BioProfiling.de: analytical web portal for high-throughput cell biology

Antonov, Alexey V.

doi:10.1093/nar/gkr372

Cited by 111 publications

(121 citation statements)

References 27 publications

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“…However, it still provides a useful view of the types of genes represented and whether different subsets of biological processes are affected by the main genotypic effects of the sd E3 allele and by its interactions with other loci. We also verified our results using the BioProfiling.de tool (Antonov 2011), which is a superset of the analysis done by WebGestalt.…”

Section: Gene Ontology Analysessupporting

confidence: 60%

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

et al. 2014

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The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist or about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scalloped E3 allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-byintrogression experiment and a tagged RNA gene expression dataset. In addition we used whole genome resequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative noncomplementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well. GENETICISTS often strictly control their organisms' wildtype genetic backgrounds when experimentally dissecting genetic pathways. Although this tight control is necessary to avoid faulty inferences caused by confounding variables (e.g., Burnett et al. 2011), it can often paint an incomplete or even incorrect picture; no genetic pathway or network exists in a vacuum. Instead, these networks occur in the context of all the alleles in the genome, which usually vary among individuals. There is substantial evidence that wild-type genetic background almost always modulates the phenotypic effects of mutations (e.g., McKenzie et al. 1982;Threadgill et al. 1995;Atallah et al. 2004;Milloz et al. 2008;Chandler 2010;Dowell et al. 2010;Gerke et al. 2010). The influence of wild-type genetic backgrounds also extends to interactions among mutations (Remold and Lenski 2004;Dworkin et al. 2009;Wang et al. 2013b), altering patterns of epistasis, and these complex interactions are likely widespread . Alleles that influence many mutant phenotypes segregate in most natural populations, representing a potential source of cryptic genetic variation Félix 2007;Vaistij et al. 2013). In many cases, this cryptic variation has been described phenomenologically, or via the partitioning of genetic variance components (Gibson et al. 1999;Dworkin et al. 2003;McGuigan e...

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Section: Gene Ontology Analysessupporting

confidence: 60%

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

et al. 2014

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“…49 Therefore, the overall output of the E2F1 function may differ depending on a specific type of cancer. 50 Data mining of the literature coupled with the bioinformatics approach 51,52 have established E2F1 as an oncogene in lung cancers (Eymin et al 53 and Huang et al 54 Supplementary Data; Supplementary Figure S4 and data not shown). Therefore, we performed our next experiments using lung tumour cell lines.…”

Section: Sirna-e2f1mentioning

confidence: 99%

KMTase Set7/9 is a critical regulator of E2F1 activity upon genotoxic stress

et al. 2014

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During the recent years lysine methyltransferase Set7/9 ((Su(var)-3-9, Enhancer-of-Zeste, Trithorax) domain containing protein 7/9) has emerged as an important regulator of different transcription factors. In this study, we report a novel function for Set7/9 as a critical co-activator of E2 promoter-binding factor 1 (E2F1)-dependent transcription in response to DNA damage. By means of various biochemical, cell biology, and bioinformatics approaches, we uncovered that cell-cycle progression through the G1/S checkpoint of tumour cells upon DNA damage is defined by the threshold of expression of both E2F1 and Set7/9. The latter affects the activity of E2F1 by indirectly modulating histone modifications in the promoters of E2F1-dependent genes. Moreover, Set7/9 differentially affects E2F1 transcription targets: it promotes cell proliferation via expression of the CCNE1 gene and represses apoptosis by inhibiting the TP73 gene. Our biochemical screening of the panel of lung tumour cell lines suggests that these two factors are critically important for transcriptional upregulation of the CCNE1 gene product and hence successful progression through cell cycle. These findings identify Set7/9 as a potential biomarker in tumour cells with overexpressed E2F1 activity. Cell Death and Differentiation (2014) 21, 1889-1899; doi:10.1038/cdd.2014.108; published online 15 August 2014Lysine methylation of non-histone proteins has recently emerged as a novel regulatory mechanism to control protein functions. 1-4Set7/9 ((Su(var)-3-9, Enhancer-of-Zeste, Trithorax) domain containing protein 7/9) is a founding member of the family of non-chromatin lysine methyltransferases (KMTases). Set7/9 was initially identified as a monomethylase of histone H3 lysine 4 (H3K4) in vitro. 4,5However, we and others showed that the recombinant Set7/9 failed to target nucleosomes for methylation, [6][7][8] suggesting that Set7/9 functions as a factor-specific KMTase. There have been several non-histone proteins reported as the substrates for Set7/9, including TAF10 (TATA box binding protein (TBP)-associated factor, 30 kDa), 9 oestrogen receptor a (ERa), 10 RelA, 11 PCAF (P300/CBP-associated factor), 12 Stat3,13 Yap, 14 and Suv39h1. 15 However, in most cases the functional significance of this methylation is still not clear. The beststudied targets of Set7/9-mediated methylation are p53 16 and E2 promoter-binding factor 1 (E2F1), 17 transcription factors involved in regulation of DNA damage response (DDR).In response to genotoxic stress cancer cells undergo cell-cycle arrest either in G1/S or G2/M or in both checkpoints. The presence of intact p53 in cancer cells mediates transient G1/S checkpoint arrest, 18,19 which allows cells to repair the damaged DNA before replication or, if the amount of damage is insurmountable, drives cells into apoptosis. 20,21On the contrary, the activity of the E2F family transcription factors, especially E2F1, drives cells from the G1/S block to mitosis. 22,23 Transcriptional activity of E2F1, in turn, is repressed by the retin...

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“…It provides a common interface for the collection of recently developed tools [37]; description and details of the tools can be found in original publications [38]. The gene/protein list is profiled with respect to the most information available regarding gene function, protein interactions, pathway relationships, in silico predicted microRNA to gene associations, as well as, information collected by text mining.…”

Section: Bioinformatic Functional Analysismentioning

confidence: 99%

“…The statistical significance of the inferred model is estimated based on the distribution of the model size for a random protein list. Statistical significance of the model is computed by a Monte Carlo simulation procedure [37,38]. Cytoscape software (http://www.cytoscape.org/) was used for visualizing the obtained molecular interaction networks [39].…”

Section: Bioinformatic Functional Analysismentioning

confidence: 99%

Proteomic analysis of A2780/S ovarian cancer cell response to the cytotoxic organogold(III) compound Aubipyc

Gamberi

Massai

Magherini

et al. 2014

Journal of Proteomics

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Aubipy c is an organogold(III) compound endowed with encouraging anti-proliferative properties in vitro that is being evaluated pre-clinically as a prospective anticancer agent. A classical proteomic approach is exploited here to elucidate the mechanisms of its biological actions in A2780 human ovarian cancer cells. Based on 2-D gel electrophoresis separation and subsequent mass spectrometry identification, a considerable number of differentially expressed proteins were highlighted in A2780 cancer cells treated with Aubipy c . Bioinformatic analysis of the groups of up-regulated and down-regulated proteins pointed out that Aubipy c primarily perturbs mitochondrial processes and the glycolytic pathway. Notably, some major alterations in the glycolytic pathway were validated through Western blot and metabolic investigations. Biological significanceThis is the first proteomic analysis regarding Aubipy c cytotoxicity in A2780/S ovarian cancer cell line. Aubipy c is a promising gold(III) compound which manifests an appreciable cytotoxicity toward the cell line A2780, being able to overcome resistance to platinum. The proteomic study revealed for Aubipy c different cellular alterations with respect to cisplatin as well as to other gold compound such as auranofin. Remarkably, the bioinformatic analysis of proteomic data pointed out that Aubipy c treatment affected, directly or indirectly, several glycolytic enzymes. These data suggest a new mechanism of action for this gold drug and might have an impact on the use of gold-based drug in cancer treatment.

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BioProfiling.de: analytical web portal for high-throughput cell biology

Cited by 111 publications

References 27 publications

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

KMTase Set7/9 is a critical regulator of E2F1 activity upon genotoxic stress

Proteomic analysis of A2780/S ovarian cancer cell response to the cytotoxic organogold(III) compound Aubipyc

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