Gut microbiota, nutrient sensing and energy balance.

The gastrointestinal (GI) tract is a highly specialized sensory organ that provides crucial negative feedback during a meal, partly via a gut-brain axis. More specifically, enteroendocrine cells located throughout the GI tract are able to sense and respond to specific nutrients, releasing gut peptides that act in a paracrine, autocrine or endocrine fashion to regulate energy balance, thus controlling both food intake and possibly energy expenditure. Furthermore, the gut microbiota has been shown to provide a substantial metabolic and physiological contribution to the host, and metabolic disease such as obesity has been associated with aberrant gut microbiota and microbiome. Interestingly, recent evidence suggests that the gut microbiota can impact the gut-brain axis controlling energy balance, at both the level of intestinal nutrient-sensing mechanisms, as well as potentially at the sites of integration in the central nervous system. A better understanding of the intricate relationship between the gut microbiota and host energy-regulating pathways is crucial for uncovering the mechanisms responsible for the development of metabolic diseases and for possible therapeutic strategies.

Differential effects of hypothalamic long-chain fatty acid infusions on suppression of hepatic glucose production.

Our objective was to investigate whether the direct bilateral infusion of the monounsaturated fatty acid (MUFA) oleic acid (OA) within the mediobasal hypothalamus (MBH) is sufficient to reproduce the effect of administration of OA (30 nmol) in the third cerebral ventricle, which inhibits glucose production (GP) in rats. We used the pancreatic basal insulin clamp technique (plasma insulin ∼20 mU/ml) in combination with tracer dilution methodology to compare the effect of MBH OA on GP to that of a saturated fatty acid (SFA), palmitic acid (PA), and a polyunsaturated fatty acid (PUFA), linoleic acid (LA). The MBH infusion of 200 but not 40 pmol of OA was sufficient to markedly inhibit GP (by 61% from 12.6 ± 0.6 to 5.1 ± 1.6 mg·kg(-1)·min(-1)) such that exogenous glucose had to be infused at the rate of 6.0 ± 1.2 mg·kg(-1)·min(-1) to prevent hypoglycemia. MBH infusion of PA also caused a significant decrease in GP, but only at a total dose of 4 nmol (GP 5.8 ± 1.6 mg·kg(-1)·min(-1)). Finally, MBH LA at a total dose of 0.2 and 4 nmol failed to modify GP compared with rats receiving MBH vehicle. Increased availability of OA within the MBH is sufficient to markedly inhibit GP. LA does not share the effect of OA, whereas PA can reproduce the potent effect of OA on GP, but only at a higher dose. It remains to be determined whether SFAs need to be converted to MUFAs to exert this effect or whether they activate a separate signaling pathway to inhibit GP.

Determination of the systematic and random measurement error in an LC-FTICR mass spectrometry analysis of a partially characterized complex peptide mixture.

In high-throughput proteomics, a promising approach presently being explored is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) to provide measurements of the masses of tryptic peptides in complex mixtures, which can then be used to identify the proteins which gave rise to those peptides. In order to apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error in measured masses. In this investigation, a complex mixture of peptides derived from a partially characterized sample was analyzed by LC-FTICR-MS. Through the application of a Bayesian probability model of the data, partial knowledge of the composition of the sample is sufficient both to determine any systematic error and to estimate the random error in measured masses.