Biomedical subjects

Colin Campbell

Publications and source records attributed to Colin Campbell.

29 records · Page 2Linked to original sources

PubMed2003

Genome-wide screening for complete genetic loss in prostate cancer by comparative hybridization onto cDNA microarrays.

We demonstrate that comparative genomic hybridization (CGH) onto cDNA microarrays may be used to carry out genome-wide screens for regions of genetic loss, including homozygous (complete) deletions that may represent the possible location of tumour suppressor genes in human cancer. Screening of the prostate cancer cell lines LNCaP, PC3 and DU145 allowed the mapping of specific regions where genome copy number appeared altered and led to the identification of two novel regions of complete loss at 17q21.31 (500 kb spanning STAT3) and at 10q23.1 (50-350 kb spanning SFTPA2) in the PC3 cell line.

Adenocarcinoma↗

PubMed2003

Expression profiling targeting chromosomes for tumor classification and prediction of clinical behavior.

Tumors are associated with altered or deregulated gene products that affect critical cellular functions. Here we assess the use of a global expression profiling technique that identifies chromosome regions corresponding to differential gene expression, termed comparative expressed sequence hybridization (CESH). CESH analysis was performed on a total of 104 tumors with a diagnosis of rhabdomyosarcoma, leiomyosarcoma, prostate cancer, and favorable-histology Wilms tumors. Through the use of the chromosome regions identified as variables, support vector machine analysis was applied to assess classification potential, and feature selection (recursive feature elimination) was used to identify the best discriminatory regions. We demonstrate that the CESH profiles have characteristic patterns in tumor groups and were also able to distinguish subgroups of rhabdomyosarcoma. The overall CESH profiles in favorable-histology Wilms tumors were found to correlate with subsequent clinical behavior. Classification by use of CESH profiles was shown to be similar in performance to previous microarray expression studies and highlighted regions for further investigation. We conclude that analysis of chromosomal expression profiles can group, subgroup, and even predict clinical behavior of tumors to a level of performance similar to that of microarray analysis. CESH is independent of selecting sequences for interrogation and is a simple, rapid, and widely accessible approach to identify clinically useful differential expression.

Breast Neoplasms↗

PubMed2003

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.

Algorithms↗

PubMed2002

A DNA double strand break repair defect in Fanconi anemia fibroblasts.

Fanconi anemia (FA) is a heterogeneous autosomal recessive disease characterized by congenital abnormalities, pancytopenia, and an increased incidence of cancer. Cells cultured from FA patients display elevated spontaneous chromosomal breaks and deletions and are hypersensitive to bifunctional cross-linking agents. Thus, it has been hypothesized that FA is a DNA repair disorder. We analyzed plasmid end-joining in intact diploid fibroblast cells derived from FA patients. FA fibroblasts from complementation groups A, C, D2, and G rejoined linearized plasmids with a significantly decreased efficiency compared with non-FA fibroblasts. Retrovirus-mediated expression of the respective FA cDNAs in FA cells restored their end-joining efficiency to wild type levels. Human FA fibroblasts and fibroblasts from FA rodent models were also significantly more sensitive to restriction enzyme-induced chromosomal DNA double strand breaks than were their retrovirally corrected counterparts. Taken together, these data show that FA fibroblasts have a deficiency in both extra-chromosomal and chromosomal DNA double strand break repair, a defect that could provide an attractive explanation for some of the pathologies associated with FA.

Alleles↗

PubMed2002

DNA ligase III is degraded by calpain during cell death induced by DNA-damaging agents.

A yeast two-hybrid screen identified the regulatory subunit of the calcium-dependent protease calpain as a putative DNA ligase III-binding protein. Calpain binds to the N-terminal region of DNA ligase III, which contains an acidic proline, aspartate, serine, and threonine (PEST) domain frequently present in proteins cleaved by calpain. Recombinant DNA ligase III was a substrate for calpain degradation in vitro. This calpain-mediated proteolysis was calcium-dependent and was blocked by the specific calpain inhibitor calpeptin. Western blot analysis revealed that DNA ligase III was degraded in human fibrosarcoma HT1080 cells following exposure to gamma-radiation. The degradation of DNA ligase III was prevented by pretreatment with calpeptin, which protected irradiated cells from death. Calpeptin treatment also blocked 9-amino camptothecin-induced DNA ligase III proteolysis and simultaneously protected the cells from death. HT1080 clones expressing a modified DNA ligase III that lacked a recognizable PEST domain were significantly more resistant to killing by gamma-radiation or 9- amino camptothecin than were cells that overexpressed the wild-type form of DNA ligase III. These data show that calpain-mediated proteolysis of DNA ligase III plays an essential role in DNA damage-induced cell death in human cells.

Amino Acid Motifs↗

PubMed2002

Bradykinin and ATP accelerate Ca(2+) efflux from rat sensory neurons via protein kinase C and the plasma membrane Ca(2+) pump isoform 4.

Modulation of Ca(2+) channels by neurotransmitters provides critical control of neuronal excitability and synaptic strength. Little is known about regulation of the Ca(2+) efflux pathways that counterbalance Ca(2+) influx in neurons. We demonstrate that bradykinin and ATP significantly facilitate removal of action potential-induced Ca(2+) loads by stimulating plasma membrane Ca(2+)-ATPases (PMCAs) in rat sensory neurons. This effect was mimicked in the soma and axonal varicosities by phorbol esters and was blocked by antagonists of protein kinase C (PKC). Reduced expression of PMCA isoform 4 abolished, and overexpression of isoform 4b enhanced, PKC-dependent facilitation of Ca(2+) efflux. This acceleration of PMCA4 underlies the shortening of the action potential afterhyperpolarization produced by activation of bradykinin and purinergic receptors. Thus, isoform-specific modulation of PMCA-mediated Ca(2+) efflux represents a novel mechanism to control excitability in sensory neurons.

Action Potentials↗

PubMed2002

Identification of amplified and expressed genes in breast cancer by comparative hybridization onto microarrays of randomly selected cDNA clones.

Microarray analysis using sets of known human genes provides a powerful platform for identifying candidate oncogenes involved in DNA amplification events but suffers from the disadvantage that information can be gained only on genes that have been preselected for inclusion on the array. To address this issue, we have performed comparative genome hybridization (CGH) and expression analyses on microarrays of clones, randomly selected from a cDNA library, prepared from a cancer containing the DNA amplicon under investigation. Application of this approach to the BT474 breast carcinoma cell line, which contains amplicons at 20q13, 17q11-21, and 17q22-23, identified 50 amplified and expressed genes, including genes from these regions previously proposed as candidate oncogenes. When considered together with data from microarray expression profiles and Northern analyses, we were able to propose five genes as new candidate oncogenes where amplification in breast cancer cell lines was consistently associated with higher levels of RNA expression. These included the HB01 histone acetyl transferase gene at 17q22-23 and the TRAP100 gene, which encodes a thyroid hormone receptor-associated protein coactivator, at 17q11-21. The results demonstrate the utility of this microarray-based CGH approach in hunting for candidate oncogenes within DNA amplicons.

Breast Neoplasms↗

PubMed2002

Expression of bacterial endonucleases in Saccharomyces cerevisiae mitochondria.

Expression vectors were created in which the 5' end of the Saccharomyces cerevisiae CDC9 gene, which encodes a mitochondrial targeting peptide, was cloned in-frame with the coding regions of the EcoR I, Hind III, and Pst I endonuclease genes. Expression of the EcoR I and Hind III fusion proteins inhibited growth of yeast on glycerol-containing media and resulted in the nearly quantitative restriction digestion of their mitochondrial DNA. In contrast, expression of Pst I, which does not recognize any sites within yeast mitochondrial DNA, had no effect on growth in glycerol-containing media, and did not affect the integrity of the mitochondrial genome.

Journal Article↗

PubMed2002

Bayesian automatic relevance determination algorithms for classifying gene expression data.

MOTIVATION: We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays. RESULTS: We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important.

Algorithms↗

PubMed1976

The manchild pill?

Explore the source record for details and available documents.

Abortion, Eugenic↗

PubMedDate not supplied

The latent process decomposition of cDNA microarray data sets.

We present a new computational technique (a software implementation, data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/) which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called Latent Process Decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important advantages over the standard use of dendrograms. First, the ability to objectively assess the optimal number of sample clusters. Second, the ability to represent samples and gene expression levels using a common set of latent variables (dendrograms cluster samples and gene expression values separately which amounts to two distinct reduced space representations). Third, in constrast to standard cluster models, observations are not assigned to a single cluster and, thus, for example, gene expression levels are modeled via combinations of the latent processes identified by the algorithm. We show this new method compares favorably with alternative cluster analysis methods. To illustrate its potential, we apply the proposed technique to several microarray data sets for cancer. For these data sets it successfully decomposes the data into known subtypes and indicates possible further taxonomic subdivision in addition to highlighting, in a wholly unsupervised manner, the importance of certain genes which are known to be medically significant. To illustrate its wider applicability, we also illustrate its performance on a microarray data set for yeast.

Algorithms↗