Evidence map›Paper›PMID 42885402›Full record

ArticleFASEB journal : official publication of the Federation of American Societies for Experimental Biology2026

The Maternal Blood Transcriptome Reflects Molecular Signatures Associated With Large Offspring Syndrome and Other Adverse Pregnancy Outcomes in Cattle.

Tatiane S Maia, Maria B Rabaglino, Rebecca Krisher, Paula Rodriguez-Villamil, Jeniffer Pamela J Laguna, Jeniffer F Escoto, Yanping Zhang, Abigail J Sproull, Peter J Hansen

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Article in FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

9 authors.

Tatiane S MaiaDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.
Maria B RabaglinoDepartment of Population Health Science, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.ORCID https://orcid.org/0000-0002-0099-045X
Rebecca KrisherDepartment of Animal Sciences, Pennsylvania State University, State College, Pennsylvania, USA.
Paula Rodriguez-VillamilResearch and Development, Genus Plc, DeForest, Wisconsin, USA.
Jeniffer Pamela J LagunaDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.
Jeniffer F EscotoDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.
Yanping ZhangInterdisciplinary Center for Biotechnology Research, Gene Expression and Genotyping, University of Florida, Gainesville, Florida, USA.
Abigail J SproullDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.
Peter J HansenDepartment of Animal Sciences, University of Florida, Gainesville, Florida, USA.ORCID https://orcid.org/0000-0003-3061-9333

Funding

Genus (Genus plc)L.E. "Red" Larson Endowment
6 · The paper itself

Abstract

Large offspring syndrome (LOS) in cattle is a phenomenon characterized by fetal overgrowth that is largely associated with calves derived from in vitro produced embryos. Here it was tested whether occurrence of LOS as well as two other adverse pregnancy outcomes, stillbirth and abortion, could be predicted using changes in the maternal blood transcriptome early in gestation. Blood samples were collected from Holstein and crossbred heifers at 40-50 days of gestation following embryo transfer of in vitro produced embryos. A total of 203 samples were selected for RNA-sequencing and used to compare recipients of embryos resulting in live-born calves of normal birth weight (control; n = 96) with recipients of embryos resulting in LOS calves (n = 15), stillbirths (n = 43), and abortion before 210 days of gestation (n = 49). Large offspring syndrome was defined based on birth weights greater than 2 standard deviations from the mean. The number of differentially-expressed genes (p < 0.01) was 32, 176, and 100 for comparisons of control versus LOS, stillbirth and abortion, respectively. Biomarkers were identified in a randomly selected, balanced training dataset using Random Forest after filtering for differential expression. Markers were then tested in the held-out dataset through 6 machine learning models. The best biomarkers consisted of 26, 24, and 28 genes for LOS, stillbirth and abortion, respectively. The accuracy of prediction ranged from 0.91 to 0.965 for LOS, 0.73 to 0.98 for stillbirth, and 0.62 to 0.96 for abortion. Despite the relatively small sample size, the model demonstrated high sensitivity for LOS classification, supporting that the maternal blood transcriptome in early pregnancy reflects molecular alterations associated with fetal overgrowth that can be predictive of the condition.

Indexed as

Cattle DiseasesPregnancy OutcomeTranscriptomeAnimalsBirth WeightCattleEmbryo TransferFemalePregnancyStillbirthabortiongestationlarge offspring syndromemachine learningpregnancystillbirthtranscriptome

Identifiers

PMID42885402

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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.