Biomedical subjects

Patrick O Brown

Publications and source records attributed to Patrick O Brown.

80 records · Page 5Linked to original sources

PubMed2002

Precision and functional specificity in mRNA decay.

Posttranscriptional processing of mRNA is an integral component of the gene expression program. By using DNA microarrays, we precisely measured the decay of each yeast mRNA, after thermal inactivation of a temperature-sensitive RNA polymerase II. The half-lives varied widely, ranging from approximately 3 min to more than 90 min. We found no simple correlation between mRNA half-lives and ORF size, codon bias, ribosome density, or abundance. However, the decay rates of mRNAs encoding groups of proteins that act together in stoichiometric complexes were generally closely matched, and other evidence pointed to a more general relationship between physiological function and mRNA turnover rates. The results provide strong evidence that precise control of the decay of each mRNA is a fundamental feature of the gene expression program in yeast.

Glycolysis↗

PubMed2002

Molecular characterisation of soft tissue tumours: a gene expression study.

BACKGROUND: Soft-tissue tumours are derived from mesenchymal cells such as fibroblasts, muscle cells, or adipocytes, but for many such tumours the histogenesis is controversial. We aimed to start molecular characterisation of these rare neoplasms and to do a genome-wide search for new diagnostic markers. METHODS: We analysed gene-expression patterns of 41 soft-tissue tumours with spotted cDNA microarrays. After removal of errors introduced by use of different microarray batches, the expression patterns of 5520 genes that were well defined were used to separate tumours into discrete groups by hierarchical clustering and singular value decomposition. FINDINGS: Synovial sarcomas, gastrointestinal stromal tumours, neural tumours, and a subset of the leiomyosarcomas, showed strikingly distinct gene-expression patterns. Other tumour categories--malignant fibrous histiocytoma, liposarcoma, and the remaining leiomyosarcomas--shared molecular profiles that were not predicted by histological features or immunohistochemistry. Strong expression of known genes, such as KIT in gastrointestinal stromal tumours, was noted within gene sets that distinguished the different sarcomas. However, many uncharacterised genes also contributed to the distinction between tumour types. INTERPRETATION: These results suggest a new method for classification of soft-tissue tumours, which could improve on the method based on histological findings. Large numbers of uncharacterised genes contributed to distinctions between the tumours, and some of these could be useful markers for diagnosis, have prognostic significance, or prove possible targets for treatment.

Gene Expression Profiling↗

PubMed2002

Stereotyped and specific gene expression programs in human innate immune responses to bacteria.

The innate immune response is crucial for defense against microbial pathogens. To investigate the molecular choreography of this response, we carried out a systematic examination of the gene expression program in human peripheral blood mononuclear cells responding to bacteria and bacterial products. We found a remarkably stereotyped program of gene expression induced by bacterial lipopolysaccharide and diverse killed bacteria. An intricately choreographed expression program devoted to communication between cells was a prominent feature of the response. Other features suggested a molecular program for commitment of antigen-presenting cells to antigens captured in the context of bacterial infection. Despite the striking similarities, there were qualitative and quantitative differences in the responses to different bacteria. Modulation of this host-response program by bacterial virulence mechanisms was an important source of variation in the response to different bacteria.

Bacteria↗

PubMed2002

Autoantigen microarrays for multiplex characterization of autoantibody responses.

We constructed miniaturized autoantigen arrays to perform large-scale multiplex characterization of autoantibody responses directed against structurally diverse autoantigens, using submicroliter quantities of clinical samples. Autoantigen microarrays were produced by attaching hundreds of proteins, peptides and other biomolecules to the surface of derivatized glass slides using a robotic arrayer. Arrays were incubated with patient serum, and spectrally resolvable fluorescent labels were used to detect autoantibody binding to specific autoantigens on the array. We describe and characterize arrays containing the major autoantigens in eight distinct human autoimmune diseases, including systemic lupus erythematosus and rheumatoid arthritis. This represents the first report of application of such technology to multiple human disease sera, and will enable validated detection of antibodies recognizing autoantigens including proteins, peptides, enzyme complexes, ribonucleoprotein complexes, DNA and post-translationally modified antigens. Autoantigen microarrays represent a powerful tool to study the specificity and pathogenesis of autoantibody responses, and to identify and define relevant autoantigens in human autoimmune diseases.

Animals↗

PubMed2002

Gene expression patterns in human liver cancers.

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Using cDNA microarrays to characterize patterns of gene expression in HCC, we found consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues. The expression patterns in HCC were also readily distinguished from those associated with tumors metastatic to liver. The global gene expression patterns intrinsic to each tumor were sufficiently distinctive that multiple tumor nodules from the same patient could usually be recognized and distinguished from all the others in the large sample set on the basis of their gene expression patterns alone. The distinctive gene expression patterns are characteristic of the tumors and not the patient; the expression programs seen in clonally independent tumor nodules in the same patient were no more similar than those in tumors from different patients. Moreover, clonally related tumor masses that showed distinct expression profiles were also distinguished by genotypic differences. Some features of the gene expression patterns were associated with specific phenotypic and genotypic characteristics of the tumors, including growth rate, vascular invasion, and p53 overexpression.

Carcinoma, Hepatocellular↗

PubMed2002

Identification of genes periodically expressed in the human cell cycle and their expression in tumors.

The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays. Transcripts of >850 genes showed periodic variation during the cell cycle. Hierarchical clustering of the expression patterns revealed coexpressed groups of previously well-characterized genes involved in essential cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion along with genes of uncharacterized function. Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle. However, some of the genes periodically expressed in the HeLa cell cycle do not have a consistent correlation with tumor proliferation. Cell cycle-regulated transcripts of genes involved in fundamental processes such as DNA replication and chromosome segregation seem to be more highly expressed in proliferative tumors simply because they contain more cycling cells. The data in this report provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery. The full dataset is available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.

Cell Cycle↗

PubMed2002

Nonparametric methods for identifying differentially expressed genes in microarray data.

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.

Carcinoma, Squamous Cell↗

PubMed2002

In vivo regulation of human skeletal muscle gene expression by thyroid hormone.

Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 microg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.

Administration, Oral↗