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. 2019 Jan 7;20(1):8.
doi: 10.1186/s12864-018-5406-2.

Identification of a metabolomic signature associated with feed efficiency in beef cattle

Francisco José Novais  1 , Pedro Ratto Lisboa Pires  1 , Pâmela Almeida Alexandre  1 , Robert A Dromms  2 , Amadeu Hoshi Iglesias  3 , José Bento Sterman Ferraz  1 , Mark Philip-Walter Styczynski  2 , Heidge Fukumasu  4
Affiliations

Identification of a metabolomic signature associated with feed efficiency in beef cattle

Francisco José Novais et al. BMC Genomics. .

Abstract

Background: Ruminants play a great role in sustainable livestock since they transform pastures, silage, and crop residues into high-quality human food (i.e. milk and beef). Animals with better ability to convert food into animal protein, measured as a trait called feed efficiency (FE), also produce less manure and greenhouse gas per kilogram of produced meat. Thus, the identification of high feed efficiency cattle is important for sustainable nutritional management. Our aim was to evaluate the potential of serum metabolites to identify FE of beef cattle before they enter the feedlot.

Results: A total of 3598 and 4210 m/z features was detected in negative and positive ionization modes via liquid chromatography-mass spectrometry. A single feature was different between high and low FE groups. Network analysis (WGCNA) yielded the detection of 19 and 20 network modules of highly correlated features in negative and positive mode respectively, and 1 module of each acquisition mode was associated with RFI (r = 0.55, P < 0.05). Pathway enrichment analysis (Mummichog) yielded the Retinol metabolism pathway associated with feed efficiency in beef cattle in our conditions.

Conclusion: Altogether, these findings demonstrate the existence of a serum-based metabolomic signature associated with feed efficiency in beef cattle before they enter the feedlot. We are now working to validate the use of metabolites for identification of feed efficient animals for sustainable nutritional management.

Keywords: Nellore; Residual feed intake; Retinol; WGCNA.

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Conflict of interest statement

Ethics approval

All animal protocols were approved by the Institutional Animal Care and Use Committee of Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo (FZEA-USP – protocol number 14.1.636.74.1). The animals belonged to FZEA-USP.

Consent for publication

Not applicable.

Competing interests

The authors declare they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
PCA (a and c, in negative and positive mode, respectively) and PLS-DA (b and d, negative and positive mode, respectively) scores plots based on LC/MS data of serum samples from HFE (red) and LFE (green). The PLS-DA models discriminated between HFE and LFE groups (R2 of 0.87 and 0.98 in negative and positive mode, respectively) but were not predictive (Q2 of 0.08 and 0.15). Considering a common heuristic for metabolomics data: R2 > 0.8 and Q2 > 0.5, the model was not overfitted. Consistent with this, a permutation test (2000 permutations) yielded P-values > 0.9 in both modes
Fig. 2
Fig. 2
Univariate differential analysis of features from bovine metabolome. a Univariate analysis corrected by multiple tests (SAM-FDR) results for positive mode features. b The difference of abundance between the HFE and LFE groups for the m/z 183.1670 peak with a retention time of 4.00 min (positive mode; SAM-FDR ≤ 0.05)
Fig. 3
Fig. 3
Network analysis of co-expressed features in the negative and positive mode of acquisition. Pearson correlation between residual feed intake (RFI) and the module eigengenes in the negative (a) and positive (b) mode. In each line the color name of modules (ME). The number in each module is the Pearson correlation between the module and RFI; In brackets the p-value of the correlation
See this image and copyright information in PMC

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