Biomedical subjects

Hongyu Zhao

Publications and source records attributed to Hongyu Zhao.

At least 73 records · Page 4Linked to original sources

PubMed2004

Discovery of tetralin carboxamide growth hormone secretagogue receptor antagonists via scaffold manipulation.

A case study of rational design of an efficient, specific, and proprietary molecular scaffold based on the structure-activity relationship (SAR) information on a screening hit is described. Potent, selective, and orally bioavailable tetralin carboxamide growth hormone secretagogue receptor (GHS-R) antagonists were discovered. Union of rational design and high throughput synthesis provided a quick access to high quality chemical leads.

Drug Design↗

PubMed2004

VitaPad: visualization tools for the analysis of pathway data.

MOTIVATION: Packages that support the creation of pathway diagrams are limited by their inability to be readily extended to new classes of pathway-related data. RESULTS: VitaPad is a cross-platform application that enables users to create and modify biological pathway diagrams and incorporate microarray data with them. It improves on existing software in the following areas: (i) It can create diagrams dynamically through graph layout algorithms. (ii) It is open-source and uses an open XML format to store data, allowing for easy extension or integration with other tools. (iii) It features a cutting-edge user interface with intuitive controls, high-resolution graphics and fully customizable appearance. AVAILABILITY: http://bioinformatics.med.yale.edu CONTACTS: matthew.holford@yale.edu; hongyu.zhao@yale.edu.

Algorithms↗

PubMed2004

Isoxazole carboxylic acids as protein tyrosine phosphatase 1B (PTP1B) inhibitors.

Guided by X-ray crystallography, we have extended the structure-activity relationship (SAR) study on an isoxazole carboxylic acid-based PTP1B inhibitor (1) and more potent and equally selective (>20-fold selectivity over the highly homologous T-cell PTPase, TCPTP) PTP1B inhibitors were identified. Inhibitor 7 demonstrated good cellular activity against PTP1B in COS 7 cells.

Animals↗

PubMed2004

A computational approach for ordering signal transduction pathway components from genomics and proteomics Data.

BACKGROUND: Signal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited. RESULTS: We have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components. CONCLUSIONS: As demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms.

Computational Biology↗

PubMed2004

A cluster of metabolic defects caused by mutation in a mitochondrial tRNA.

Hypertension and dyslipidemia are risk factors for atherosclerosis and occur together more often than expected by chance. Although this clustering suggests shared causation, unifying factors remain unknown. We describe a large kindred with a syndrome including hypertension, hypercholesterolemia, and hypomagnesemia. Each phenotype is transmitted on the maternal lineage with a pattern indicating mitochondrial inheritance. Analysis of the mitochondrial genome of the maternal lineage identified a homoplasmic mutation substituting cytidine for uridine immediately 5' to the mitochondrial transfer RNA(Ile) anticodon. Uridine at this position is nearly invariate among transfer RNAs because of its role in stabilizing the anticodon loop. Given the known loss of mitochondrial function with aging, these findings may have implications for the common clustering of these metabolic disorders.

Adult↗

PubMed2004

Novel isoxazole carboxamides as growth hormone secretagogue receptor (GHS-R) antagonists.

Novel isoxazole carboxamides have been identified as growth hormone secretagogue receptor (GHS-R) antagonists. Substituent modification off the 5-position of the isoxazole ring led to analogues with potent binding affinity and functional antagonism of GHS-R. A potent analogue (32) with high aqueous solubility and good GPCR selectivity was also identified as a potential pharmacological tool for in vivo studies.

Amides↗

PubMed2004

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Artificial Intelligence↗

PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗

PubMed2004

A semiparametric approach for marker gene selection based on gene expression data.

MOTIVATION: Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification. RESULTS: A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates. CONTACT: hongyu.zhao@yale.edu.

Biomarkers, Tumor↗

PubMed2004

Handling multiple testing while interpreting microarrays with the Gene Ontology Database.

BACKGROUND: The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally. RESULTS: We present the results from a preliminary study of the distribution one would expect for analyzing sets of genes extracted from Drosophila, S. cerevisiae, Wormbase, and Gramene databases using the Gene Ontology Database. CONCLUSIONS: We found that the estimated distribution is not regular and is not predictable outside of a particular set of genes. Permutation-based simulations may be necessary to determine the confidence in results of such analyses.

Animals↗

PubMed2004

Relay ring-closing metathesis (RRCM): a strategy for directing metal movement throughout olefin metathesis sequences.

The title concept involves the use of structurally modified RCM substrates that contain extender arms, terminating in a remote reactive alkene. Initiation of an RCM sequence at that reactive alkene is followed by rapid intramolecular relay of the metal center to an initially less reactive alkene in the parent substrate. This permits one to control the relative timing (or direction) of a metathesis sequence. For example, one can reverse the inherent tendency of an unsymmetrical alpha,omega-diene substrate to close, say, left-to-right, to that of right-to-left. Four distinct types of application of the RRCM concept are demonstrated. Among other things, they show the preparation of tetrasubstituted electron-deficient alkenes using G1 [(Cy3P)2(Cl2)Ru=CHPh], complementary control of directionality (endedness), auxiliary benefits (enzyme specificity) from the incorporation of additional steric bulk, the activation of otherwise ineffective substrates for RCM closure, the use of unorthodox alkenes as initiation sites for ring closure, and control of product olefin geometry.

Alkenes↗

PubMed2004

Expression profiling reveals novel pathways in the transformation of melanocytes to melanomas.

Affymetrix and spotted oligonucleotide microarrays were used to assess global differential gene expression comparing normal human melanocytes with six independent melanoma cell strains from advanced lesions. The data, validated at the protein level for selected genes, confirmed the overexpression in melanoma cells relative to normal melanocytes of several genes in the growth factor/receptor family that confer growth advantage and metastasis. In addition, novel pathways and patterns of associated expression in melanoma cells not reported before emerged, including the following: (a) activation of the NOTCH pathway; (b) increased Twist expression and altered expression of additional transcriptional regulators implicated in embryonic development and epidermal/mesenchymal transition; (c) coordinated activation of cancer/testis antigens; (d) coordinated down-regulation of several immune modulation genes, in particular in the IFN pathways; (e) down-regulation of several genes implicated in membrane trafficking events; and (f) down-regulation of growth suppressors, such as the Prader-Willi gene NECDIN, whose function was confirmed by overexpression of ectopic Flag-necdin. Validation of differential expression using melanoma tissue microarrays showed that reduced ubiquitin COOH-terminal esterase L1 in primary melanoma is associated with worse outcome and that increased expression of the basic helix-loop-helix protein Twist is associated with worse outcome. Some differentially expressed genes reside on chromosomal regions displaying common loss or gain in melanomas or are known to be regulated by CpG promoter methylation. These results provide a comprehensive view of changes in advanced melanoma relative to normal melanocytes and reveal new targets that can be used in assessing prognosis, staging, and therapy of melanoma patients.

Animals↗

PubMed2004

A statistical method for identifying differential gene-gene co-expression patterns.

MOTIVATION: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states. RESULTS: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.

Algorithms↗

PubMed2004

HAPLORE: a program for haplotype reconstruction in general pedigrees without recombination.

MOTIVATION: Haplotype reconstruction is an essential step in genetic linkage and association studies. Although many methods have been developed to estimate haplotype frequencies and reconstruct haplotypes for a sample of unrelated individuals, haplotype reconstruction in large pedigrees with a large number of genetic markers remains a challenging problem. METHODS: We have developed an efficient computer program, HAPLORE (HAPLOtype REconstruction), to identify all haplotype sets that are compatible with the observed genotypes in a pedigree for tightly linked genetic markers. HAPLORE consists of three steps that can serve different needs in applications. In the first step, a set of logic rules is used to reduce the number of compatible haplotypes of each individual in the pedigree as much as possible. After this step, the haplotypes of all individuals in the pedigree can be completely or partially determined. These logic rules are applicable to completely linked markers and they can be used to impute missing data and check genotyping errors. In the second step, a haplotype-elimination algorithm similar to the genotype-elimination algorithms used in linkage analysis is applied to delete incompatible haplotypes derived from the first step. All superfluous haplotypes of the pedigree members will be excluded after this step. In the third step, the expectation-maximization (EM) algorithm combined with the partition and ligation technique is used to estimate haplotype frequencies based on the inferred haplotype configurations through the first two steps. Only compatible haplotype configurations with haplotypes having frequencies greater than a threshold are retained. RESULTS: We test the effectiveness and the efficiency of HAPLORE using both simulated and real datasets. Our results show that, the rule-based algorithm is very efficient for completely genotyped pedigree. In this case, almost all of the families have one unique haplotype configuration. In the presence of missing data, the number of compatible haplotypes can be substantially reduced by HAPLORE, and the program will provide all possible haplotype configurations of a pedigree under different circumstances, if such multiple configurations exist. These inferred haplotype configurations, as well as the haplotype frequencies estimated by the EM algorithm, can be used in genetic linkage and association studies. AVAILABILITY: The program can be downloaded from http://bioinformatics.med.yale.edu.

Algorithms↗

PubMed2004

Mining the structural genomics pipeline: identification of protein properties that affect high-throughput experimental analysis.

Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized "pipeline schematics". We find that the properties of a protein that are most significant are: (i.) whether it is conserved across many organisms; (ii). the percentage composition of charged residues; (iii). the occurrence of hydrophobic patches; (iv). the number of binding partners it has; and (v). its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/.

Algorithms↗

PubMed2004

BGI-RIS: an integrated information resource and comparative analysis workbench for rice genomics.

Rice is a major food staple for the world's population and serves as a model species in cereal genome research. The Beijing Genomics Institute (BGI) has long been devoting itself to sequencing, information analysis and biological research of the rice and other crop genomes. In order to facilitate the application of the rice genomic information and to provide a foundation for functional and evolutionary studies of other important cereal crops, we implemented our Rice Information System (BGI-RIS), the most up-to-date integrated information resource as well as a workbench for comparative genomic analysis. In addition to comprehensive data from Oryza sativa L. ssp. indica sequenced by BGI, BGI-RIS also hosts carefully curated genome information from Oryza sativa L. ssp. japonica and EST sequences available from other cereal crops. In this resource, sequence contigs of indica (93-11) have been further assembled into Mbp-sized scaffolds and anchored onto the rice chromosomes referenced to physical/genetic markers, cDNAs and BAC-end sequences. We have annotated the rice genomes for gene content, repetitive elements, gene duplications (tandem and segmental) and single nucleotide polymorphisms between rice subspecies. Designed as a basic platform, BGI-RIS presents the sequenced genomes and related information in systematic and graphical ways for the convenience of in-depth comparative studies (http://rise.genomics.org.cn/).

China↗

PubMed2004

The impacts of errors in individual genotyping and DNA pooling on association studies.

Case-control association studies using unrelated individuals may offer an effective approach for identifying genetic variants that have small to moderate disease risks. In general, two different strategies may be employed to establish associations between genotypes and phenotypes: (1) collecting individual genotypes or (2) quantifying allele frequencies in DNA pools. These two technologies have their respective advantages. Individual genotyping gathers more information, whereas DNA pooling may be more cost effective. Recent technological advances in DNA pooling have generated great interest in using DNA pooling in association studies. In this article, we investigate the impacts of errors in genotyping or measuring allele frequencies on the identification of genetic associations with these two strategies. We find that, with current technologies, compared to individual genotyping, a larger sample is generally required to achieve the same power using DNA pooling. We further consider the use of DNA pooling as a screening tool to identify candidate regions for follow-up studies. We find that the majority of the positive regions identified from DNA pooling results may represent false positives if measurement errors are not appropriately considered in the design of the study.

Algorithms↗

PubMed2004

The estimation of sibling genetic risk parameters revisited.

This report points out that some sibling genetic risk parameters can be regarded as the ratios of the characteristic values in the ascertainment subpopulation. Based on this observation, we reconsider Olson and Cordell's ([2000] Genet. Epidemiol. 18:217-235) and Cordell and Olson's ([2000] Genet. Epidemiol. 18:307-321) estimators, and re-derive these estimators. Furthermore, we provide the closed-form variance estimators. Simulation results suggest that our proposed estimators perform very well, and single ascertainment may be better than complete ascertainment for estimating these genetic parameters.

Alleles↗