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1.  Changes in Corneal Endothelial Cell after Ahmed Glaucoma Valve Implantation and Trabeculectomy: 1-Year Follow-up 
To compare changes in corneal endothelial cell density (CECD) after Ahmed glaucoma valve (AGV) implantation and trabeculectomy.
Changes in corneal endothelium in patients that underwent AGV implantation or trabeculectomy were prospectively evaluated. Corneal specular microscopy was performed at the central cornea using a non-contact specular microscope before surgery and 6 months and 12 months after surgery. The CECD, hexagonality of the endothelial cells, and the coefficient of variation of the cell areas were compared between the two groups.
Forty eyes of 40 patients with AGV implantation and 28 eyes of 28 patients with trabeculectomy were studied. Intraocular pressure in the AGV implantation group was significantly higher than that in the trabeculectomy group (p < 0.001), but there was no significant difference in other clinical variables between the two groups. In the AGV implantation group, the mean CECD significantly decreased by 9.4% at 6 months and 12.3% at 12 months compared with baseline values (both, p < 0.001), while it decreased by 1.9% at 6 months and 3.2% at 12 months in the trabeculectomy group (p = 0.027 and p = 0.015, respectively). The changes at 6 months and 12 months in the AGV implantation group were significantly higher than those in the trabeculectomy group (p = 0.030 and p = 0.027, respectively). In the AGV implantation group, there was a significant decrease in the CECD between baseline and 6 months and between 6 months and 12 months (p < 0.001 and p = 0.005, respectively). However, in the trabeculectomy group, a significant decrease was observed only between baseline and 6 months (p = 0.027).
Both the AGV implantation group and the trabeculectomy group showed statistically significant decreases in the CECD 1 year after surgery. The decrease in CECD in the AVG implantation group was greater and persisted longer than that in the trabeculectomy group.
PMCID: PMC5156615  PMID: 27980360
Corneal endothelial cell loss; Glaucoma drainage implants; Trabeculectomy
2.  Influence of Biometric Variables on Refractive Outcomes after Cataract Surgery in Angle-closure Glaucoma Patients 
To evaluate the influence of biometric variables on refractive outcomes after cataract surgery in angle-closure glaucoma (ACG) patients.
In this case-control study, 42 ACG patients, 40 open-angle glaucoma (OAG) patients, and 35 controls without glaucoma who had undergone conventional cataract surgery were enrolled consecutively. Electronic medical records, including preoperative biometric variables (keratometric diopter, axial length, anterior chamber depth, and lens thickness), the refractive change (RC), and the absolute value of refractive change (ARC) were reviewed.
In the control and OAG patients, the anterior chamber depth was negatively correlated with the ARC (r = -0.344, p = 0.043 and r = -0.431, p = 0.006, respectively), whereas there was no correlation in the ACG patients. Lens thickness was positively correlated with the RC, but not with the ARC, in the control and OAG groups (r = 0.391, p = 0.020 and r = 0.501, p = 0.001, respectively). In contrast, lens thickness in the ACG group was not correlated with the RC but was positively correlated with the ARC (r = 0.331, p = 0.032).
In contrast with the anterior chamber depth, preoperatively measured lens thickness may be a useful predictor of the direction of the RC after cataract surgery in control and OAG patients. However, in ACG patients, a thicker lens was correlated with a larger RC, regardless of the direction of the shift (hyperopic or myopic).
PMCID: PMC4965603  PMID: 27478355
Absolute value of the refractive change; Angle-closure glaucoma; Anterior chamber depth; Lens thickness; Refractive change
3.  Performance of and Pressure Elevation Formed by Small-diameter Microtubes Used in Constant-flow Sets 
We explored the performance of and pressure elevation caused by small-diameter microtubes used to reduce overfiltration.
Using a syringe pump-driven constant-flow setting (2 µL/min), pressures were measured for polytetrafluoroethylene (PTFE) microtubes 5 mm in length with inner diameters of 51, 64, and 76 µm and for polyether block amide (PEBAX) microtubes with an inner diameter of 76 µm. Experiments (using microtubes only) were initially performed in air, water, and enucleated pig eyes and were repeated under the same conditions using intraluminal 9/0 nylon stents.
The pressures measured in air in 51-, 64-, and 76-µm-diameter PTFE microtubes differed significantly (22.1, 16.9, and 12.2 mmHg, respectively; p < 0.001), and that of the 76-µm-diameter PEBAX microtube was 15.8 mmHg (p < 0.001 compared to the 12.2 mmHg of the 76-µm-diameter PTFE microtube). The pressures measured in water also differed significantly among the three microtubes at 3.9, 3.0, and 1.4 mmHg, respectively, while that in the PEBAX microtube was 2.6 mmHg (all p < 0.001). Using the intraluminal stent, the pressure in water of the three different PTFE microtubes increased to 22.6, 18.0, and 4.1 mmHg, respectively, and that in the PEBAX microtube increased to 10.5 mmHg (all p < 0.001). Similar trends were evident when measurements were performed in pig eyes.
Although microtubes of smaller diameter experienced higher pressure in air, reduction of the inner diameter to 51 µm did not adequately increase the pressure attained in water or pig eyes. Insertion of an intraluminal stent effectively elevated the latter pressures. PEBAX microtubes created higher pressures than did PTFE microtubes.
PMCID: PMC4878983  PMID: 27247522
Intraluminal stent; Intraocular pressure; Microtube; Polyether block amide; Polytetrafluoroethylene
4.  Expression-associated polymorphisms of CAV1-CAV2 affect intraocular pressure and high-tension glaucoma risk 
Molecular Vision  2015;21:548-554.
The human CAV1-CAV2 locus has been associated with susceptibility to primary open-angle glaucoma in four studies of Caucasian, Chinese, and Pakistani populations, although not in several other studies of non-Korean populations. In this study with Korean participants, the CAV1-CAV2 locus was investigated for associations with susceptibility to primary open-angle glaucoma accompanied by elevated intraocular pressure (IOP), namely, high-tension glaucoma (HTG), as well as with IOP elevation, which is a strong risk factor for glaucoma.
Two single nucleotide polymorphisms (SNPs) were genotyped in 1,161 Korean participants including 229 patients with HTG and 932 healthy controls and statistically examined for association with HTG susceptibility and IOP. One SNP was rs4236601 G>A, which had been reported in the original study, and the other SNP was rs17588172 T>G, which was perfectly correlated (r2=1) with another reported SNP rs1052990. Expression quantitative trait loci (eQTL) analysis was performed using GENe Expression VARiation (Genevar) data.
Both SNPs were associated with HTG susceptibility, but the rs4236601 association disappeared when adjusted for the rs17588172 genotype and not vice versa. The minor allele G of rs17588172 was associated significantly with 1.5-fold increased susceptibility to HTG (p=0.0069) and marginally with IOP elevation (p=0.043) versus the major allele T. This minor allele was also associated with decreased CAV1 and CAV2 mRNA in skin and adipose according to the Genevar eQTL analysis.
The minor allele G of rs17588172 in the CAV1-CAV2 locus is associated with decreased expression of CAV1 and CAV2 in some tissues, marginally with IOP elevation, and consequently with increased susceptibility to HTG.
PMCID: PMC4431411  PMID: 26015768
5.  A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping 
BMC Bioinformatics  2016;17:211.
Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user’s perspective.
We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a “gene signature progression” method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin.
In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1066-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4864913  PMID: 27170106
Connectivity mapping; Differentially expressed genes; Gene signature progression; Disease inhibitory compounds; Lung cancer; Breast cancer
7.  Expansive marker analysis replicating the association of glaucoma susceptibility with human chromosome loci 1q43 and 10p12.31 
Three human chromosome loci (1q43, 10p12.31, and 12q21.31) were recently associated with the susceptibility to primary open-angle glaucoma (POAG) in a Japanese population; however, this was not replicated in three subsequent studies using South Indian, Afro-Caribbean, and Chinese populations. To identify genetic markers that are robustly associated across ethnic populations, numerous markers in addition to the six in the three reported loci were examined in this study. A total of 31 single-nucleotide polymorphism (SNP) markers were genotyped for 1115 Korean participants, and many neighboring SNPs were imputed using the Korean HapMap Project genotype data. Each SNP was statistically tested for association with POAG susceptibility by comparisons among 211 POAG patients with 904 unaffected controls. A strong and statistically significant association was found with a previously unreported SNP, rs7098387 (odds ratio, OR=2.0 (1.4–3.0), P=0.00038) at the 10p12.31 locus (where 11 SNPs were typed and 38 imputed) in contrast to the reported rs7081455, which was too poorly correlated with newly associated rs7098387 (r2=0.003, D′=0.40) to show association. Additionally, a modest association was observed with the reported rs693421 (OR=1.4 (1.1–1.7), P=0.0082) and several other SNPs located within and around ZP4 at the 1q43 locus (10 SNPs typed and 14 imputed). However, no association was observed with the reported rs7961953 SNP or any other SNPs at the 12q21.31 locus, upstream of TMTC2 (10 SNPs typed and 29 imputed). Accordingly, POAG susceptibility association was replicated using rs7098387 (C) rather than rs7081455 (T) at the 10p12.31 locus and additionally with rs693421 (T) at the 1q43 locus.
PMCID: PMC3925277  PMID: 23838595
primary open-angle glaucoma; single-nucleotide polymorphism; replicative association study; 1q43; 10p12.31
8.  Variation in the ICAM1–ICAM4–ICAM5 locus is associated with systemic lupus erythematosus susceptibility in multiple ancestries 
Annals of the rheumatic diseases  2012;71(11):1809-1814.
Systemic lupus erythematosus (SLE; OMIM 152700) is a chronic autoimmune disease for which the aetiology includes genetic and environmental factors. ITGAM, integrin αΜ (complement component 3 receptor 3 subunit) encoding a ligand for intracellular adhesion molecule (ICAM) proteins, is an established SLE susceptibility locus. This study aimed to evaluate the independent and joint effects of genetic variations in the genes that encode ITGAM and ICAM.
The authors examined several markers in the ICAM1–ICAM4–ICAM5 locus on chromosome 19p13 and the single ITGAM polymorphism (rs1143679) using a large-scale case–control study of 17 481 unrelated participants from four ancestry populations. The single marker association and gene–gene interaction were analysed for each ancestry, and a meta-analysis across the four ancestries was performed.
The A-allele of ICAM1–ICAM4–ICAM5 rs3093030, associated with elevated plasma levels of soluble ICAM1, and the A-allele of ITGAM rs1143679 showed the strongest association with increased SLE susceptibility in each of the ancestry populations and the trans-ancestry meta-analysis (ORmeta=1.16, 95% CI 1.11 to 1.22; p=4.88×10−10 and ORmeta=1.67, 95% CI 1.55 to 1.79; p=3.32×10−46, respectively). The effect of the ICAM single-nucleotide polymorphisms (SNPs) was independent of the effect of the ITGAM SNP rs1143679, and carriers of both ICAM rs3093030-AA and ITGAM rs1143679-AA had an OR of 4.08 compared with those with no risk allele in either SNP (95% CI 2.09 to 7.98; p=3.91×10−5).
These findings are the first to suggest that an ICAM–integrin-mediated pathway contributes to susceptibility to SLE.
PMCID: PMC3466387  PMID: 22523428
9.  Analysis of an extended chromosome locus 2p14–21 for replication of the 2p16.3 association with glaucoma susceptibility 
Molecular Vision  2011;17:1136-1143.
Susceptibility to primary open-angle glaucoma (POAG) has recently associated with three intergenic single-nucleotide polymorphisms (SNPs) on human chromosome 2p16.3, just outside of the POAG-linkage locus GLC1H (2p15–16.2), in an Afro-Caribbean population. Especially, association of one SNP (rs12994401) was very strong (odds ratio 35) and later replicated in Afro-Americans but not in Ghanaians or Japanese. An extended region was examined in this study to look for SNPs of cross-population association.
The three reported SNPs and all 63 SNPs considerably correlating with rs12994401 (r2≥0.3) in the African-descendent Yoruba were examined for POAG susceptibility association in a Korean population of 1,159 unrelated participants including 226 cases with glaucoma. As these 66 SNPs were spread from 2p14 to 2p21, all SNPs in this extended region were imputed for susceptibility association tests.
No susceptibility association was detected with rs12994401 in comparisons between 933 controls and 188 POAG (or 175 high-tension glaucoma) cases (statistical power of 100%), as well as with all 19 other typed SNPs, using logistic regression with adjustment for age and gender. The other 46 SNPs were deemed non-polymorphic in Koreans. Among 21,201 SNPs located in 2p14–21, only 4,260 were imputed to be non-monomorphic, but none of them passed a significance level of multiple testing. No association was observed when the samples were stratified by age or gender.
No typed or imputed SNPs within 2p14–21 showed association with susceptibility to POAG, suggesting that the population inconsistency in 2p16.3 association was unlikely due to linkage disequilibrium differences.
PMCID: PMC3087448  PMID: 21552472
10.  Mutation spectrum of CYP1B1 and MYOC genes in Korean patients with primary congenital glaucoma 
Molecular Vision  2011;17:2093-2101.
To elucidate the incidence of cytochrome P450 1B1 (CYP1B1) and myocillin (MYOC) mutations in Korean patients with primary congenital glaucoma (PCG).
Genomic DNA was collected from peripheral blood of 85 unrelated Korean patients who were diagnosed as having PCG by standard ophthalmological examinations and screened for mutations in the CYP1B1 and MYOC genes by using bi-directional sequencing.
Among 85 patients with PCG, 22 patients (22/85; 25.9%) had either one (n=11) or two (n=11) mutant alleles of the CYP1B1 gene. Among 11 different CYP1B1 mutations identified, a frameshift mutation (c.970_971dupAT; p.T325SfsX104) was the most frequent mutant allele (6/33; 18.2%) while p.G329S and p.V419Gfs11X were novel. In the MYOC gene, two variants of unknown significance (p.L228S and p.E240G) were identified in two PCG patients (2/85; 2.4%), respectively. No patient had mutations in both genes.
Although CYP1B1 mutations are major causes of PCG in Korea, ~70% of PCG patients have neither CYP1B1 nor MYOC mutations suggesting a high degree of genetic heterogeneity. Furthermore, the fact that 11 out of 22 patients had only one mutant allele in the CYP1B1 gene necessitates further investigation for other genetic backgrounds underlying PCG.
PMCID: PMC3156779  PMID: 21850185
11.  Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast 
BMC Genomics  2009;10:440.
Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction.
Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes.
A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.
PMCID: PMC2753555  PMID: 19761608
12.  A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data 
BMC Bioinformatics  2008;9:56.
The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a number of different ways to identify cell cycle dependent genes. In this study, we pose the hypothesis that cell cycle dependent genes are considered as oscillating systems with a rhythm, i.e. systems producing response signals with period and frequency. Therefore, we are motivated to apply the theory of multivariate phase synchronization for clustering cell cycle specific genome-wide expression data.
We propose the strategy to find groups of genes according to the specific biological process by analyzing cell cycle specific gene expression data. To evaluate the propose method, we use the modified Kuramoto model, which is a phase governing equation that provides the long-term dynamics of globally coupled oscillators. With this equation, we simulate two groups of expression signals, and the simulated signals from each group shares their own common rhythm. Then, the simulated expression data are mixed with randomly generated expression data to be used as input data set to the algorithm. Using these simulated expression data, it is shown that the algorithm is able to identify expression signals that are involved in the same oscillating process. We also evaluate the method with yeast cell cycle expression data. It is shown that the output clusters by the proposed algorithm include genes, which are closely associated with each other by sharing significant Gene Ontology terms of biological process and/or having relatively many known biological interactions. Therefore, the evaluation analysis indicates that the method is able to identify expression signals according to the specific biological process. Our evaluation analysis also indicates that some portion of output by the proposed algorithm is not obtainable by the traditional clustering algorithm with Euclidean distance or linear correlation.
Based on the evaluation experiments, we draw the conclusion as follows: 1) Based on the theory of multivariate phase synchronization, it is feasible to find groups of genes, which have relevant biological interactions and/or significantly shared GO slim terms of biological process, using cell cycle specific gene expression signals. 2) Among all the output clusters by the proposed algorithm, the cluster with relatively large size has a tendency to include more known interactions than the one with relatively small size. 3) It is feasible to understand the cell cycle specific gene expression patterns as the phenomenon of collective synchronization. 4) The proposed algorithm is able to find prominent groups of genes, which are not obtainable by traditional clustering algorithm.
PMCID: PMC2335309  PMID: 18221564
13.  Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks 
BMC Bioinformatics  2007;8:251.
A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.
This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|Hi), which is used as confidence level. The unit network with higher P(D|Hi) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|Hi), which is a unique property of the proposed algorithm.
The algorithm is evaluated with synthetic and Saccharomyces cerevisiae expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.
The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and Saccharomyces cerevisiae expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.
From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.
PMCID: PMC1959566  PMID: 17626641

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