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At least 847 records · Page 47Linked to original sources
PubMed2005

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Animals↗
PubMed2000

Genomic structure and expression profile of LPHH1, a 7TM gene variably expressed in breast cancer cell lines.

Gene identification studies, centred on a region of overlapping loss of heterozygosity in breast tumours within band 1p31.1, lead to the characterisation of LPHH1, a novel human 7TM gene. The coding sequence of LPHH1 extends over a 60 kb region and comprises in excess of 28 exons. Alternative splicing occurs minimally at five positions, four of which are within the coding sequence. The fifth region of alternative splicing occurs at the extreme 5' end of the transcript. A clear tissue specific bias in alternative exon selection is observed to some degree at all five positions, including the extreme 5' region, which raises the possibility of multiple and perhaps tissue specific promoters. One such putative promoter region, which appears to be utilised predominantly in breast cancer cells, has been identified. LPHH1 is highly evolutionarily conserved, with the simplest (19 exon) gene product being 95% identical between human and rat. Comparison of the alternatively spliced exons between three species, where data are available, has so far revealed 100% identity in the encoded peptide sequences, suggesting conservation of a functional aspect of this splicing. Gene expression has been observed in all tissues and cell lines tested, with the exception of lymphoblastoid and multiple myeloma lines, where there appears to be only a very low level of transcription. LPHH1 also appears to be downregulated in human bone marrow. These data are consistent with a role for the gene products in adhesion-mediated signalling. Analysis of a panel of breast tumour cell lines revealed that a number apparently overexpressed the gene whilst others showed very low levels of transcription. In one case, the overexpression correlated with a low level increase in gene copy number in the tumour line. In addition to differences in the overall levels of expression, LPHH1 mRNAs were alternatively spliced to varying degrees with shifts in the major gene product to truncated or altered forms in some lines. No somatic LPHH1 mutations were detected through sequence analysis of four primary breast tumours that showed loss of the adjacent 1p31.1 marker D1S207.

Alternative Splicing↗
PubMed2003

Human SLITRK family genes: genomic organization and expression profiling in normal brain and brain tumor tissue.

Slitrk family proteins are characterized as integral membrane proteins that have two leucine-rich repeat (LRR) domains and a carboxy-terminal domain that is partially similar to trk neurotrophin receptor proteins. The LRR domains are similar to those of slit proteins. In a previous study, we showed that mouse Slitrk genes are expressed predominantly in neural tissue and have neurite-modulating activity in cultured neuronal cells. Their expression profiles as well as their functions vary among the family members. In this paper, we characterized the human SLITRK1, SLITRK2, SLITRK3, SLITRK4, SLITRK5, and SLITRK6 genes. The six genes are located in three clusters, on 3q, 13q, and Xq, respectively. Their expression was detected mainly in the brain, but the expression profile of each SLITRK was unique. SLITRK expression was also investigated in various types of brain tumor tissue. The results showed that all SLITRK genes are differentially expressed in brain tumors, including astrocytoma, oligodendroglioma, glioblastoma, medulloblastoma, and supratentorial primitive neuroectodermal tumor (PNET). Particularly interesting findings were that SLITRK3 expression was enhanced in tissue from several different types of tumors and SLITRK6 expression was highly selective. These results suggest that the human SLITRK genes are useful molecular indicators of brain tumor properties.

Adolescent↗
PubMed2001

Genome-wide gene expression profiles of the developing mouse hippocampus.

We have analyzed the developmental molecular programs of the mouse hippocampus, a cortical structure critical for learning and memory, by means of large-scale DNA microarray techniques. Of 11,000 genes and expressed sequence tags examined, 1,926 showed dynamic changes during hippocampal development from embryonic day 16 to postnatal day 30. Gene-cluster analysis was used to group these genes into 16 distinct clusters with striking patterns that appear to correlate with major developmental hallmarks and cellular events. These include genes involved in neuronal proliferation, differentiation, and synapse formation. A complete list of the transcriptional changes has been compiled into a comprehensive gene profile database (http://BrainGenomics.Princeton.edu), which should prove valuable in advancing our understanding of the molecular and genetic programs underlying both the development and the functions of the mammalian brain.

Animals↗
PubMed2004

A statistical framework for genomic data fusion.

MOTIVATION: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. RESULTS: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. AVAILABILITY: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm

Algorithms↗
PubMed2006

Functional genomics of the beta-cell: short-chain 3-hydroxyacyl-coenzyme A dehydrogenase regulates insulin secretion independent of K+ currents.

Recent advances in functional genomics afford the opportunity to interrogate the expression profiles of thousands of genes simultaneously and examine the function of these genes in a high-throughput manner. In this study, we describe a rational and efficient approach to identifying novel regulators of insulin secretion by the pancreatic beta-cell. Computational analysis of expression profiles of several mouse and cellular models of impaired insulin secretion identified 373 candidate genes involved in regulation of insulin secretion. Using RNA interference, we assessed the requirements of 10 of these candidates and identified four genes (40%) as being essential for normal insulin secretion. Among the genes identified was Hadhsc, which encodes short-chain 3-hydroxyacyl-coenzyme A dehydrogenase (SCHAD), an enzyme of mitochondrial beta-oxidation of fatty acids whose mutation results in congenital hyperinsulinism. RNA interference-mediated gene suppression of Hadhsc in insulinoma cells and primary rodent islets revealed enhanced basal but normal glucose-stimulated insulin secretion. This increase in basal insulin secretion was not attenuated by the opening of the KATP channel with diazoxide, suggesting that SCHAD regulates insulin secretion through a KATP channel-independent mechanism. Our results suggest a molecular explanation for the hyperinsulinemia hypoglycemic seen in patients with SCHAD deficiency.

Animals↗
PubMed2006

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles.

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.

Bayes Theorem↗
PubMed2003

Transcriptional profiling and drug discovery.

The availability of whole-genome nucleotide sequence data from an ever-growing list of microbial genomes, including complete genomes for multiple strains within a species, presents an opportunity to overcome the challenges presented by antimicrobial drug resistance. The development of DNA microarrays provides a unique tool to understand pathogenic microbial genomes from a global perspective. Genome-wide expression profiles can facilitate the characterization both of the mechanisms of action and of the mechanisms of resistance to antimicrobial agents. Expression data have also been used to initiate the characterization of genes of unknown function, potentially leading to the identification of novel drug targets. Initial studies using DNA microarrays to analyse the host and pathogen responses to infection have also been performed, impacting our understanding of pathogenesis and the strategies taken to combat infectious diseases.

Drug Design↗
PubMed2002

Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors.

In approximately 70% of the families with a high frequency of early-onset breast and/or ovarian cancer, BRCA1 or BRCA2 germline mutations cannot be identified with the current screening regime. Therefore, we used data mining to identify a somatic genetic signature to differentiate BRCA1 mutation carriers from non-BRCA1 carriers based on the genetic characteristics of their breast carcinomas. For this purpose, we developed a molecular classifier, which assigns a given tumor to either the BRCA1 or control group based on somatic genetic profiles as revealed by comparative genomic hybridization. This was performed on breast tumors selected from two groups of patients: 28 proven BRCA1 germline mutation carriers; and a control group consisting of 42 breast tumors from patients with unknown BRCA1 or BRCA2 status. We show that BRCA1 breast carcinomas exhibit specific somatic genetic aberrations and can be distinguished from control tumors with an accuracy of 84% (sensitivity of 96% and specificity of 76%). Chromosomal bands used by this classifier include regions on chromosomes 3p, 3q, and 5q. The classifier miss-assigned one patient with a BRCA1 mutation to the non-BRCA1 class. The germline mutation in this patient is a 62bp deletion in the last exon of BRCA1 (5622del62). Possibly, this mutation may give a different phenotypic effect than do mutations in other regions of the gene. Validation on an independent set of BRCA1 and sporadic tumors showed that the BRCA1 classifier correctly identified all 6 BRCA1 tumors and assigned 4 of the 19 control patients to the BRCA1 class. The resulting accuracy on the validation set is 84%.

Breast Neoplasms↗
PubMed1997

Recent emergence of new variants of Yersinia pestis in Madagascar.

Yersinia pestis, the causative agent of plague, has been responsible for at least three pandemics. During the last pandemic, which started in Hong Kong in 1894, the microorganism colonized new, previously unscathed geographical areas where it has become well established. The aim of this longitudinal study was to investigate the genetic stability of Y. pestis strains introduced into a new environment just under a century ago and to follow the epidemiology of any new genetic variant detected. In the present study, 187 strains of Y. pestis isolated between 1939 and 1996 from different regions of Madagascar and responsible mainly for human cases of bubonic and pneumonic plague were studied. Our principal genotyping method was rRNA gene profiling (ribotyping), which has previously been shown to be an effective scheme for typing Y. pestis strains of different geographical origins. We report that all studied Y. pestis strains isolated in Madagascar before 1982 were of classical ribotype B, the ribotype attributed to the Y. pestis clone that spread around the world during the third pandemic. In 1982, 1983, and 1994, strains with new ribotypes, designated R, Q, and T, respectively, were isolated on the high-plateau region of the island. Analysis of other genotypic traits such as the NotI genomic restriction profiles and the EcoRV plasmid restriction profiles revealed that the new variants could also be distinguished by specific genomic and/or plasmid profiles. A follow-up of these new variants indicated that strains of ribotypes Q and R have become well established in their ecosystem and have a tendency to spread to new geographical areas and supplant the original classical strain.

Animals↗
PubMed2005

Integrating microarray gene expression object model and clinical document architecture for cancer genomics research.

Systematic integration of genomic-scale expression profiles with clinical information may facilitate cancer genomics research. MAGE-OM (Microarray Gene Expression Object Model) defines standard objects for genomic but not for clinical data. HL7 CDA (Clinical Document Architecture) is a document model for clinical information, describing syntax (generic structure) but not semantics. We designed a document template in XML Schema with additional constraints for CDA to define content semantics, enabling data model-level integration of MAGE-OM and CDA for cancer genomics research.

Gene Expression Profiling↗
PubMed2000

Sequence analysis by additive scales: DNA structure for sequences and repeats of all lengths.

MOTIVATION: DNA structure plays an important role in a variety of biological processes. Different di- and tri-nucleotide scales have been proposed to capture various aspects of DNA structure including base stacking energy, propeller twist angle, protein deformability, bendability, and position preference. Yet, a general framework for the computational analysis and prediction of DNA structure is still lacking. Such a framework should in particular address the following issues: (1) construction of sequences with extremal properties; (2) quantitative evaluation of sequences with respect to a given genomic background; (3) automatic extraction of extremal sequences and profiles from genomic databases; (4) distribution and asymptotic behavior as the length N of the sequences increases; and (5) complete analysis of correlations between scales. RESULTS: We develop a general framework for sequence analysis based on additive scales, structural or other, that addresses all these issues. We show how to construct extremal sequences and calibrate scores for automatic genomic and database extraction. We show that distributions rapidly converge to normality as Nincreases. Pairwise correlations between scales depend both on background distribution and sequence length and rapidly converge to an analytically predictable asymptotic value. For di- and tri-nucleotide scales, normal behavior and asymptotic correlation values are attained over a characteristic window length of about 10-15 bp. With a uniform background distribution, pairwise correlations between empirically-derived scales remain relatively small and roughly constant at all lengths, except for propeller twist and protein deformability which are positively correlated. There is a positive (resp. negative) correlation between dinucleotide base stacking (resp. propeller twist and protein deformability) and AT-content that increases in magnitude with length. The framework is applied to the analysis of various DNA tandem repeats. We derive exact expressions for counting the number of repeat unit classes at all lengths. Tandem repeats are likely to result from a variety of different mechanisms, a fraction of which is likely to depend on profiles characterized by extreme structural features.

Animals↗
PubMed2004

Metabolic profiling: a new tool in the study of wood formation.

In the realm of plant genomics, metabolic profiling has become a valuable tool with which to assess the effect of genetic and/or environmental factors on plant development. This paper reports the first application of metabolic profiling on differentiating xylem tissue of loblolly pine. A protocol is presented for the analysis of loblolly pine xylem tissue. The effects of sample preparation, extraction, and derivatization on the corresponding metabolite profiles and yields have been investigated and are reported. Gas chromatography-mass spectroscopy has been used to quantify >60 polar and lipophilic metabolites from wood-forming tissue. It was possible to assign chemical structures to approximately half of these compounds. Comparison of six loblolly pine genotypes, three high cellulose (50-52%) and three medium (45-48%) cellulose, showed distinct metabolic profiles. Principal component analysis enabled the assignment of metabolic phenotypes using these large data sets. Metabolic phenotype clustering occurred in which the three high-cellulose genotypes were segregated from the medium-cellulose genotypes. These results demonstrate the use of metabolic profiling for the study of wood-forming tissue and as a tool in functional genomics.

Cellulose↗
PubMed2001

Cis-acting sites contributing to expression of divergently transcribed DAL1 and DAL4 genes in S. cerevisiae: a word of caution when correlating cis-acting sequences with genome-wide expression analyses.

Correlating genome-wide expression profiles with sequence searches of promoter regions is being used as a technique to identify putative binding sites for transacting factors or to refine consensus sequences of those already known. To evaluate the limitations of such an approach in our studies of GATA-mediated transcription in Saccharomyces cerevisiae, we identified the relative contributions made to DAL1 and DAL4 expression by each of five Gln3p-, and/or Gat1p-, and three Dal82p-binding site homologous sequences situated in the 829-bp intergenic region separating these highly related, divergently transcribed genes. Our data suggest that although the correlation of repeated sequences or sequence homologies appearing within promoter regions with expression profiles obtained from genome-wide transcription analyses can provide useful starting points for analyses of cis-acting sites, significant limitations and possibilities for misinterpretation also abound.

Amidohydrolases↗
PubMed2026

The agroenvironmental-clinical link of Proteus mirabilis: Genomic epidemiology, clonal relationships, and shared resistance and virulence profiles.

Proteus mirabilis is an opportunistic pathogen frequently associated with urinary tract infections (UTIs), with its pathogenicity driven by coordinated virulence traits such as adhesion, biofilm formation, and toxin production. The systemic emergence of antimicrobial resistance (AMR) within this species raises critical concerns regarding its persistence across clinical and environmental niches. This study investigated the virulence profiles, AMR determinants, and molecular epidemiology of P. mirabilis isolates recovered from retail vegetables and human community-acquired UTIs (CA-UTIs) in southern Brazil. A total of 310 isolates were analyzed (110 from vegetables and 200 from UTIs). Multidrug resistance was observed in 36.6-42.0% of vegetable isolates and 16.0% of UTI isolates, while extended-spectrum β-lactamase (ESBL) production reached 32.0% in the vegetable group. Notably, the carbapenemase gene blaKPC-2 was identified in vegetable isolates, representing a critical food safety concern. High-consequence resistance genes, including blaCTX-M variants, fosA3, and qnrD, were widely distributed. Furthermore, all isolates harbored multi-element virulence profiles-particularly genes encoding fimbriae, proteases, and iron acquisition systems-and exhibited strong or very strong biofilm-forming phenotypes. Clonal analysis revealed tight genetic relatedness between vegetable and clinical isolates, including indistinguishable profiles. Whole-genome sequencing identified shared sequence types (STs), most notably the high-risk clone ST773, alongside internationally reported lineages such as ST135 and ST336. Moreover, conserved mobile genetic environments flanking blaKPC-2 were structurally characterized. These findings demonstrate that food-associated P. mirabilis serves as an active agroenvironmental reservoir for virulent and multidrug-resistant lineages, posing an unmonitored risk for zoonotic dissemination and human infection within the One Health framework.

bla KPC−2↗
PubMed2005

Application of expression genomics for predicting treatment response in cancer.

During tumor progression, multiple genetic changes in the genome vastly alter the transcriptomes of cancers. Some of these changes, including the mutations of various growth regulatory genes as well as alterations in the transcription of a large number of genes, may lead to resistance to treatment. Therefore, capturing such genomic information of the tumors would enable a physician to decide on the course of treatment options clinically available. Currently, it is still not feasible to identify all the genetic mutations that have occurred in a patient's cancer genome. However, the advent of DNA microarray coupled with the completion of the human genome sequence and the identification of all its genes, have made possible genome-wide gene expression profiling of the cancer genome. In this review, we will focus on the application of expression genomics for identifying signature gene expression profiles in primary cancers to predict response to either radio- or chemotherapy. We envision that transcription profiling of the cancer genomes ultimately will not only reveal how altered gene expression results in resistance to treatment, but also be exploited for predicting and personalizing cancer therapy.

Animals↗