Yukihiro Maki

DEVELOPMENT OF A SYSTEM FOR THE INFERENCE OF LARGE SCALE GENETIC NETWORKS

We propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and introduce two top-down approaches for the inference of interrelated mechanism among genes in genetic network that is based on the steady state and temporal analyses of gene expression patterns against some kinds of gene perturbations such as disruption or overexpression. The former analysis is performed by a static Boolean network model based on multi-level digraph, and the latter one is by S-system model. By integrating these two analyses, we show our strategy is flexible and rich in structure to treat gene expression patterns; we applied our strategy to the inference of a genetic network that is composed of 30 genes as a case study. Given the gene expression time-course data set under the conditions of wild-type and the deletion of one gene, our system enabled us to reconstruct the same network architecture as original one.

A new diabetes model induced by neonatal alloxan treatment in rats

Rats treated with streptozotocin (STZ) during the neonatal period have been used as a model of non-insulin-dependent diabetes mellitus. The present study was designed to produce another diabetes model by substituting alloxan for STZ. Male Sprague-Dawley rats of 2, 4 or 6 days of age were injected intraperitoneally with 200 mg/kg of alloxan monohydrate after 16 h fast. Control rats received vehicle alone at 6 days of age. Non-fasting plasma glucose levels in alloxan-treated rats significantly increased after 8 weeks as compared with control, as the age of alloxan treatment advanced (6.6 +/- 0.2 (S.E.M.) mM in control, 8.3 +/- 0.3 mM in 2 days, P < 0.05, 9.8 +/- 0.9 mM in 4 days, P < 0.05, 17.1 +/- 3.5 mM in 6 days, P < 0.05). For the long-term observation, alloxan-treated rats were divided into mild and severe diabetes groups. Hyperglycemia persisted in both groups until 52 weeks (6.5 +/- 0.1 mM in control, 10.3 +/- 0.7 mM in mild diabetes group, 25.3 +/- 3.6 mM in severe group), but significant albuminuria developed only in severe diabetes group. The diabetogenicity of alloxan rapidly increased during the neonatal period, and the neonatal alloxan diabetes model may be useful for studying chronic diabetic complications.

A new model of Type 2 (non-insulin-dependent) diabetes mellitus in spontaneously hypertensive rats: diabetes induced by neonatal streptozotocin treatment

SummaryThis study was designed to develop an animal model of Type 2 (non-insulin-dependent) diabetes with persistent hypertension. Male spontaneously hypertensive rats were treated with 25.0, 37.5, 50.0, 62.5 or 75.0 mg/kg of streptozotocin given intraperitoneally at 2 days of age and maintained for 12 weeks. In the rats which received 50.0 mg/kg or more streptozotocin, overt hyperglycaemia gradually and consistently developed following incomplete recovery from an initial hyperglycaemia. Compared to vehicle-treated controls, body weight gain in these animals did not differ for the first 8 weeks; thereafter, it was slightly but significantly (p < 0.05) reduced. The animals treated with 25.0 or 37.5 mg/kg streptozotocin developed mild to moderate hyperglycaemia, but their body weight gain was similar to controls. The relationships between streptozotocin dose and metabolic responses (plasma glucose, glycosylated haemoglobin, urinary glucose, food intake, etc.) were clearly demonstrated. Systolic blood pressure rose with progressing age in both controls and streptozotocin-treated rats, irrespective of dosage or metabolic response. This new rat model of Type 2 diabetes associated with persistent hypertension may be useful in studying these combined effects on small and large vessels.

Diabetes induced by neonatal streptozotocin treatment in spontaneously hypertensive and normotensive rats

The development of non-insulin-dependent diabetes mellitus (NIDDM) induced by neonatal streptozotocin (STZ) treatment was compared between male spontaneously hypertensive rats (SHR) and normotensive Wistar Kyoto rats (WKY). The animals were intraperitoneally given 37.5, 50.0, 62.5, or 75.0 mg/kg of STZ at two days of age. At two days after STZ injection, plasma glucose was elevated in both groups of rats according to the dose of STZ, but the level was higher in SHR than in corresponding WKY. At ten days of age, plasma glucose in WKY returned to the similar level to that in vehicle-treated control irrespective of the doses of STZ, while in SHR it remained above control and its level was significantly higher than that in WKY. At 12 weeks of age, plasma glucose was within the control range in WKY, while in SHR it was markedly and dose-dependently elevated. The present study indicates that SHR are susceptible to NIDDM induced by neonatal STZ treatment. The difference in response to STZ between SHR and WKY was discussed.

Multi-layered network structure of amino acid (AA) metabolism characterized by each essential AA-deficient condition

Summary.The concentrations of free amino acids in plasma change coordinately and their profiles show distinctive features in various physiological conditions; however, their behavior can not always be explained by the conventional flow-based metabolic pathway network. In this study, we have revealed the interrelatedness of the plasma amino acids and inferred their network structure with threshold-test analysis and multilevel-digraph analysis methods using the plasma samples of rats which are fed diet deficient in single essential amino acid.In the inferred network, we could draw some interesting interrelations between plasma amino acids as follows: 1) Lysine is located at the top control level and has effects on almost all of the other plasma amino acids. 2) Threonine plays a role in a hub in the network, which has direct links to the most number of other amino acids. 3) Threonine and methionine are interrelated to each other and form a loop structure.

AN INTEGRATED COMPREHENSIVE WORKBENCH FOR INFERRING GENETIC NETWORKS: VOYAGENE

We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.

Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression.

Recent advances in technologies such as DNA microarrays have provided an abundance of gene expression data on the genomic scale. One of the most important projects in the post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data are an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from various network candidates. Common core binomial genetic interactions are reliable interactions with a higher possibility of existence, and are important for understanding the dynamic behavior of gene expression networks. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy (Y. Maki, Y. Takahashi, Y. Arikawa, S. Watanabe, K. Aoshima, Y. Eguchi, T. Ueda, S. Aburatani, S. Kuhara, M. Okamoto, An integrated comprehensive workbench for inferring genetic networks: Voyagene, Journal of Bioinformatics and Computational Biology 2(3) (2004) 533.) with an analysis method for extracting common core binomial genetic interactions.

Determining important regulatory relations of amino acids from dynamic network analysis of plasma amino acids

The changes in the concentrations of plasma amino acids do not always follow the flow-based metabolic pathway network. We have previously shown that there is a control-based network structure among plasma amino acids besides the metabolic pathway map. Based on this network structure, in this study, we performed dynamic analysis using time-course data of the plasma samples of rats fed single essential amino acid deficient diet. Using S-system model (conceptual mathematical model represented by power-law formalism), we inferred the dynamic network structure which reproduces the actual time-courses within the error allowance of 13.17%. By performing sensitivity analysis, three of the most dominant relations in this network were selected; the control paths from leucine to valine, from methionine to threonine, and from leucine to isoleucine. This result is in good agreement with the biological knowledge regarding branched-chain amino acids, and suggests the biological importance of the effect from methionine to threonine.

Early development of nephropathy in a new model of spontaneously hypertensive rat with non-insulin-dependent diabetes mellitus

SummaryWe designed the present study to clarify whether the development of nephropathy was accelerated by a combination of hypertension and non-insulin-dependent diabetes. Spontaneously hypertensive rats with non-insulin-dependent diabetes induced by neonatal streptozotocin treatment (25.0–75.0 mg/kg) were separated into severely or mildly diabetic groups according to their non-fasting plasma glucose levels at 12 weeks of age and the findings were compared with the data on a control group treated with citrate buffer alone. The natural courses of urinary excretion rate of total protein, the molecular composition by sodium dodecyl sulfate polyacrylamide gel electrophoresis with laser desitometer and N-acetyl-β-D-glucosaminidase were measured in the three groups from 12 weeks until 36 weeks of age. Total urinary protein in the control group decreased with age (p<0.05), while in the mildly diabetic group changes were nil; in the severely diabetic group, however, the excretion rates of total urinary protein and high molecular weight protein consistently and progressively increased with age (p<0.05). The low molecular weight protein continuously decreased with age in the mildly diabetic and control groups (p<0.05), while in the severely diabetic group there was no decrease after 28 weeks of age. The urinary N-acetyl-β-D-glucosaminidase markedly increased (p<0.05) in the severely diabetic group throughout the period compared with findings in the control group, but drastically decreased (p<0.05) in the mildly diabetic group with age. There were significant correlations between the mean glycosylated haemoglobin levels and all the urinary parameters measured (p<0.05). These observations suggest that development of nephropathy is accelerated by the glycaemic level in hypertensive rats. This new model should be appropriate for studying the combined effects of hypertension and diabetes mellitus on the kidney.

Design of virtual-labo-system for metabolic engineering : Development of biochemical engineering system analyzing tool-kit (BEST KIT)

Abstract BEST-KIT is an efficient and user-friendly “biochemical engineering system analyzing tool-kit” integrated the following key modules: 1) mathematical modeling and editing of reaction-scheme, 2) automatic derivation of differential equations, 3) numerical calculation, 4) nonlinear optimization, 5) visualization, 6) retrieve the information on reaction mechanism and kinetic parameters from data-base of metabolic pathways. The users of this simulator are assumed to be unfamiliar with computer technology and with computer programming. The integrated interface (UNIX version) is based on Xlib, XToolkit and OSF/Motif Widget.