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Relevant bibliographies by topics / Gene expression and cDNA microarray

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Academic literature on the topic 'Gene expression and cDNA microarray'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Gene expression and cDNA microarray"

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Pérez-Enciso, Miguel, Miguel A. Toro, Michel Tenenhaus, and Daniel Gianola. "Combining Gene Expression and Molecular Marker Information for Mapping Complex Trait Genes: A Simulation Study." Genetics 164, no. 4 (2003): 1597–606. http://dx.doi.org/10.1093/genetics/164.4.1597.

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Abstract A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (3) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.

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Lonnstedt, I. "Hierarchical Bayes models for cDNA microarray gene expression." Biostatistics 6, no. 2 (2005): 279–91. http://dx.doi.org/10.1093/biostatistics/kxi009.

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Skotheim, Rolf I., Anne Kallioniemi, Bodil Bjerkhagen та ін. "Topoisomerase-IIα Is Upregulated in Malignant Peripheral Nerve Sheath Tumors and Associated With Clinical Outcome". Journal of Clinical Oncology 21, № 24 (2003): 4586–91. http://dx.doi.org/10.1200/jco.2003.07.067.

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Purpose: To identify target genes of clinical significance for patients with malignant peripheral-nerve sheath tumor (MPNST), an aggressive cancer for which no consensus therapy exists. Materials and Methods: Biopsies and clinical data from 51 patients with MPNST were included in this study. Based on our previous research implicating chromosome arm 17q amplification in MPNST, we performed gene expression analyses of 14 MPNSTs using chromosome 17–specific cDNA microarrays. Copy numbers of selected gene probes and centromere probes were then determined by interphase fluorescence in situ hybridization in 16 MPNSTs. Finally, we generated a tissue microarray containing 79 samples from 44 MPNSTs, on which in situ protein expressions of candidate genes were examined and related to clinical end points. Results: Among several deregulated genes found by cDNA microarray analyses, topoisomerase IIα (TOP2A) was the most overexpressed gene in MPNSTs compared with benign neurofibromas. Excess copies of the TOP2A were also seen at the DNA level in 10 of 16 cases, and high expression of the TOP2A protein was seen in 83% of the tumors on the tissue microarray. The TOP2A-expressing tumors were associated with poor cancer-specific survival and presence of metastases. Conclusion: We have identified TOP2A as a target gene in MPNST, using a focused gene expression profiling followed by a DNA copy number evaluation and clinical validation of the encoded protein using a tissue microarray. This study is the first to suggest that TOP2A expression may be a predictive factor for adverse outcome in MPNST.

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Ghazizadeh, Mohammad, Oichi Kawanami, and Tsutomu Araki. "Assessment of Gene Expression Profile by cDNA Microarray Analysis." Journal of Nippon Medical School 68, no. 6 (2001): 460–61. http://dx.doi.org/10.1272/jnms.68.460.

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Schofield, Deborah, and Timothy J. Triche. "cDNA microarray analysis of global gene expression in sarcomas." Current Opinion in Oncology 14, no. 4 (2002): 406–11. http://dx.doi.org/10.1097/00001622-200207000-00007.

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Aittokallio, Tero, Markus Kurki, Olli Nevalainen, Tuomas Nikula, Anne West, and Riitta Lahesmaa. "Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments." Journal of Bioinformatics and Computational Biology 01, no. 03 (2003): 541–86. http://dx.doi.org/10.1142/s0219720003000319.

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Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

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Dumur, Catherine I., Suhail Nasim, Al M. Best, et al. "Evaluation of Quality-Control Criteria for Microarray Gene Expression Analysis." Clinical Chemistry 50, no. 11 (2004): 1994–2002. http://dx.doi.org/10.1373/clinchem.2004.033225.

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Abstract Background: Development of quality-control criteria to ensure reproducibility of microarray results for potential clinical application is still in its infancy. Methods: In the present studies we developed quality-control criteria and evaluated their effect in microarray data analysis using total RNA from cell lines, frozen tumors, and a commercially available reference RNA. Quality-control criteria such as A260/A280 ratios, percentage of rRNA, and median size of cDNA and cRNA synthesis products were evaluated for robustness in microarray analysis. Furthermore, precision studies using a reference material were performed on the Affymetrix® HG-U133A high-density oligonucleotide microarrays. The same reference RNA sample was examined in 16 different chips run on 2 different days in the four different modules of the Affymetrix fluidics workstation. Fresh and frozen fragmented cRNAs were also compared. An ANOVA model was fit to identify the main sources of variation. Results: Good-quality samples showed &gt;30% rRNA in the electropherograms and cDNA and cRNA synthesis products with median sizes of 2.0 and 3.0 kb, respectively. Precision studies showed that the main source of variation was the day-to-day variability, minimally affecting hybridization exogenous control genes. Altogether, the results showed that the Affymetrix Genechip® system is highly reproducible when RNA that meet the quality-control criteria are used (overall P &gt;0.01). Conclusions: These results confirm the need to establish defined quality-control criteria for sample quality to distinguish between analytical and biological variability.

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Magbanua, M. M., J. E. Lang, J. Scott, et al. "Gene expression profiling of circulating tumor cells from breast cancer patients." Journal of Clinical Oncology 24, no. 18_suppl (2006): 10769. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.10769.

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10769 Background: Levels of circulating tumor cells (CTC) have prognostic and predictive significance in metastatic breast cancer. However, since CTCs are extremely rare, little is known about the actual phenotype of these cells. In order to characterize these cells, we performed cDNA microarray analyses of CTC isolated from peripheral blood (PB) of breast cancer patients. Methods: CTCs were directly isolated via immunomagnetic enrichment (IE) followed by fluorescence activated cell sorting (FACS). Total RNA was then subjected to two rounds of linear amplification and hybridized to cDNA microarrays (∼40,000 cDNAs). Validation studies used spiked BT474 cells. Clinical studies used PB (10–20 ml) from patients with metastatic breast cancer. Results: Rare spiked tumor cells (e.g., 320 cells in 10 mL PB) were efficiently recovered by IE/FACS (50% yield). Expression profiles of recovered cells, both by TaqMan of a 37 gene panel as well as by global gene expression analysis, matched that of BT474 cells in culture. In contrast, these profiles were clearly distinct from that of normal PB, ruling out significant contamination from blood elements. In clinical studies, IE/FACS isolated small numbers of CTCs (10–1000 cells). Expression profiles of CTCs were compared to that of normal blood, primary breast tumors, and normal epithelial samples. Unsupervised hierarchical clustering revealed that CTC profiles were readily distinguished from that of normal blood and normal epithelium; and further analysis revealed that CTC cluster with a subset of primary breast tumors, particularly the basal-like phenotype. Candidate genes associated with the CTC phenotype were also identified. Conclusions: We have developed and validated a method to isolate rare CTCs and profile them via cDNA microarray analysis. In addition, our gene expression analyses of CTC further provide evidence to the malignant nature of these cells. Further expression profiling of CTC may yield insights into their phenotype, pathophysiology and potential as biomarkers. [Table: see text]

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Eckel, Jeanette E., Antje Hoering, and Irene Ghobrial. "Experimental Design & Analysis of Protein Array Data: Applying Methods from cDNA Arrays." Blood 104, no. 11 (2004): 4280. http://dx.doi.org/10.1182/blood.v104.11.4280.4280.

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Abstract It appears that a number of recent manuscripts using protein microarray technology are using equivalent analysis procedures that the gene-expression microarray community implemented in their infancy. That is, utilizing a classic reference design such that the ratio of the sample of interest to a reference sample is the response of interest and assessing fold change to determine differential expression. For example, recent publications have concluded that proteins with a fold change less than 0.7 or greater than 1.3 demonstrate significant down- or up-regulated differential expression, respectively. However, fold change is an unreliable measure of differential expression and statistical models that distinguish true signal from random noise should be utilized instead of fold changes. Over the last half decade a tremendous amount of research has been devoted to gene-expression microarrays to vastly improve on the areas of experimental design, normalization and statistical analyses to assess differential expression and classification and these methods are directly applicable to protein microarray technology. Thus, the objective is to review the statistical methodology that has been developed for two-color cDNA arrays that is directly applicable to protein arrays. Examples are provided from a mantle-cell lymphoma protein-array experiment.

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Lau, W.-y., P. B. S. Lai, M.-f. Leung, et al. "Differential Gene Expression of Hepatocellular Carcinoma Using cDNA Microarray Analysis." Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics 12, no. 2 (2001): 59–69. http://dx.doi.org/10.3727/096504001108747530.

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Dissertations / Theses on the topic "Gene expression and cDNA microarray"

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Zhao, Hongya. "Statistical analysis of gene expression data in cDNA microarray experiments." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/657.

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Kan, Takatsugu. "Gene expression profiling in human esophageal cancers using cDNA microarray." Kyoto University, 2003. http://hdl.handle.net/2433/148738.

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Wu, Meng. "Data mining cDNA microarray experiment with a GEE approach /." Electronic version (PDF), 2004. http://dl.uncw.edu/etd/2004/wum/mengwu.pdf.

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Howell, Brandon George. "Gene expression profiling of UV-induced skin cancer using cDNA microarray technology." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63108.pdf.

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Van, Laar Ryan. "Optimisation of cDNA microarray tumour profiling and molecular analysis of epithelial ovarian cancer /." Connect to thesis, 2005. http://eprints.unimelb.edu.au/archive/00002764.

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Jouenne, Vincent Y. "Critical Issues in the Processing of cDNA Microarray Images." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/33960.

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Microarray technology enables simultaneous gene expression level monitoring for thousands of genes. While this technology has now been recognized as a powerful and cost-effective tool for large-scale analysis, the many systematic sources of experimental variations introduce inherent errors in the extracted data. Data is gathered by processing scanned images of microarray slides. Therefore robust image processing is particularly important and has a large impact on downstream analysis. The processing of the scanned images can be subdivided in three phases: gridding, segmentation and data extraction. To measure the gene expression levels, the processing of cDNA microarray images must overcome a large set of issues in these three phases that motivates this study. This study presents automatic gridding methods and compares their performances. Two segmentation techniques already used, the Seeded Region Growing Algorithm and the Mann-Whitney Test, are examined. We present limitations of these techniques. Finally, we studied the data extraction method used in MicroArray Suite (MS), a microarray analysis software, via synthetic images and explain its intricacies.<br>Master of Science

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Mantri, Nitin Laxminarayan, and nitin_mantri@rediffmail com. "Gene expression profiling of chickpea responses to drought, cold and high-salinity using cDNA microarray." RMIT University. Applied Sciences, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080509.160714.

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Cultivated chickpea (Cicer arietinum) has a narrow genetic base making it difficult for breeders to produce new elite cultivars with durable resistance to major biotic and abiotic stresses. As an alternative to genome mapping, microarrays have recently been applied in crop species to identify and assess the function of putative genes thought to be involved in plant abiotic stress and defence responses. In the present study, a cDNA microarray approach was taken in order to determine if the transcription of genes, from a set of previously identified putative stress-responsive genes from chickpea and its close relative Lathyrus sativus, were altered in chickpea by the three abiotic stresses; drought, cold and high-salinity. For this, chickpea genotypes known to be tolerant and susceptible to each abiotic stress were challenged and gene expression in the leaf, root and/or flower tissues was studied. The transcripts that were differentially expressed among stressed an d unstressed plants in response to the particular stress were analysed in the context of tolerant/susceptible genotypes. The transcriptional change of more than two fold was observed for 109, 210 and 386 genes after drought, cold and high-salinity treatments, respectively. Among these, two, 15 and 30 genes were consensually differentially expressed (DE) between tolerant and susceptible genotypes studied for drought, cold and high-salinity, respectively. The genes that were DE in tolerant and susceptible genotypes under abiotic stresses code for various functional and regulatory proteins. Significant differences in stress responses were observed within and between tolerant and susceptible genotypes highlighting the multiple gene control and complexity of abiotic stress response mechanism in chickpea. The annotation of these genes suggests that they may have a role in abiotic stress response and are potential candidates for tolerance/susceptibility.

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Yang, Xiao. "Optimal Design of Single Factor cDNA Microarray experiments and Mixed Models for Gene Expression Data." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/26379.

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Microarray experiments are used to perform gene expression profiling on a large scale. E- and A-optimality of mixed designs was established for experiments with up to 26 different varieties and with the restriction that the number of arrays available is equal to the number of varieties. Because the IBD setting only allows for a single blocking factor (arrays), the search for optimal designs was extended to the Row-Column Design (RCD) setting with blocking factors dye (row) and array (column). Relative efficiencies of these designs were further compared under analysis of variance (ANOVA) models. We also compared the performance of classification analysis for the interwoven loop and the replicated reference designs under four scenarios. The replicated reference design was favored when gene-specific sample variation was large, but the interwoven loop design was preferred for large variation among biological replicates. We applied mixed model methodology to detection and estimation of gene differential expression. For identification of differential gene expression, we favor contrasts which include both variety main effects and variety by gene interactions. In terms of t-statistics for these contrasts, we examined the equivalence between the one- and two-step analyses under both fixed and mixed effects models. We analytically established conditions for equivalence under fixed and mixed models. We investigated the difference of approximation with the two-step analysis in situations where equivalence does not hold. The significant difference between the one- and two-step mixed effects model was further illustrated through Monte Carlo simulation and three case studies. We implemented the one-step analysis for mixed models with the ASREML software.<br>Ph. D.

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Stolf, Beatriz Simonsen. "Identificação de marcadores moleculares para o câncer de tireóide por cDNA microarrays." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/46/46131/tde-21072008-101124/.

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Doenças tireoideanas são bastante comuns, sendo sua maioria benigna. A relação entre os diversos tipos de doenças tireoideanas, bem como seus aspectos moleculares, são pouco conhecidos. O bócio (hiperplasia), por exemplo, é descrito por alguns como relacionado com carcinoma (tumor maligno) papilífero, enquanto que outros afirmam não haver relação causal entre as duas doenças. A questão mais desafiante, porém, refere-se à distinção entre adenoma (tumor benigno) e carcinoma folicular, que atualmente é feita apenas após à cirurgia, não permitindo tratamento diferenciado para os dois tipos de tumor. Este trabalho buscou identificar genes diferencialmente expressos entre tecido tireoideano normal, bócio, adenoma e carcinoma papilífero utilizando microarrays. Carcinomas foliculares não foram incluídos devido ao número e tamanho reduzidos das amostras. Dois tipos de array foram utilizados: arrays em membranas de nylon, contendo 213 clones obtidos por DDRT-PCR de amostras de tireóide, e arrays em vidro, contendo 3800 clones ORESTES. Experimentos utilizando o primeiro tipo de array identificaram três genes diferencialmente expressos, cuja expressão foi analisada por RT-PCR em 10 amostras de cada tipo de tecido. Dois deles foram capazes de diferenciar carcinomas papilíferos de tecido normal e bócio com 89% de precisão para o tumor maligno e 80% para os tecidos não malignos. Os arrays em vidro foram utilizados para avaliar o perfil de expressão de aproximadamente 10 amostras de cada tipo de tecido tireoideano. Foram identificados 160 clones diferencialmente expressos entre quaisquer dois tipos de tecido, cujas seqüências foram determinadas e comparadas com as dos bancos de dados. Dentre os genes mais interessantes destacam-se o correspondente à ATPase Na/K, cuja expressão está reduzida nos carcinomas em relação a tecidos normais e adenomas, o da proteína PDCD4, envolvida em morte celular programada, mais expresso em adenomas e tecidos normais em comparação com carcinomas e bócios, e os da calgizzarin (S100A11) e da α1-anti-tripsina, ambos mais ativos nos carcinomas do que nos demais tecidos. Todos esses genes já foram descritos como diferencialmente expressos em algum tipo de tumor. Este trabalho levou à padronização da metodologia de microarray em lâminas de vidro em nosso laboratório, bem como à identificação de genes que podem elucidar as alterações envolvidas na formação do bócio, adenoma e carcinoma papilífero. A implantação da técnica de amplificação de mRNA em nosso laboratório viabilizou a utilização de 10 amostras de carcinoma folicular, cuja massa de RNA total era insuficiente para as hibridizações. Essas amostras serão hibridizadas, juntamente com 10 amostras de adenoma, com microarrays contendo 4800 genes humanos conhecidos para a busca de genes diferencialmente expressos, de grande interesse diagnóstico.<br>Thyroid diseases are very common and are usually benign. The causal relationships among the different types of disease, as well as their molecular aspects, are not well understood. The goiter (hyperplasia), for instance, is described by some as related to papillary carcinoma (a malignant tumor), while others say there is no causal relationship between the two diseases. The most defying question, however, concerns the distinction between adenoma (benign tumor) and follicular carcinoma, which is currently made only after surgery, not allowing distinct treatments for the two kinds of tumor. This work aimed to identify differentially expressed genes among normal thyroid tissue, goiter, adenoma and papillary carcinoma using microarrays. Follicular carcinomas were not included due to the reduced number and size of the samples. Two kinds of array were used: arrays in nylon membranes, with 213 clones isolated from thyroid samples by differential display (DDRT-PCR); and glass slide arrays containing 3800 ORESTES clones.Experiments using the first type of array identified three differentially expressed genes, whose expression was analyzed by RT-PCR in 10 samples of each kind of tissue. Two of these genes were able to differentiate papillary carcinomas from goiters and normal tissues with precisions of 89% for the malignant tumor and 80% for the non-malignant tissues. Glass slide arrays were used to evaluate gene expression profile of approximately 10 samples of each type of thyroid tissue. 160 clones differentially expressed between any two tissues were identified, and their sequences were determined and compared with databases. Among the most interesting genes are Na/K ATPase gene, whose expression is reduced in carcinomas compared to normal tissues and adenomas, the gene corresponding to PDCD4 protein, involved in program cell death, with elevated expression in adenomas and normal tissues than carcinomas and goiters, and the genes of calgizzarin (S100A11) and α1-antitrypsin, both more active in carcinomas than the other tissues. All these genes have already been described as differentially expressed in at least one type of human cancer. This work led to the standardization of glass slide microarray technology in our laboratory, and to the identification of genes that may clarify the alterations involved in the formation of goiter, adenoma and follicular carcinoma. The implementation of mRNA amplification technique in our laboratory allowed the utilization of 10 samples of follicular carcinoma, whose mass was insufficient for microarray hybridizations. These samples will be hybridized along with 10 samples of adenomas, with microarrays containing 4800 known human genes to search for differentially expressed genes, of great diagnostic interest.

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Camilo, Cesar Moisés. "Regulação da expressão gênica por oxigênio no fungo aquático Blastocladiella emersonii." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/46/46131/tde-28042010-082547/.

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Neste trabalho realizamos a análise das variações na expressão gênica global do fungo aquático Blastocladiella emersonii submetido ao estresse de carência de oxigênio (hipóxia), utilizando a técnica de microarranjos de cDNA em lâminas contendo 3773 genes distintos. Nos experimentos de hipóxia gradual (diminuição gradual da concentração de oxigênio dissolvido, seguido de reoxigenação) e hipóxia direta (diminuição direta da concentração de oxigênio dissolvido, seguido de reoxigenação) observamos que 650 genes foram diferencialmente expressos em pelo menos uma das condições de estresse e que 534 deles mostraram-se afetados (direta ou indiretamente) pela disponibilidade de oxigênio, uma vez que apresentaram recuperação (ou tendência à recuperação) da sua expressão aos níveis normais, quando as células foram reoxigenadas. Além de modular a expressão de diversos genes sem função conhecida, B. emersonii responde à hipóxia reajustando a expressão de genes responsáveis pela produção e consumo de energia. Pelo menos transcricionalmente, este fungo favorece o metabolismo anaeróbico, através da indução de genes que codificam enzimas da via glicolítica e lactato desidrogenase, ao passo que no ciclo do ácido cítrico, a maioria dos genes encontram-se reprimidos ou não sofrem alteração na expressão. Processos dispendiosos em energia como síntese protéica, metabolismo de aminoácidos, enovelamento de proteínas e transporte por membrana apresentaram perfis predominantemente de repressão gênica quando em carência de oxigênio. Ainda utilizando a técnica de microarranjos, mostramos semelhanças entre os perfis transcricionais nos experimentos hipóxia e de carência de Fe2+ (tratamento com quelante de Fe2+ 2,2´-dipyridyl) sugerem que estes estresses estão de alguma forma relacionados, fornecendo bons indícios de que o íon Fe2+ possa ter um papel importante no mecanismo sensor de oxigênio e/ou de resposta a hipóxia em B. emersonii. Além disso, o tratamento prévio de células submetidas à hipóxia com o antibiótico geldanamicina, um conhecido inibidor da proteína de choque térmico HSP90, levou à diminuição da indução de certos genes de hipóxia, indicando que este fungo pode possuir algum mecanismo semelhante ao do fator de transcrição de hipóxia HIF1-α de mamíferos, uma vez que este fator também é afetado por geldanamicina. Adicionalmente, desenvolvemos um protocolo para transformação de B. emersonii mediada por Agrobacterium tumefasciens que se mostrou promissor. A transferência do T-DNA contendo um gene de resistência a higromicina B, presente no vetor binário pBINPLUS-Hph, foi evidenciada pelo crescimento normal e esporulação das células transformadas, na presença do antibiótico e pela amplificação do gene de resistência no DNA genômico de células transformadas.<br>In this work we analyzed global gene expression changes in the aquatic fungus Blastocladiella emersonii submitted to oxygen deprivation (hypoxia), using cDNA microarrays containing 3,773 distinct genes. In gradual hypoxia (gradual decrease in dissolved oxygen concentration, followed by reoxygenation) and direct hypoxia (direct decrease of dissolved oxygen concentration, followed by reoxygenation) we observed 650 differentially expressed genes in at least one of the stress conditions tested, 534 of them being affected (directly or indirectly) by oxygen availability, since they showed recovery of normal expression levels or a tendency to recover, when cells were reoxygenated. Besides modulating many genes with no previously assigned function, B. emersonii responds to hypoxia by readjusting the expression levels of genes responsible for energy production and consumption. At least transcriptionally, this fungus seems to favour anaerobic metabolism through the induction of genes encoding glycolytic enzymes and lactate dehydrogenase, while in the TCA-cycle, most genes were repressed or unchanged. Energy-costly processes like protein synthesis, amino acid metabolism, protein folding and transport had their gene expression profiles predominantly repressed during oxygen deprivation. Microarray experiments also showed similarities between the transcriptional profile of genes in hypoxia and iron (II) deprivation (treatment with the iron (II) chelator 2,2\'-dipyridyl), suggesting that these stresses are somehow related, giving good evidence that Fe2+ ion could have a role in the mechanism of oxygen sensing and/or response to hypoxia in B. emersonii. Furthermore, pretreatment of cells subjected to hypoxia with the antibiotic geldanamycin, a known inhibitor of the heat shock protein HSP90, caused a significant decrease in the induction of certain hypoxic genes, indicating that this fungus could have a mechanism similar to that of the mammalian hypoxia transcription factor HIF-1α, which is also affected by geldanamycin. Additionally, we developed an Agrobacterium tumefasciens-mediated protocol for transformation of B. emersonii that has shown to be promising. The capacity to transfer the T-DNA containing a hygromycin B resistance gene, present in the pBINPLUSHph binary vector, was evidenced by the normal growth and sporulation of the transformed cells in the presence of antibiotic and by amplification of the resistance gene from the genomic DNA of transformed cells

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Books on the topic "Gene expression and cDNA microarray"

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Howell, Brandon George. Gene expression profiling of UV-induced skin cancer using cDNA microarray technology. National Library of Canada, 2001.

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Rintala, Nina. Differential gene expression in radiosensitive and radioresistant breast cancer cells using cDNA microarray analysis. Laurentian University, 2001.

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Handley, Heather Martin. Zebrafish cardiovascular cDNA microarrays: Expression profiling and gene discovery in embryos exposed to 2,3,7,8-Tetrachlorodibenzo-P-dioxin. Massachusetts Institute of Technology, 2003.

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McLachlan, Geoffrey J., Kim-Anh Do, and Christophe Ambroise. Analyzing Microarray Gene Expression Data. John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172842x.

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McLachlan, Geoffrey J., Kim-Anh Do, and Christophe Ambroise. Analyzing Microarray Gene Expression Data. John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172842x.

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Lee, Mei-Ling Ting. Analysis of microarray gene expression data. Kluwer Academic, 2004.

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John, Quackenbush, and Brazma Alvis, eds. Microarray gene expressions data analysis: A beginner's guide. Blackwell, 2003.

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Microarray analysis. Wiley-Liss, 2003.

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Liu, Ivy S. C. Cloning of cDNA for the murine CHX10 gene and developmental expression studies. National Library of Canada, 1993.

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Seale, David Andrew. A statistical model of microarray images and an estimator of gene expression ratio. National Library of Canada, 2002.

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Book chapters on the topic "Gene expression and cDNA microarray"

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Peck, Konan, and Yuh-Pyng Sher. "cDNA Microarrays on Nylon Membranes with Enzyme Colorimetric Detection." In DNA Microarrays: Gene Expression Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_3.

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Grimmond, Sean, and Andy Greenfield. "Expression Profiling with cDNA Microarrays: A User’s Perspective and Guide." In DNA Microarrays: Gene Expression Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_2.

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Loriod, Béatrice, Geneviève Victorero, and Catherine Nguyen. "cDNA Macroarrays and Microarrays on Nylon Membranes with Radioactive Detection." In DNA Microarrays: Gene Expression Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56517-5_4.

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Komorowski, Jan, Torgeir R. Hvidsten, Tor-Kristian Jenssen, et al. "Towards Knowledge Discovery from cDNA Microarray Gene Expression Data." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_53.

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Bittner, Michael, Yidong Chen, Sally A. Amundson, et al. "Obtaining and Evaluating Gene Expression Profiles with cDNA Microarrays." In Genomics and Proteomics. Springer US, 2002. http://dx.doi.org/10.1007/0-306-46823-9_2.

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Thiago.M., Venancio, DeMarco Ricardo, Oliveira Katia C.P., Simoes Ana Carolina Quirino, da Silva Aline Maria, and Verjovski-Almeida Sergio. "Genomics and Gene Expression Management Tools for the Schistosoma Mansoni cDNA Microarray Project." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11532323_22.

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Neima, Paul E., Joan Burnside, Katrina Elsaesser, et al. "Analysis of gene expression, copy number and palindrome formation with a DT40 enriched CDNA microarray." In Subcellular Biochemistry. Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-4896-8_14.

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Meltzer, Paul S., Michael Bittner, Mervi Heiskanen, Tiffany Hoffman, Yidong Chen, and Jeffrey M. Trent. "Use of cDNA Microarrays to Assess DNA Gene Expression Patterns in Cancer." In The Biology of Tumors. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4899-1352-4_10.

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Paez, Gerardo L., Barbara Zangerl, Kimberly Sellers, Gregory M. Acland, and Gustavo D. Aguirre. "Characterization of Gene Expression Profiles of Normal Canine Retina and Brain Using a Retinal cDNA Microarray." In Advances in Experimental Medicine and Biology. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-74904-4_20.

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Nino-Soto, M. I., R. J. Jozani, B. Bridle, and B. A. Mallard. "cDNA Microarray Analysis of Gene Expression Patterns in Blood Mononuclear Cells of SLA-DRB1-Defined Yorkshire Pigs." In Animal Genomics for Animal Health. KARGER, 2008. http://dx.doi.org/10.1159/000317177.

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Conference papers on the topic "Gene expression and cDNA microarray"

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Dougherty, Edward R., Yidong Chen, Sinan Batman, and Michael L. Bittner. "Digital measurement of gene expression in a cDNA microarray." In Medical Imaging 1997, edited by Kenneth M. Hanson. SPIE, 1997. http://dx.doi.org/10.1117/12.274178.

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Kim, Seungchan, Edward R. Dougherty, Michael L. Bittner, et al. "Automated analysis of multivariate nonlinear gene relations based on cDNA microarray expression data." In BiOS 2000 The International Symposium on Biomedical Optics, edited by Patrick A. Limbach, John C. Owicki, Ramesh Raghavachari, and Weihong Tan. SPIE, 2000. http://dx.doi.org/10.1117/12.380506.

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Ong, Eng-Kok, Tim Sawbridge, Tracie Webster, et al. "Gene expression analysis of perennial ryegrass ( Lolium perenne ) using cDNA microarrays." In Biomedical Optics 2003, edited by Dan V. Nicolau and Ramesh Raghavachari. SPIE, 2003. http://dx.doi.org/10.1117/12.478075.

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Sakwe, Amos M. "Abstract B022: cDNA microarray analysis of genes associated with reduced annexin A6 expression in invasive breast cancer cells." In Abstracts: AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications - October 3-6, 2013; San Diego, CA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1557-3125.advbc-b022.

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Kuruoglu, Ercan E., Diego Salas, and Diego Pablo Ruiz. "Microarray Gene Expression and Stable Laws." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298832.

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Yang, Andy C., Hui-Huang Hsu, and Ming-Da Lu. "Applying gene ontology to microarray gene expression data analysis." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551740.

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McMunn-Coffran, Cameron, Christina Schweikert, and D. Frank Hsu. "Microarray Gene Expression Analysis Using Combinatorial Fusion." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.70.

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Yip, Timothy T. C., Dora L. W. Kwong, Roger K. C. Ngan, et al. "Abstract 3903: Differential transcript expression in nasopharyngeal carcinoma by cDNA microarray analysis." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-3903.

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Yip, Timothy T. C., Dora L. W. Kwong, Roger K. C. Ngan, et al. "Abstract 3437: Differential transcript expression in nasopharyngeal carcinoma by cDNA microarray analysis." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-3437.

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Lu, Jianping, and Yue Wang. "Extended Iterative Nonlinear Regression Normalization for cDNA Gene Expression Data." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162838.

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Reports on the topic "Gene expression and cDNA microarray"

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Huang, Shixia, and Harold Varmus. The Use of cDNA Microarray to Study Gene Expression in Wnt-1 Induced Mammary Tumors. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada411264.

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Szallasi, Zoltan. CDNA Microarray Based Comparative Gene Expression Analysis of Primary Breast Tumors Versus In Vitro Transformed Neoplastic Breast Epithelium. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada401181.

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Gottardo, Raphael, Adrian E. Raftery, Ka Y. Yeung, and Roger E. Bumgarner. Bayesian Robust Inference for Differential Gene Expression in cDNA Microarrays with Multiple Samples. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada478418.

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Tooker, B. C., and Timothy S. Stahly. Microarray Analysis of Gene Expression Essential to Energetic Efficiency in a Porcine Model of Obesity. Iowa State University, 2005. http://dx.doi.org/10.31274/ans_air-180814-1077.

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