- Project 4 The increase in obesity, particularly in industrialized countries has taken on epidemic proportions. In the US two-thirds of adults (20 years of age and older) are overweight or obese, with a body mass index or BMI above 25kg/m2. This epidemic extends to the pregnant population where more than half of all American women enter pregnancy with an increased BMI and as a consequence obesity is one of the most common high-risk obstetric syndromes. Maternal overweight and obesity are associated with disturbances in fetal growth leading to poor neonatal outcomes and a predisposition for cardiovascular disease and metabolic disorders later in life. The placenta serves as an interface between the fetal and maternal circulation and one of its key functions is to supply nutrients to the fetus. As the quantity of nutrients available to the fetus is a significant determinant of fetal growth, the placenta has been implicated in fetal overgrowth. The mechanisms linking in utero nutrient excess, fetal overgrowth and disease development later in life are poorly understood and are thought to include alterations in placental nutrient transport, genetic and epigenetic changes. We propose to develop a gene therapy approach to address this problem using a unique piggyBac transposon- based transfection system that was developed at our institute in Phase I of this COBRE. These GENIE plasmids are able to efficiently introduce genes into the host genome. Moreover, this non-viral approach presents many advantages over virus-based systems, including low toxicity and low immune response. We have recently employed a minimally invasive in vivo method (Ultrasound Targeted Microbubble Destruction, UTMD) for the delivery of GENIE plasmids achieving long-term expression of a reporter gene in the liver of mice. UTMD can mediate site-specific delivery of bioactive molecules to ultrasound-accessible target organs such as the placenta. Specifically, we will first attempt to knockdown the Glut1 expression in mice by stably introducing shRNAmir constructs into the genome of the placenta via UTMD. We will then determine the effects of Glut1 knockdown on placental glucose transport capacity on fetal growth and the metabolic syndrome in the offspring. We will also determine the effects of Glut1 knockdown on the function of primary human trophoblast (PHT) cells. Since the placenta is a tissue with a finite life span, essential for fetal development but discarded after birth, it is well suited for improving fetal wellbeing through genetic modifications, without causing problems associated with gene transfer directly into the host genome. The hypothesis that we will test in this application is that a placenta-specific reduction in Glut1 expression and consequent decrease of glucose transport into the placenta will attenuate fetal overgrowth and subsequent metabolic syndrome in offspring from dams fed a high-fat diet. Furthermore, placental gene transfer may serve as an early intervention strategy to reduce fetal overgrowth.

Public Health Relevance

- Project 4 As half of all American women enter pregnancy with an increased BMI, infants often display fetal overgrowth, which is associated with long-term consequences for fetal and neonatal health. The placenta supplies nutrients to the fetus and has been implicated in fetal overgrowth. We intend to modulate the glucose placental transport by gene transfer and evaluate the effects on fetal overgrowth. This research may be a step towards an early intervention strategy to avoid or reduce fetal overgrowth in obese mothers.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
2P20GM103457-06A1
Application #
8737530
Study Section
Special Emphasis Panel (ZGM1)
Project Start
2014-07-01
Project End
2019-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
6
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Hawaii
Department
Type
DUNS #
City
Honolulu
State
HI
Country
United States
Zip Code
96822
Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res 24:1248-1259
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Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data. J Proteome Res 17:337-347

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