bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.10.22.683996

Multi-omic integration reveals dynamic changes in human placental metabolism across gestation

Abstract

ObjectivesMetabolic demands of the developing conceptus are highly dynamic during pregnancy. While placental metabolism has been well described at term and in cell lines, changes in the placental metabolome during development remains understudied. We investigated the placental metabolome, metabolite trajectories, and altered pathways across trimesters in normal human pregnancy by integrating metabolomic and transcriptomic data. MethodsTargeted aqueous metabolomic profiling of 372 metabolites was conducted on placental biopsies from samples collected in the first (n=12), second (n=13), and third (n=11) trimesters of normal pregnancy using liquid chromatography-tandem mass spectrometry. Robust linear models identified differentially abundant metabolites across trimesters in models adjusted for fetal sex and total protein. We conducted pathway analysis using a human metabolic reconstruction. To further aid in biological interpretation, we leveraged publicly available transcriptomics data to conduct pathway-level multi-omic integration throughout gestation. ResultsSamples clustered by trimester in principal component analysis and we identified 5 metabolite trajectories. Out of 193 detectable metabolites, 149 (77%) differed by trimester (FDR<0.05). Using pathway-level multi-omic integration, pathways involved in extracellular transport, and pyruvate, amino acid, NAD, and membrane lipid metabolism are up-regulated in the second trimester compared to the first. In the late third trimester, pathways involved in amino acid metabolism, redox balance, mitochondrial transport, and biomolecule synthesis were down-regulated compared to second trimester. ConclusionsPlacental metabolite abundances change substantially across gestation and integration with metabolic gene expression provides insight into dynamic metabolic function during pregnancy. Observed pathway-level changes potentially reflect the metabolic response to invading maternal circulation in the first-to-second trimester transition, as well as changing maternal and fetal metabolic requirements. Differences observed at term may reflect placental senescence and preparation for parturition. These data can inform other molecular analyses of the placenta by providing enhanced resolution of metabolic changes across pregnancy.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Parenti, M., Rosen, S., Pettes, M., Djukovic, D., Raftery, D., Glass, I. A., Birth Defects Research Laboratory (BDRL),, Gray, K. J., Paquette, A. G., McCartney, S. A.. 2025-10-23. Multi-omic integration reveals dynamic changes in human placental metabolism across gestation. https://doi.org/10.1101/2025.10.22.683996

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

INFORME: coupling information-theoretic experimental design with nonlinear mixed-effects modeling for efficient observation scheduling

Mathematical models of treatment response can inform individualized therapy, but their calibration often requires longitudinal measurements that are costly, burdensome, and collected on fixed schedules. Such schedules may be inefficient, over-sampling patients whose response is already well characterized while delaying informative measurements for those whose model parameters remain uncertain. We present INFORME (INFORmation-theoretic design with Mixed Effects), a framework that combines Bayesian information-theoretic experimental design with nonlinear mixed-effects modeling to adaptively select each patients next measurement time. Population and response-subgroup parameter distributions learned from an existing cohort provide informative priors, allowing candidate measurement times to be ranked by their expected reduction in patient-specific parameter uncertainty. As observations accumulate, priors can be updated to reflect the response subgroup most consistent with the patients data. We evaluate INFORME in two radiotherapy datasets: 150 synthetic tumor volume trajectories from a hybrid cellular automaton model of prostate cancer spheroids (HD1) and longitudinal tumor volumes from 39 patients with head-and-neck cancer (HD2). In HD1, population priors allowed omission of both pretreatment scans, while adaptive scheduling reduced the protocol from nine scans to three or four, with the response group identified from a single post-treatment scan on day 27. In HD2, the adaptive schedule used three scans instead of six and improved prediction by delaying the first on-treatment scan from week 1 to week 2, avoiding transient dynamics that produced false-positive and false-negative response projections. Across both datasets, the adaptive schedules used a mean of 2.7 scans in stead of seven and advanced completion of the patient-specific prediction by a mean of 15.5 days (95% CI, 6.7-24.3) relative to the equidistant protocol, while treatment duration remained unchanged. INFORME therefore reduces measurement burden and accelerates patient-specific prediction by concentrating observations at times that are most informative for model calibration.

systems biology↗

Sobetirome, a thyroid hormone receptor beta agonist, is a potential therapeutic agent for pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease with limited treatment options. Our group previously identified the antifibrotic potential of thyroid hormone, triiodothyronine (T3); however, clinical translation of thyroid hormone therapy is limited by its systemic adverse effects. In this study, we investigate whether sobetirome, a selective and well tolerated thyroid hormone receptor beta (THRB) agonist, offers antifibrotic benefits of thyroid hormone while minimizing systemic toxicity. Our study reveals that sobetirome, administered via intraperitoneal or inhalational routes, effectively mitigates bleomycin-induced pulmonary fibrosis in mice, with no evidence of toxicity. We identified that sobetirome restores mitochondrial homeostasis via activating the THRB-PPARGC1a axis. This protects alveolar type II epithelial cells from injury-induced apoptosis while selectively inducing apoptosis and metabolic reprogramming in apoptosis resistant IPF fibroblasts. Cell-specific deletion of Ppargc1a in either alveolar epithelial cells or fibroblasts abolishes sobetirome-mediated protection, establishing PPARGC1a as an essential mediator of therapeutic response. Importantly, sobetirome reverses fibrosis-associated transcriptional programs in human IPF lung tissue, reducing expression of key fibrosis-associated genes, including collagen I alpha 1 (COL1A1), collagen III alpha 1 (COL3A1), periostin (POSTN), cathepsin K (CTSK), and Chitinase 3 Like 1 (CHI3L1), while promoting extracellular matrix remodeling, epithelial restoration, and tissue homeostasis. Collectively, our findings identify THRB activation as a novel metabolic strategy for reversing pulmonary fibrosis. Across complementary in vitro, in vivo, and human ex vivo models, sobetirome restores mitochondrial function, modulates apoptotic pathways in pathogenic cells, and promotes fibrosis resolution, highlighting its potential as a lung-targeted therapeutic approach for IPF and other fibrotic lung diseases.

systems biology↗

Mechanistic modeling of bacterial translation initiation across growth conditions

Translation frequency in bacteria depends on how ribosomes, mRNAs, and initiation factors are allocated across growth conditions. Here, we developed a mechanistic ODE-based model of Escherichia coli translation that represents initiation, elongation, termination, and coupled auxiliary processes. Growth-dependent abundances were derived from physiological relationships and reprocessed omics data, and simulated outputs were compared with translation-frequency and active-ribosome references. The model predicts a continuous shift from complex-formation-limited toward ribosome-limited behavior as growth increases. This shift is characterized by a decline in free-ribosome abundance, whereas initiation-factor pools remain largely unbound and do not become depleted in parallel. Together with the implemented IF-dependent kinetic term, this preserved availability provides a model-internal route through which productive initiation can be maintained despite increasing ribosome utilization. Consistently, transcript-wide ribosome loading remains below its theoretical maximum, while COG-level simulations reveal distinct sector-specific translation-frequency trajectories. The study therefore provides a resource-allocation framework for interpreting how mRNA--ribosome interactions shape bacterial translation across growth conditions.

systems biology↗