Evidence map›Paper›PMID 41186200›Full record

ArticleFASEB journal : official publication of the Federation of American Societies for Experimental Biology2025

The Maternal Blood Transcriptome Reflects Changes in Fetal Growth and Is an Accurate Predictor of Birth Weight in Cattle.

Laura Thompson, Pim G van Helvoort, Maurice Duijn, Daphne D Reinders, Eliza M Murphy, Michael McDonald, Alan D Crowe, Stephen T Butler, Patrick Lonergan, Maria Belen Rabaglino

Abstract read
In one paragraph

Article in FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. The Maternal Blood Transcriptome Reflects Changes in Fetal Growth and Is an Accurate Predictor of Birth Weight in Cattle.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
    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

10 authors.

Laura ThompsonSchool of Agriculture and Food Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0003-2968-762X
Pim G van HelvoortDepartment of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID https://orcid.org/0009-0003-3024-4065
Maurice DuijnDepartment of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID https://orcid.org/0009-0009-6259-9673
Daphne D ReindersDepartment of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID https://orcid.org/0009-0003-7761-0499
Eliza M MurphySchool of Agriculture and Food Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0009-0005-9367-1608
Michael McDonaldSchool of Agriculture and Food Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-4063-4441
Alan D CroweSchool of Agriculture and Food Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0001-6108-9254
Stephen T ButlerTeagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co., Cork, Ireland.ORCID https://orcid.org/0000-0003-1542-8344
Patrick LonerganSchool of Agriculture and Food Science, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0001-5598-5044
Maria Belen RabaglinoDepartment of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.ORCID https://orcid.org/0000-0002-0099-045X

Funding

Department of Agriculture, Food and the Marine, Ireland (DAFM) 2021R665
6 · The paper itself

Abstract

Harnessing information from maternal blood to predict fetal growth is an emerging area of research in livestock production, offering a noninvasive tool to monitor development. This study aimed to investigate temporal changes in blood gene expression during cow gestation through a bioinformatic approach and to determine the association between transcriptomic modifications in maternal blood at day 63 of gestation and calf birth weight (BW). Publicly available gene datasets from gestational days 0, 21, 42, 56, 63, and 105 were integrated to investigate maternal gene expression changes across gestation. Coexpression clustering identified four clusters, with three enriched for relevant biological processes, including interferon response genes from day 0 to 21, oxidative phosphorylation at day 42, and immune response genes by day 105. Additionally, maternal blood samples from 20 recipient cows carrying female fetuses derived from in vitro-produced embryos were subjected to RNA sequencing. Unsupervised weighted gene co-expression network analysis of these data identified four modules of coexpressed genes correlated with calf BW (p < 0.05). Supervised analysis revealed 189 differentially expressed genes (DEG; FDR < 0.05) associated with calf BW. The 106 positively associated DEG enriched phosphorylation and protein modification, while the 83 negatively associated DEG enriched immune processes. Biomarker genes were best determined using genes affected by gestational age and DEG, yielding 26 and 17 biomarkers, respectively, with R

Indexed as

Birth WeightFetal DevelopmentTranscriptomeAnimalsCattleFemaleGene Expression Regulation, DevelopmentalPregnancybiomarkersgene expression profilinggestationmachine learningtranscriptomics

Identifiers

PMID41186200
PMCPMC12584144

What OpenQuestion holds

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LicenceCC BY
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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.