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Zhang X, Chen K, Meng Z, Jia R, Lian F, Lin F. Cadmium-induced preeclampsia-like phenotype in the rat is related to decreased progesterone synthesis in the placenta. Xenobiotica 2022; 52:625-632. [DOI: 10.1080/00498254.2022.2124204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Xiaojie Zhang
- Department of Obstetrics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Kai Chen
- Wenzhou Medical University, Wenzhou 325000, China
| | - Zhu Meng
- Wenzhou Medical University, Wenzhou 325000, China
| | - Ru Jia
- Wenzhou Medical University, Wenzhou 325000, China
| | - Feifei Lian
- Wenzhou Medical University, Wenzhou 325000, China
| | - Feng Lin
- Department of Obstetrics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Sylvester KG, Hao S, Li Z, Han Z, Tian L, Ladella S, Wong RJ, Shaw GM, Stevenson DK, Cohen HJ, Whitin JC, McElhinney DB, Ling XB. Gestational Dating by Urine Metabolic Profile at High Resolution Weekly Sampling Timepoints: Discovery and Validation. FRONTIERS IN MOLECULAR MEDICINE 2022; 2:844280. [PMID: 39086969 PMCID: PMC11285704 DOI: 10.3389/fmmed.2022.844280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/29/2022] [Indexed: 08/02/2024]
Abstract
Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a "metabolic clock" of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals (n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l-carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.
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Affiliation(s)
- Karl G. Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, United States
- Clinical and Translational Research Program, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Palo Alto, CA, United States
| | - Zhen Li
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Zhi Han
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Lu Tian
- Department of Health Research and Policy, Stanford University, Stanford, CA, United States
| | - Subhashini Ladella
- Department of Obstetrics and Gynecology, University of California San Francisco-Fresno, Fresno, Fresno, CA, United States
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Harvey J. Cohen
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - John C. Whitin
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Doff B. McElhinney
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, United States
- Clinical and Translational Research Program, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Palo Alto, CA, United States
| | - Xuefeng B. Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
- Clinical and Translational Research Program, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Palo Alto, CA, United States
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