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Longitudinal muscle gene expression patterns associated with differential intramuscular fat in cattle

Published online by Cambridge University Press:  13 November 2014

N. J. Hudson ,
A. Reverter ,
P. L. Greenwood ,
B. Guo ,
L. M. Cafe  and
B. P. Dalrymple
N. J. Hudson*
Affiliation:
Computational and Systems Biology, CSIRO Agriculture Flagship, 306 Carmody Road, St Lucia, Brisbane, QLD 4067, Australia
A. Reverter
Affiliation:
Computational and Systems Biology, CSIRO Agriculture Flagship, 306 Carmody Road, St Lucia, Brisbane, QLD 4067, Australia
P. L. Greenwood
Affiliation:
NSW Department of Primary Industries Beef Industry Centre, University of New England, Armidale, NSW 2350, Australia
B. Guo
Affiliation:
Computational and Systems Biology, CSIRO Agriculture Flagship, 306 Carmody Road, St Lucia, Brisbane, QLD 4067, Australia
L. M. Cafe
Affiliation:
NSW Department of Primary Industries Beef Industry Centre, University of New England, Armidale, NSW 2350, Australia
B. P. Dalrymple
Affiliation:
Computational and Systems Biology, CSIRO Agriculture Flagship, 306 Carmody Road, St Lucia, Brisbane, QLD 4067, Australia
*
†E-mail: [email protected]
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Abstract

Intramuscular fat (IMF) can improve meat product quality through its impact on flavour and juiciness. High marbling cuts can command premium prices in some countries and grading systems, but there is substantial cost involved in choosing to grain feed animals in an effort to deposit more IMF. There would be value in developing methods to predict predisposition to ‘marble’ well. Unfortunately, the biological mechanisms underpinning marbling remain a mystery: the key adipocyte cell populations have not been defined, there are no reliable DNA markers, no known (if any) causal mutations and gene expression analyses in the main have tended to characterise increases in expression of end-point fat metabolism proteins such as the fatty acid-binding proteins. To shed light on expression-based markers of marbling potential, we contrasted LD gene expression in high IMF Wagyu cross animals with a low IMF Piedmontese cross at various time points. The expected divergence in the fat metabolism genes FABP4, THRSP, CIDEC and ACACA between the breeds occurs surprisingly late in postnatal development at about 20 months. On the other hand, divergent expression of WISP2, RAI14 and CYP4F2 was discovered in animals at or before 12 months of age, suggesting these genes may have potential as earlier predictors of marbling potential. In line with other researchers, we found intriguing links between IMF development and connective tissue remodelling. WISP2 – a novel adipokine highly expressed and secreted by adipose precursor cells and an inhibitor of the pro-fibrotic connective tissue growth factor – emerges as a particularly attractive candidate. It is relatively upregulated in high marbling Wagyu before admission to feedlotting, somewhere between 7 and 12 months. This difference is subsequently maintained until 25 months, but not thereafter. RAI14, thought to play a role in porcine adipocyte differentiation and with links to retinoic acid metabolism, has an unusual expression profile. Its expression level increases monotonically with postnatal development, and is always higher in Wagyu than Piedmontese. Strong, sustained upregulation of the anti-inflammatory CYP4F2 in Piedmontese is consistent with Wagyu adiposity being a pro-inflammatory state. Application of regulatory impact factor analysis, a network method for identifying causal effector molecules, suggests marbling roles for transcription factors previously implicated in (1) the formation of liposarcoma (unconstrained fatty masses) (YEATS4, MDM2), (2) adipogenesis (CREBL2, SP1, STAT1) and (3) inflammation (ISGF3G, HOXB13, PML).

Keywords

marblingcattlePiedmonteseWagyuadipocyte
Type
Research Article
Information
animal , Volume 9 , Issue 4 , April 2015 , pp. 650 - 659
Copyright
© The Animal Consortium 2014 

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Supplementary material: File

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Table S1

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Supplementary material: File

Hudson Supplementary Material

Table S2

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Hudson Supplementary Material

Table S3

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