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High dietary energy and protein supply is common practice in livestock nutrition, aiming to maximize growth and production performance. However, a chronic nutritional surplus induces obesity, promotes insulin insensitivity, and triggers low-grade inflammation. Thirty Holstein bulls were randomly assigned to two groups, low energy and protein (LEP), and high energy and protein (HEP) intake, provided from the 13th to the 20th month of life. Body weight, carcass composition, laminitis score, and circulating insulin and glucose concentrations were assessed. The expression and extent of phosphorylation of insulin signaling proteins were measured in the liver, muscle, and adipose tissue. The sphingolipid metabolome was quantified by a targeted liquid chromatography-mass spectrometry based metabolomics approach. The HEP bulls were obese, had hyperinsulinemia with euglycemia, and expressed clinical signs of chronic laminitis. In the liver, protein kinase B (PKB) phosphorylation was decreased and this was associated with a higher tissue concentration of ceramide 16:0, a sphingolipid that diminishes insulin action by dephosphorylating PKB. In the adipose tissue, insulin receptor expression was lower in HEP bulls, associated with higher concentration of hexosylceramide, which reduces the abundance of functional insulin receptors. Our findings confirm that diet-induced metabolic inflammation triggers ceramide accumulation and disturbs insulin signaling. As insulin insensitivity exacerbates metabolic inflammation, this self-reinforcing cycle could explain the deterioration of metabolic health apparent as chronic laminitis. By demonstrating molecular relationships between insulin signaling and sphingolipid metabolism in three major tissues, our data extend our mechanistic understanding of the role of ceramides in diet-induced metabolic inflammation.

Additional file1: Table S1. List of metabolites measured bythe Absolute-IDQ p180 Kit.

Supplemental material to the journal article: Xu, Grindler et al., 2021. Increased plasma and milk short-chain acylcarnitines concentrations reflect systemic LPS response in mid-lactation dairy cows

Additional file 1: Figure S1. Shannon index of RS bacterial communities in R and BS samples of different age group calves. Figure S2. Overlap of RS-OTUs covering V1-V2 region of bacterial 16S rRNA. Figure S3. Average relative abundances of RS bacterial phylum- and genus-level taxa in rumen and buccal swab samples of different age group calves. Figure S4. a Principal coordinates analysis plot of bacterial communities in 186 BS samples of different age group calves, after exclusion of potential RS taxa by mathematical filtering approach. Figure S5. Average relative abundances of OS bacterial genus-level taxa in BS samples of different age group calves. Figure S6. Shannon index of OS bacterial communities in BS samples of different weaning groups of calves. Figure S7. Bar-plot depicting within group similarity (mean and standard deviations) along the time. Figure S8. The Spearman correlation coefficients (R-values) between OTU’s relative abundance along the d140R samples with its abundance over the RS portion of the d140BS samples.

Background Insulin dysregulation (ID) is an equine endocrine disorder, which is often accompanied by obesity and various metabolic perturbations. The relationship between weight variations and fluctuations of the insulin response to oral glucose tests (OGT) as well as the metabolic impact of ID have been described previously. The present study seeks to characterize the concomitant metabolic impact of variations in the insulin response and bodyweight during repeated OGTs using a metabolomics approach. Methods Nineteen Icelandic horses were subjected to five OGTs over one year and their bodyweight, insulin and metabolic response were monitored. Analysis of metabolite concentrations depending on time (during the OGT), relative bodyweight (rWeight; defined as the bodyweight at one OGT divided by the mean bodyweight across all OGTs) and relative insulin response (rAUCins; defined accordingly from the area under the insulin curve during OGT) was performed using linear models. Additionally, the pathways significantly associated with time, rWeight and rAUCins were identified by rotation set testing. Results The results suggested that weight gain and worsening of ID activate distinct metabolic pathways. The metabolic profile associated with weight gain indicated an increased activation of arginase, while the pathways associated with time and rAUCins were consistent with the expected effect of glucose and insulin, respectively. Overall, more metabolites were significantly associated with rWeight than with rAUCins.