Biomedical subjects

Tomoyuki Higuchi

Publications and source records attributed to Tomoyuki Higuchi.

8 recordsLinked to original sources

PubMed2006

Post-mortem changes in gene expression of the muscle tissue of rainbow trout, Oncorhynchus mykiss.

A subtracted cDNA library was used to identify specific genes that increase in post-mortem muscle of rainbow trout (Oncorhynchus mykiss) during on-ice storage. Of the 200 cDNAs analyzed, 82 had significant homologies to previously identified genes from salmonids and other species such as homologues of troponin I, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and so on, whereas 40 had no significant homologies and were designated as unknown. Comparison of gene expression profiles by dot blot hybridization confirmed an increase or induction of mRNA in the muscle after 3 h of on-ice storage compared to that at 0 h after death. Real-time reverse transcriptase-Polymerase Chain Reaction analysis showed that troponin I and GAPDH mRNAs were increased by 24 h and, in particular, that the change in troponin I mRNA was greater than that of GAPDH mRNA. These results suggest that the increased mRNAs in rainbow trout muscle occurred by transcription immediately after death.

Amino Acid Sequence↗

PubMed2006

Effect of lipids from Erabu sea snake, Laticauda semifasciata, on plasma glucose, insulin, and adipocytokine concentrations of normal and streptozotocin-diabetic mice.

AIMS: To clarify the influence of Erabu sea snake lipid on levels of plasma insulin and adipocyte-derived hormones. METHODS: Normal male mice (6 months) and streptozotocin-diabetic mice (a single administration, 150 mg/kg i.p.) were fed diets containing 10% fat as either lard or a mixture of 5% lard and 5% sea snake lipid for 4 months. RESULTS: The normal mice on the sea snake lipid diet had a significantly lower plasma glucose concentration (198 +/- 16 mg/dl; 148 +/- 11 mg/dl) than those mice on the lard diet. Although plasma insulin concentrations were not influenced by the dietary fat type, leptin (10 +/- 1 ng/ml; 16 +/- 2 ng/ml) and adiponectin (18 +/- 1 mug/ml; 21 +/- 1 mug/ml) concentrations were significantly higher in the sea snake lipid group than in the lard group. However, these effects of a sea snake lipid intake were not evident in the streptozotocin-diabetic mice. CONCLUSION: The results suggest that the decrease in plasma glucose following the intake of sea snake lipids could be related to a corresponding increase in leptin and adiponectin level.

Adiponectin↗

PubMed2006

ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles.

SUMMARY: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.

Bayes Theorem↗

PubMed2006

Reduction in plasma glucose after lipid changes in mice fed fish oil, docosahexaenoic acid, and eicosapentaenoic acid diets.

BACKGROUND/AIM: Effects of the dietary docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and fish oil on weekly changes in plasma glucose, total cholesterol, triacylglycerol, and phospholipid concentrations of male Crlj:CD-1 mice were investigated. METHOD: Animals were fed five different experimental diets which contained 6.0% lard (lard diet), 6.0% fish oil (fish oil diet), 1.5% DHA-ethyl ester+4.5% lard (DHA diet), 0.4% EPA-ethyl ester+5.6% lard (EPA diet), and 1.5% DHA-ethyl ester+0.4% EPA ethyl ester+4.1% lard (DHA+EPA diet) for 17 weeks. Glucose and lipid concentrations in plasma collected at weeks 0, 1, 2, 4, 6, 8, 12, and 16 were measured. RESULTS: Plasma phospholipid concentrations were significantly lower in mice fed the fish oil and DHA+EPA diets than in those fed the lard diet at week 2. Plasma total cholesterol and glucose concentrations were significantly lower in the fish oil diet group than in the lard diet group at weeks 6 and 8, respectively. CONCLUSION: These results indicate that the decreases in plasma glucose concentrations in response to intakes of DHA and EPA in mice take place over a longer period of time than similar decreases in the plasma lipid concentrations.

Animals↗

PubMed2005

Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching.

In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

Algorithms↗

PubMed2004

A mixed factors model for dimension reduction and extraction of a group structure in gene expression data.

When we cluster tissue samples on the basis of genes, the number of observations to be grouped is much smaller than the dimension of feature vector. In such a case, the applicability of conventional model-based clustering is limited since the high dimensionality of feature vector leads to overfitting during the density estimation process. To overcome such difficulty, we attempt a methodological extension of the factor analysis. Our approach enables us not only to prevent from the occurrence of overfitting, but also to handle the issues of clustering, data compression and extracting a set of genes to be relevant to explain the group structure. The potential usefulness are demonstrated with the application to the leukemia dataset.

Algorithms↗

PubMed2004

Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Algorithms↗

PubMed2003

Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks.

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

Bayes Theorem↗