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. 2025 Dec 1;22(1):8.
doi: 10.1007/s11306-025-02384-3.

Unveiling long-term prenatal nutrition biomarkers in beef cattle via multi-tissue and multi-OMICs analysis

Guilherme Henrique Gebim Polizel  1   2 , Ángela Cánovas  3 , Wellison J S Diniz  4 , German D Ramírez-Zamudio  5 , Aline Silva Mello Cesar  6 , Heidge Fukumasu  7 , Arícia Christofaro Fernandes  5 , Édison Furlan  5 , Miguel Henrique de Almeida Santana  5
Affiliations

Unveiling long-term prenatal nutrition biomarkers in beef cattle via multi-tissue and multi-OMICs analysis

Guilherme Henrique Gebim Polizel et al. Metabolomics. .

Abstract

Introduction: Maternal nutrition during gestation plays a crucial role in shaping offspring development, metabolism, and long-term health, yet the underlying molecular mechanisms remain poorly understood.

Objectives: This study investigated potential biomarkers through multi-OMICs and multi-tissue analyses in offspring of beef cows subjected to different gestational nutrition regimes.

Methods: A total of 126 cows were allocated to three groups: NP (control, mineral supplementation only), PP (protein-energy supplementation in the last trimester), and FP (protein-energy supplementation throughout gestation). Post-finishing phase, samples (blood, feces, ruminal fluid, fat, liver, and longissimus muscle/meat) were collected from 63 male offspring. RNA sequencing was performed on muscle and liver, metabolomics on plasma, fat, liver, and meat, and 16S rRNA sequencing on feces and ruminal fluid. Data were analyzed via DIABLO (mixOmics, R).

Results: The muscle transcriptome showed strong cross-block correlations (|r| > 0.7), highlighting its sensitivity to maternal nutrition. Plasma glycerophospholipids (PC ae C30:0, PC ae C38:1, lysoPC a C28:0) were key biomarkers, particularly for FP. The PP group exhibited liver-associated markers (IL4I1 gene, butyrylcarnitine), reflecting late-gestation effects, while NP had reduced ruminal Clostridia (ASV151, ASV241), suggesting impaired microbial energy metabolism.

Conclusions: This integrative multi-OMICs approach provided deeper insights than single-layer analyses, distinguishing nutritional groups and revealing tissue- and OMIC-specific patterns. These findings demonstrate the value of combining transcriptomic, metabolomic, and microbiome data to identify biomarkers linked to maternal nutrition in beef cattle.

Keywords: Beef prenatal nutrition; Metabolomics; Metagenomics; Systems biology; Transcriptomics.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval and consent to participate: This study was approved by the Research Ethics Committee of the Faculty of Animal Science and Food Engineering, University of São Paulo, under protocol No. 1843241117, in compliance with the National Council for the Control of Animal Experimentation recommendations. In addition, the Faculty of Animal Science and Food Engineering provided the animals to carry out this study. Consent for publication: Not applicable.

Figures

Fig. 1
Fig. 1
Performance plot showing the optimal number of components (X-axis) with their corresponding error rates (Y-axis), along with the best distance metric for enhancing model prediction accuracy
Fig. 2
Fig. 2
Overview of the experimental design and multi-OMICs and multi-tissue analyses
Fig. 3
Fig. 3
Sample plot from multiblock sPLS-DA. The samples are plotted according to their scores on the first two components for each data set. Samples are colored by maternal nutrition groups (NP, PP and FP). The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set. A Each block represented individually. B Arrow plot integrating all blocks, where the base of each arrow represents the centroid across all data sets for a given sample, and the arrow tip indicates the sample’s position within each individual block
Fig. 4
Fig. 4
Correlation component plot from multiblock sPLS-DA. Samples are colored by maternal nutrition groups (NP, PP and FP) and 95% confidence ellipse plots are represented. The bottom-left numbers represent the Pearson correlation coefficients for the first components between each dataset
Fig. 5
Fig. 5
The correlation circle plot highlights the contribution of each selected variable to each component, while the circos plot shows the relationships across all -OMICs × tissues. Specifically, A displays the correlation circle plot of transcriptome blocks, B the correlation circle plot of metagenome blocks, and C the correlation circle plot of metabolome blocks. Lastly, D illustrates the circos plot, showing correlations between variables from different blocks and their expression levels across maternal nutrition groups (NP, PP, and FP)
Fig. 6
Fig. 6
Network representation of correlations above |0.8|, visualized using a circular degree-sorted layout across the -OMICs × tissue blocks. To enhance clarity, the hub variable in the network has been slightly distanced from the others to better highlight its central role
Fig. 7
Fig. 7
Lollipop chart representing the potential candidate biomarkers in each -OMICs-tissue block with their respective loading scores in component 1. The color of each lollipop indicates the maternal nutrition group in which the variable is a potential candidate biomarker
Fig. 8
Fig. 8
Lollipop chart representing the potential candidate biomarkers in each -OMICs-tissue block with their respective loading scores in component 2. The color of each lollipop indicates the maternal nutrition group in which the variable is a potential candidate biomarker
See this image and copyright information in PMC

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