Biomedical subjects

Lance D Miller

Publications and source records attributed to Lance D Miller.

At least 19 recordsLinked to original sources

PubMed2006

The major chemotaxis gene cluster of Rhizobium leguminosarum bv. viciae is essential for competitive nodulation.

Rhizobium leguminosarum biovar viciae strain 3841 is a motile alpha-proteobacterium that can establish a nitrogen-fixing symbiosis within the roots of pea plants. In order to determine the contribution of chemotaxis to the lifestyle of R. leguminosarum, we have characterized the function of two chemotaxis gene clusters (che1 and che2) in controlling motility behaviour. We have found that both chemotaxis gene clusters modulate the motility swimming bias of R. leguminosarum cells and that the che1 cluster is the major pathway controlling swimming bias and chemotaxis. The che2 cluster also contributes to swimming bias, but has a minor effect on chemotaxis. Using competitive nodulation assays, we have demonstrated that a functional che1 cluster, but not the che2 cluster, promotes competitive nodulation of the peas. This finding implies that the environmental cue(s) triggering chemotaxis of R. leguminosarum bv. viciae cells towards the roots of pea and facilitating colonization are likely to be processed through the che1 cluster despite the contribution of both che clusters to swimming behaviour. A phylogenetic analysis of the distribution of che1 and che2 orthologues in the alpha-proteobacteria together with our results allow us to propose that che1 homologues are major controllers of chemotaxis and host association in the Rhizobiaceae.

Bacterial Proteins↗

PubMed2006

Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.

Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.

Algorithms↗

PubMed2006

Transcriptome kinetics of arsenic-induced adaptive response in zebrafish liver.

Arsenic is a prominent environmental toxicant and carcinogen; however, its molecular mechanism of toxicity and carcinogenicity remains poorly understood. In this study, we performed microarray-based expression profiling on liver of zebrafish exposed to 15 parts/million (ppm) arsenic [As(V)] for 8-96 h to identify global transcriptional changes and biological networks involved in arsenic-induced adaptive responses in vivo. We found that there was an increase of transcriptional activity associated with metabolism, especially for biosyntheses, membrane transporter activities, cytoplasm, and endoplasmic reticulum in the 96 h of arsenic treatment, while transcriptional programs for proteins in catabolism, energy derivation, and stress response remained active throughout the arsenic treatment. Many differentially expressed genes encoding proteins involved in heat shock proteins, DNA damage/repair, antioxidant activity, hypoxia induction, iron homeostasis, arsenic metabolism, and ubiquitin-dependent protein degradation were identified, suggesting strongly that DNA and protein damage as a result of arsenic metabolism and oxidative stress caused major cellular injury. These findings were comparable with those reported in mammalian systems, suggesting that the zebrafish liver coupled with the available microarray technology present an excellent in vivo toxicogenomic model for investigating arsenic toxicity. We proposed an in vivo, acute arsenic-induced adaptive response model of the zebrafish liver illustrating the relevance of many transcriptional activities that provide both global and specific information of a coordinated adaptive response to arsenic in the liver.

Adaptation, Physiological↗

PubMed2006

Gene expression preferentially regulated by tamoxifen in breast cancer cells and correlations with clinical outcome.

The beneficial effect of the selective estrogen receptor (ER) modulator tamoxifen in the treatment and prevention of breast cancer is assumed to be through its ability to antagonize the stimulatory actions of estrogen, although tamoxifen can also have some estrogen-like agonist effects. Here, we report that, in addition to these mixed agonist/antagonist actions, tamoxifen can also selectively regulate a unique set of >60 genes, which are minimally regulated by estradiol (E2) or raloxifene in ERalpha-positive MCF-7 human breast cancer cells. This gene regulation by tamoxifen is mediated by ERalpha and reversed by E2 or ICI 182,780. Introduction of ERbeta into MCF-7 cells reverses tamoxifen action on approximately 75% of these genes. To examine whether these genes might serve as markers of tamoxifen sensitivity and/or the development of resistance, their expression level was examined in breast cancers of women who had received adjuvant therapy with tamoxifen. High expression of two of the tamoxifen-stimulated genes, YWHAZ/14-3-3z and LOC441453, was found to correlate significantly with disease recurrence following tamoxifen treatment in women with ER-positive cancers and hence seem to be markers of a poor prognosis. Our data indicate a new dimension in tamoxifen action, involving gene expression regulation that is tamoxifen preferential, and identify genes that might serve as markers of tumor responsiveness or resistance to tamoxifen therapy. This may have a potential effect on the choice of tamoxifen versus aromatase inhibitors as adjuvant endocrine therapy.

Breast Neoplasms↗

PubMed2006

Hormone-replacement therapy influences gene expression profiles and is associated with breast-cancer prognosis: a cohort study.

BACKGROUND: Postmenopausal hormone-replacement therapy (HRT) increases breast-cancer risk. The influence of HRT on the biology of the primary tumor, however, is not well understood. METHODS: We obtained breast-cancer gene expression profiles using Affymetrix human genome U133A arrays. We examined the relationship between HRT-regulated gene profiles, tumor characteristics, and recurrence-free survival in 72 postmenopausal women. RESULTS: HRT use in patients with estrogen receptor (ER) protein positive tumors (n = 72) was associated with an altered regulation of 276 genes. Expression profiles based on these genes clustered ER-positive tumors into two molecular subclasses, one of which was associated with HRT use and had significantly better recurrence free survival despite lower ER levels. A comparison with external data suggested that gene regulation in tumors associated with HRT was negatively correlated with gene regulation induced by short-term estrogen exposure, but positively correlated with the effect of tamoxifen. CONCLUSION: Our findings suggest that post-menopausal HRT use is associated with a distinct gene expression profile related to better recurrence-free survival and lower ER protein levels. Tentatively, HRT-associated gene expression in tumors resembles the effect of tamoxifen exposure on MCF-7 cells.

Breast Neoplasms↗

PubMed2006

A modular analysis of breast cancer reveals a novel low-grade molecular signature in estrogen receptor-positive tumors.

PURPOSE: Previous reports using genome-wide gene expression data to classify breast tumors have typically used standard unsupervised or supervised techniques, both of which have known limitations. We hypothesized that novel clinically relevant information could be revealed in these data sets by an alternative analytic approach. Using a recently described algorithm, signature analysis (SA), we identified "modules," comprising groups of tightly coexpressed genes that are conditionally linked to particular tumors, in a series of breast tumor gene expression profiles. EXPERIMENTAL DESIGN AND RESULTS: The SA successfully identified multiple breast cancer modules specifically linked to distinct biological functions. We identified a novel module, TuM1, whose presence was not readily discernible by conventional clustering techniques. The TuM1 module is expressed in a subset of estrogen receptor (ER)-positive tumors and is significantly enriched with genes involved in apoptosis and cell death. Clinically, TuM1-expressing tumors are associated with low histopathologic grade, and this association is independent of the inherent ER status of a tumor. We confirmed the robustness and general applicability of TuM1 module by demonstrating its association with low tumor grade in multiple independent breast cancer data sets generated using different array technologies. In vitro, the TuM1 module is down-regulated in ER+ MCF7 cells upon treatment with tamoxifen, suggesting that TuM1 expression may be dependent on active signaling by ER. Initial data is also suggestive that TuM1 expression may be clinically associated with a patient's response to antihormonal therapy. CONCLUSION: Our results suggest that modular-based approaches toward gene expression data can prove useful in identifying novel, robust, and biologically relevant signatures even from data sets that have been the subject of substantial prior analysis.

Breast Neoplasms↗

PubMed2006

Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients.

MOTIVATION: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. RESULTS: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis.

Biomarkers, Tumor↗

PubMed2006

A global map of p53 transcription-factor binding sites in the human genome.

The ability to derive a whole-genome map of transcription-factor binding sites (TFBS) is crucial for elucidating gene regulatory networks. Herein, we describe a robust approach that couples chromatin immunoprecipitation (ChIP) with the paired-end ditag (PET) sequencing strategy for unbiased and precise global localization of TFBS. We have applied this strategy to map p53 targets in the human genome. From a saturated sampling of over half a million PET sequences, we characterized 65,572 unique p53 ChIP DNA fragments and established overlapping PET clusters as a readout to define p53 binding loci with remarkable specificity. Based on this information, we refined the consensus p53 binding motif, identified at least 542 binding loci with high confidence, discovered 98 previously unidentified p53 target genes that were implicated in novel aspects of p53 functions, and showed their clinical relevance to p53-dependent tumorigenesis in primary cancer samples.

Binding Sites↗

PubMed2006

In the pursuit of complexity: systems medicine in cancer biology.

Adler et al., in a paper appearing in Nature Genetics, exploited the intersect of genetic information from expression profiles with that from array comparative genomic hybridization in human breast cancers to identify genes that may induce the transcription of the prognostic "wound response" expression signature. The amplification of two genes, MYC and CSN5, appeared to be correlated with the wound response cassette. In vitro validation showed that the wound signature could be induced in MCF10A cells only when MYC and CSN5 were coexpressed. This work shows that the intersect analysis of gene amplification and transcriptional expression on a genome-wide scale can uncover complex conditional interactions embedded in the systems map of transcriptional regulation.

Gene Expression Regulation, Neoplastic↗

PubMed2005

Conservation of gene expression signatures between zebrafish and human liver tumors and tumor progression.

The zebrafish (Danio rerio) has been long advocated as a model for cancer research, but little is known about the real molecular similarities between zebrafish and human tumors. Comparative analysis of microarray data from zebrafish liver tumors with those from four human tumor types revealed molecular conservation at various levels between fish and human tumors. This approach provides a useful strategy for identifying an expression signature that is strongly associated with a disease phenotype.

Amino Acid Sequence↗

PubMed2005

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.

Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.

Breast Neoplasms↗

PubMed2005

Transcriptome analysis of zebrafish embryogenesis using microarrays.

Zebrafish (Danio rerio) is a well-recognized model for the study of vertebrate developmental genetics, yet at the same time little is known about the transcriptional events that underlie zebrafish embryogenesis. Here we have employed microarray analysis to study the temporal activity of developmentally regulated genes during zebrafish embryogenesis. Transcriptome analysis at 12 different embryonic time points covering five different developmental stages (maternal, blastula, gastrula, segmentation, and pharyngula) revealed a highly dynamic transcriptional profile. Hierarchical clustering, stage-specific clustering, and algorithms to detect onset and peak of gene expression revealed clearly demarcated transcript clusters with maximum gene activity at distinct developmental stages as well as co-regulated expression of gene groups involved in dedicated functions such as organogenesis. Our study also revealed a previously unidentified cohort of genes that are transcribed prior to the mid-blastula transition, a time point earlier than when the zygotic genome was traditionally thought to become active. Here we provide, for the first time to our knowledge, a comprehensive list of developmentally regulated zebrafish genes and their expression profiles during embryogenesis, including novel information on the temporal expression of several thousand previously uncharacterized genes. The expression data generated from this study are accessible to all interested scientists from our institute resource database (http://giscompute.gis.a-star.edu.sg/~govind/zebrafish/data_download.html).

Journal Article↗

PubMed2005

Gene expression profiling to identify oncogenic determinants of autocrine human growth hormone in human mammary carcinoma.

We have exploited a discrepancy in the oncogenic potential of autocrine and exogenous human growth hormone (hGH) in an attempt to identify molecules that could potentially be involved in oncogenic transformation of the human mammary epithelial cell. Microarray analysis of 19,000 human genes identified a subset of 305 genes in a human mammary carcinoma cell line that were remarkably different in their response to autocrine and exogenous hGH. Autocrine and exogenous hGH also regulated 167 common genes. Semiquantitative reverse transcription-PCR confirmed differential regulation of genes by either autocrine or exogenous hGH. Functional analysis of one of the identified autocrine hGH-regulated genes, TFF3, determined that its expression is sufficient to support anchorage-independent growth of human mammary carcinoma cells. Small interfering RNA-mediated knockdown of TFF3 concordantly abrogated anchorage-independent growth of mammary carcinoma cells and abrogated the ability of autocrine hGH to stimulate oncogenic transformation of immortalized human mammary epithelial cells. Further functional characterization of the identified subset of specifically autocrine hGH regulated genes will delineate additional novel oncogenes for the human mammary epithelial cell.

Base Sequence↗

PubMed2005

Correlation test to assess low-level processing of high-density oligonucleotide microarray data.

BACKGROUND: There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data. RESULTS: We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout. CONCLUSION: Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.

Algorithms↗

PubMed2005

Molecular changes from dysplastic nodule to hepatocellular carcinoma through gene expression profiling.

Progression of hepatocellular carcinoma (HCC) is a stepwise process that proceeds from pre-neoplastic lesions--including low-grade dysplastic nodules (LGDNs) and high-grade dysplastic nodules (HGDNs)--to advanced HCC. The molecular changes associated with this progression are unclear, however, and the morphological cues thought to distinguish pre-neoplastic lesions from well-differentiated HCC are not universally accepted. To understand the multistep process of hepato-carcinogenesis at the molecular level, we used oligo-nucleotide microarrays to investigate the transcription profiles of 50 hepatocellular nodular lesions ranging from LGDNs to primary HCC (Edmondson grades 1-3). We demonstrated that gene expression profiles can discriminate not only between dysplastic nodules and overt carcinoma but also between different histological grades of HCC via unsupervised hierarchical clustering with 10,376 genes. We identified 3,084 grade-associated genes, correlated with tumor progression, using one-way ANOVA and a one-versus-all unpooled t test. Functional assignment of these genes revealed discrete expression clusters representing grade-dependent biological properties of HCC. Using both diagonal linear discriminant analysis and support vector machines, we identified 240 genes that could accurately classify tumors according to histological grade, especially when attempting to discriminate LGDNs, HGDNs, and grade 1 HCC. In conclusion, a clear molecular demarcation between dysplastic nodules and overt HCC exists. The progression from grade 1 through grade 3 HCC is associated with changes in gene expression consistent with plausible functional consequences.

Carcinoma, Hepatocellular↗

PubMed2004

Identification of cell cycle-regulated genes in fission yeast.

Cell cycle progression is both regulated and accompanied by periodic changes in the expression levels of a large number of genes. To investigate cell cycle-regulated transcriptional programs in the fission yeast Schizosaccharomyces pombe, we developed a whole-genome oligonucleotide-based DNA microarray. Microarray analysis of both wild-type and cdc25 mutant cell cultures was performed to identify transcripts whose levels oscillated during the cell cycle. Using an unsupervised algorithm, we identified 747 genes that met the criteria for cell cycle-regulated expression. Peaks of gene expression were found to be distributed throughout the entire cell cycle. Furthermore, we found that four promoter motifs exhibited strong association with cell cycle phase-specific expression. Examination of the regulation of MCB motif-containing genes through the perturbation of DNA synthesis control/MCB-binding factor (DSC/MBF)-mediated transcription in arrested synchronous cdc10 mutant cell cultures revealed a subset of functional targets of the DSC/MBF transcription factor complex, as well as certain gene promoter requirements. Finally, we compared our data with those for the budding yeast Saccharomyces cerevisiae and found approximately 140 genes that are cell cycle regulated in both yeasts, suggesting that these genes may play an evolutionarily conserved role in regulation of cell cycle-specific processes. Our complete data sets are available at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

Algorithms↗

PubMed2004

Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells.

BACKGROUND: Estrogens and their receptors are important in human development, physiology and disease. In this study, we utilized an integrated genome-wide molecular and computational approach to characterize the interaction between the activated estrogen receptor (ER) and the regulatory elements of candidate target genes. RESULTS: Of around 19,000 genes surveyed in this study, we observed 137 ER-regulated genes in T-47D cells, of which only 89 were direct target genes. Meta-analysis of heterogeneous in vitro and in vivo datasets showed that the expression profiles in T-47D and MCF-7 cells are remarkably similar and overlap with genes differentially expressed between ER-positive and ER-negative tumors. Computational analysis revealed a significant enrichment of putative estrogen response elements (EREs) in the cis-regulatory regions of direct target genes. Chromatin immunoprecipitation confirmed ligand-dependent ER binding at the computationally predicted EREs in our highest ranked ER direct target genes, NRIP1, GREB1 and ABCA3. Wider examination of the cis-regulatory regions flanking the transcriptional start sites showed species conservation in mouse-human comparisons in only 6% of predicted EREs. CONCLUSIONS: Only a small core set of human genes, validated across experimental systems and closely associated with ER status in breast tumors, appear to be sufficient to induce ER effects in breast cancer cells. That cis-regulatory regions of these core ER target genes are poorly conserved suggests that different evolutionary mechanisms are operative at transcriptional control elements than at coding regions. These results predict that certain biological effects of estrogen signaling will differ between mouse and human to a larger extent than previously thought.

Binding Sites↗

PubMed2004

BEARR: Batch Extraction and Analysis of cis-Regulatory Regions.

Transcription factors play important roles in regulating biological and disease processes. Microarray technology has enabled researchers to simultaneously monitor changes in the expression of thousands of transcripts. By identifying specific transcription factor binding sites in the cis-regulatory regions of differentially expressed genes, it is then possible to identify direct targets of transcription factors, model transcriptional regulatory networks and mine the dataset for relevant targets for experimental and clinical manipulation. We have developed web-based software to assist biologists in efficiently carrying out the analysis of microarray data from studies of specific transcription factors. Batch Extraction and Analysis of cis-Regulatory Regions, or BEARR, accepts gene identifier lists from microarray data analysis tools and facilitates identification, extraction and analysis of regulatory regions from the large amount of data that is typically generated in these types of studies. The software is publicly available at http://giscompute.gis.a-star.edu.sg/~vega/BEARR1.0/.

Binding Sites↗