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At least 1,657 records · Page 92Linked to original sources

PubMed2004

Pharmacogenomics of drug transporters: a new approach to functional analysis of the genetic polymorphisms of ABCB1 (P-glycoprotein/MDR1).

In the 21st century, emerging genomic technologies (i.e., bioinformatics, functional genomics, and pharmacogenomics) are shifting the paradigm of drug discovery research and improving the strategy of medical care for patients. In order to realize the personalized medicine, it is critically important to understand molecular mechanisms underlying inter-individual differences in the drug response, namely, pharmacological effect vs. side effect. Evidence is now accumulating to strongly suggest that drug transporters are one of the determinant factors governing the pharmacokinetic profile of drugs. Effort has been made to identify genetic variation in drug transporter genes. In particular, genetic variations of the human ABCB1 (P-glycoprotein/MDR1) gene have been most extensively studied. Hitherto more than fifty single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms in the ABCB1 gene have been reported. However, at the present time, information is still limited with respect to the actual effect of those genetic polymorphisms on the function of ABCB1. In this context, we have undertaken functional analyses of ABCB1 polymorphisms. To quantify the impact of genetic polymorphisms on the substrate specificity of ABCB1, we have developed a high-speed screening system and a new structure-activity relationship (SAR) analysis method. This review addresses functional aspects of the genetic polymorphism of ABCB1 and provides the standard method to evaluate the effect of polymorphisms on the function.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2005

Shortest triplet clustering: reconstructing large phylogenies using representative sets.

BACKGROUND: Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. RESULTS: We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC), to reconstruct phylogenies. The main idea is the introduction of a natural definition of so-called k-representative sets. Based on k-representative sets, shortest triplets are reconstructed and serve as building blocks for the STC algorithm to agglomerate sequences for tree reconstruction in O(n2) time for n sequences. Simulations show that STC gives better topological accuracy than other tested methods that also build a first starting tree. STC appears as a very good method to start the tree reconstruction. However, all tested methods give similar results if balanced nearest neighbor interchange (BNNI) is applied as a post-processing step. BNNI leads to an improvement in all instances. The program is available at http://www.bi.uni-duesseldorf.de/software/stc/. CONCLUSION: The results demonstrate that the new approach efficiently reconstructs phylogenies for large data sets. We found that BNNI boosts the topological accuracy of all methods including STC, therefore, one should use BNNI as a post-processing step to get better topological accuracy.

Algorithms↗

PubMed2002

[Linkage disequilibrium analysis of the single nucleotide polymorphisms in the PRKCZ gene].

OBJECTIVE: To search for the disease-associated haplotype in the PRKCZ gene, a susceptibility gene for type 2 diabetes in Han population of North China, by case-control study and linkage disequilibrium (LD) analysis using single nucleotide polymorphisms (SNPs). METHODS: SNPs located in the PRKCZ gene were chosen from public SNP domain by bioinformatic methods and single base extension (SBE) method was used to genotype the loci in 173 sporadic type 2 diabetes patients and 152 normal individuals to perform case-control study and LD analysis. Haplotype block were constructed in these populations. RESULTS: Several SNPs in the PRKCZ gene were found to be associated with the disease. The SNPs formed different haplotype block pattern in case and control groups. The frequencies of the haplotypes formed by 5 SNPs were statistically different between the two groups. CONCLUSION: The haplotype formed by 5 SNPs in the PRKCZ gene may be associated with type 2 diabetes in Han population of China, which is confirmed from statistics to be a susceptibility gene for the disease.

Alleles↗

PubMed2005

HTself: self-self based statistical test for low replication microarray studies.

Different statistical methods have been used to classify a gene as differentially expressed in microarray experiments. They usually require a number of experimental observations to be adequately applied. However, many microarray experiments are constrained to low replication designs for different reasons, from financial restrictions to scarcely available RNA samples. Although performed in a high-throughput framework, there are few experimental replicas for each gene to allow the use of traditional or state-of-art statistical methods. In this work, we present a web-based bioinformatics tool that deals with real-life problems concerning low replication experiments. It uses an empirically derived criterion to classify a gene as differentially expressed by combining two widely accepted ideas in microarray analysis: self-self experiments to derive intensity-dependent cutoffs and non-parametric estimation techniques. To help laboratories without a bioinformatics infrastructure, we implemented the tool in a user-friendly website (http://blasto.iq.usp.br/~rvencio/HTself).

Algorithms↗

PubMed2004

hOLF44, a secreted glycoprotein with distinct expression pattern, belongs to an uncharacterized olfactomedin-like subfamily newly identified by phylogenetic analysis.

Secreted proteins are indispensable for the development and differentiation of multicellular organisms. Cloning and characterization of novel or hypothetical genes encoding these proteins are therefore inviting great incentives. Using bioinformatics tools and experimental approaches, we isolated and characterized a human secreted glycoprotein, hOLF44, which contains a highly conserved olfactomedin-like (OLF) domain in the C-terminal. However, phylogenetic analysis revealed that hOLF44 is not clustered into any of the OLF subfamilies containing characterized members, and obviously falls into a newly identified uncharacterized OLF subfamily. Western blot analysis showed that hOLF44 protein is robustly secreted from the transfected COS-7 cells. Expression levels of hOLF44 mRNA are abundant in placenta, moderate in liver and heart, whereas fairly weak in other tissues examined. Immunohistochemical study on human term placenta demonstrated that hOLF44 is mainly localized extracellularly surrounding the syncytiotrophoblastic cells and very rarely expressed in the maternal decidua layer. These results suggest that hOLF44 may have matrix-related function involved in human placental and embryonic development, or play a similar role in other physiological processes. The further functional characterization of hOLF44 may provide insights into a better understanding of the newly identified OLF subfamily.

Amino Acid Sequence↗

PubMed2006

Bioconductor: an open source framework for bioinformatics and computational biology.

This chapter describes the Bioconductor project and details of its open source facilities for analysis of microarray and other high-throughput biological experiments. Particular attention is paid to concepts of container and workflow design, connections of biological metadata to statistical analysis products, support for statistical quality assessment, and calibration of inference uncertainty measures when tens of thousands of simultaneous statistical tests are performed.

Animals↗

PubMed2004

Stratification of acute myeloid leukemia based on gene expression profiles.

Acute myeloid leukemia (AML) is characterized by clonal growth of immature leukemic blasts and develops either de novo or secondarily to anticancer treatment or to other hematologic disorders. Given that the current classification of AML, which is based on blast karyotype and morphology, is not sufficiently robust to predict the prognosis of each affected individual, new stratification schemes that are of better prognostic value are needed. Global profiling of gene expression in AML blasts has the potential both to identify a small number of genes whose expression is associated with clinical outcome and to provide insight into the molecular pathogenesis of this condition. Emerging genomics tools, especially DNA microarray analysis, have been applied in attempts to isolate new molecular markers for the differential diagnosis of AML and to identify genes that contribute to leukemogenesis. Progress in bioinformatics has also yielded means with which to classify patients according to clinical parameters such as long-term prognosis. The application of such analysis to large sets of gene expression data has begun to provide the basis for a new AML classification that is more powerful with regard to prediction of prognosis.

Biomarkers, Tumor↗

PubMed2025

Analysis of ferroptosis-related genes in cerebral ischemic stroke via immune infiltration and single-cell RNA-sequencing.

Ischemic stroke (IS) represents a harmful neurological disorder with limited treatment options. Ferroptosis accounts for the iron-dependent, nonapoptotic cell death pattern, which shows the feature of fatal lipid ROS accumulation. Nonetheless, ferroptosis-related biomarkers for identifying IS early are currently lacking. The present study focused on investigating the possible ferroptosis-related biomarkers for IS and analyzing their effects on immune infiltration. Altogether five hub differentially expressed ferroptosis-related genes (DEFRGs) were identified from the relevant databases. Additionally, single-cell RNA-sequencing (seq) analysis was conducted for the comprehensive mapping of cell populations based on the IS database. These five hub DEFRGs were analyzed using gene set enrichment analysis, miRNA prediction, and single-cell RNA-seq analysis. A transient middle cerebral artery occlusion mouse model was constructed. We also adopted bioinformatics methods combined with western blot, changes to mitochondria, hematoxylin & eosin staining, Nissl staining, ROS fluorescence staining, immunohistochemistry, and quantitative real-time polymerase chain reaction (qRT-PCR) to show the involvement of ferroptosis in IS progression. The results revealed that nuclear factor erythroid-derived 2-like 2 (Nfe2l2) was the potential candidate biomarker for IS diagnosis, and ferroptosis may be suppressed via the Nfe2l2/HO-1 pathway. Thus, drug targeting Nfe2l2 can shed novel lights on IS treatment.

Ferroptosis↗

PubMed2002

Global gene expression profiling in Barrett's esophagus and esophageal cancer: a comparative analysis using cDNA microarrays.

In order to identify and contrast global gene expression profiles defining the premalignant syndrome, Barrett's esophagus, as well as frank esophageal cancer, we utilized cDNA microarray technology in conjunction with bioinformatics tools. We hybridized microarrays, each containing 8000 cDNA clones, to RNAs extracted from 13 esophageal surgical or endoscopic biopsy specimens (seven Barrett's metaplasias and six esophageal carcinomas). Hierarchical cluster analysis was performed on these results and displayed using a color-coded graphic representation (Treeview). The esophageal samples clustered naturally into two principal groups, each possessing unique global gene expression profiles. After retrieving histologic reports for these tissues, we found that one main cluster contained all seven Barrett's samples, while the remaining principal cluster comprised the six esophageal cancers. The cancers also clustered according to histopathological subtype. Thus, squamous cell carcinomas (SCCAs) constituted one group, adenocarcinomas (ADCAs) clustered separately, and one signet-ring carcinoma was in its own cluster, distinct from the ADCA cluster. We conclude that cDNA microarrays and bioinformatics show promise in the classification of esophageal malignant and premalignant diseases, and that these methods can be applied to small biopsy samples.

Barrett Esophagus↗

PubMed2005

Efficiency and limits of the Serial Analysis of Gene Expression (SAGE) method: discussions based on first results in bovine trypanotolerance.

Post genomic biotechnologies, such as transcriptome analysis, are now efficient enough to characterize the full complement of genes involved in the expression of specific biological functions. One of them is the Serial Analysis of Gene Expression (SAGE) technique. SAGE involves the construction of transcript libraries for a quantitative analysis of the entire set of genes expressed or inactivated at particular stages of cellular activation. Bioinformatic comparisons in hosts and pathogens genomic databases allow the identification of several up- and down-regulated genes, ESTs and unknown transcripts directly involved in the host-pathogen immunological interaction mechanisms. Based on the first results obtained during an experimental Trypanosoma congolense infection in trypanotolerant cattle, the efficiency and limits of such a technique, from the data acquisition level to the data analysis level, is discussed in this analysis.

Animals↗

PubMed2006

Stubb: a program for discovery and analysis of cis-regulatory modules.

Given the DNA-binding specificities (motifs) of one or more transcription factors, an important bioinformatics problem is to discover significant clusters of binding sites for the transcription factors(s). Such clusters often correspond to cis-regulatory modules mediating regulation of an adjacent gene. In earlier work, we developed the Stubb program that uses a probabilistic model and a maximum likelihood approach to efficiently detect cis-regulatory modules over genomic scales. It may optionally exploit a second related genome to improve module prediction accuracy. We describe here the use of a web-based interface for the Stubb program. The interface is equipped with a special post-processing step for in-depth analysis of specific modules, in order to reveal individual binding sites predicted in the module. The web server may be accessed at the URL http://stubb.rockefeller.edu/.

Algorithms↗

PubMed2004

A comparative phylogenetic approach for dating whole genome duplication events.

MOTIVATION: Whole genome duplications have played a major role in determining the structure of eukaryotic genomes. Current evidence revealing large blocks of duplicated chromatin yields new insights into the evolutionary history of species, but also presents a major challenge for researchers attempting to utilize comparative genomics techniques. Understanding the timing of duplication events relative to divergence among taxa is critical to accurate and comprehensive cross-species comparisons. RESULTS: We describe a large-scale approach to estimate the timing of duplication events in a phylogenetic context. The methodology has been previously utilized for analysis of Arabidopsis and Saccharomyces duplication events. This new implementation provides a more flexible and reusable framework for these analyses. Scripts written in the Python programming language drive a number of freely available bioinformatics programs, creating a no-cost tool for researchers. The usefulness of the approach is demonstrated through genome-scale analysis of Arabidopsis and Oryza (rice) duplications. AVAILABILITY: Software and documentation are freely available from http://plantgenome.agtec.uga.edu/bioinformatics/dating/

Algorithms↗

PubMedDate not supplied

Computational systems analysis of developmental toxicity: design, development and implementation of a Birth Defects Systems Manager (BDSM).

Birth defects and developmental disabilities remain an important public health issue worldwide. With the availability of genomic sequences from a growing number of human and model organisms and the rapid expansion of the public repositories holding large-scale gene expression datasets, a computational systems analysis of developmental toxicology can incorporate this vast digital information toward the realization of predictive models for complex disease. Here we describe the initial design, development and implementation of a Birth Defects Systems Manager (BDSM). The project was motivated by the need for a computational-bioinformatics infrastructure to manage vast digital information from functional genomics and for a new knowledge environment specifically engineered for the analysis of developmental processes and toxicities. Proof-of-concept tested BDSM using meta-analysis of gene expression data collected from different laboratories, technology platforms, and study models. The composite dataset incorporated 232 microarray comparisons of RNA samples by single or dual microarray platforms, cDNA or oligonucleotide based probes, and human or mouse sequence information. Preliminary results identified system-level features in the embryonic transcriptome as it reacted to various developmental-teratological stimuli. BDSM is open access through the worldwide web (http://systemsanalysis.louisville.edu/) and can be integrated with other bioinformatics tools and resources to advance the pace of discovery in birth defects research.

Animals↗

PubMed2024

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Stomach Neoplasms↗

PubMed2006

PATRIC: the VBI PathoSystems Resource Integration Center.

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project.

Bioterrorism↗

PubMed2005

Fractionation and identification of proteins by 2-DE and MS: towards a proteomic analysis of Plasmodium falciparum.

Since completion of genome sequencing of the malarial parasite Plasmodium falciparum, proteomic tools for the identification of parasite proteins have become particularly attractive as they allow a more thorough interpretation of these data. Recent advances in 2-D PAGE, MS, and bioinformatics have created great opportunities for mapping and characterization of protein populations. We employed these improvements in a proteomic approach for the analysis of proteins detected in two blood stages of P. falciparum, (i) in the schizont stage and (ii) in the merozoite stage. For the isolation of merozoites, we introduced a new protocol based on the preparation of clustered structures of merozoites upon treatment of cultures with the common cysteine proteinase inhibitor E64. Peptide mass fingerprints of excised and trypsinated protein spots, acquired by MALDI-TOF MS were generated to identify a variety of proteins. Moreover, prefractionation procedures were used to enrich and map low-abundance proteins in protein samples. The data demonstrate that classic proteomic analyses using 2-D PAGE are now feasible for P. falciparum and represent the first step in the direction of creating 2-D reference maps for this medically most relevant protozoon.

Animals↗

PubMedDate not supplied

Mass spectrometry-based metabolomics.

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.

Databases, Protein↗

PubMed2002

A framework for integrating the songbird brain.

Biological systems by default involve complex components with complex relationships. To decipher how biological systems work, we assume that one needs to integrate information over multiple levels of complexity. The songbird vocal communication system is ideal for such integration due to many years of ethological investigation and a discreet dedicated brain network. Here we announce the beginnings of a songbird brain integrative project that involves high-throughput, molecular, anatomical, electrophysiological and behavioral levels of analysis. We first formed a rationale for inclusion of specific biological levels of analysis, then developed high-throughput molecular technologies on songbird brains, developed technologies for combined analysis of electrophysiological activity and gene regulation in awake behaving animals, and developed bioinformatic tools that predict causal interactions within and between biological levels of organization. This integrative brain project is fitting for the interdisciplinary approaches taken in the current songbird issue of the Journal of Comparative Physiology A and is expected to be conducive to deciphering how brains generate and perceive complex behaviors.

Animals↗