Number of probe sets showing a significantly altered abundance in muscle tissue.

<p>The number of altered probe sets between adjacent developmental stages in AP or HP offspring are indicated at horizontal arrows; the number of commonly altered probe sets between stages in AP and HP offspring are indicated at intersections; the number of probe sets showing a different abundance between HP and AP offspring at the same developmental stage are indicated at vertical arrows; small arrows at the numbers indicate a higher or lower probe set abundance, respectively.</p

Functional annotation of muscle transcripts showing altered abundance between two developmental stages within either dietary group HP or AP (Ingenuity Pathway Analysis).

<p>Up and down indicate higher and lower abundance in later compared to earlier stages, respectively. P-value: significance of association between dataset and IP-pathways; Fischer's exact test.</p

Functional annotation of muscle transcripts showing altered abundance depending on the dietary group (HP vs. AP) within different developmental stages (Ingenuity Pathway Analysis).

<p>Up and down indicate higher and lower abundance in HP compared to AP, respectively. P-value: significance of association between dataset and IP-pathways; Fischer's exact test.</p

Affected pathways in muscle tissue between developmental stages and diets.

<p>Listed pathways between AP stages (white boxes) indicate shifts during development that are not found in HP offspring (black boxes) at the corresponding period. Pathways between HP stages indicate alterations that occur in HP offspring but not in AP offspring in the corresponding period. (Arrows between boxes show direction of comparison; small arrows indicate higher and lower transcript abundance, respectively. OXPHOS, oxidative phosphorylation; PLK, Polo-like kinase; mTOR, mammalian target of rapamycin; AMPK, AMP-activated protein kinase; IGF1, insulin-like growth factor 1; FA, Fatty acid; RAN, Ras-related nuclear protein).</p

Experimental design.

<p>Fetuses and offspring of divergently fed sows were collected at 4 developmental stages. Fetuses were derived from 3 sows per dietary group. Offspring were full sibs of six litters per dietary group collected at 3 consecutive postnatal stages; HP = high protein, CP = crude protein, AP = adequate protein.</p

Multi-tissue gene expression profiling of cows with a genetic predisposition for low and high milk urea levels

Milk urea (MU) concentration is proposed as an indicator trait for breeding toward reduced nitrogen (N) emissions and leaching in dairy. We selected 20 German Holstein cows based on MU breeding values, with 10 cows each having low (LMUg) and high (HMUg) MU genetic predisposition. Using RNA-seq, we characterized these cows to unravel molecular pathways governing post-absorptive body N pools focusing on renal filtration and reabsorption of nitrogenous compounds, hepatic urea formation and mammary gland N excretion. While we observed minor adjustments in cellular energy metabolism in different tissues associated with different MU levels, no transcriptional differences in liver ammonia detoxification were detected, despite significant differences in MU between the groups. Differential expression of AQP3 and SLC38A2 in the kidney provides evidence for higher urea concentration in the collecting duct of LMU cows than HMU cows. The mammary gland exhibited the most significant differences, particularly in tricarboxylic acid (TCA) cycle genes, amino acid transport, tRNA binding, and casein synthesis. These findings suggest that selecting for lower MU could lead to altered urinary urea (UU) handling and changes in milk protein synthesis. However, given the genetic variability in N metabolism components, the long-term effectiveness of MU-based selection in reducing N emissions remains uncertain.</p

Additional file 1 of Effect of metabolically divergent pig breeds and tissues on mesenchymal stem cell expression patterns during adipogenesis

Supplementary Material

Comparison of microarray and quantitative PCR (qPCR) results for selected transcripts (<i>CD69</i>, <i>GNAZ</i>, <i>ITGA2B</i>) to verify microarray data.

<p>Values were calculated by factorial normalisation on <i>IQGAP1</i> and <i>TSC22D2</i> expression values. Fold-changes displayed in red circles indicate significant differences in mRNA abundances between HR and LR at either microarray (solid lined circles) or qPCR data (dashed lined circles). Positive values display increased mRNA abundances in HR (HR > LR). Correlation of normalized expression values was calculated by Spearman (n = 176). * p = 0.06.</p

Specific Ingenuity biofunctions of transcripts with higher and lower expression between HR and LR PRMC samples at day 0.

<p>Metadata of involved genes are displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120153#pone.0120153.s005" target="_blank">S4 Table</a>. HR—High resisting; LR—Low resisting.</p><p>Specific Ingenuity biofunctions of transcripts with higher and lower expression between HR and LR PRMC samples at day 0.</p

Heatmap displaying probe-sets with significantly altered mRNA abundances.

<p>Effects mediated by coping group appeared to dominate early sampling points (day 0, day 14, day 28). Later, age-specific effects were more pronounced as visualized by young adult subgroups (day 140). Columns = variance component coping group x time; Rows = transcripts showing altered mRNA abundances between HR and LR on at least one time point; HR—High resisting; LR—Low resisting.</p