Quantification of Transcriptome Responses of the Rumen Epithelium to Butyrate Infusion using RNA-seq Technology

Short-chain fatty acids (SCFAs), such as butyrate, produced by gut microorganisms, play a critical role in energy metabolism and physiology of ruminants as well as in human health. In this study, the temporal effect of elevated butyrate concentrations on the transcriptome of the rumen epithelium was quantified via serial biopsy sampling using RNA-seq technology. The mean number of genes transcribed in the rumen epithelial transcriptome was 17,323.63 ± 277.20 (±SD; N = 24) while the core transcriptome consisted of 15,025 genes. Collectively, 80 genes were identified as being significantly impacted by butyrate infusion across all time points sampled. Maximal transcriptional effect of butyrate on the rumen epithelium was observed at the 72-h infusion when the abundance of 58 genes was altered. The initial reaction of the rumen epithelium to elevated exogenous butyrate may represent a stress response as Gene Ontology (GO) terms identified were predominantly related to responses to bacteria and biotic stimuli. An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10−11). Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate. Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health

Correlations between milk and plasma levels of amino and carboxylic acids in dairy cows.

The objective of this study was to investigate the relationship between the concentrations of 19 amino acids, glucose, and seven carboxylic acids in the blood and milk of dairy cows and their correlations with established markers of ketosis. To that end, blood plasma and milk specimens were collected throughout lactation in two breeds of dairy cows of different milk yield. Plasma concentrations of glucose, pyruvate, lactate, α-aminobutyrate, β-hydroxybutyrate (BHBA), and most amino acids, except for glutamate and aspartate, were on average 9.9-fold higher than their respective milk levels. In contrast, glutamate, aspartate, and the Krebs cycle intermediates succinate, fumarate, malate, and citrate were on average 9.1-fold higher in milk than in plasma. For most metabolites, with the exception of BHBA and threonine, no significant correlations were observed between their levels in plasma and milk. Additionally, milk levels of acetone showed significant direct relationships with the glycine-to-alanine ratio and the BHBA concentration in plasma. The marked decline in plasma concentrations of glucose, pyruvate, lactate, and alanine in cows with plasma BHBA levels above the diagnostic cutoff point for subclinical ketosis suggests that these animals fail to meet their glucose demand and, as a consequence, rely increasingly on ketone bodies as a source of energy. The concomitant increase in plasma glycine may reflect not only the excessive depletion of protein reserves but also a potential deficiency of vitamin B6

Modeling And Control Of A Nonsquare Drug Infusion System

: A model predictive control strategy is developed and tested on an nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Several cases are studied, including congestive heart failure, post-operative hypertension and a patient that moves from hypertensive to hypotensive conditions. The "nonsquare" (more process inputs than outputs) control system allows the independent management of the hemodynamic and venous circulation. The model predictive controller, which uses a different linear model depending on the patient condition, allows constraints to be explicitly enforced. The controller is initially tuned based on linear plant responses, then tested on the nonlinear plant model; the simulations verify the robustness of the control strategy. Keywords: Biomedical control systems, Predictive control, Physiological models 1. INTRODUCTION Critical care patients have often suffered a "disturbance" to the normal operation of their physiol..