Maternal blood transcriptome as a sensor of fetal organ maturation at the end of organogenesis in cattle†

Maria Belen Rabaglino*, José María Sánchez, Michael McDonald, Elena O'Callaghan, Pat Lonergan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Harnessing information from the maternal blood to predict fetal growth is attractive yet scarcely explored in livestock. The objectives were to determine the transcriptomic modifications in maternal blood and fetal liver, gonads, and heart according to fetal weight and to model a molecular signature based on the fetal organs allowing the prediction of fetal weight from the maternal blood transcriptome in cattle. In addition to a contemporaneous maternal blood sample, organ samples were collected from 10 male fetuses at 42 days of gestation for RNA-sequencing. Fetal weight ranged from 1.25 to 1.69 g (mean = 1.44 ± 0.15 g). Clustering data analysis revealed clusters of co-expressed genes positively correlated with fetal weight and enriching ontological terms biologically relevant for the organ. For the heart, the 1346 co-expressed genes were involved in energy generation and protein synthesis. For the gonads, the 1042 co-expressed genes enriched seminiferous tubule development. The 459 co-expressed genes identified in the liver were associated with lipid synthesis and metabolism. Finally, the cluster of 571 co-expressed genes determined in maternal blood enriched oxidative phosphorylation and thermogenesis. Next, data from the fetal organs were used to train a regression model of fetal weight, which was predicted with the maternal blood data. The best prediction was achieved when the model was trained with 35 co-expressed genes overlapping between heart and maternal blood (root-mean-square error = 0.04, R2 = 0.93). In conclusion, linking transcriptomic information from maternal blood with that from the fetal heart unveiled maternal blood as a predictor of fetal development.

Original languageEnglish
Pages (from-to)749-758
Number of pages10
JournalBiology of Reproduction
Volume109
Issue number5
Early online date2 Sept 2023
DOIs
Publication statusPublished - 15 Nov 2023
Externally publishedYes

Keywords

  • Cattle
  • Gestation
  • Machine learning
  • Organ development
  • Transcriptomics

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