• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.

• Interdisciplinary Bio Central
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• v.5 no.2
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• pp.3.1-3.7
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• 2013

Recently, the productivity of drug discovery has gradually decreased as the limitations of single-target-based drugs for various and complex diseases become exposed. To overcome these limitations, drug combinations have been proposed, and great efforts have been made to predict efficacious drug combinations by statistical methods using drug databases. However, previous methods which did not take into account biological networks are insufficient for elaborate predictions. Also, increased evidences to support the fact that drug effects are closely related to metabolic enzymes suggested the possibility for a new approach to the study drug combinations. Therefore, in this paper we suggest a novel approach for analyzing drug combinations using a metabolic network in a systematic manner. The influence of a drug on the metabolic network is described using the distance between the drug target and an enzyme. Target-enzyme distances are converted into influence scores, and from these scores we calculated the correlations between drugs. The result shows that the influence score derived from the targetenzyme distance reflects the mechanism of drug action onto the metabolic network properly. In an analysis of the correlation score distribution, efficacious drug combinations tended to have low correlation scores, and this tendency corresponded to the known properties of the drug combinations. These facts suggest that our approach is useful for prediction drug combinations with an advanced understanding of drug mechanisms.

• BMB Reports
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• v.41 no.8
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• pp.609-614
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• 2008

The concentrations and catalytic activities of enzymes control metabolic rates. Previous studies have focused on enzyme concentrations because there are no genome-wide techniques used for the measurement of enzyme activity. We propose a method for evaluating the significance of enzyme activity by integrating metabolic network topologies and genome-wide microarray gene expression profiles. We quantified the enzymatic activity of reactions and report the 388 significant reactions in five perturbation datasets. For the 388 enzymatic reactions, we identified 70 that were significantly regulated (P-value < 0.001). Thirty-one of these reactions were part of anaerobic metabolism, 23 were part of low-pH aerobic metabolism, 8 were part of high-pH anaerobic metabolism, 3 were part of low-pH aerobic reactions, and 5 were part of high-pH anaerobic metabolism.

• IMMUNE NETWORK
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• v.21 no.3
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• pp.20.1-20.18
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• 2021

The gut is an important organ with digestive and immune regulatory function which consistently harbors microbiome ecosystem. The gut microbiome cooperates with the host to regulate the development and function of the immune, metabolic, and nervous systems. It can influence disease processes in the gut as well as extra-intestinal organs, including the brain. The gut closely connects with the central nervous system through dynamic bidirectional communication along the gut-brain axis. The connection between gut environment and brain may affect host mood and behaviors. Disruptions in microbial communities have been implicated in several neurological disorders. A link between the gut microbiota and the brain has long been described, but recent studies have started to reveal the underlying mechanism of the impact of the gut microbiota and gut barrier integrity on the brain and behavior. Here, we summarized the gut barrier environment and the 4 main gut-brain axis pathways. We focused on the important function of gut barrier on neurological diseases such as stress responses and ischemic stroke. Finally, we described the impact of representative environmental sensors generated by gut bacteria on acute neurological disease via the gut-brain axis.

• BMB Reports
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• v.35 no.2
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• pp.213-219
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• 2002

Malonyl-CoA decarboxylase (E.C.4.1.1.9) catalyzes the conversion of malonyl-CoA to acetyl-CoA. Although the metabolic role of this enzyme has not been fully defined, it has been reported that its deficiency is associated with mild mental retardation, seizures, hypotonia, cadiomyopathy, developmental delay, vomiting, hypoglycemia, metabolic acidosis, and malonic aciduria. Here, we isolated a cDNA clone for malonyl CoA decarboxylase from a rat brain cDNA library, expressed it in E. coli, and characterized its biochemical properties. The full-length cDNA contained a single open-reading frame that encoded 491 amino acid residues with a calculated molecular weight of 54, 762 Da. Its deduced amino acid sequence revealed a 65.6% identity to that from the goose uropigial gland. The sequence of the first 38 amino acids represents a putative mitochondrial targeting sequence, and the last 3 amino acid sequences (SKL) represent peroxisomal targeting ones. The expression of malonyl CoA decarboxylase was observed over a wide range of tissues as a single transcript of 2.0 kb in size. The recombinant protein that was expressed in E. coli was used to characterize the biochemical properties, which showed a typical Michaelis-Menten substrate saturation pattern. The $K_m$ and $V_{max}$ were calculated to be $68\;{\mu}M$ and $42.6\;{\mu}mol/min/mg$, respectively.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Oriental Neuropsychiatry
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• v.26 no.1
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• pp.63-78
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• 2015

Objectives: Pinelliae Rhizoma has traditionally been used as an anti-depressant in oriental medicine. This study is to investigate the effect of Pinelliae Rhizoma extract (PRe) on psychological stress in genome wild expression of mice. Methods: After giving physical stress to mice, PRe was orally administered with 100 mg/kg/day for five days. After extracting whole brain tissue from the mice, their genome changes were observed by micorarray analysis method. The genome changes were analyzed by IMAGENE 4.0, TREEVIEW, FatiGo algorithems, BOND database, cytoscape program, etc. Results: 1. PRe administered group were remained at normal level; 60% of increase was shown in expressed genes by physical stress, and 65% of decrease was shown in expressed genes by psychological stress. 2. Genes with increased expression in control group that remained at a normal state in PRe administered group were involved with the gene of a cellular metabolic process on biological process, protein binding on molecular function, and cell part on cell composition. The pathway was found to be cytokin-cytokin receptor interaction. 3. Genes with decreased expression in control group that remained at a normal state in PRe administered group were involved with the gene of a cellular metabolic process on biologiacl detail and coupled ATPaes activity on molecular function. This gene related path was Ubiquintin mediated proteolysis etc. 4. Core node genes analyzed by protein interaction network were Vinculin, Cell sdivision cycle 42 homolog (S. cerevisiae) etc. They played an important role in maintaining cytoskeleton and controlling cell cycle. Conclusions: Several genes were up-regulated and down-regulated in response to psychological stress. The expression of most of the genes that were altered in response to psychological stress was restored to normal levels in PRe treated mice. When the interaction network information was analyzed, the recovery of the core node genes in PRe treated mice indicates that this final set of genes may be the effective target of PRe.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.233-240
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• 2004

Purpose: We developed a computer-aided classifier using artificial neural network (ANN) to discriminate the cerebral metabolic pattern of medial and lateral temporal lobe epilepsy (TLE). Materials and Methods: We studied brain F-18-FDG PET images of 113 epilepsy patients sugically and pathologically proven as medial TLE (left 41, right 42) or lateral TLE (left 14, right 16). PET images were spatially transformed onto a standard template and normalized to the mean counts of cortical regions. Asymmetry indices for predefined 17 mirrored regions to hemispheric midline and those for medial and lateral temporal lobes were used as input features for ANN. ANN classifier was composed of 3 independent multi-layered perceptrons (1 for left/right lateralization and 2 for medial/lateral discrimination) and trained to interpret metabolic patterns and produce one of 4 diagnoses (L/R medial TLE or L/R lateral TLE). Randomly selected 8 images from each group were used to train the ANN classifier and remaining 51 images were used as test sets. The accuracy of the diagnosis with ANN was estimated by averaging the agreement rates of independent 50 trials and compared to that of nuclear medicine experts. Results: The accuracy in lateralization was 89% by the human experts and 90% by the ANN classifier Overall accuracy in localization of epileptogenic zones by the ANN classifier was 69%, which was comparable to that by the human experts (72%). Conclusion: We conclude that ANN classifier performed as well as human experts and could be potentially useful supporting tool for the differential diagnosis of TLE.