Autonomic tone in branches of the respiratory-hemodynamic system of junior schoolchildren

Periodic variations in the cardiac, vascular, and respiratory branches of the respiratory-hemodynamic system (RHDS) were studied in children 8–11 years of age at rest using spectral analysis. By analogy with studying the autonomous regulation of the cardiac rhythm, the averaged pattern of the autonomic tone (BAT) was determined in all RHDS branches. We aimed at studying various combinations of the autonomic balance (AB) in the same pattern and identifying individual typological peculiarities of the BAT patterns of the RHDS. Groups of children with different autonomic RHDS patterns were determined using cluster analysis; these groups differed significantly in many spectral and hemodynamic parameters.     

Autonomic involvement in the permanent metabolic programming of hyperinsulinemia in the high-carbohydrate rat model

Exposure to a high-carbohydrate (HC) milk formula during the suckling period results in permanent metabolic programming of hyperinsulinemia in HC rats. Previous studies have shown that hyperinsulinemia in HC rats involves a programmed hyperresponsiveness to glucose. However, the immediate onset and persistence of enhanced insulin secretion throughout life suggests a role for numerous factors that control insulin secretion. Present in vivo and in vitro studies have shown a role for altered autonomic activity, including increased parasympathetic and decreased sympathetic activities, in the maintenance of hyperinsulinemia in 100-day-old HC rats. HC rats were shown to be more sensitive to cholinergic-induced potentiation of glucose-stimulated insulin secretion (GSIS) in response to acetylcholine and showed increased sensitivity to blockade of cholinergic-induced insulin secretion by the muscarinic-type 3 receptor-specific antagonist 4-diphenylacetoxy-N-methylpiperidine. In addition, HC rats were less sensitive to adrenergic-induced inhibition of insulin secretion by oxymetazoline, whereas treatment with yohimbine resulted in increased GSIS. Furthermore, HC rats showed greater reductions in plasma insulin levels after vagotomy, as well as an attenuation of yohimbine-induced potentiation of GSIS, suggesting that yohimbine-mediated changes are mediated by parasympathetic activity. Changes in autonomic regulation of GSIS are supported by increased mRNA levels of the parasympathetic signaling molecules muscarinic-type 3 receptor, phospholipase Cbeta1, and protein kinase C-alpha and decreased levels of alpha(2a)-adrenergic receptors in islets from adult HC rats. In conclusion, metabolic programming of hyperinsulinemia throughout adulthood of HC rats involves changes in autonomic activity in response to the HC dietary intervention in the suckling period.     

Enumeration of minimal stoichiometric precursor sets in metabolic networks

Background

What an organism needs at least from its environment to produce a set of metabolites, e.g. target(s) of interest and/or biomass, has been called a minimal precursor set. Early approaches to enumerate all minimal precursor sets took into account only the topology of the metabolic network (topological precursor sets). Due to cycles and the stoichiometric values of the reactions, it is often not possible to produce the target(s) from a topological precursor set in the sense that there is no feasible flux. Although considering the stoichiometry makes the problem harder, it enables to obtain biologically reasonable precursor sets that we call stoichiometric. Recently a method to enumerate all minimal stoichiometric precursor sets was proposed in the literature. The relationship between topological and stoichiometric precursor sets had however not yet been studied.

Results

Such relationship between topological and stoichiometric precursor sets is highlighted. We also present two algorithms that enumerate all minimal stoichiometric precursor sets. The first one is of theoretical interest only and is based on the above mentioned relationship. The second approach solves a series of mixed integer linear programming problems. We compared the computed minimal precursor sets to experimentally obtained growth media of several Escherichia coli strains using genome-scale metabolic networks.

Conclusions

The results show that the second approach efficiently enumerates minimal precursor sets taking stoichiometry into account, and allows for broad in silico studies of strains or species interactions that may help to understand e.g. pathotype and niche-specific metabolic capabilities. sasita is written in Java, uses cplex as LP solver and can be downloaded together with all networks and input files used in this paper at http://sasita.gforge.inria.fr/.

     

Computing complex metabolic intervention strategies using constrained minimal cut sets

The model-driven search for gene deletion strategies that increase the production performance of microorganisms is an essential part of metabolic engineering. One theoretical approach is based on Minimal Cut Sets (MCSs) which are minimal sets of knockouts disabling the operation of a specified set of target elementary modes. A limitation of the current approach is that MCSs can induce side effects disabling also desired functionalities. We, therefore, generalize MCSs to Constrained MCSs (cMCSs) allowing for the additional definition of a set of desired modes of which a minimum number must be preserved. Exemplarily for ethanol production by Escherichia coli, we demonstrate that this approach offers enormous flexibility in defining and solving knockout problems. Moreover, many existing methods can be reformulated as special cMCS problems. The cMCSs approach allows systematic enumeration of all equivalent gene deletion combinations and also helps to determine robust knockout strategies for coupled product and biomass synthesis.     

Association of the MC4R V103I polymorphism with the metabolic syndrome: the KORA Study

Objective: Epidemiological studies showing an association between the melanocortin‐4‐receptor (MC4R) 103I variant (rs2229616) and decreased BMI are complemented by functional studies; these suggest a mechanism for appetite regulation and a linkage signal for physical activity and dietary intake for the region encompassing the MC4R. This study aims to provide epidemiological evidence for showing the association of this polymorphism with features of the metabolic syndrome and with parameters related to energy expenditure and dietary habits as potential mediators. Methods and Procedures: We analyzed this polymorphism in 7,888 adults of a population‐based cross‐sectional study applying regression‐based statistical models. Results: Carriers of the MC4R 103I (3.7%) exhibited a significantly decreased waist circumference (–1.46 cm, P = 0.020), decreased glycosylated hemoglobin (HbA_1c) (–0.09%, P = 0.040), and increased HDL‐cholesterol (HDL‐C) (+1.76 mg/dl, P = 0.056), but no change in blood pressure. The odds of having three or more components of the metabolic syndrome were substantially reduced among carriers of MC4R 103I (odds ratio (OR) = 0.46, P = 0.003). Controlling for BMI reduced the HbA_1c and HDL‐C association. Mediator analyses revealed a borderline association of MC4R 103I with carbohydrate intake (OR = 1.26, P = 0.059) possibly mediating association with leanness. Discussion: Our representative study of well‐phenotyped Europeans is the first to describe the association of the MC4R V103I with the metabolic syndrome and with a nutrient‐related phenotype. Our data support the idea that this polymorphism plays a role in appetite regulation that not only affects BMI, but also other features of the metabolic syndrome. It further establishes that the association of the MC4R V103I with obesity and related phenotypes is genuine.     

Testing the role of genetic variation of the MC4R gene in Chinese population in antipsychotic-induced metabolic disturbance

Antipsychotic-induced metabolic disturbance(AIMD) is a common adverse effect of antipsychotics with genetics partly underpinning variation in susceptibility among schizophrenia patients. Melanocortin4 receptor(MC4 R) gene, one of the candidate genes for AIMD, has been under-studied in the Chinese patients. We conducted a pharmacogenetic study in a large cohort of Chinese patients with schizophrenia. In this study, we investigated the genetic variation of MC4 R in Chinese population by genotyping two SNPs(rs489693 and rs17782313) in 1,991 Chinese patients and examined association of these variants with the metabolic effects that were often observed to be related to AIMD. Metabolic measures, including body mass index(BMI), waist circumference(WC), glucose, triglyceride, high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels were assessed at baseline and after 6-week antipsychotic treatment. We found that interaction of SNP×medication status(drug-na?ve/medicated) was significantly associated with BMI, WC, and HDL change %, respectively. Both SNPs were significantly associated with baseline BMI and WC in the medicated group. Moderate association of rs489693 with WC, Triglyceride, and HDL change % were observed in the whole sample. In the drug-na?ve group, we found recessive effects of rs489693 on BMI gain more than 7%, WC and Triglyceride change %, with AA incurring more metabolic adverse effects. In conclusion, the association between rs489693 and the metabolic measures is ubiquitous but moderate. Rs17782313 is less involved in AIMD. Two SNPs confer risk of AIMD to patients treated with different antipsychotics in a similar way.     

A biologically inspired validity measure for comparison of clustering methods over metabolic data sets

In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.     

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

Metabolic flux analysis (MFA) methods use external flux and isotopic measurements to quantify the magnitude of metabolic flows in metabolic networks. A key question in this analysis is choosing a set of measurements that is capable of yielding a unique flux distribution (identifiability). In this article, we introduce an optimization-based framework that uses incidence structure analysis to determine the smallest (or most cost-effective) set of measurements leading to complete flux elucidation. This approach relies on an integer linear programming formulation OptMeas that allows for the measurement of external fluxes and the complete (or partial) enumeration of the isotope forms of metabolites without requiring any of these to be chosen in advance. We subsequently query and refine the measurement sets suggested by OptMeas for identifiability and optimality. OptMeas is first tested on small to medium-size demonstration examples. It is subsequently applied to a large-scale E. coli isotopomer mapping model with more than 17,000 isotopomers. A number of additional measurements are identified leading to maximum flux elucidation in an amorphadiene producing E. coli strain.     

Primary structure of the chromosomal proteins MC1a, MC1b, and MC1c from the archaebacterium Methanothrix soehngenii

The chromosomal protein MC1 of Methanothrix soehngenii is a family of three variants a, b, and c. These are small basic polypeptides of 89, 87, and 90 residues, respectively. Their primary structures have been determined from automated sequence analyses of the intact proteins and from structural data provided by peptides derived from the variants by cleavage at aspartic acid, glutamic acid, arginine, and methionine residues. By comparison with variant b taken as reference, variants a and c present 18 and 24 differences, respectively. The extent of sequence homologies between protein MC1 from M. soehngenii and proteins MC1 from two other species of Methanosarcinaceae is only 60%. The sequences 17-35 and 45-58 of the protein MC1 appear well conserved. Deletions are observed in region 36-44. Many changes, most of them nonconservative, occur in the carboxyl-terminal third of the protein. However, proline residues at positions 68, 72, 76, and 82 remain strictly conserved. Predictive methods for secondary structures indicate a low content of alpha helix and beta sheet structures in proteins MC1.