Evidence map›Paper›PMID 29939228›Full record

ArticleBiology of reproduction2018

Temporal transcriptomic analysis of metabolic genes in maternal organs and placenta during murine pregnancy.

Alison Paquette, Priyanka Baloni, Anisa B Holloman, Sanjay Nigam, Theo Bammler, Qingcheng Mao, Nathan D Price

Open access · bronzeAbstract read
In one paragraph

Article in Biology of reproduction, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.1field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Modeling human placental biology: a review of organoid technologies.Frontiers in cell and developmental biology · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Regulation of maternal-fetal metabolic communication.Cellular and molecular life sciences : CMLS · 2021
    Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Alison PaquetteInstitute for Systems Biology, Seattle, Washington, USA.
Priyanka BaloniInstitute for Systems Biology, Seattle, Washington, USA.
Anisa B HollomanInstitute for Systems Biology, Seattle, Washington, USA.
Sanjay NigamDepartments of Pediatrics and Medicine, University of California San Diego, San Diego, California, USA.
Theo BammlerDepartment of Environmental and Occupational Health Science, School of Public Health, University of Washington, Seattle, Washington, USA.
Qingcheng MaoDepartment of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington, USA.
Nathan D PriceInstitute for Systems Biology, Seattle, Washington, USA.
Institute for Systems Biology · USUniversity of Washington · USUniversity of California San Diego · US

Funding

XENOBIOTIC BIOTRANSFORMATION AND DISPOSITIONP30ES007033 · NIEHS · UNIVERSITY OF WASHINGTON · PI Sheela Sathyanarayana · 1995 to 2026
$42.5M
Understanding Fetal Exposure to Cannabinoids Through In Vitro and In Vivo StudiesP01DA032507 · NIDA · UNIVERSITY OF WASHINGTON · PI UNADKAT, JASHVANT D · 2013 to 2023
$12.4M
Harnessing "omics": A Systems Biology approach to discovery of biologicalpathways in placental development and parturitionR01HD091527 · NICHD · UNIVERSITY OF FLORIDA · PI BALONI, PRIYANKA, JONES, HELEN N · 2017 to 2021
$2.7M
Safety and Dose Optimization of High-Dose Anakinra in Kawasaki DiseaseU54HD090259 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NIGAM, SANJAY K · 2016 to 2018
$2.2M
Safety and Dose Optimization of High-Dose Anakinra in Kawasaki DiseaseP50HD090259 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CAPPARELLI, EDMUND VINCENT, NIZET, VICTOR · 2019 to 2020
$1.4M
NICHD NIH HHS P50 HD090259NICHD NIH HHS R01 HD091527NICHD NIH HHS U54 HD090259NIDA NIH HHS P01 DA032507NIEHS NIH HHS P30 ES007033
6 · The paper itself

Abstract

Maternal pregnancy adaptation is crucial for fetal development and long-term health. Complex interactions occur between maternal digestive and excretory systems as they interface with the developing fetus through the placenta, and transcriptomic regulation in these organs throughout pregnancy is poorly understood. Our objective is to characterize transcriptomic changes across gestation in maternal organs and placenta. Gene expression was quantified in the kidney, liver, and small intestine harvested from nonpregnant and pregnant FVB mice at four time points and placenta at three time points (N = 5/time point) using Affymetrix Mouse Gene 1.0 ST arrays. In maternal organs, we identified 476 genes in the liver, 207 genes in the kidney, and 27 genes in the small intestine that were differentially expressed across gestation (False Discovery Rate [FDR] adjusted q < 0.05). The placenta had a total of 1576 differentially expressed genes between the placenta at either/gd15 or gd19 compared to gd10. We identified a number of pathways enriched for genes differentially expressed across gestation, including 5 pathways in the placenta, 9 pathways in the kidney, and 28 pathways in the liver, including the citrate cycle, retinol metabolism, bile acid synthesis, and steroid bile synthesis, which play functional roles in fetal development and pregnancy maintenance. Characterization of normal longitudinal changes that occur in pregnancy provides context to understand how perturbations in these biochemical pathways and perturbations in nutrient signaling may impact pregnancy.

Indexed as

Gene Expression ProfilingAnimalsFemaleGene Expression RegulationIntestine, SmallKidneyLiverMiceMultivariate AnalysisPlacentaPregnancyTranscriptome

Identifiers

PMID29939228
PMCPMC6692859
OpenAlexW2810926259

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.