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At least 919 records · Page 51Linked to original sources
PubMed2003

Differential gene expression in the rat caudate putamen after "binge" cocaine administration: advantage of triplicate microarray analysis.

Rat genome U34A (Affymetrix) oligonucleotide microarrays were used to analyze changes in gene expression in the caudate putamen (CPu) of Fischer rats induced by 1 and 3 days of "binge" cocaine (or saline) administration. A triplicate array assay of pooled RNA of each treatment group was used to evaluate the technical variability and sensitivity of microarrays. Cocaine-regulated genes were identified using the Affymetrix MAS 5.0 and Data Mining Tool v. 3. Eighty-nine upregulated and eight downregulated genes/ESTs were found after 1 day of "binge" cocaine. Following 3 days of cocaine treatment we identified 21 upregulated and 17 downregulated genes/ESTs. RNase protection assays of selected genes confirmed reliability of changes identified by the microarrays at the level of > or =1.40-fold increase. Many genes upregulated in the CPu by cocaine were immediate early genes for transcription factors and for "effector" proteins (e.g., vesl/Homer1a, Arc, synaptotagmin IV). Acute "binge" cocaine also increased mRNA levels for glutamate receptor GluR2, dopamine receptor D1, and a number of phosphatases. Genes downregulated by cocaine include several genes associated with energy metabolism in mitochondria, as well as the phosphatydylinositol-4 kinase and the regulator of G-protein signaling protein 4 (RGS4). A differential expression of somatostatin receptor SSTR2, not known to be a cocaine-responsive gene, as well as the clock gene Per2, were found by microarrays and confirmed by RNase protection assay. These results demonstrate the potential of microarrays in profiling gene expression with > or =40% increase or > or =14% decrease in mRNA levels for discovery of novel cocaine-responsive genes.

Animals↗
PubMed2006

Whole genome association mapping by incompatibilities and local perfect phylogenies.

BACKGROUND: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. RESULTS: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set--the DeltaF508 mutation for cystic fibrosis--where the susceptibility variant is already known--and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. CONCLUSION: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours.

Chromosome Mapping↗
PubMed2006

Multi-exon out of frame deletion of the FBN1 gene leading to a severe juvenile onset cardiovascular phenotype in Marfan syndrome.

Marfan syndrome is caused by mutations in fibrillin-1, a large gene spanning approximately 200 kb of genomic DNA on chromosome 15q21. So far, more than 600 different mutations have been identified, accounting for 60-90% of all Marfan syndrome cases, the vast majority being single nucleotide exchanges as well as small deletions and insertions. Only four major rearrangements have been described in the literature so far. We have screened 11 individuals fulfilling the diagnostic criteria of Marfan syndrome but negative for point mutations in the fibrillin-1 gene by SSCP and/or direct sequencing, for large rearrangements. We report here the largest known de novo and out of frame deletion in the fibrillin-1 gene in a patient fulfilling the diagnostic criteria of Marfan syndrome. We identified the deletion breakpoints at the genomic and transcript levels and studied the expression of the mutated allele at the transcript and protein level. We conclude that large rearrangements may account for a non-negligible proportion of all Marfan cases.

Alleles↗
PubMed2000

The microarray explorer tool for data mining of cDNA microarrays: application for the mammary gland.

The Microarray Explorer (MAExplorer) is a versatile Java-based data mining bioinformatic tool for analyzing quantitative cDNA expression profiles across multiple microarray platforms and DNA labeling systems. It may be run as either a stand-alone application or as a Web browser applet over the Internet. With this program it is possible to (i) analyze the expression of individual genes, (ii) analyze the expression of gene families and clusters, (iii) compare expression patterns and (iv) directly access other genomic databases for clones of interest. Data may be downloaded as required from a Web server or in the case of the stand-alone version, reside on the user's computer. Analyses are performed in real-time and may be viewed and directly manipulated in images, reports, scatter plots, histograms, expression profile plots and cluster analyses plots. A key feature is the clone data filter for constraining a working set of clones to those passing a variety of user-specified logical and statistical tests. Reports may be generated with hypertext Web access to UniGene, GenBank and other Internet databases for sets of clones found to be of interest. Users may save their explorations on the Web server or local computer and later recall or share them with other scientists in this groupware Web environment. The emphasis on direct manipulation of clones and sets of clones in graphics and tables provides a high level of interaction with the data, making it easier for investigators to test ideas when looking for patterns. We have used the MAExplorer to profile gene expression patterns of 1500 duplicated genes isolated from mouse mammary tissue. We have identified genes that are preferentially expressed during pregnancy and during lactation. One gene we identified, carbonic anhydrase III, is highly expressed in mammary tissue from virgin and pregnant mice and in gene knock-out mice with underdeveloped mammary epithelium. Other genes, which include those encoding milk proteins, are preferentially expressed during lactation.

Animals↗
PubMed2003

From immunogenetics to immunomics: functional prospecting of genes and transcripts.

Human and mouse genome and transcriptome projects have expanded the field of 'immunogenetics' beyond the traditional study of the genetics and evolution of MHC, TCR and Ig loci into the new interdisciplinary area of 'immunomics'. Immunomics is the study of the molecular functions associated with all immune-related coding and non-coding mRNA transcripts. To unravel the function, regulation and diversity of the immunome requires that we identify and correctly categorize all immune-related transcripts. The importance of intercalated genes, antisense transcripts and non-coding RNAs and their potential role in regulation of immune development and function are only just starting to be appreciated. To better understand immune function and regulation, transcriptome projects (e.g. Functional Annotation of the Mouse, FANTOM), that focus on sequencing full-length transcripts from multiple tissue sources, ideally should include specific immune cells (e.g. T cell, B cells, macrophages, dendritic cells) at various states of development, in activated and unactivated states and in different disease contexts. Progress in deciphering immune regulatory networks will require the cooperative efforts of immunologists, immunogeneticists, molecular biologists and bioinformaticians. Although primary sequence analysis remains useful for annotation of new transcripts it is less useful for identifying novel functions of known transcripts in a new context (protein interaction network or pathway). The most efficient approach to mine useful information from the vast a priori knowledge contained in biological databases and the scientific literature, is to use a combination of computational and expert-driven knowledge discovery strategies. This paper will illustrate the challenges posed in attempts to functionally infer transcriptional regulation and interaction of immune-related genes from text and sequence-based data sources.

Alternative Splicing↗
PubMed1999

Nucleotide sequence databases: a gold mine for biologists.

The rapid expansion of nucleotide sequence data available in public databases is revolutionizing biomedical research. These databases have a variety of uses, including the discovery of novel genes, identification of homologous genes, analysis of alternative splicing, chromosomal localization of genes, and detection of polymorphisms. Data sets such as the human transcript map will undoubtedly accelerate identification of candidate genes in positional-cloning approaches. Careful in silico analysis can significantly reduce the amount of lab work required. Approximately half of all human genes are represented in these databases; therefore, one need not wait for the entire human genome to be sequenced before performing genome-wide studies.

Base Sequence↗
PubMed2005

Genomic analysis of rodent pulmonary tissue following bis-(2-chloroethyl) sulfide exposure.

Bis-(2-chloroethyl) sulfide (sulfur mustard, SM) is a carcinogenic alkylating agent that has been utilized as a chemical warfare agent. To understand the mechanism of SM-induced lung injury, we analyzed global changes in gene expression in a rat lung SM exposure model. Rats were injected in the femoral vein with liquid SM, which circulates directly to the pulmonary vein and then to the lung. Rats were exposed to 1, 3, or 6 mg/kg of SM, and lungs were harvested at 0.5, 1, 3, 6, and 24 h postinjection. Three biological replicates were used for each time point and dose tested. RNA was extracted from the lungs and used as the starting material for the probing of replicate oligonucleotide microarrays. The gene expression data were analyzed using principal component analysis and two-way analysis of variance to identify the genes most significantly changed across time and dose. These genes were ranked by p value and categorized based on molecular function and biological process. Computer-based data mining algorithms revealed several biological processes affected by SM exposure, including protein catabolism, apoptosis, and glycolysis. Several genes that are significantly upregulated in a dose-dependent fashion have been reported as p53 responsive genes, suggesting that cell cycle regulation and p53 activation are involved in the response to SM exposure in the lung. Thus, SM exposure induces transcriptional changes that reveal the cellular response to this potent alkylating agent.

Animals↗
PubMed2006

Iron metabolism in insect disease vectors: mining the Anopheles gambiae translated protein database.

All animals require iron for survival. This requirement reflects the role of this mineral as a cofactor of numerous proteins. However, under physiological conditions, Fe(2+) oxidizes to Fe(3+) encouraging the formation of toxic free radicals. In mammals, the potential for oxidative damage from iron is minimized by binding iron to proteins. Mammalian iron metabolism is complex and numerous proteins are involved in iron absorption, transport, uptake and utilization. We have analyzed the Anopheles gambiae translated protein database for candidates that show identity to proteins involved in mammalian iron metabolism (Holt et al., 2002. The genome sequence of the malaria mosquito Anopheles gambiae. Science 298, 129-149). Our results indicate that proteins involved in iron absorption and intracellular iron utilization are, for the most part, conserved in A. gambiae. In contrast, proteins involved in the pathways of iron export from the gut, transport in hemolymph and uptake at peripheral tissues in mosquitos differ from those for mammals.

Animals↗
PubMed2006

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets.

BACKGROUND: Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). RESULTS: We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). CONCLUSION: The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions.

Algorithms↗
PubMed2002

Proteomic profiling and neurodegeneration in Alzheimer's disease.

Quantitative proteome analysis of Alzheimer's disease (AD) brains was performed using 2-D gels to identify disease specific changes in protein expression. The task of characterizing the proteome and its components is now practically achievable because of the development and integration of four important tools: protein, EST, and complete genome sequence databases, mass spectrometry, matching software for protein sequences and protein separation technology. Mass spectrometry (MS) instrumentation has undergone a tremendous change over the past decade, culminating in the development of highly sensitive, robust instruments that can reliably analyze biomolecules, particularly proteins and peptides; we identified 35 proteins from over 100 protein spots on a 2-D gel. Using this current technology, protein-expression profiling, which is actually a specialized form of mining, is an important principal application of proteomics. The information obtained has tremendous potential as a means of determining the pathogenesis, and detecting disease markers and potential targets for drug therapy in AD.

Aged↗
PubMedDate not supplied

Ovarian cancer in the proteomics era: diagnosis, prognosis, and therapeutics targets.

Ovarian cancer is clinically quiet as it plants seeds of metastases in the peritoneal cavity, even during early stages when there is highest potential for cure. The only available biomarker is CA125, which has an unacceptably low sensitivity and specificity for diagnostic use. Highly sensitive and specific tools to further optimize early diagnosis and treatment are needed. We propose that proteomic technologies have an important role to play in the development of these tools. Mass spectrometry platforms, such as surface-enhanced laser desorption/ionization time-of-flight, may be used to mine patient's serum for proteomic signatures that are shed by tumor and stroma. Such signatures could serve as a diagnostic tool during early-stage disease and as a remission-monitoring tool in later-stage disease. Reverse-phase protein microarrays are a new microproteomic tool to profile signaling pathways in ovarian cancer, thus identifying therapeutic targets while simultaneously suggesting prognostic indicators. Proteomic technologies have the capacity to build upon our genomic and clinical understanding of ovarian cancer by moving the focal point to the tumor and its microenvironment. This unique proteomic vantage point allows the creation of tools that will aid clinicians in making rational decisions in the diagnosis and treatment of women with ovarian cancer.

Biomarkers, Tumor↗
PubMed2004

Prediction of protein function in the absence of significant sequence similarity.

Tremendous progress in DNA sequencing has yielded the genomes of a host of important organisms. The utilisation of these resources requires understanding of the function of each gene. Standard methods of functional assignment involve sequence alignment to a gene of known function; however such methods often fail to find any significant matches. Here we discuss a number of recent alternative methods that may be of use when sequence alignment fails. Function can be defined in a number of ways including E.C. number and MIPS and KEGG functional classes. Phylogenetic profiles show the pattern of presence or absence of a protein between genomes. Protein-protein interactions can be identified by searching for interacting pairs of proteins that are fused to a single protein chain in another organism. The gene neighbour method uses the observation that if the genes that encode two proteins are close on a chromosome, the proteins tend to be functionally related. More general methods use sequence properties such as amino acid composition, mean hydrophobicity, predicted secondary structure and post-translational modification sites. Data mining methods devise rules in the form of IF... THEN statements that make predictions of function using sequence based attributes, predicted secondary structure and sequence similarity. Finally, structural features can be used, after modelling the structure of a protein from its sequence or solving its structure. Protein fold class can be strongly indicative of function, while other structural features, such as secondary structure content, cleft size and 3D structural motifs are also useful.

Computer Simulation↗
PubMed2026

From spillover to systems: evidence gaps in One Health preparedness for emerging infectious diseases in Latin America and the Caribbean.

Latin America and the Caribbean are a global hotspot for emerging and re-emerging infectious diseases, yet regional One Health preparedness remains uneven and incompletely operationalized. This narrative Mini Review synthesizes evidence published mainly between 2015 and 2026 on One Health preparedness for emerging infectious diseases in the region, emphasizing how environmental disruption and climate change shape zoonotic and vector-borne spillover risk. Available regional surveys suggest broad professional familiarity with the One Health concept but limited operational implementation, with environmental health frequently identified as the least-integrated domain. We argue that spillover risk-and the failure to detect and contain spillover once it occurs-should be understood as a system-level outcome shaped by ecological disruption, socioeconomic vulnerability, surveillance capacity, and governance, rather than as an isolated biological event: deforestation, agricultural and extractive expansion-including illegal mining and logging-unplanned urbanization, and climate variability generate new human-animal-vector interfaces, while fragmented governance, uneven and poorly decentralized laboratory capacity, and limited reservoir and environmental surveillance leave these interfaces unmonitored. Environmental and climatic drivers are robustly linked to spillover, although the pathways are disease-specific rather than universal, and socioeconomic vulnerability concentrates the resulting burden in Indigenous, rural, and marginalized populations. We identify priority gaps in integrated surveillance, decentralized diagnostics, genomic capacity, reservoir ecology, governance, financing, and equity, and propose an agenda for anticipatory, climate-informed, and context-sensitive preparedness.

Latin America↗
PubMed2006

Genome-wide review of transcriptional complexity in mouse protein kinases and phosphatases.

BACKGROUND: Alternative transcripts of protein kinases and protein phosphatases are known to encode peptides with altered substrate affinities, subcellular localizations, and activities. We undertook a systematic study to catalog the variant transcripts of every protein kinase-like and phosphatase-like locus of mouse http://variant.imb.uq.edu.au. RESULTS: By reviewing all available transcript evidence, we found that at least 75% of kinase and phosphatase loci in mouse generate alternative splice forms, and that 44% of these loci have well supported alternative 5' exons. In a further analysis of full-length cDNAs, we identified 69% of loci as generating more than one peptide isoform. The 1,469 peptide isoforms generated from these loci correspond to 1,080 unique Interpro domain combinations, many of which lack catalytic or interaction domains. We also report on the existence of likely dominant negative forms for many of the receptor kinases and phosphatases, including some 26 secreted decoys (seven known and 19 novel: Alk, Csf1r, Egfr, Epha1, 3, 5,7 and 10, Ephb1, Flt1, Flt3, Insr, Insrr, Kdr, Met, Ptk7, Ptprc, Ptprd, Ptprg, Ptprl, Ptprn, Ptprn2, Ptpro, Ptprr, Ptprs, and Ptprz1) and 13 transmembrane forms (four known and nine novel: Axl, Bmpr1a, Csf1r, Epha4, 5, 6 and 7, Ntrk2, Ntrk3, Pdgfra, Ptprk, Ptprm, Ptpru). Finally, by mining public gene expression data (MPSS and microarrays), we confirmed tissue-specific expression of ten of the novel isoforms. CONCLUSION: These findings suggest that alternative transcripts of protein kinases and phosphatases are produced that encode different domain structures, and that these variants are likely to play important roles in phosphorylation-dependent signaling pathways.

Alternative Splicing↗
PubMed2003

Mining meiosis and gametogenesis with DNA microarrays.

Gametogenesis is a key developmental process that involves complex transcriptional regulation of numerous genes including many that are conserved between unicellular eukaryotes and mammals. Recent expression-profiling experiments using microarrays have provided insight into the co-ordinated transcription of several hundred genes during mitotic growth and meiotic development in budding and fission yeast. Furthermore, microarray-based studies have identified numerous loci that are regulated during the cell cycle or expressed in a germ-cell specific manner in eukaryotic model systems like Caenorhabditis elegans, Mus musculus as well as Homo sapiens. The unprecedented amount of information produced by post-genome biology has spawned novel approaches to organizing biological knowledge using currently available information technology. This review outlines experiments that contribute to an emerging comprehensive picture of the molecular machinery governing sexual reproduction in eukaryotes.

Animals↗
PubMed2002

Information management systems for pharmacogenomics.

The value of high-throughput genomic research is dramatically enhanced by association with key patient data. These data are generally available but of disparate quality and not typically directly associated. A system that could bring these disparate data sources into a common resource connected with functional genomic data would be tremendously advantageous. However, the integration of clinical and accurate interpretation of the generated functional genomic data requires the development of information management systems capable of effectively capturing the data as well as tools to make that data accessible to the laboratory scientist or to the clinician. In this review these challenges and current information technology solutions associated with the management, storage and analysis of high-throughput data are highlighted. It is suggested that the development of a pharmacogenomic data management system which integrates public and proprietary databases, clinical datasets, and data mining tools embedded in a high-performance computing environment should include the following components: parallel processing systems, storage technologies, network technologies, databases and database management systems (DBMS), and application services.

Animals↗
PubMed2002

DNA microarrays in prostate cancer.

DNA microarray technology provides a means to examine large numbers of molecular changes related to a biological process in a high throughput manner. This review discusses plausible utilities of this technology in prostate cancer research, including definition of prostate cancer predisposition, global profiling of gene expression patterns associated with cancer initiation and progression, identification of new diagnostic and prognostic markers, and discovery of novel patient classification schemes. The technology, at present, has only been explored in a limited fashion in prostate cancer research. Some hurdles to be overcome are the high cost of the technology, insufficient sample size and repeated experiments, and the inadequate use of bioinformatics. With the completion of the Human Genome Project and the advance of several highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (eg, single-nucleotide polymorphism chips), and amenable bioinformatics software, this technology will become widely used by investigators in the field. The large amount of novel, unbiased hypotheses and insights generated by this technology is expected to have a significant impact on the diagnosis, treatment, and prevention of prostate cancer. Finally, this review emphasizes existing, but currently underutilized, data-mining tools, such as multivariate statistical analyses, neural networking, and machine learning techniques, to stimulate wider usage.

Algorithms↗
PubMed2006

CytoAccess, a relational laboratory information management system for a clinical cytogenetics laboratory.

We developed a CytoAccess laboratory management system based on the widely used Microsoft Access software to facilitate data processing, result reporting, and quality management for a full-service cytogenetics laboratory. The CytoAccess system consists of four functional modules. The data entry module is for logging in patient information. The result entry module is used to generate chromosome, fluorescent in situ hybridization (FISH), and array comparative genomic hybridization (aCGH) reports. The administrative module enables periodic monitoring of quality control and quality improvement (QA/QI) parameters and produces billing forms. The maintenance module allows users to update clinical demographics, report templates, code tables, and to refresh data links. We have integrated into this system over 15,000 chromosome and FISH results from prenatal, postnatal, and cancer cases for the past six years. This system is cost-effective, user-friendly, flexible in updating, and potentially adaptable for data mining.

Journal Article↗