46 research outputs found

    Putative cold acclimation pathways in Arabidopsis thaliana identified by a combined analysis of mRNA co-expression patterns, promoter motifs and transcription factors

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    <p>Abstract</p> <p>Background</p> <p>With the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.</p> <p>Results</p> <p>By means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from ~24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.</p> <p>Conclusion</p> <p>The rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.</p

    Evaluation of Combining Several Statistical Methods with a Flexible Cutoff for Identifying Differentially Expressed Genes in Pairwise Comparison of EST Sets

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    The detection of differentially expressed genes from EST data is of importance for the discovery of potential biological or pharmaceutical targets, especially when studying biological processes in less characterized organisms and where large-scale microarrays are not an option. We present a comparison of five different statistical methods for identifying up-regulated genes through pairwise comparison of EST sets, where one of the sets is generated from a treatment and the other one serves as a control. In addition, we specifically address situations where the sets are relatively small (~2,000–10,000 ESTs) and may differ in size. The methods were tested on both simulated and experimentally derived data, and compared to a collection of cold stress induced genes identified by microarrays. We found that combining the method proposed by Audic and Claverie with Fisher’s exact test and a method based on calculating the difference in relative frequency was the best combination for maximizing the detection of up-regulated genes. We also introduced the use of a flexible cutoff, which takes the size of the EST sets into consideration. This could be considered as an alternative to a static cutoff. Finally, the detected genes showed a low overlap with those identified by microarrays, which indicates, as in previous studies, low overall concordance between the two platforms

    Generation and analysis of 9792 EST sequences from cold acclimated oat, Avena sativa

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    BACKGROUND: Oat is an important crop in North America and northern Europe. In Scandinavia, yields are limited by the fact that oat cannot be used as a winter crop. In order to develop such a crop, more knowledge about mechanisms of cold tolerance in oat is required. RESULTS: From an oat cDNA library 9792 single-pass EST sequences were obtained. The library was prepared from pooled RNA samples isolated from leaves of four-week old Avena sativa (oat) plants incubated at +4°C for 4, 8, 16 and 32 hours. Exclusion of sequences shorter than 100 bp resulted in 8508 high-quality ESTs with a mean length of 710.7 bp. Clustering and assembly identified a set of 2800 different transcripts denoted the Avena sativa cold induced UniGene set (AsCIUniGene set). Taking advantage of various tools and databases, putative functions were assigned to 1620 (58%) of these genes. Of the remaining 1180 unclassified sequences, 427 appeared to be oat-specific since they lacked any significant sequence similarity (Blast E values > 10(-10)) to any sequence available in the public databases. Of the 2800 UniGene sequences, 398 displayed significant homology (BlastX E values ≤ 10(-10)) to genes previously reported to be involved in cold stress related processes. 107 novel oat transcription factors were also identified, out of which 51 were similar to genes previously shown to be cold induced. The CBF transcription factors have a major role in regulating cold acclimation. Four oat CBF sequences were found, belonging to the monocot cluster of DREB family ERF/AP2 domain proteins. Finally in the total EST sequence data (5.3 Mbp) approximately 400 potential SSRs were found, a frequency similar to what has previously been identified in Arabidopsis ESTs. CONCLUSION: The AsCIUniGene set will now be used to fabricate an oat biochip, to perform various expression studies with different oat cultivars incubated at varying temperatures, to generate molecular markers and provide tools for various genetic transformation experiments in oat. This will lead to a better understanding of the cellular biology of this important crop and will open up new ways to improve its agronomical properties

    The Vulnerability of the Developing Brain : Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

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    The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer’s, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.CC BY 4.0The author(s) received no financial support for the research, authorship, and/or publication of this article.</p

    Deriving Genetic Networks from Gene Expression Data and Prior Knowledge

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    In this work three different approaches for deriving genetic association networks were tested. The three approaches were Pearson correlation, an algorithm based on the Boolean network approach and prior knowledge. Pearson correlation and the algorithm based on the Boolean network approach derived associations from gene expression data. In the third approach, prior knowledge from a known genetic network of a related organism was used to derive associations for the target organism, by using homolog matching and mapping the known genetic network to the related organism. The results indicate that the Pearson correlation approach gave the best results, but the prior knowledge approach seems to be the one most worth pursuin

    A systems biology view of the spliceosome component Phf5a in relation to estrogen and cancer

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    Cancer is a broad term for a wide spectrum of diseases and which involves the alteration in expression levels of several hundreds of genes. As such, the study of the disease from a systems biology point of view becomes rational, as the properties of a system as a whole may be very different from the properties of its individual components. However, understanding a network at the systems level not only requires knowledge about the components of the network, but also the interactions between them. Here, a systems biology view of the rat PHD finger protein 5A (Phf5a) gene was attempted; a gene previously identified as aberrantly expressed in estrogen dependent endometrial adenocarcinoma tumors from both rat and human. Phf5 ais a highly conserved cysteine rich (C4HC3) zinc finger and such proteins predominantly have a role in chromatin mediated transcriptional regulation. Moreover, PHF5A is a component of the macromolecular complex spliceosome that takes part in pre-mRNA splicing and spliceosome component coding genes have previously been shown to be implicated in various cancer types and suggested to potentially be novel antitumor drugs. To derive a systems biology view, in this study, a weighted gene network was inferred from a list of genes having correlated expression profiles to Phf5a as nodes, and common transcription factors and microRNAs regulating these genes together with annotation about biological process ontology term(s) and pathway(s) as edge weights. In the inferred network a higher weight indicates more annotation shared between two genes and, hence, the network facilitates the identification of closely interacting genes with Phf5a. The results show that highly weighted edges connect Phf5a to other spliceosome components, but also to genes involved in the metabolism of proteins, proteasome and DNA replication, repair and recombination. The results also link Phf5a to the Myc/Rb/E2F pathway, one of the central pathways associated with cancer. The proposed method for inferring a weighted gene network can easily be applied to other genes and diseases. CC BYCopyright: © 2014 Vallabhu R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</p

    How to choose a normalization strategy for miRNA quantitative real-time (QPCR) arrays

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    Low-density arrays for quantitative real-time PCR (qPCR) are increasingly being used as an experimental technique for miRNA expression profiling. As with gene expression profiling using microarrays, data from such experiments needs effective analysis methods to produce reliable and high-quality results. In the pre-processing of the data, one cruciala nalysis step is normalization, which aims to reduce measurement errors and technical variability among arrays that might have arisen during the execution of the experiments. However, there are currently a number of different approaches to choose among and an unsuitable applied method may induce misleading effects, which could affect the subsequent analysis steps and thereby any conclusions drawn from the results. The hoice of normalization method is hence an important issue to consider. In this study we present the comparison of a number of data-driven normalization methods for TaqManlow-density arrays for qPCR and different descriptive statistical techniques that can facilitate the choice of normalization method. The performance of the normalization methods was assessed and compared against each other as well as against standard normalization using endogenous controls. The results clearly show that the data-driven methods reduce variation and represent robust alternatives to using endogenous controls

    Low DLG2 gene expression, a link between 11q-deleted and MYCN-amplified neuroblastoma, causes forced cell cycle progression, and predicts poor patient survival

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    BACKGROUND: Neuroblastoma (NB) is a childhood neural crest tumor. There are two groups of aggressive NBs, one with MYCN amplification, and another with 11q chromosomal deletion; these chromosomal aberrations are generally mutually exclusive. The DLG2 gene resides in the 11q-deleted region, thus makes it an interesting NB candidate tumor suppressor gene. METHODS: We evaluated the association of DLG2 gene expression in NB with patient outcomes, stage and MYCN status, using online microarray data combining independent NB patient data sets. Functional studies were also conducted using NB cell models and the fruit fly. RESULTS: Using the array data we concluded that higher DLG2 expression was positively correlated to patient survival. We could also see that expression of DLG2 was inversely correlated with MYCN status and tumor stage. Cell proliferation was lowered in both 11q-normal and 11q-deleted NB cells after DLG2 over expression, and increased in 11q-normal NB cells after DLG2 silencing. Higher level of DLG2 increased the percentage of cells in the G2/M phase and decreased the percentage of cells in the G1 phase. We detected increased protein levels of Cyclin A and Cyclin B in fruit fly models either over expressing dMyc or with RNAi-silenced dmDLG, indicating that both events resulted in enhanced cell cycling. Induced MYCN expression in NB cells lowered DLG2 gene expression, which was confirmed in the fly; when dMyc was over expressed, the dmDLG protein level was lowered, indicating a link between Myc over expression and low dmDLG level. CONCLUSION: We conclude that low DLG2 expression level forces cell cycle progression, and that it predicts poor NB patient survival. The low DLG2 expression level could be caused by either MYCN-amplification or 11q-deletion. Video Abstract.CC BY 4.0</p

    Global expression profiling of low temperature induced genes in the chilling tolerant japonica rice jumli marshi

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    Low temperature is a key factor that limits growth and productivity of many important agronomical crops worldwide. Rice (Oryza sativa L.) is negatively affected already at temperatures below +10°C and is therefore denoted as chilling sensitive. However, chilling tolerant rice cultivars exist and can be commercially cultivated at altitudes up to 3,050 meters with temperatures reaching as low as +4°C. In this work, the global transcriptional response to cold stress (+4°C) was studied in the Nepalese highland variety Jumli Marshi (spp. japonica) and 4,636 genes were identified as significantly differentially expressed within 24 hours of cold stress. Comparison with previously published microarray data from one chilling tolerant and two sensitive rice cultivars identified 182 genes differentially expressed (DE) upon cold stress in all four rice cultivars and 511 genes DE only in the chilling tolerant rice. Promoter analysis of the 182 genes suggests a complex cross-talk between ABRE and CBF regulons. Promoter analysis of the 511 genes identified over-represented ABRE motifs but not DRE motifs, suggesting a role for ABA signaling in cold tolerance. Moreover, 2,101 genes were DE in Jumli Marshi alone. By chromosomal localization analysis, 473 of these cold responsive genes were located within 13 different QTLs previously identified as cold associated
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