Early milk-feeding regimes in calves exert long-term effects on the development of ovarian granulosa cells

Abstract

Background: Nutrition has not only an impact on the general wellbeing of an animal but can also affect reproductive processes. In cattle, feeding regimes can influence the age of puberty onset and alter gonadal development. We analyzed effects of different milk replacer (MR) feeding regimes during rearing on ovarian physiology with specific emphasis on the numbers as well as gene expression characteristics of granulosa cells (GCs) at the age of puberty onset. Two groups of calves received either 10% or 20% of bodyweight MR per day during their first 8 weeks. After weaning, both groups were fed the same mixed ration ad libitum until slaughter at 8 months.

Results: Animals of the 20% feeding group had a significantly higher body weight, but the proportion of animals having a corpus luteum at the time of slaughter was not different between groups, suggesting a similar onset of puberty. Calves of the 10% group showed a constant GC count regardless of the number of follicles (r = 0.23) whereas in the 20% group increasing numbers of GCs were detected with a higher follicle count (r = 0.71). As a first effort to find a possible molecular explanation for this unexpected limitation of GC numbers in the 10% group, we comparatively analyzed GC transcriptomes in both diet groups. The mRNA microarray analysis revealed a total of 557 differentially expressed genes comparing both groups (fold change > |1.5| and p < 0.05). OAS1X, MX2 and OAS1Z were among the top downregulated genes in the 20% vs. the 10% group, whereas top upregulated genes comprised BOLA and XCL1. All of these genes are known to be regulated by interferon. Subsequent signaling pathway analysis revealed the involvement of several immune response mechanisms in accordance with a number of interferons as upstream regulators.

Conclusions: The results indicate that the plane of MR feeding in early life has an impact on the number and physiology of GCs later in life. This might influence the overall reproductive life initiated by the onset of puberty in cattle. In addition, the observed alterations in GCs of calves fed less MR might be a consequence of interferon regulated immunological pathways.

Keywords: Follicle count; Gene expression; Innate immune system; Interferon; Plane of nutrition.

Fig. 1

Different animal characteristics of the 10% (blue) and 20% (red) feeding group at time of slaughter. (a) The bodyweight (BW) at slaughter differed significantly between both feeding groups (indicated by asterisk, n = 11, t-test, p < 0.05). (b) Follicle cell count as well as (c) granulosa cell count of all animals were not significantly different (n = 11, t-test, p < 0.05). (d) The granulosa cell count of animals with 40 or more follicles differs significantly (n = 5, Mann-Whitney Rank test, p < 0.05). (e) The amount of GCs increased with increasing follicle numbers in the 20% feeding group (animals: H1-H11; r = 0.71, p < 0.05) but remained constant in the 10% feeding group (animals L1-L11; r = 0.23, p = 0.49)

Fig. 2

Principal component analysis (PCA) and loadings. In the PCA (a) each dot represents one animal either from the 10% (blue; L1-L5) or the 20% (red; H1-H5) feeding group. (b) The variable coefficients used to define the principal component 1 are shown while the length of each bar represents their importance. The left side of (b) displays the contribution of all variables. A more detailed information is obtained when only twenty variables are displayed (right)

Fig. 3

Heatmap and hierarchical clustering of the top 15 up- and downregulated genes. The heatmap visualizes the different signal intensities from low (blue) to high expression (red) for the respective genes in all animal samples. The fold change of displayed genes is shown by comparing the 20% vs. the 10% feeding group

Fig. 4

The top 15 affected signaling pathways predicted by IPA. The blue bars represent a predicted suppression of the respective pathway in GCs of the 20% vs. the 10% feeding group indicated by a negative z-score, whereas no prediction could be made for signaling pathways when bars appear grey

Fig. 5

Network of the top upstream regulators Interferon alpha, STAT1, IFNG and IRF7 (bold). The created network was simplified according to the appearance of the different molecules in at least three different upstream regulator networks (modified after analysis by IPA).