Placental Metabolomics for Assessment of Sex-specific Differences in Fetal Development During Normal Gestation

Abstract

The placenta is a metabolically active interfacial organ that plays crucial roles in fetal nutrient delivery, gas exchange and waste removal reflecting dynamic maternal and fetal interactions during gestation. There is growing evidence that the sex of the placenta influences fetal responses to external stimuli in utero, such as changes in maternal nutrition and exposure to environmental stressors. However, the exact biochemical mechanisms associated with sex-specific metabolic adaptations during pregnancy and its link to placental function and fetal development remain poorly understood. Herein, multisegment injection-capillary electrophoresis-mass spectrometry is used as a high throughput metabolomics platform to characterize lyophilized placental tissue (~2 mg dried weight) from C57BL/6J mice fed a standardized diet. Over 130 authentic metabolites were consistently measured from placental extracts when using a nontargeted metabolomics workflow with stringent quality control and robust batch correction. Our work revealed distinct metabolic phenotype differences that exist between male (n = 14) and female (n = 14) placentae collected at embryonic day E18.5. Intracellular metabolites associated with fatty acid oxidation and purine degradation were found to be elevated in females as compared to male placentae (p < 0.05, effect size >0.40), including uric acid, valerylcarnitine, hexanoylcarnitine, and 3-hydroxyhexanolycarnitine. This murine model sheds new insights into sexspecific differences in placental mitochondrial function and protective mechanisms against deleterious oxidative stress that may impact fetal growth and birth outcomes later in life.

Fetal Brain Elicits Sexually Conflicting Transcriptional Response to the Ablation of Uterine Forkhead Box A2 (Foxa2) in Mice

“…In rodents, the placenta develops at a relative faster rate in males compared to females [13]. Relationships between fetal sex and placental functions have been demonstrated in many studies [14][15][16][17][18][19]. Recent study shows that uterine Foxa2 influences the growth of fetus and placenta in a sexual dimorphic manner [20].…”

Let’s Talk about Placental Sex, Baby: Understanding Mechanisms That Drive Female- and Male-Specific Fetal Growth and Developmental Outcomes

“…Evidence of sex-specific intrauterine growth rates and outcomes have been observed throughout gestation with males, on average, having accelerated growth rates and increased growth outcomes relative to females [1][2][3][4][5][6][7][8][9][10][11][12][13]. In fact, the first observation of sexspecific neonatal outcomes was reported by Dr. Clarke during the late 18th century [14].…”

New Advances in Tissue Metabolomics: A Review

“…Both solution NMR and HRMAS-NMR were used in about 25% of published tissue metabolomic studies as they are non-destructive methods that also allow for direct measurement of metabolites within intact tissue [ 57 ]. Interestingly, while CE-MS is ideal for the analysis of mass-restricted tissue specimens (<5 mg dried tissue mass), it remains an underutilized technique in tissue metabolomics (~4%) as compared to more established hyphenated-MS based platforms [ 42 , 82 , 83 , 84 , 85 ]. Furthermore, DI-MS and FIA-MS are also underrepresented instrumental platforms in tissue metabolomics studies (~1–2%) due to their low specificity and issues with ion suppression matrix effects and isomeric/isobaric interferences.…”

“…A common strategy in MS-based workflows is to normalize feature responses to internal standards, based on the assumption that systematic error exclusively contributes to the variance observed in the internal standards. For tissue metabolomics studies, feature responses are also often normalized to the (wet or dry) weight of tissue specimen analyzed, to account for variations in tissue specimens analyzed [ 42 , 85 ]. Mathematical transformations (i.e., log transformation) can also be employed to correct for heteroscedasticity and skewed distribution in the datasets [ 96 ].…”