Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.

Application of a target array comparative genomic hybridization to prenatal diagnosis.

BACKGROUND: While conventional G-banded karyotyping still remains a gold standard in prenatal genetic diagnoses, the widespread adoption of array Comparative Genomic Hybridization (array CGH) technology for postnatal genetic diagnoses has led to increasing interest in the use of this same technology for prenatal diagnosis. We have investigated the value of our own designed DNA chip as a prenatal diagnostic tool for detecting submicroscopic deletions/duplications and chromosome aneuploidies. METHODS: We designed a target bacterial artificial chromosome (BAC)-based aCGH platform (MacArray M-chip), which specifically targets submicroscopic deletions/duplications for 26 known genetic syndromes of medical significance observed prenatally. To validate the DNA chip, we obtained genomic DNA from 132 reference materials generated from patients with 22 genetic diseases and 94 clinical amniocentesis samples obtained for karyotyping. RESULTS: In the 132 reference materials, all known genomic alterations were successfully identified. In the 94 clinical samples that were also subjected to conventional karyotyping, three cases of balanced chromosomal aberrations were not detected by aCGH. However, we identified eight cases of microdeletions in the Yq11.23 chromosomal region that were not found by conventional karyotyping. This region harbors the DAZ gene, and deletions may lead to non-obstructive spermatogenesis. CONCLUSIONS: We have successfully designed and applied a BAC-based aCGH platform for prenatal diagnosis. This platform can be used in conjunction with conventional karyotyping and will provide rapid and accurate diagnoses for the targeted genomic regions while eliminating the need to interpret clinically-uncertain genomic regions.

Gene expression profiling of human gastrointestinal stromal tumors according to its malignant potential.

PURPOSE: Surgical removal or treatment with Imatinib mesylate (STI-571/Gleevec) is shown to be highly effective in gastrointestinal stromal tumors (GISTs). However, it is unclear the understanding of the molecular basis in GISTs according to its malignant potential. The aim of this study was therefore to determine the gene expression profiles according to GISTs risk progresses. RESULTS: In this study, we performed a cDNA microarray with 30 human GIST tissues using the Mac Array-Express 10K chip (10,800 genes), and compared their gene expression profiles among low (n=10), intermediate (n=8), and high-risk groups (n=12) according to NIH consensus criteria. A total of 181 genes were identified to be expressed differentially according to GISTs risk category. After clustering by self-organizing maps, the expression profiles of 32 genes sequentially increased as the tumor risk increased, and those of 37 genes sequentially decreased as the tumor risk increased. Identified targets have been cross referenced against their involvements in different cellular pathways, according to GenMAPP, KEGG, and BioCarta. In pathway-enrichment analysis, eight up-regulated pathways and ten down-regulated pathways were significantly enriched. CONCLUSIONS: Our results showed a remarkably distinct and uniform expression pattern in GISTs progression. Moreover, the expression profiling of GISTs may be used as a basic reference to better understand the molecular basis of GISTs tumorigenesis and to identify a novel target molecule for replacing KIT and PDGFRA for a complementary diagnosis and effective curative treatments.

Improving gene expression similarity measurement using pathway-based analytic dimension.

BACKGROUND: Gene expression similarity measuring methods were developed and applied to search rapidly growing public microarray databases. However, current expression similarity measuring methods need to be improved to accurately measure similarity between gene expression profiles from different platforms or different experiments. RESULTS: We devised new gene expression similarity measuring method based on pathway information. In short, newly devised method measure similarity between gene expression profiles after converting them into pathway based expression profiles. To evaluate pathway based gene expression similarity measuring method, we conducted cell type classification test. Pathway based similarity measuring method shows higher classification accuracy. Especially, pathway based methods outperform at most 50% and 10% over conventional gene expression similarity method when search databases are limited to cross-platform profiles and cross-experiment profiles. CONCLUSION: The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods. Considering the fact that public microarray database is consist of gene expression profiles of various experiments with various type of platform, pathway based gene expression similarity measuring method could be successfully applied for searching large public microarray databases.

Frequent Users of Hospital Emergency Departments in Korea Characterized by Claims Data from the National Health Insurance: A Cross Sectional Study.

The Korean National Health Insurance, which provides universal coverage for the entire Korean population, is now facing financial instability. Frequent emergency department (ED) users may represent a medically vulnerable population who could benefit from interventions that both improve care and lower costs. To understand the nature of frequent ED users in Korea, we analyzed claims data from a population-based national representative sample. We performed both bivariate and multivariable analyses to investigate the association between patient characteristics and frequent ED use (4+ ED visits in a year) using claims data of a 1% random sample of the Korean population, collected in 2009. Among 156,246 total ED users, 4,835 (3.1%) were frequent ED users. These patients accounted for 14% of 209,326 total ED visits and 17.2% of $76,253,784 total medical expenses generated from all ED visits in the 1% data sample. Frequent ED users tended to be older, male, and of lower socio-economic status compared with occasional ED users (p < 0.001 for each). Moreover, frequent ED users had longer stays in the hospital when admitted, higher probability of undergoing an operative procedure, and increased mortality. Among 8,425 primary diagnoses, alcohol-related complaints and schizophrenia showed the strongest positive correlation with the number of ED visits. Among the frequent ED users, mortality and annual outpatient department visits were significantly lower in the alcohol-related patient subgroup compared with other frequent ED users; furthermore, the rate was even lower than that for non-frequent ED users. Our findings suggest that expanding mental health and alcohol treatment programs may be a reasonable strategy to decrease the dependence of these patients on the ED.

Identification of NUCKS1 as a putative oncogene and immunodiagnostic marker of hepatocellular carcinoma.

Although the molecular mechanisms underpinning hepatocellular carcinoma (HCC) are unknown, gene copy number and associated mRNA expression changes are frequently reported. Comparative genomic hybridization arrays spotted with 4041 bacterial artificial chromosome clones were used to assess copy number changes in 45 HCC tissues. Seventy more HCC tissues were used to validate candidate genes by using western blots and immunohistochemistry. A total of 259 clones were associated with copy number changes that significantly differed between normal liver and HCC samples. The chromosomal region 1q32.1 containing the nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) gene was associated with tumor vascular invasion. Western blot analysis demonstrated that NUCKS1 was up-regulated in 37 of 70 (52.8%) HCC tissues compared with adjacent non-tumor tissues, and over-expressed in a vast majority of HCCs (44/52, 84.6%) as determined by immunohistochemical staining. Furthermore, immunostaining of both NUCKS1 and glypican-3 improved the diagnostic prediction of HCC. Knock-down of NUCKS1 by siRNA implied the decrease in cell viability of the Hep3B cell line and reduced tumor formation in a xenograft mouse model. NUCKS1 was identified as a potential oncogene at chromosomal 1q32.1 in patients with HCC, and it might be a valuable immunodiagnostic marker for HCC.

Similarity-based disease risk assessment for personal genomes: proof of concept.

The increasing availability of personal genome data has led to escalating needs by consumers to understand the implications of their gene sequences. At present, poorly integrated genetic knowledge has not met these needs. This proof-of-concept study proposes a similarity-based approach to assess the disease risk predisposition for personal genomes. We hypothesize that the semantic similarity between a personal genome and a disease can indicate the disease risks in the person. We developed a knowledge network that integrates existing knowledge of genes, diseases, and symptoms from six sources using the Semantic Web standard, Resource Description Framework (RDF). We then used latent relationships between genes and diseases derived from our knowledge network to measure the semantic similarity between a personal genome and a genetic disease. For demonstration, we showed the feasibility of assessing the disease risks in one personal genome and discussed related methodology issues.

Common genetic variants associated with breast cancer in Korean women and differential susceptibility according to intrinsic subtype.

BACKGROUND: Recently identified genetic variants from genome-wide association studies (GWAS) on breast cancer have not been validated in Asian populations, except in China. In this study, we sought to confirm the association between known variants and breast cancer in Korean women and further evaluate the associations of individual single nucleotide polymorphisms (SNP) with different intrinsic subtypes of breast cancer. METHODS: In total, 3,321 invasive breast cancer patients and 3,500 healthy controls were genotyped for five SNPs by using the TaqMan assay. The SNPs genotyped included rs2046210 (6q25.1), rs2981582 (FGFR2), rs889312 (MAP3K1), rs3803662 (TOX3/TNRC9), and rs4973768 (SLC4A7). Tumors were classified into four intrinsic subtypes based on estrogen receptor (ER), progesterone receptor, HER2, and Ki67 expression. RESULTS: All five SNPs were significantly associated with risk of breast cancer in dominant, recessive, and additive models. ORs per risk allele (95% CI) were 1.29 (1.16-1.43), 1.40 (1.18-1.68), 1.22 (1.06-1.41), 1.52 (1.30-1.77), and 1.20 (1.08-1.33) for rs2046210, rs2981582, rs889312, rs3803662, and rs4973768, respectively. A multigene logistic regression risk model was generated with the SNPs. In subtype analysis, all 5 SNPs were associated with the Luminal A subtype. Two SNPs (rs2046210 and rs3803662) were linked to the ER(-)HER2(+) subtype, and only rs2046210 SNP was associated with the triple-negative subtype. CONCLUSIONS: The five SNPs from GWAS were significantly associated with breast cancer risk in Korean women. Associations were heterogeneous according to the intrinsic subtype of breast cancer. IMPACT: Our result is an important contribution to the literature about genetic susceptibility for breast cancer in nonwhite populations.

17p12 deletion in breast cancer predicts resistance to neoadjuvant chemotherapy.

Numerous studies have attempted to identify gene expression profiles which can be utilized to predict responses to neoadjuvant chemotherapy (NAC), but their findings are not clinically applicable at present. In the present study, we sought to determine DNA copy number alterations (CNAs) in breast cancer tissues which are associated with the response to NAC. Frozen tumor tissues from 63 breast cancer patients were obtained using core needle biopsy prior to NAC (3 cycles of docetaxel plus adriamycin) and were microdissected. Array comparative genomic hybridization (array CGH) with 4,045 bacterial artificial chromosome (BAC) probes was performed to identify the CNAs. Changes in tumor size in response to NAC were measured via magnetic resonance imaging. Fluorescence in situ hybridization (FISH) was conducted to verify array CGH results and for independent validation studies. CNAs at eight chromosomal loci encompassing 24 clones were correlated with changes in tumor size after NAC (p<0.05; t-test). Two CNAs were selected, 17p12 deletion and 17q21.32-33 gain, which were significantly associated with a smaller reduction in tumor size following NAC, via prioritization of the regions containing the candidate genes. In an independent validation set of samples from 39 patients, FISH assay further showed that the 17p12 deletion was markedly associated with smaller changes in tumor size (p=0.006), while the 17q21.32-33 gain was not significant (p=0.309). In conclusion, we successfully identified a 17p12 deletion in breast cancer tissue which can be applied in predicting tumor resistance to NAC.

Identifying regulatory relationships among genomic loci, biological pathways, and disease.

OBJECTIVE: Elucidating genetic factors of complex diseases is one of the most important challenges in biomedical research. Recently, a genetical genomics approach of mapping genotype to transcripts has been used in complex disease analysis. This approach treats messenger ribonucleic acid (mRNA) expression as a quantitative trait and identifies putative regulatory loci for the expression of an individual gene. However, the single-gene approach could not detect single nucleotide polymorphisms (SNP's) associated with the concerted activity of multiple genes. Since complex diseases result from the interactions of multiple genes, it is important to consider associations between genotype and multiple gene expression. METHODS AND MATERIALS: We developed the differential allelic co-expression (DACE) that identifies regulatory loci that affect the inter-correlation structure of multiple genes or a gene set. We applied DACE to two benchmark datasets: the normal human lymphoblastoid cell dataset and the glioblastoma dataset. These datasets consist of both SNPs and mRNA expression profiles for each sample. When analyzing the lymphoblastoid cell dataset, principal component analysis (PCA) was compared with the DACE test. RESULTS: While PCA identified associations found by single-gene analysis, the DACE test detected associations not identified by the single-gene approach. Using the DACE test, seven putative regulatory loci of immune-related pathways were identified in lymphoblastoid cells after controlling for family-wise error rate. In the glioblastoma dataset, DACE identified 4582 SNPs associated with six pathways. In 231 of the 4582 SNPs, patient survival length was correlated significantly with the SNP genotype. This finding suggests that our integrative approach may provide a biological explanation for the putative relationship between sequence level variation and disease outcome, via expression of a functional pathway. CONCLUSION: The DACE test shows promise for finding regulatory relationships between a genomic locus and sets of genes which may be related to disease outcome.

Modification of the bulk properties of the porous poly(lactide-co-glycolide) scaffold by irradiation with a cyclotron ion beam with high energy for its application in tissue engineering.

Understanding the bulk properties of a prefabricated scaffold for handling and degradation during cell culture may be advantageous to its application in tissue engineering. Modification of the bulk properties of the porous poly(lactide-co-glycolide) (PLGA) scaffold was performed by irradiation with a high energy cyclotron proton ion beam. The porous PLGA scaffolds were fabricated in advance by the gas-foaming method by employing ammonium bicarbonate particles as porogens. Irradiation with ion beams was performed with 40 MeV for 3, 6 and 9 min on the scaffolds at a distance of 30 cm from the beam exit to the scaffold surface. The bulk area of the ion beam-treated PLGA scaffold apparently demonstrated no color changes when observed with a digital camera. The chemical structures of the untreated samples seemed to be kept well when analyzed by both Fourier transformed infrared but a subtle change was observed in its x-ray photoelectron spectroscopy. The results of in vitro tissue culture with smooth muscle cells for up to 4 weeks also demonstrated no significant difference in terms of its handling stability during cell culture and cellular behavior between the untreated PLGA scaffolds and the ion beam-treated ones. However, significant changes were observed in its molecular weight as measured by gel permeation chromatography, indicating a significant reduction of its molecular weights. These results of in vitro tests and GPC measurements indicated that while bulk modification of the scaffold was processed, its handling was stable during in vitro cell culture for up to 4 weeks.

The JNK-dependent CaMK pathway restrains the reversion of committed cells during osteoclast differentiation.

Osteoclastogenesis involves the commitment of macrophage-lineage precursors to tartrate-resistant acid phosphatase-positive (TRAP+) mononuclear pre-osteoclasts (pOCs) and subsequent fusion of pOCs to form multinuclear mature osteoclasts. Despite many studies on osteoclast differentiation, little is known about the signaling mechanisms that specifically mediate the osteoclastic commitment. In this study, we found that inhibition of JNK at the pOC stage provoked reversion of TRAP(+) cells to TRAP(-) cells. The conversion to TRAP(-) cells occurred with concomitant return to the state with higher expression of macrophage antigens, and greater activity of phagocytosis and dendritic-differentiation potential. JNK inhibition at the pOC stage reduced NFATc1 and CaMK levels, and addition of active NFATc1 partially rescued the effect of JNK inhibition. In addition, the level of NFATc1 was decreased by knockdown of CaMK by RNAi and by catalytic inhibition of CaMK, which both caused the reversion of pOCs to macrophages. These data suggest that JNK activity is specifically required for maintaining the committed status during osteoclastogenesis and that the CaMK-NFATc1 pathway is the key element in that specific role of JNK.

Finding pathway regulators: gene set approach using peak identification algorithms.

Recently, a number of different approaches have been used to examine variation in gene expression and to identify genes whose level of transcript differed greatly among unrelated individuals. Previous studies have commonly focused on identifying determinants that regulate gene expressions by targeting individual genes. However, it is difficult to detect true differences in the level of gene expression among genotypes from noise due to issues such as multiple testing and limited sample size. To increase the statistical power for detecting this difference, we consider a 'gene set' approach by focusing on subtle but coordinated changes in gene expression across multiple genes rather than individual genes. We defined a 'gene set' as a set of genes in the same biological pathway and focused on identifying common regulators based on an assumption that the genes within the same pathway are controlled by common regulators. We applied the gene set approach to the expression data of mRNA in Centre d'Etude du Polymorphisme Humain lymphoblast cells to identify regulators controlling the genes in a biological pathway. Our gene set approach successfully identified potent regulators controlling gene expression in an inflammatory response pathway.

Identifying Genomic Regulators of Set-Wise Co-Expression.

The genetical genomics approach has been used to study the genetic basis of variation in gene expression, where putative transcriptional regulators of genes are identified via genetic quantitative trait mapping. The genetic regulators identified through such efforts can partially account for an individual gene's natural variation. However, genes in a molecular pathway often exhibit coordinated activities, the patterns and levels of which are also regulated. In an effort to understand these complicated mechanisms, we propose a method that searches for the genomic regulators of set-wise co-expression of related genes, based on current genetical genomics data. Using this method, we studied genomic regulators of 233 biological pathways for a BXD RI data set. For 15 pathways, we obtained significant regulatory loci after controlling for the false discovery rate. The results presented in this paper constitute important evidence of the heritability of mRNA co-expression between individuals. We have shown that, by defining new phenotypes using existing genetical genomics data, evidence on regulation of co-expression can be derived.

Genome-wide association analyses of expression phenotypes.

A number of issues arise when analyzing the large amount of data from high-throughput genotype and expression microarray experiments, including design and interpretation of genome-wide association studies of expression phenotypes. These issues were considered by contributions submitted to Group 1 of the Genetic Analysis Workshop 15 (GAW15), which focused on the association of quantitative expression data. These contributions evaluated diverse hypotheses, including those relevant to cancer and obesity research, and used various analytic techniques, many of which were derived from information theory. Several observations from these reports stand out. First, one needs to consider the genetic model of the trait of interest and carefully select which single nucleotide polymorphisms and individuals are included early in the design stage of a study. Second, by targeting specific pathways when analyzing genome-wide data, one can generate more interpretable results than agnostic approaches. Finally, for datasets with small sample sizes but a large number of features like the Genetic Analysis Workshop 15 dataset, machine learning approaches may be more practical than traditional parametric approaches.