G. Invernizzi1,2,3, B. Thering2,3, M. Bionaz2,3, G. Savoini1 and J.J. Loor2,3
1Department of Sciences and Technologies for Food Safety, University of Milan, 20133 Milan, Italy
2Mammalian NutriPhysioGenomics; 3Department of Animal Sciences and Division of Nutritional
Sciences, University of Illinois at Urbana-Champaign, Urbana, 61801 Illinois, USA
Introduction
Milk fat synthesis and milk FA profiles can be greatly affected by nutrition. Several studies have already explored these changes obtained by supplementing in both unsaturated and saturated fat the diet of lactating cows without completely clarifying the systematic adaptations in mammary tissue. A functional genomics approach permits a deeper and broader understanding of the physiological response of the mammary gland to dietary treatments. The goal of this study was to understand the systemic adaptations of biological processes in the mammary gland consequent to dietary supplementation of saturated fat or fish oil.
Material and methods
A 13,257 bovine oligonucleotide microarray in a dye-swap reference design (Loor et al., 2007) was used for transcript profiling of mammary biopsies harvested on d 0, 7 and 21 from the beginning of treatments on 13 Holstein mid-lactation cows (41.7 kg milk/d). The study lasted 4 weeks and the experimental design consisted of three dietary treatments: 4 cows fed a control diet (CTR), 4 cows fed the same diet supplemented with a blend of fish oil and soybean oil (10 g/kg and 25 g/ kg DM; FSO) and 5 cows fed the control diet supplemented with 25 g/kg DM of saturated lipid (Energy Booster 100; EB100). Loess normalization and array centering and scaling were used on data obtained from 72 microarrays prior to statistical analysis. Statistics were performed using a mixed effect model on the adjusted ratios of each oligonucleotide with Proc MIXED (SAS®). The model included treatments, time and dye as fixed effects and cow as a random variable. The significant probability values for the treatment and time effects were adjusted for the number of comparisons using Benjamini and Hochberg’s false discovery rate (FDR). Data mining was carried out with Ingenuity Pathway Analysis® (IPA) and DAVID (Huang et al., 2009; Dennis et al., 2003). Canonical pathway analysis identified those pathways within the IPA Knowledge Base that were most significantly enriched in the data set. Genes from the data set that met the FDR ≤0.05 and post-hoc P-value <0.01 cut-off and were associated with a canonical pathway in the IPA Knowledge Base were considered for the analysis.
Results and discussion
Data on enrichment of most significant canonical signalling and metabolic pathways in the comparison between EB100 and FSO at d 21 are summarized in Table 1. Most enriched metabolic pathways were related to energy metabolism, carbohydrate metabolism and lipid metabolism. The overall effect of EB100 vs. FSO at d 21 on those pathways was a marked inhibition of β-oxidation, glycolysis and TCA cycle. In addition, oxidative phosphorylation as well as sulfur metabolism, all energy-related pathways, was inhibited. The putative inhibition of the pentose phosphate shunt as well that of isocitrate dehydrogenase, the two major sources of NADPH for mammary cells, could be part of the same coordinated mechanism likely directed to a reduction of de novo fatty acids synthesis and, in turn, affect glutathione metabolism which is also dependent on NADPH. Similar to our results, soybean oil supplementation in mice decreased mRNA expression of enzymes of the pentose phosphate shunt, mitochondrial citrate transporter, and enzymes of fatty acid synthesis (Rudolph et
and cholesterol clearance in non-mammary cells (HDLBP) were up-regulated, likely in response to increased availability of this compound in blood. The up-regulation of 11β-hydroxysteroid dehydrogenase 2 (HSD11B2), which converts cortisol to cortisone, could be a response to a local increase of cortisol from exogenous cholesterol. One of the genes most up-regulated (3.3-fold) was FOXO1, a transcription factor (TF) whose function is controlled by insulin signalling. This TF together with PPARGC1A, encoding for the protein PGC-1α (↓), plays a critical role in regulating gluconeogenesis and glycolysis, both of which are classified in the IPA knowledge base as being part of the FXR/RXR activation pathway. Pathways analysis suggested an inhibitory effect of saturated fat on cellular energy production and an enhanced steroid metabolism in mammary tissue.
Table 1. Top metabolic and signalling pathways from Ingenuity Pathways Analysis (IPA) among DEG at d 21 between EB100 and FSO.
Ingenuity canonical pathways P-value Ratio DEG ↑/↓ Effect Class (KEGG orthology)
Metabolic pathways
Glutathione metabolism 0.0891 0.11 11 3/8 ⇓ Metabolism of other amino acids Oxidative phosphorylation 0.110 0.13 22 0/22 ⇓ Energy metabolism
Fructose and mannose metabolism 0.110 0.07 10 3/7 ⇓⇓ Carbohydrate metabolism Citrate cycle 0.119 0.14 8 0/8 ⇓⇓ Carbohydrate metabolism Androgen and estrogen metabolism 0.153 0.06 9 2/7 ⇔ Lipid metabolism Fatty acid metabolism 0.157 0.07 14 1/13 ⇓⇓ Lipid metabolism Glycolysis/gluconeogenesis 0.182 0.09 13 0/13 ⇓ Carbohydrate metabolism Pentose phosphate pathway 0.191 0.08 7 0/7 ⇓⇓ Carbohydrate metabolism Urea cycle and metabolism of amino
groups 0.191 0.09 7 0/7 ⇓ Amino acid metabolism
Sulfur metabolism 0.191 0.07 4 1/3 ↓ Energy metabolism
Signalling pathways Function (IPA)
FXR/RXR activation 0.0229 0.16 16 3/13 Modulate bile, lipid and glucose homeostasis.
Mitochondrial dysfunction 0.0229 0.14 24 1/23 Affect oxidative stress, apoptosis and mitochondrial DNA damage Coagulation system 0.0794 0.24 9 1/8 Maintain a fine balance between formation and dissolution of a clot Xenobiotic metabolism signalling 0.153 0.10 28 9/19 Induction xenobiotic metabolism, elimination and/or detoxification The P-value denotes the significance of the enrichment of a function within the DEG adjusted by Benjamini and Hochberg’s FDR ≤0.2. Shown also are the ratio (DEG/number of genes in the pathways), the total number of DEG in the pathway, the number of up-(↑) and down- (↓) regulated DEG in the pathway, the overall effect of the pathways (denoted by ↓ likely inhibited; ⇓ inhibited; ⇓⇓ evidently inhibited; ⇔ equilibrium).
References
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