic clearance. CONCLUSIONS: Overall, our findings suggest that perturbed metabolic signaling and mitochondrial dysfunction are common pathogenic mechanisms in patients with HCM. These results highlight potential new drug targets for attenuation of the clinical disease through improving metabolic function and reducing mitochondrial injury.

Multi-omic profiling of primary mouse neutrophils predicts a pattern of sex and age-related functional regulation.

Neutrophils are the most abundant human white blood cell and constitute a first line of defense in the innate immune response. Neutrophils are short-lived cells, and thus the impact of organismal aging on neutrophil biology, especially as a function of biological sex, remains poorly understood. Here, we describe a multi-omic resource of mouse primary bone marrow neutrophils from young and old female and male mice, at the transcriptomic, metabolomic and lipidomic levels. We identify widespread regulation of neutrophil 'omics' landscapes with organismal aging and biological sex. In addition, we leverage our resource to predict functional differences, including changes in neutrophil responses to activation signals. To date, this dataset represents the largest multi-omics resource for neutrophils across sex and ages. This resource identifies neutrophil characteristics which could be targeted to improve immune responses as a function of sex and/or age.

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

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

Depletion of mitochondrial inorganic polyphosphate (polyP) in mammalian cells causes metabolic shift from oxidative phosphorylation to glycolysis.

Inorganic polyphosphate (polyP) is a linear polymer composed of up to a few hundred orthophosphates linked together by high-energy phosphoanhydride bonds, identical with those found in ATP. In mammalian mitochondria, polyP has been implicated in multiple processes, including energy metabolism, ion channels function, and the regulation of calcium signaling. However, the specific mechanisms of all these effects of polyP within the organelle remain poorly understood. The central goal of this study was to investigate how mitochondrial polyP participates in the regulation of the mammalian cellular energy metabolism. To accomplish this, we created HEK293 cells depleted of mitochondrial polyP, through the stable expression of the polyP hydrolyzing enzyme (scPPX). We found that these cells have significantly reduced rates of oxidative phosphorylation (OXPHOS), while their rates of glycolysis were elevated. Consistent with this, metabolomics assays confirmed increased levels of metabolites involved in glycolysis in these cells, compared with the wild-type samples. At the same time, key respiratory parameters of the isolated mitochondria were unchanged, suggesting that respiratory chain activity is not affected by the lack of mitochondrial polyP. However, we detected that mitochondria from cells that lack mitochondrial polyP are more fragmented when compared with those from wild-type cells. Based on these results, we propose that mitochondrial polyP plays an important role as a regulator of the metabolic switch between OXPHOS and glycolysis.

Metabolomic profiling for juvenile Chinook salmon exposed to contaminants of emerging concern.

Both targeted and non-targeted metabolomic analyses were conducted on juvenile ocean-type fall Chinook salmon (Oncorhynchus tshawytscha) residing in two estuaries receiving wastewater treatment plant (WWTP) effluent and one reference estuary. The data show that the metabolome patterns for fish from the two WWTP-receiving estuaries were more similar to each other compared to that for the reference site fish. Also, a comparison of the metabolome for fish from the reference site and fish from a hatchery upstream of one of the effluent-receiving estuaries indicated no differences, implying that residency for fish in the contaminated estuary resulted in major changes to the metabolome. Based on general health parameters including whole-body lipid content and condition factor, plus the availability of prey for these fish, we conclude that juvenile Chinook salmon in these contaminated estuaries may have been experiencing metabolic disruption without any overt signs of impairment. Additionally, a non-targeted analysis was performed on hatchery summer Chinook salmon from a laboratory study where fish were dosed for 32 days with feed containing 16 of the most common contaminants of emerging concern (CECs) detected in wild fish. In the laboratory experiment a relationship was observed between dose and the number of liver metabolites that were different between control and treatment fish. Laboratory fish were exposed to only 16 CECs, but are generally exposed to hundreds of these compounds in contaminated aquatic environments. These results have implications for the health of juvenile Chinook salmon and the likelihood of a successful life cycle when exposed to effluent-related chemicals.

Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.