Glucose production during an IVGTT by deconvolution : validation with the tracer-to-tracee clamp technique

Recently, a new method, based on a two-compartment minimal model and deconvolution [A. Caumo and C. Cobelli. Am. J. Physiol 264 (Endocrinol. Metab. 37): E829-E841, 1993; P. Vicini, G. Sparacino, A. Caumo, and C. Cobelli. Comput. Meth. Prog. Biomed. 52: 147-156, 1997], has been proposed to estimate endogenous glucose production (EGP) from labeled intravenous glucose tolerance test (IVGTT) data. Our aim here is to compare this EGP profile with that independently obtained with the reference method, based on the tracer-to-tracee ratio (TTR) clamp. An insulin-modified (0.03 U/kg body wt infused over 5 min) [6,6-2H2]glucose-labeled IVGTT (0.33 g/kg of glucose) was performed in 10 normal subjects. A second tracer ([U-13C]glucose) was also infused during the test in a variable fashion to clamp endogenous glucose TTR. The TTR clamp was quite successful. As a result, the EGP profile, reconstructed from [U-13C]glucose data with the models of Steele and Radziuk, were almost superimposable. The deconvolution-obtained EGP profile, calculated from [6,6-2H2]glucose data, showed remarkable agreement with that obtained from the TTR clamp. Some differences between the two profiles were noted in the estimated basal EGP and in the initial modalities of EGP inhibition. A high interindividual variability was also observed with both methods in the resumption of EGP to baseline; variability was high in both the timing and the extent of resumption. In conclusion, the use of the two-compartment minimal model of the IVGTT and deconvolution allows the estimation of a profile of EGP that is in very good agreement with that independently obtained with a TTR clamp

Undermodeling affects minimal model indexes : insights from a two-compartment model

The classic (hereafter cold) and the labeled (hereafter hot) minimal models are powerful tools to investigate glucose metabolism. The cold model provides, from intravenous glucose tolerance test (IVGTT) data, indexes of glucose effectiveness (SG) and insulin sensitivity (SI) that measure the effect of glucose and insulin, respectively, to enhance glucose disappearance and inhibit endogenous glucose production. The hot model provides, from hot IVGTT data, indexes of glucose effectiveness (SG*) and insulin sensitivity (SI*) that, respectively, measure the effects of glucose and insulin on glucose disappearance only. Recent reports call for a reexamination of some of the assumptions of the minimal models. We have previously pointed out the criticality of the single-compartment description of glucose kinetics on which both the minimal models are founded. In this paper we evaluate the impact of single-compartment undermodeling on SG, SI*, and by using a two-compartment model to describe the glucose system. The relationships of the minimal model indexes to the analogous indexes measured with the glucose clamp technique are also examined. Theoretical analysis and simulation studies indicate that cold indexes are more affected than hot indexes by undermodeling. In particular, care must be exercised in the physiological interpretation of SG, because this index is a local descriptor of events taking place in the initial portion of the IVGTT. As a consequence, SG not only reflects glucose effect on glucose uptake and production but also the rapid exchange of glucose between the accessible and nonaccessible glucose pools that occurs in the early part of the test

The nutritional phenotype in the age of metabolomics

The concept of the nutritional phenotype is proposed as a defined and integrated set of genetic, proteomic, metabolomic, functional, and behavioral factors that, when measured, form the basis for assessment of human nutritional status. The nutritional phenotype integrates the effects of diet on disease/wellness and is the quantitative indication of the paths by which genes and environment exert their effects on health. Advances in technology and in fundamental biological knowledge make it possible to define and measure the nutritional phenotype accurately in a cross section of individuals with various states of health and disease. This growing base of data and knowledge could serve as a resource for all scientific disciplines involved in human health. Nutritional sciences should be a prime mover in making key decisions that include: what environmental inputs (in addition to diet) are needed; what genes/proteins/metabolites should be measured; what end-point phenotypes should be included; and what informatics tools are available to ask nutritionally relevant questions. Nutrition should be the major discipline establishing how the elements of the nutritional phenotype vary as a function of diet. Nutritional sciences should also be instrumental in linking the elements that are responsive to diet with the functional outcomes in organisms that derive from them. As the first step in this initiative, a prioritized list of genomic, proteomic, and metabolomic as well as functional and behavioral measures that defines a practically useful subset of the nutritional phenotype for use in clinical and epidemiological investigations must be developed. From this list, analytic platforms must then be identified that are capable of delivering highly quantitative data on these endpoints. This conceptualization of a nutritional phenotype provides a concrete form and substance to the recognized future of nutritional sciences as a field addressing diet, integrated metabolism, and health

Structure-based design of non-natural macrocyclic peptides that inhibit protein-protein interactions

Macrocyclic peptides can interfere with challenging biomolecular targets including protein-protein interactions. Whereas there are various approaches that facilitate the identification of peptide-derived ligands, their evolution into higher affinity binders remains a major hurdle. We report a virtual screen based on molecular docking that allows the affinity maturation of macrocyclic peptides taking non-natural amino acids into consideration. These macrocycles bear large and flexible substituents that usually complicate the use of docking approaches. A virtual library containing more than 1400 structures was screened against the target focusing on docking poses with the core structure resembling a known bioactive conformation. Based on this screen, a macrocyclic peptide 22 involving two non-natural amino acids was evolved showing increased target affinity and biological activity. Predicted binding modes were verified by X-ray crystallography. The presented workflow allows the screening of large macrocyclic peptides with diverse modifications thereby expanding the accessible chemical space and reducing synthetic efforts