Selective gene action in the specification of cell surface structure.
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Chronic prenatal ethanol exposure does not affect the expression of selected genes in rat brain development.
To investigate possible mechanisms contributing to the teratogenic effects of ethanol on the nervous system, we examined the effects of in utero exposure to ethanol on gene expression in the postnatal brain. Pregnant rats were exposed to ethanol by constant inhalation of ethanol vapor during gestational days 8-21, generating a mean blood alcohol level of approximately 150 mg/100 ml during the last week of gestation. Particular care was given to ensure proper nutrition and weight gain in both control and EtOH-treated groups. At different times after birth, the brains of pups from ethanol-treated dams and from a parallel series of untreated dams were dissected into major regions. Cytoplasmic RNA was assayed for different mRNAs encoding proteins representative of both glial and neuronal cell types, by Northern blot and solution hybridization. In situ hybridization was performed with T alpha-1 tubulin and PLP. There were no significant differences between pups from ethanol-treated and control dams in the amount of mRNAs encoding the myelin protein proteolipid protein (PLP), the astrocyte glial fibrillary acidic protein (GFAP), the neurally enriched T alpha-1 isotype of alpha-tubulin, the nerve terminal components synaptophysin p38 and SNAP 25. The mRNA encoding the alpha 1-subunit of the receptor for gamma-amino butyric acid (GABA) was slightly decreased in expression in the hindbrain but not cortex of pups from EtOH-treated dams. These results suggest that, when weight gain is controlled, chronic exposure of rats to ethanol during the last 2 weeks of gestation has little influence on the postnatal expression of these cell type specific genes.
Animals↗
A novel adenoviral vector which mediates hypoxia-inducible gene expression selectively in neurons.
Selective gene expression in neurons is still a challenge. We have developed several expression vectors using a combination of neuron restrictive silencer elements (NRSEs), hypoxia responsive elements (HREs) and CMV minimal promoter (CMVmp). These elements were packaged into replication defective adenovirus to target gene expression selectively in neurons in a hypoxia-regulated manner. Neuronal selectivity and responsiveness to hypoxia of these novel constructs were determined empirically in both neural cell lines and primary cerebellar granule neurons (CGNs). The construct p5HRE-3NRSE exhibited not only the highest level of reporter gene expression in neuronal cells but also in an oxygen concentration-dependent manner when compared with all other constructs. As expected, this construct did not elicit reporter gene expression in non-neuronal cells including human HEK293A and HT29 cells, rat NRK cells, mouse 3T6 cells and 3T3 L1 cells. This construct was packaged into a replication defective adenoviral vector (Ad/5HRE-3NRSE) to determine neuron-selective and hypoxia-inducible gene expression in cultured mouse postmitotic primary CGNs and differentiated human NT2 neurons (NT2/Ns). Remarkably, in response to hypoxia, Ad/5HRE-3NRSE showed strong hypoxia-inducible gene expression selectively in neurons (12-fold induction in CGNs and 22-fold in NT2/Ns), but not in glial cells. Taken together, this vector with restricted gene expression to neurons under the regulation of hypoxia will be a useful tool for investigations of mechanisms of neuronal damage caused by ischemic insult.
Adenoviridae↗
Integration of text- and data-mining using ontologies successfully selects disease gene candidates.
Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.
Anatomy↗
Reliable gene signatures for microarray classification: assessment of stability and performance.
MOTIVATION: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. METHODS: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. RESULTS: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. AVAILABILITY: R package at http://www.bio.ifi.lmu.de/~davis/edaprakt
Algorithms↗
Expansion of genetically corrected neutrophils in chronic granulomatous disease mice by cotransferring a therapeutic gene and a selective amplifier gene.
Hematopoietic stem cell gene therapy has not provided clinical success in disorders such as chronic granulomatous disease (CGD), where genetically corrected cells do not show a selective advantage in vivo. To facilitate selective expansion of transduced cells, we have developed a fusion receptor system that confers drug-induced proliferation. Here, a 'selective amplifier gene (SAG)' encodes a chimeric receptor (GcRER) that generates a mitotic signal in response to estrogen. We evaluated the in vivo efficacy of SAG-mediated cell expansion in a mouse disease model of X-linked CGD (X-CGD) that is deficient in the NADPH oxidase gp91phox subunit. Bone marrow cells from X-CGD mice were transduced with a bicistronic retrovirus encoding GcRER and gp91phox, and transplanted to lethally irradiated X-CGD recipients. Estrogen was administered to a cohort of the transplants, and neutrophil superoxide production was monitored. A significant increase in oxidase-positive cells was observed in the estrogen-treated mice, and repeated estrogen administration maintained the elevation of transduced cells for 20 weeks. In addition, oxidase-positive neutrophils were increased in the X-CGD transplants given the first estrogen even at 9 months post-transplantation. These results showed that the SAG system would enhance the therapeutic effects by boosting genetically modified, functionally corrected cells in vivo.
Animals↗
Co-transformation using a negative selectable marker gene for the production of selectable marker gene-free transgenic plants.
A negative selectable marker gene, codA, was successfully co-transformed with a GUS reporter gene to develop selectable marker gene-free transgenic plants. The pNC binary vector contained a T-DNA harboring the codA gene next to the nptII gene, while a second binary vector, pHG, contained a GUS reporter gene. Tobacco plants ( Nicotiana tabacum cv. Samsun NN) were co-transformed via the mixture method with Agrobacterium tumefaciens LBA4404 strains harboring pNC and pHG, respectively. Seeds harvested from the co-transformants were sown on germination media containing 5-fluorocytosine (5-FC). Analysis of the progeny by GUS staining and PCR amplification revealed that all of the 5-FC-resistant R(1) plants were codA free, and that the codA gene segregated independently of the GUS gene. Because codA-free seedlings developed normally on 5-FC-containing medium, we suggest that co-transformation with negatively selectable markers is a viable method for the production of easily distinguished, selectable marker gene-free transgenic plants.
Agrobacterium tumefaciens↗
Non-random selection of gene segment 3 and random selection of gene segment 5 observed in reassortants generated in vitro between rotavirus SA11 and RRV.
A total of 154 clones of reassortants between rotavirus RRV and SA11 were isolated in vitro in order to compare the selection modes of each RNA segment in reassortant formation with those in the in vivo experiment (carried out by Gombold and Ramig) using the same parent strains in which the occurrence of non-random selection of gene segments 3 and 5 was observed. In reassortants isolated from a cross RRV x SA11-L2 (66 isolates) and a cross RRV x SA11-S1 (88 isolates), 36 and 57 different genotypes were identified, respectively. Segregation rates of SA11 RNA segments 1, 5, 8, 9 and 11 were significantly different as compared with those previously reported in the in vivo experiment. While RNA segment 3 was preferentially selected from an SA11 parent, the segregation rate of SA11 gene 5 was relatively low in vitro. Thus, the selection mode of rotavirus RNA segments was found to be different between in vivo and in vitro reassortments.
Animals↗
Evolutionary expressed sequence tag analysis of Drosophila female reproductive tracts identifies genes subjected to positive selection.
Genes whose products are involved in reproduction include some of the fastest-evolving genes found within the genomes of several organisms. Drosophila has long been used to study the function and evolutionary dynamics of genes thought to be involved in sperm competition and sexual conflict, two processes that have been hypothesized to drive the adaptive evolution of reproductive molecules. Several seminal fluid proteins (Acps) made in the Drosophila male reproductive tract show evidence of rapid adaptive evolution. To identify candidate genes in the female reproductive tract that may be involved in female-male interactions and that may thus have been subjected to adaptive evolution, we used an evolutionary bioinformatics approach to analyze sequences from a cDNA library that we have generated from Drosophila female reproductive tracts. We further demonstrate that several of these genes have been subjected to positive selection. Their expression in female reproductive tracts, presence of signal sequences/transmembrane domains, and rapid adaptive evolution indicate that they are prime candidates to encode female reproductive molecules that interact with rapidly evolving male Acps.
Animals↗
Novel retroviral vector transferring a suicide gene and a selectable marker gene with enhanced gene expression by using a tetracycline-responsive expression system.
A retroviral vector for the enhanced expression of the herpes simplex virus thymidine kinase (HSV tk) gene was developed by using a tetracycline-responsive expression system (TRES). The two components of the TRES, the chimeric transactivator (tTA) and the corresponding tTA-binding cis element (tetO), were both incorporated into a retroviral vector and resulted in high levels of tk gene expression from tetO in target cells. Amphotropic virus supernatants from stable producer cells, generated by the retroviral vector containing the TRES, gave titers of 10(4) to 10(5) G418-resistant CFU/ml on murine NIH 3T3 cells. The retroviral vector (G1tTA-[tetOTkINa]R), in which tetO was used in the opposite orientation relative to viral transcription, was capable of transducing tk and neo genes into murine NIH 3T3 cells to yield a high level of tk gene expression. TK enzyme activity in NIH 3T3 cells transduced by this vector was 417-fold higher than in control cells. This increased TK activity was returned to basal levels in the presence of tetracycline. The level of tk gene expression driven by tetO from G1tTA-[tetOTkINa]R vector in NIH 3T3 cells was fourfold higher at both the mRNA level and the TK enzyme level than that produced by the long terminal repeat of G1Tk1SvNa, the vector being used in the ongoing brain tumor gene therapy trial. Retroviral vectors containing the TRES may be useful therefore in achieving higher levels of tk gene expression, which should facilitate gene therapy approaches in the treatment of cancer.
3T3 Cells↗
The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.
BACKGROUND: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. RESULTS: The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function. CONCLUSION: The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.
Animals↗
[Obtaining of intertribal Brassica juncea+Arabidopsis thaliana somatic hybrids and study of transgenic trait behaviour].
Intertribal somatic hybridization between wild type Brassica juncea (L.) Czern. & Coss. and transgenic A. thaliana L. has been carried out. Genome of A. thaliana plants contained heterologous transposable element Spm/dSpm, reporter GUS gene, selective genes for kanamycin- (npt II) and phosphinothricin (bar) resistance. Hybrid nature of obtained plants was confirmed with their morphology, GUS hystochemical assay, PCR-RFLP, RAPD and isozyme analyses. It was determined that heterologous transposable element Spm/dSpm is able to function in hybrid plants. There was no complete elimination of A. thaliana genetic material in the hybrids and the transgenes were stably maintained.
Amylases↗
Genomics and circulating tumor cells: promising tools for choosing and monitoring adjuvant therapy in patients with early breast cancer?
PURPOSE OF REVIEW: Gene expression profiling using a class discovery approach has consistently shown that breast cancer emerges as a group of different disease entities: the basal-like, the human epidermal growth factor receptor 2 type, the normal breast-like, and at least two different hormone receptor positive or luminal types. Initial reports have also suggested that certain expression patterns are associated with relapse. Current data on adjuvant therapy are only beginning to consider this biologic heterogeneity (estrogen receptor and human epidermal growth factor receptor 2 status). Similarly, the search for isolated tumor cells in blood or bone marrow has been regarded as another approach for defining prognosis and tailoring adjuvant therapies. The purpose of this review is to highlight the recent data emerging from both approaches as a means of assessing prognosis and tailoring therapy in patients with early breast cancer. RECENT FINDINGS: The initial reports on prognosis assessment of breast cancer using the expression profile have been corroborated, but the differences between the actual genes selected for the different prognostic signatures remain difficult to explain. Others have introduced added signatures, such as those associated with proliferation or with wound healing, or selected subgroups on the basis of, for example, estrogen receptor level prior to class comparison. The data on the quantification of expression of a limited number of selected genes are promising for both prognosis and prediction. The segregation of histological grade 2 tumors into genetically defined grade 1 or 3 tumors and its associated prognostic significance is a critical observation. The standardization of methods for measuring isolated tumor cells in blood or bone marrow has resulted in validated data both on prognosis in early breast cancer and in the advanced setting. Methylation of circulating DNA might become another method for selecting patients for extended adjuvant regimens. SUMMARY: The new molecular knowledge must be incorporated into prospective clinical studies in patients with early breast cancer. This is the challenge for the years to come.
Biomarkers, Tumor↗
Regularized Least Squares Cancer classifiers from DNA microarray data.
BACKGROUND: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information provided by DNA microarrays allows an approach to the problem of cancer analysis from a quantitative rather than qualitative point of view. Cancer classification requires well founded mathematical methods which are able to predict the status of new specimens with high significance levels starting from a limited number of data. In this paper we assess the performances of Regularized Least Squares (RLS) classifiers, originally proposed in regularization theory, by comparing them with Support Vector Machines (SVM), the state-of-the-art supervised learning technique for cancer classification by DNA microarray data. The performances of both approaches have been also investigated with respect to the number of selected genes and different gene selection strategies. RESULTS: We show that RLS classifiers have performances comparable to those of SVM classifiers as the Leave-One-Out (LOO) error evaluated on three different data sets shows. The main advantage of RLS machines is that for solving a classification problem they use a linear system of order equal to either the number of features or the number of training examples. Moreover, RLS machines allow to get an exact measure of the LOO error with just one training. CONCLUSION: RLS classifiers are a valuable alternative to SVM classifiers for the problem of cancer classification by gene expression data, due to their simplicity and low computational complexity. Moreover, RLS classifiers show generalization ability comparable to the ones of SVM classifiers also in the case the classification of new specimens involves very few gene expression levels.
Algorithms↗
Expression of housekeeping genes during the asexual cell cycle of Plasmodium falciparum.
In an earlier study, we found that calmodulin displayed an atypical expression for a housekeeping gene during the erythrocytic cycle of Plasmodium falciparum. The expression pattern was that of an inducible gene linked to the cell cycle, with a peak prior to replication, and not one of a gene that expresses itself in a constitutive way. In this work, we examined the expression pattern of other housekeeping genes, selecting genes from two functionally very different groups: those for three enzymes involved in carbohydrate metabolism--glucose-phosphate-isomerase (GPI), aldolase and glucose-6-phosphate-dehydrogenase (G6PD)--and for three proteins with structural and motor functions--actin-I, beta-tubulin and myosin. The mRNA of each gene was measured by reverse transcription-polymerase chain reaction in synchronic parasite samples that were 14, 28, 40 and 48 h old. GPI and G6PD achieved their maximum expression at 28 h, then declined, while aldolase increased its expression up to 40 h and remained high, but less so at 48 h. Actin and myosin showed the same pattern, increasing up to 48 h, while beta-tubulin expression peaked at 40 h. These findings confirm unconventional behavior in the expression of certain Plasmodium housekeeping genes and suggest the existence of different expression patterns for distinct functional groups.
Animals↗
Classification of microarrays to nearest centroids.
MOTIVATION: Classification of biological samples by microarrays is a topic of much interest. A number of methods have been proposed and successfully applied to this problem. It has recently been shown that classification by nearest centroids provides an accurate predictor that may outperform much more complicated methods. The 'Prediction Analysis of Microarrays' (PAM) approach is one such example, which the authors strongly motivate by its simplicity and interpretability. In this spirit, I seek to assess the performance of classifiers simpler than even PAM. RESULTS: I surprisingly show that the modified t-statistics and shrunken centroids employed by PAM tend to increase misclassification error when compared with their simpler counterparts. Based on these observations, I propose a classification method called 'Classification to Nearest Centroids' (ClaNC). ClaNC ranks genes by standard t-statistics, does not shrink centroids and uses a class-specific gene-selection procedure. Because of these modifications, ClaNC is arguably simpler and easier to interpret than PAM, and it can be viewed as a traditional nearest centroid classifier that uses specially selected genes. I demonstrate that ClaNC error rates tend to be significantly less than those for PAM, for a given number of active genes. AVAILABILITY: Point-and-click software is freely available at http://students.washington.edu/adabney/clanc.
Algorithms↗
Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes.
MOTIVATION: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better. RESULTS: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.
Algorithms↗
Expression profiling reveals differences in metabolic gene expression between exercise-induced cardiac effects and maladaptive cardiac hypertrophy.
While cardiac hypertrophy elicited by pathological stimuli eventually leads to cardiac dysfunction, exercise-induced hypertrophy does not. This suggests that a beneficial hypertrophic phenotype exists. In search of an underlying molecular substrate we used microarray technology to identify cardiac gene expression in response to exercise. Rats exercised for seven weeks on a treadmill were characterized by invasive blood pressure measurements and echocardiography. RNA was isolated from the left ventricle and analysed on DNA microarrays containing 8740 genes. Selected genes were analysed by quantitative PCR. The exercise program resulted in cardiac hypertrophy without impaired cardiac function. Principal component analysis identified an exercise-induced change in gene expression that was distinct from the program observed in maladaptive hypertrophy. Statistical analysis identified 267 upregulated genes and 62 downregulated genes in response to exercise. Expression changes in genes encoding extracellular matrix proteins, cytoskeletal elements, signalling factors and ribosomal proteins mimicked changes previously described in maladaptive hypertrophy. Our most striking observation was that expression changes of genes involved in beta-oxidation of fatty acids and glucose metabolism differentiate adaptive from maladaptive hypertrophy. Direct comparison to maladaptive hypertrophy was enabled by quantitative PCR of key metabolic enzymes including uncoupling protein 2 (UCP2) and fatty acid translocase (CD36). DNA microarray analysis of gene expression changes in exercise-induced cardiac hypertrophy suggests that a set of genes involved in fatty acid and glucose metabolism could be fundamental to the beneficial phenotype of exercise-induced hypertrophy, as these changes are absent or reversed in maladaptive hypertrophy.
Animals↗