Atlas of fetal metabolism during mid-to-late gestation and diabetic pregnancy.

Mounting evidence suggests metabolism instructs stem cell fate decisions. However, how fetal metabolism changes during development and how altered maternal metabolism shapes fetal metabolism remain unexplored. We present a descriptive atlas of in vivo fetal murine metabolism during mid-to-late gestation in normal and diabetic pregnancy. Using 13C-glucose and liquid chromatography-mass spectrometry (LC-MS), we profiled the metabolism of fetal brains, hearts, livers, and placentas harvested from pregnant dams between embryonic days (E)10.5 and 18.5. Our analysis revealed metabolic features specific to a hyperglycemic environment and signatures that may denote developmental transitions during euglycemic development. We observed sorbitol accumulation in fetal tissues and altered neurotransmitter levels in fetal brains isolated from hyperglycemic dams. Tracing 13C-glucose revealed disparate fetal nutrient sourcing depending on maternal glycemic states. Regardless of glycemic state, histidine-derived metabolites accumulated in late-stage fetal tissues. Our rich dataset presents a comprehensive overview of in vivo fetal tissue metabolism and alterations due to maternal hyperglycemia.

A parallel glycolysis provides a selective advantage through rapid growth acceleration.

Glycolysis is a universal metabolic process that breaks down glucose to produce adenosine triphosphate (ATP) and biomass precursors. The Entner-Doudoroff (ED) pathway is a glycolytic pathway that parallels textbook glycolysis but yields half as much ATP. Accordingly, in organisms that possess both glycolytic pathways (for example, Escherichia coli), its raison d'être remains a mystery. In this study, we found that the ED pathway provides a selective advantage during growth acceleration. Upon carbon and nitrogen upshifts, E. coli accelerates growth faster with than without the ED pathway. Concurrent isotope tracing reveals that the ED pathway flux increases faster than that of textbook glycolysis. We attribute the fast response time of the ED pathway to its strong thermodynamic driving force and streamlining of glucose import. Intermittent nutrient supply manifests the evolutionary advantage of the parallel glycolysis; thus, the dynamic nature of an ostensibly redundant pathway's role in promoting rapid adaptation constitutes a metabolic design principle.

Effects of excess thromboxane A2 on placental development and nutrient transporters in a Mus musculus model of fetal growth restriction.

Hypertensive disease of pregnancy (HDP) with placental insufficiency is the most common cause of fetal growth restriction (FGR) in the developed world. Despite the known negative consequences of HDP both to the mother and fetus, little is known about the longitudinal placental changes that occur as HDP progresses in pregnancy. This is because longitudinal sampling of human placentae during each gestation is impossible. Therefore, using a mouse model of thromboxane A2-analog infusion to mimic human HDP in the last trimester, we calculated placental efficiencies based on fetal and placental weights; quantified spongiotrophoblast and labyrinth thicknesses and vascular density within these layers; examined whether hypoxia signaling pathway involving vascular endothelial growth factor A (VEGFA) and its receptors (VEGFR1, VEGFR2) and matrix metalloproteinases (MMPs) contributed to vascular change; and examined nutrient transporter abundance including glucose transporters 1 and 3 (GLUT1, GLUT3), neutral amino acid transporters 1, 2, and 4 (SNAT1, SNAT2, and SNAT4), fatty acid transporters 2 and 4 (FATP2, FATP4), and fatty acid translocase (CD36) from embryonic day 15.5 to 19 in a 20-day C57Bl/6J mouse gestation. We conclude that early-to-mid gestation hypertensive placentae show compensatory mechanisms to preserve fetal growth by increasing placental efficiencies and maintaining abundance of important nutrient transporters. As placental vascular network diminishes over late hypertension, placental efficiency diminishes and fetal growth fails. Neither hypoxia signaling pathway nor MMPs mediated the vascular diminution in this model. Hypertensive placentae surprisingly exhibit a sex-differential expression of nutrient transporters in late gestation despite showing fetal growth failure in both sexes.

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes.

Complete understanding of a biological system requires quantitation of metabolic fluxes that reflect its dynamic state. Various analytical chemistry tools, enzyme-based probes, and microscopy enable flux measurement. However, any method alone falls short of comprehensive flux quantitation. Here we show that integrating these techniques results in a systems-level quantitative map of absolute metabolic fluxes that constitute an indispensable dimension of characterizing phenotypes. Stable isotopes, mass spectrometry, and NMR spectroscopy reveal relative pathway fluxes. Biochemical probes reveal the physical rate of environmental changes. FRET-based and SRS-based microscopy reveal targeted metabolite and chemical bond formation. These techniques are complementary and can be computationally integrated to reveal actionable information on metabolism. Integrative metabolic flux analysis using various quantitative techniques advances biotechnology and medicine.