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ABSTRACT: Considering the similarities between the transcriptional programming involved in cancer progression and somatic cell reprogramming, we tried to identify drugs that would be effective against malignant cancers. We used the EOS (Early transposon Oct4 and Sox2 enhancer) system to select human prostate cancer (PCA) cells expressing high levels of OCT4. Patients with metastatic castration-resistant PCA that does not respond to treatment with docetaxel have few therapeutic options. Indeed, the OCT4-expressing PCA cells selected using the EOS system showed increased tumorigenicity and high resistance to docetaxel, both in vitro and in vivo. By using their gene expression data, expression signature-based prediction for compound candidates identified an anti-viral drug, ribavirin, as a conversion modulator from drug-resistance to sensitivity. Treatment of PCA cells with ribavirin decreased their resistance against treatment with docetaxel. This indicated that ribavirin reversed the gene expression, including that of humoral factors, in the OCT4-expressing PCA cells selected using the EOS system. Thereby, ribavirin increased the efficacy of docetaxel for cancer cells. We propose a novel cell reprogramming approach, named Drug Efficacy Reprogramming, as a new model for identifying candidate anti-tumor drugs. This article is protected by copyright. All rights reserved.
Cancer Science 04/2013; · 3.33 Impact Factor
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Teruyuki Ueda,
Masao Honda, Katsuhisa Horimoto,
Sachiyo Aburatani,
Shigeru Saito,
Taro Yamashita,
Yoshio Sakai,
Mikiko Nakamura,
Hajime Takatori,
Hajime Sunagozaka,
Shuichi Kaneko
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ABSTRACT: Background & Aims Gene expression profiling of hepatocellular carcinoma (HCC) and background liver has been studied extensively; however, the relationship between the gene expression profiles of different lesions has not been assessed. METHODS: We examined the expression profiles of 34 HCC specimens (17 hepatitis B virus [HBV]-related and 17 hepatitis C virus [HCV]-related) and 71 non-tumor liver specimens (36 chronic hepatitis B [CH-B] and 35 chronic hepatitis C [CH-C]) using an in-house cDNA microarray consisting of liver-predominant genes. Graphical Gaussian modeling (GGM) was applied to elucidate the interactions of gene clusters among the HCC and non-tumor lesions. RESULTS: In CH-B-related HCC, the expression of vascular endothelial growth factor-family signaling and regulation of T cell differentiation, apoptosis, and survival, as well as development-related genes was up-regulated. In CH-C-related HCC, the expression of ectodermal development and cell proliferation, wnt receptor signaling, cell adhesion, and defense response genes was also up-regulated. Many of the metabolism-related genes were down-regulated in both CH-B- and CH-C-related HCC. GGM analysis of the HCC and non-tumor lesions revealed that DNA damage response genes were associated with AP1 signaling in non-tumor lesions, which mediates the expression of many genes in CH-B-related HCC. In contrast, signal transducer and activator of transcription 1 and phosphatase and tensin homolog were associated with early growth response protein 1 signaling in non-tumor lesions, which potentially promotes angiogenesis, fibrogenesis, and tumorigenesis in CH-C-related HCC. CONCLUSIONS: Gene expression profiling of HCC and non-tumor lesions revealed the predisposing changes of gene expression in HCC. This approach has potential for the early diagnosis and possible prevention of HCC.
Genomics 02/2013; · 3.02 Impact Factor
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Go Nagamatsu,
Takeo Kosaka,
Shigeru Saito,
Hiroaki Honda,
Keiyo Takubo,
Taisuke Kinoshita,
Hideo Akiyama,
Tetsuo Sudo, Katsuhisa Horimoto,
Mototsugu Oya,
Toshio Suda
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ABSTRACT: Germ cells are similar to pluripotent stem cells in terms of gene expression patterns and the capacity to convert to pluripotent stem cells in culture. The factors involved in germ cell development are also able to reprogram somatic cells. This suggests that germ cells are useful tools for investigating the mechanisms responsible for somatic cell reprogramming. In this study, the expression of reprogramming factors in primordial germ cells (PGCs) was analyzed. PGCs expressed Oct3/4, Sox2 and c-Myc, but not Klf4. However, Klf2, Klf5, Essrb, or Essrg, which were expressed in PGCs, could compensate for Klf4 during somatic cell reprogramming. Furthermore, PGCs could be converted to a pluripotent state by infection with any of the known reprogramming factors (Oct3/4, Sox2, Klf4 and c-Myc). These cells were designated as multipotent PGCs (mPGCs). Contrary to differences in the origins of somatic cells in somatic cell reprogramming, we hypothesized that the gene expression levels of the reprogramming factors would vary in mPGCs. Candidate genes involved in the regulation of tumorigenicity and/or reprogramming efficiency were identified by comparing the gene expression profiles of mPGCs generated by the exogenous expression of c-Myc or L-Myc.
Stem Cells 12/2012; · 7.78 Impact Factor
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ABSTRACT: Somatic cell reprogramming is achieved by four reprogramming transcription factors (RTFs), Oct3/4, Sox2, Klf4, and c-Myc. However, in addition to the induction of pluripotent cells, these RTFs also generate pseudo-pluripotent cells, which do not show Nanog promoter activity. Therefore, it should be possible to fine-tune the RTFs to produce only fully pluripotent cells. For this study, a tagging system was developed to sort induced pluripotent stem (iPS) cells according to the expression levels of each of the four RTFs. Using this system, the most effective ratio (Oct3/4-high, Sox2-low, Klf4-high, c-Myc-high) of the RTFs was 88 times more efficient at producing iPS cells than the worst effective ratio (Oct3/4-low, Sox2-high, Klf4-low, c-Myc-low). Among the various RTF combinations, Oct3/4-high and Sox2-low produced the most efficient results. To investigate the molecular basis, microarray analysis was performed on iPS cells generated under high (Oct3/4-high and Sox2-low) and low (Oct3/4-low and Sox2-high) efficiency reprogramming conditions. Pathway analysis revealed that the G protein-coupled receptor (GPCR) pathway was up-regulated significantly under the high efficiency condition and treatment with the chemokine, C-C motif ligand 2, a member of the GPCR family, enhanced somatic cell reprogramming 12.3 times. Furthermore, data from the analysis of the signature gene expression profiles of mouse embryonic fibroblasts at 2 days after RTF infection revealed that the genetic modifier, Whsc1l1 (variant 1), also improved the efficiency of somatic cell reprogramming. Finally, comparison of the overall gene expression profiles between the high and low efficiency conditions will provide novel insights into mechanisms underlying somatic cell reprogramming.
Journal of Biological Chemistry 09/2012; 287(43):36273-82. · 4.77 Impact Factor
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ABSTRACT: To understand mechanisms underlying acquisition of pluripotency, it is critical to identify cells that can be converted to pluripotent stem cells. For this purpose, we focused on unipotent primordial germ cells (PGCs), which can be reprogrammed into pluripotent embryonic germ (EG) cells under defined conditions. Treatment of PGCs with combinations of signaling inhibitors, including inhibitors of MAP2K (MEK), GSK3B (GSK-3beta), and TGFB (TGFbeta) type 1 receptors, induced cells to enter a pluripotent state at a high frequency (12.1%) by Day 10 of culture. When we employed fluorescence-activated cell sorting to monitor conversion of candidate cells to a pluripotent state, we observed a cell cycle shift to S phase, indicating enrichment of pluripotent cells, during the early phase of EG formation. Transcriptome analysis revealed that PGCs retained expression of some pluripotent stem cell-associated genes, such as Pou5f1 and Sox2, during EG cell formation. On the other hand, PGCs lost their germ lineage characteristics and acquired expression of pluripotent stem cell markers, such as Klf4 and Eras. The overall gene expression profiles revealed by this system provide novel insight into how pluripotency is acquired in germ-committed cells.
Biology of Reproduction 03/2012; 86(6):182. · 4.01 Impact Factor
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ABSTRACT: We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.
TheScientificWorldJOURNAL 01/2012; 2012:957620. · 1.66 Impact Factor
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Hiroaki Tateno,
Masashi Toyota,
Shigeru Saito,
Yasuko Onuma,
Yuzuru Ito,
Keiko Hiemori,
Mihoko Fukumura,
Asako Matsushima,
Mio Nakanishi,
Kiyoshi Ohnuma,
Hidenori Akutsu,
Akihiro Umezawa, Katsuhisa Horimoto,
Jun Hirabayashi,
Makoto Asashima
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ABSTRACT: Induced pluripotent stem cells (iPSCs) can now be produced from various somatic cell (SC) lines by ectopic expression of the four transcription factors. Although the procedure has been demonstrated to induce global change in gene and microRNA expressions and even epigenetic modification, it remains largely unknown how this transcription factor-induced reprogramming affects the total glycan repertoire expressed on the cells. Here we performed a comprehensive glycan analysis using 114 types of human iPSCs generated from five different SCs and compared their glycomes with those of human embryonic stem cells (ESCs; nine cell types) using a high density lectin microarray. In unsupervised cluster analysis of the results obtained by lectin microarray, both undifferentiated iPSCs and ESCs were clustered as one large group. However, they were clearly separated from the group of differentiated SCs, whereas all of the four SCs had apparently distinct glycome profiles from one another, demonstrating that SCs with originally distinct glycan profiles have acquired those similar to ESCs upon induction of pluripotency. Thirty-eight lectins discriminating between SCs and iPSCs/ESCs were statistically selected, and characteristic features of the pluripotent state were then obtained at the level of the cellular glycome. The expression profiles of relevant glycosyltransferase genes agreed well with the results obtained by lectin microarray. Among the 38 lectins, rBC2LCN was found to detect only undifferentiated iPSCs/ESCs and not differentiated SCs. Hence, the high density lectin microarray has proved to be valid for not only comprehensive analysis of glycans but also diagnosis of stem cells under the concept of the cellular glycome.
Journal of Biological Chemistry 04/2011; 286(23):20345-53. · 4.77 Impact Factor
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ABSTRACT: We developed a method in which the relationship between chemical compounds, characterized by the secondary dimensional descriptors by a standard method, is first determined by network inference, and then the inferred network is divided into the compound groups by network clustering. We applied this method to 279 active inhibitors of factor Xa found by the first screening. A large network of 266 active compounds connected with 408 edges emerged and was divided into 10 clusters. Surprisingly, the chemical structures that were common within the clusters, but diverse between them, could be extracted. The activity differences between the clusters provide rational clues for the systematic synthesis of derivatives in the lead optimization process, instead of empirical and intuitive inspections. Thus, our method for automatically grouping the chemical compounds by a network approach is useful to improve the efficiency of the drug discovery process.
Journal of Chemical Information and Modeling 01/2011; 51(1):61-8. · 4.68 Impact Factor
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Shigeru Saito,
Yasuko Onuma,
Yuzuru Ito,
Hiroaki Tateno,
Masashi Toyoda,
Akutsu Hidenori,
Koichiro Nishino,
Emi Chikazawa,
Yoshihiro Fukawatase,
Yoshitaka Miyagawa,
Hajime Okita,
Nobutaka Kiyokawa,
Yohichi Shimma,
Akihiro Umezawa,
Jun Hirabayashi, Katsuhisa Horimoto,
Makoto Asashima
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ABSTRACT: Human iPS cells (hiPSCs) have attracted considerable attention for applications to drug screening and analyses of disease mechanisms, and even as next generation materials for regenerative medicine. Genetic reprogramming of human somatic cells to a pluripotent state was first achieved by the ectopic expression of four factors (Sox2, Oct4, Klf4 and c-Myc), using a retrovirus. Subsequently, this method was applied to various human cells, using different combinations of defined factors. However, the transcription factor-induced acquisition of replication competence and pluripotency raises the question as to how exogenous factors induce changes in the inner and outer cellular states.
We analyzed both the RNA profile, to reveal changes in gene expression, and the glycan profile, to identify changes in glycan structures, between 51 cell samples of four parental somatic cell (SC) lines from amniotic mesodermal, placental artery endothelial, and uterine endometrium sources, fetal lung fibroblast (MRC-5) cells, and nine hiPSC lines that were originally established. The analysis of this information by standard statistical techniques combined with a network approach, named network screening, detected significant expression differences between the iPSCs and the SCs. Subsequent network analysis of the gene expression and glycan signatures revealed that the glycan transfer network is associated with known epitopes for differentiation, e.g., the SSEA epitope family in the glycan biosynthesis pathway, based on the characteristic changes in the cellular surface states of the hiPSCs.
The present study is the first to reveal the relationships between gene expression patterns and cell surface changes in hiPSCs, and reinforces the importance of the cell surface to identify established iPSCs from SCs. In addition, given the variability of iPSCs, which is related to the characteristics of the parental SCs, a glycosyltransferase expression assay might be established to define hiPSCs more precisely and thus facilitate their standardization, which are important steps towards the eventual therapeutic applications of hiPSCs.
BMC Systems Biology 01/2011; 5 Suppl 1:S17. · 3.15 Impact Factor
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Yasuaki Oda,
Yasuhide Yoshimura,
Hiroe Ohnishi,
Mika Tadokoro,
Yoshihiro Katsube,
Mari Sasao,
Yoko Kubo,
Koji Hattori,
Shigeru Saito, Katsuhisa Horimoto,
Shunsuke Yuba,
Hajime Ohgushi
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ABSTRACT: The expression of four transcription factors (OCT3/4, SOX2, KLF4, and MYC) can reprogram mouse as well as human somatic cells to induced pluripotent stem (iPS) cells. We generated iPS cells from mesenchymal stromal cells (MSCs) derived from human third molars (wisdom teeth) by retroviral transduction of OCT3/4, SOX2, and KLF4 without MYC, which is considered as oncogene. Interestingly, some of the clonally expanded MSCs could be used for iPS cell generation with 30-100-fold higher efficiency when compared with that of other clonally expanded MSCs and human dermal fibroblasts. Global gene expression profiles demonstrated some up-regulated genes regarding DNA repair/histone conformational change in the efficient clones, suggesting that the processes of chromatin remodeling have important roles in the cascade of iPS cells generation. The generated iPS cells resembled human embryonic stem (ES) cells in many aspects, including morphology, ES marker expression, global gene expression, epigenetic states, and the ability to differentiate into the three germ layers in vitro and in vivo. Because human third molars are discarded as clinical waste, our data indicate that clonally expanded MSCs derived from human third molars are a valuable cell source for the generation of iPS cells.
Journal of Biological Chemistry 09/2010; 285(38):29270-8. · 4.77 Impact Factor
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ABSTRACT: Signaling pathways are often represented by networks where each node corresponds to a protein and each edge corresponds to a relationship between nodes such as activation, inhibition and binding. However, such signaling pathways in a cell may be affected by genetic and epigenetic alteration. Some edges may be deleted and some edges may be newly added. The current knowledge about known signaling pathways is available on some public databases, but most of the signaling pathways including changes upon the cell state alterations remain largely unknown. In this paper, we develop an integer programming-based method for inferring such changes by using gene expression data. We test our method on its ability to reconstruct the pathway of colorectal cancer in the KEGG database.
Genome informatics. International Conference on Genome Informatics 07/2010; 24(1):193-203.
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Masao Honda,
Akito Sakai,
Tatsuya Yamashita,
Yasunari Nakamoto,
Eishiro Mizukoshi,
Yoshio Sakai,
Taro Yamashita,
Mikiko Nakamura,
Takayoshi Shirasaki, Katsuhisa Horimoto,
Yasuhito Tanaka,
Katsushi Tokunaga,
Masashi Mizokami,
Shuichi Kaneko
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ABSTRACT: Multiple viral and host factors are related to the treatment response to pegylated-interferon and ribavirin combination therapy; however, the clinical relevance and relationship of these factors have not yet been fully evaluated.
We studied 168 patients with chronic hepatitis C who received pegylated-interferon and ribavirin combination therapy. Gene expression profiles in the livers of 91 patients were analyzed using an Affymetrix genechip (Affymetrix, Santa Clara, CA). The expression of interferon-stimulated genes (ISGs) was evaluated in all samples by real-time polymerase chain reaction. Genetic variation in interleukin 28B (IL28B; rs8099917) was determined in 91 patients.
Gene expression profiling of the liver differentiated patients into 2 groups: patients with up-regulated ISGs and patients with down-regulated ISGs. A high proportion of patients with no response to treatment was found in the up-regulated ISGs group (P=.002). Multivariate logistic regression analysis showed that ISGs (<3.5) (odds ratio [OR], 16.2; P<.001), fibrosis stage (F1-F2) (OR, 4.18; P=.003), and ISDR mutation (>or=2) (OR, 5.09; P=.003) were strongly associated with the viral response. The IL28B polymorphism of 91 patients showed that 66% were major homozygotes (TT), 30% were heterozygotes (TG), and 4% were minor homozygotes (GG). Interestingly, hepatic ISGs were associated with the IL28B polymorphism (OR, 18.1; P<.001), and its expression was significantly higher in patients with the minor genotype (TG or GG) than in those with the major genotype (TT).
The expression of hepatic ISGs is strongly associated with treatment response and genetic variation of IL28B. The differential role of host and viral factors as predicting factors may also be present.
Gastroenterology 04/2010; 139(2):499-509. · 11.68 Impact Factor
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ABSTRACT: Acute coronary syndrome is sometimes accompanied by accelerated coagulability, lipid metabolism, and inflammatory responses, which are not attributable to the cardiac events alone. We hypothesized that the liver plays a pivotal role in the pathophysiology of acute coronary syndrome. We simultaneously analyzed the gene expression profiles of the liver and heart during acute myocardial ischemia in mice.
-Mice were divided into 3 treatment groups: sham operation, ischemia/reperfusion, and myocardial infarction. Mice with liver ischemia/reperfusion were included as additional controls. Marked changes in hepatic gene expression were observed after 24 hours, despite the lack of histological changes in the liver. Genes related to tissue remodeling, adhesion molecules, and morphogenesis were significantly upregulated in the livers of mice with myocardial ischemia/reperfusion or infarction but not in those with liver ischemia/reperfusion. Myocardial ischemia, but not changes in the hemodynamic state, was postulated to significantly alter hepatic gene expression. Moreover, detailed analysis of the signaling pathway suggested the presence of humoral factors that intervened between the heart and liver. To address these points, we used isolated primary hepatocytes and showed that osteopontin released from the heart actually altered the signaling pathways of primary hepatocytes to those observed in the livers of mice under myocardial ischemia. Moreover, osteopontin stimulated primary hepatocytes to secrete vascular endothelial growth factor-A, which is important for tissue remodeling.
Hepatic gene expression is potentially regulated by cardiac humoral factors under myocardial ischemia. These results provide new insights into the pathophysiology of acute coronary syndrome.
Circulation Cardiovascular Genetics 02/2010; 3(1):68-77. · 6.11 Impact Factor
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ABSTRACT: The investigation of network dynamics is a major issue in systems and synthetic biology. One of the essential steps in a dynamics investigation is the parameter estimation in the model that expresses biological phenomena. Indeed, various techniques for parameter optimization have been devised and implemented in both free and commercial software. While the computational time for parameter estimation has been greatly reduced, due to improvements in calculation algorithms and the advent of high performance computers, the accuracy of parameter estimation has not been addressed.
We propose a new approach for parameter optimization by using differential elimination, to estimate kinetic parameter values with a high degree of accuracy. First, we utilize differential elimination, which is an algebraic approach for rewriting a system of differential equations into another equivalent system, to derive the constraints between kinetic parameters from differential equations. Second, we estimate the kinetic parameters introducing these constraints into an objective function, in addition to the error function of the square difference between the measured and estimated data, in the standard parameter optimization method. To evaluate the ability of our method, we performed a simulation study by using the objective function with and without the newly developed constraints: the parameters in two models of linear and non-linear equations, under the assumption that only one molecule in each model can be measured, were estimated by using a genetic algorithm (GA) and particle swarm optimization (PSO). As a result, the introduction of new constraints was dramatically effective: the GA and PSO with new constraints could successfully estimate the kinetic parameters in the simulated models, with a high degree of accuracy, while the conventional GA and PSO methods without them frequently failed.
The introduction of new constraints in an objective function by using differential elimination resulted in the drastic improvement of the estimation accuracy in parameter optimization methods. The performance of our approach was illustrated by simulations of the parameter optimization for two models of linear and non-linear equations, which included unmeasured molecules, by two types of optimization techniques. As a result, our method is a promising development in parameter optimization.
BMC Systems Biology 01/2010; 4 Suppl 2:S9. · 3.15 Impact Factor
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ABSTRACT: Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) not only causes apoptotic cell death in tumor cells, but also activates some transcription factors and affects several other cellular functions. In this study, we observed the effect of administration of TRAIL on gene expression downstream of the cyclic AMP responsive element (CRE) enhancer by using the signal transduction reporter cis-element plasmid pCRE-d2EGFP. Western blotting showed that after administration of TRAIL, the expression level of reporter protein d2EGFP was down-regulated in NIH3T3 cells. To confirm the TRAIL-induced down-regulation of CRE enhancer controlled gene expression, DNA Chip time series analysis of the intrinsic genes expressed in NIH3T3 cells was carried out. As a result, the expression levels of six genes, which have CRE sequence in their promoter region, were slightly down-regulated within three hours after administration of TRAIL.
Biochemical and Biophysical Research Communications 03/2009; 381(4):533-6. · 2.48 Impact Factor
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Mathematics in Computer Science. 01/2009; 2:399-400.
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Algorithmic Learning Theory, 20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009. Proceedings; 01/2009
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ABSTRACT: The assessment of co-expressed genes is an initial step to investigate the gene regulatory system in a microarray analysis. We designed a simple method to assess co-expressed genes from the expression profiles, based on the path consistency (PC) algorithm, which systematically infers a causal graph. The PC algorithm is simply modified for the frequent occurrence of high correlations between expression profiles, which causes an accidental stop of the algorithm by the violation of a numerical calculation. By utilizing the flexible features of the algorithm, biological information about co-expressed genes can be introduced into the algorithm. The performance is illustrated by the application to the gene profiles of operons: 1168 gene profiles in 137 known operons in Escherichia coli. By the improved algorithm, more than 80% of the operons were correctly detected, and less than one gene per operon was falsely detected. The performance promises the wide feasibility of the present method for the assessment of co-expressed genes from microarray data on a genomic scale.
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA, 11-14 October 2009; 01/2009
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ABSTRACT: MicroRNA (miRNA) plays an important role in the pathology of various diseases, including infection and cancer. Using real-time polymerase chain reaction, we measured the expression of 188 miRNAs in liver tissues obtained from 12 patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) and 14 patients with hepatitis C virus (HCV)-related HCC, including background liver tissues and normal liver tissues obtained from nine patients. Global gene expression in the same tissues was analyzed via complementary DNA microarray to examine whether the differentially expressed miRNAs could regulate their target genes. Detailed analysis of the differentially expressed miRNA revealed two types of miRNA, one associated with HBV and HCV infections (n = 19), the other with the stage of liver disease (n = 31). Pathway analysis of targeted genes using infection-associated miRNAs revealed that the pathways related to cell death, DNA damage, recombination, and signal transduction were activated in HBV-infected liver, and those related to immune response, antigen presentation, cell cycle, proteasome, and lipid metabolism were activated in HCV-infected liver. The differences in the expression of infection-associated miRNAs in the liver correlated significantly with those observed in Huh7.5 cells in which infectious HBV or HCV clones replicated. Out of the 31 miRNAs associated with disease state, 17 were down-regulated in HCC, which up-regulated cancer-associated pathways such as cell cycle, adhesion, proteolysis, transcription, and translation; 6 miRNAs were up-regulated in HCC, which down-regulated anti-tumor immune response. CONCLUSION: miRNAs are important mediators of HBV and HCV infection as well as liver disease progression, and therefore could be potential therapeutic target molecules.
Hepatology 12/2008; 49(4):1098-112. · 11.66 Impact Factor
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ABSTRACT: A knowledge-based network, which is constructed by extracting as many relationships identified by experimental studies as possible and then superimposing them, is one of the promising approaches to investigate the associations between biological molecules. However, the molecular relationships change dynamically, depending on the conditions in a living cell, which suggests implicitly that all of the relationships in the knowledge-based network do not always exist. Here, we propose a novel method to estimate the consistency of a given network with the measured data: i) the network is quantified into a log-likelihood from the measured data, based on the Gaussian network, and ii) the probability of the likelihood corresponding to the measured data, named the graph consistency probability (GCP), is estimated based on the generalized extreme value distribution.
The plausibility and the performance of the present procedure are illustrated by various graphs with simulated data, and with two types of actual gene regulatory networks in Escherichia coli: the SOS DNA repair system with the corresponding data measured by fluorescence, and a set of 29 networks with data measured under anaerobic conditions by microarray. In the simulation study, the procedure for estimating GCP is illustrated by a simple network, and the robustness of the method is scrutinized in terms of various aspects: dimensions of sampling data, parameters in the simulation study, magnitudes of data noise, and variations of network structures. In the actual networks, the former example revealed that our method operates well for an actual network with a size similar to those of the simulated networks, and the latter example illustrated that our method can select the activated network candidates consistent with the actual data measured under specific conditions, among the many network candidates.
The present method shows the possibility of bridging between the static network from the literature and the corresponding measurements, and thus will shed light on the network structure variations in terms of the changes in molecular interaction mechanisms that occur in response to the environment in a living cell.
BMC Systems Biology 11/2008; 2:84. · 3.15 Impact Factor
