2021
DOI: 10.3390/ijms22147654
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Placental Transcriptome Adaptations to Maternal Nutrient Restriction in Sheep

Abstract: Placental development is modified in response to maternal nutrient restriction (NR), resulting in a spectrum of fetal growth rates. Pregnant sheep carrying singleton fetuses and fed either 100% (n = 8) or 50% (NR; n = 28) of their National Research Council (NRC) recommended intake from days 35–135 of pregnancy were used to elucidate placentome transcriptome alterations at both day 70 and day 135. NR fetuses were further designated into upper (NR NonSGA; n = 7) and lower quartiles (NR SGA; n = 7) based on day 1… Show more

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Cited by 8 publications
(5 citation statements)
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“…To illuminate gestational age-dependent transcriptional changes in across a healthy pregnancy, we collected whole mouse placentas at 48 hr intervals spanning embryonic day 13.5–17.5 (e13.5–e17.5) and quantified mRNA and protein targets that emerged from network analysis of an independently published microarray dataset from healthy mouse pregnancies. Upon searching Gene Expression Omnibus (GEO) ( Edgar et al, 2002 ) datasets for ‘placenta AND transcriptome’, we were surprised that among 326 results, only 9 included data from normal placenta across a series of gestational timepoints extending to late pregnancy ( Knox and Baker, 2008 ; Zhou et al, 2009 ; Loux et al, 2019 ; Soncin et al, 2018 ; Maeda et al, 2019 ; Morey et al, 2021 ; Steinhauser et al, 2021 ; Figure 1 ). We applied weighted gene correlation network analysis (WGCNA) to a microarray study of mouse placenta ( Knox and Baker, 2008 ) (GEO accession GSE11224) spanning e8.5 to postnatal day 0 (p0) to assess the mRNA signature of the aging mouse placenta, using the dataset with the best temporal resolution across gestation.…”
Section: Resultsmentioning
confidence: 99%
“…To illuminate gestational age-dependent transcriptional changes in across a healthy pregnancy, we collected whole mouse placentas at 48 hr intervals spanning embryonic day 13.5–17.5 (e13.5–e17.5) and quantified mRNA and protein targets that emerged from network analysis of an independently published microarray dataset from healthy mouse pregnancies. Upon searching Gene Expression Omnibus (GEO) ( Edgar et al, 2002 ) datasets for ‘placenta AND transcriptome’, we were surprised that among 326 results, only 9 included data from normal placenta across a series of gestational timepoints extending to late pregnancy ( Knox and Baker, 2008 ; Zhou et al, 2009 ; Loux et al, 2019 ; Soncin et al, 2018 ; Maeda et al, 2019 ; Morey et al, 2021 ; Steinhauser et al, 2021 ; Figure 1 ). We applied weighted gene correlation network analysis (WGCNA) to a microarray study of mouse placenta ( Knox and Baker, 2008 ) (GEO accession GSE11224) spanning e8.5 to postnatal day 0 (p0) to assess the mRNA signature of the aging mouse placenta, using the dataset with the best temporal resolution across gestation.…”
Section: Resultsmentioning
confidence: 99%
“…To illuminate gestational age-dependent transcriptional changes in across a healthy pregnancy, we collected whole mouse placentas at 48h intervals spanning embryonic day network analysis of an independently published microarray dataset from healthy mouse pregnancies. Upon searching Gene Expression Omnibus (GEO) (21) datasets for "placenta AND transcriptome", we were surprised that among 326 results, only nine included data from normal placenta across a series of gestational timepoints extending to late pregnancy (22)- (28) (Figure 1). We applied weighted gene correlation network analysis (WGCNA) to a microarray study of mouse placenta (22) (GEO accession GSE11224) spanning e8.5 to postnatal day 0 (p0) to assess the mRNA signature of the aging mouse placenta, using the dataset with the best temporal resolution across gestation.…”
Section: Resultsmentioning
confidence: 99%
“…To illuminate gestational age-dependent transcriptional changes in across a healthy pregnancy, we collected whole mouse placentas at 48h intervals spanning embryonic day 13.5 to 17.5 (e13.5-e17.5) and quantified mRNA and protein targets that emerged from network analysis of an independently published microarray dataset from healthy mouse pregnancies. Upon searching Gene Expression Omnibus (GEO) 21 datasets for "placenta AND transcriptome", we were surprised that among 326 results, only nine included data from normal placenta across a series of gestational timepoints extending to late pregnancy [22][23][24][25][26][27][28] (Figure 1). We applied weighted gene correlation network analysis (WGCNA) to a microarray study of mouse placenta 22 (GEO accession GSE11224) spanning e8.5 to postnatal day 0 (p0) to assess the mRNA signature of the aging mouse placenta, using the dataset with the best temporal resolution across gestation.…”
Section: Resultsmentioning
confidence: 99%