Seed-based systematic discovery of specific transcription factor target genes

Paul-Ehrlich-Zentrum für Experimentelle Medizin, AG Systems Biology-Computational Physiology, Tucholskystrasse 2, Berlin, Germany.
FEBS Journal (Impact Factor: 4). 07/2008; 275(12):3178-92. DOI: 10.1111/j.1742-4658.2008.06471.x
Source: PubMed


Reliable prediction of specific transcription factor target genes is a major challenge in systems biology and functional genomics. Current sequence-based methods yield many false predictions, due to the short and degenerated DNA-binding motifs. Here, we describe a new systematic genome-wide approach, the seed-distribution-distance method, that searches large-scale genome-wide expression data for genes that are similarly expressed as known targets. This method is used to identify genes that are likely targets, allowing sequence-based methods to focus on a subset of genes, giving rise to fewer false-positive predictions. We show by cross-validation that this method is robust in recovering specific target genes. Furthermore, this method identifies genes with typical functions and binding motifs of the seed. The method is illustrated by predicting novel targets of the transcription factor nuclear factor kappaB (NF-kappaB). Among the new targets is optineurin, which plays a key role in the pathogenesis of acquired blindness caused by adult-onset primary open-angle glaucoma. We show experimentally that the optineurin gene and other predicted genes are targets of NF-kappaB. Thus, our data provide a missing link in the signalling of NF-kappaB and the damping function of optineurin in signalling feedback of NF-kappaB. We present a robust and reliable method to enhance the genome-wide prediction of specific transcription factor target genes that exploits the vast amount of expression information available in public databases today.

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Available from: Ralf Mrowka, Jul 30, 2015
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    • "The data contain a collection of different independent investigations, including expression profiles from tumor samples, cancer cell lines, and different organ-specific tissue samples, as well as expression data from studies of diverse biological processes such as cell cycle, stress, signaling, and apoptosis . We correlated HIF-1α mRNA levels with randomly chosen genes or with verified HIF-1 target genes (Semenza, 2003), as described earlier (Mrowka et al., 2008). Of note, we found a significant positive correlation between HIF-1α mRNA levels and HIF-1 target gene expression (Figure 1C), supporting the view that HIF-1α mRNA level, and thus mRNA stability, is important for HIF-1 function. "
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    ABSTRACT: Hypoxia-inducible factor-1 (HIF-1) is a well-studied transcription factor mediating cellular adaptation to hypoxia. It also plays a crucial role under normoxic conditions, such as in inflammation, where its regulation is less well understood. The 3'-untranslated region (UTR) of HIF-1α mRNA is among the most conserved UTRs in the genome, hinting toward posttranscriptional regulation. To identify potential trans factors, we analyzed a large compilation of expression data. In contrast to its known function of being a negative regulator, we found that tristetraprolin (TTP) positively correlates with HIF-1 target genes. Mathematical modeling predicts that an additional level of posttranslational regulation of TTP can explain the observed positive correlation between TTP and HIF-1 signaling. Mechanistic studies revealed that TTP indeed changes its mode of regulation from destabilizing to stabilizing HIF-1α mRNA upon phosphorylation by p38 mitogen-activated protein kinase (MAPK)/MAPK-activated protein kinase 2. Using a model of monocyte-to-macrophage differentiation, we show that TTP-driven HIF-1α mRNA stabilization is crucial for cell migration. This demonstrates the physiological importance of a hitherto-unknown mechanism for multilevel regulation of HIF-1α in normoxia.
    Molecular biology of the cell 08/2012; 23(20):4129-41. DOI:10.1091/mbc.E11-11-0949 · 4.47 Impact Factor
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    • "interaction in this pathway. Interestingly, Sudhakar and colleagues characterized optineurin itself as a target gene of NF-κB [23], [24]. Optineurin recruitment to TNFR1 has been postulated to negatively regulate TNF-induced NF-κB activation due to competition of optineurin with the regulatory subunit of IκB kinase (NF-κB essential modulator, NEMO) for ubiquitinated RIP1 [11]. "
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    ABSTRACT: Escape from the host immune system is essential for intracellular pathogens. The adenoviral protein E3-14.7K (14.7K) is known as a general inhibitor of tumor necrosis factor (TNF)-induced apoptosis. It efficiently blocks TNF-receptor 1 (TNFR1) internalization but the underlying molecular mechanism still remains elusive. Direct interaction of 14.7K and/or associated proteins with the TNFR1 complex has been discussed although to date not proven. In our study, we provide for the first time evidence for recruitment of 14.7K and the 14.7K interacting protein optineurin to TNFR1. Various functions have been implicated for optineurin such as regulation of receptor endocytosis, vesicle trafficking, regulation of the nuclear factor κB (NF-κB) pathway and antiviral signaling. We therefore hypothesized that binding of optineurin to 14.7K and recruitment of both proteins to the TNFR1 complex is essential for protection against TNF-induced cytotoxic effects. To precisely dissect the individual role of 14.7K and optineurin, we generated and characterized a 14.7K mutant that does not confer TNF-resistance but is still able to interact with optineurin. In H1299 and KB cells expressing 14.7K wild-type protein, neither decrease in cell viability nor cleavage of caspases was observed upon stimulation with TNF. In sharp contrast, cells expressing the non-protective mutant of 14.7K displayed reduced viability and cleavage of initiator and effector caspases upon TNF treatment, indicating ongoing apoptotic cell death. Knockdown of optineurin in 14.7K expressing cells did not alter the protective effect as measured by cell viability and caspase activation. Taken together, we conclude that optineurin despite its substantial role in vesicular trafficking, endocytosis of cell surface receptors and recruitment to the TNFR1 complex is dispensable for the 14.7K-mediated protection against TNF-induced apoptosis.
    PLoS ONE 06/2012; 7(6):e38348. DOI:10.1371/journal.pone.0038348 · 3.23 Impact Factor
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    • "These genes were used as input for the web interface TargetFinder ( [50], which uses a seed-based sorting algorithm [80] to rank all genes in the human genome according to their similarity to a given input set of genes. The sorted list of all genes showed a strong enrichment with regard to positive EMT genes as indicated by a recovery test (p<10e-20). "
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    ABSTRACT: Somatic cells can be reprogrammed to induced pluripotent stem cells by over-expression of OCT4, SOX2, KLF4 and c-MYC (OSKM). With the aim of unveiling the early mechanisms underlying the induction of pluripotency, we have analyzed transcriptional profiles at 24, 48 and 72 hours post-transduction of OSKM into human foreskin fibroblasts. Experiments confirmed that upon viral transduction, the immediate response is innate immunity, which induces free radical generation, oxidative DNA damage, p53 activation, senescence, and apoptosis, ultimately leading to a reduction in the reprogramming efficiency. Conversely, nucleofection of OSKM plasmids does not elicit the same cellular stress, suggesting viral response as an early reprogramming roadblock. Additional initiation events include the activation of surface markers associated with pluripotency and the suppression of epithelial-to-mesenchymal transition. Furthermore, reconstruction of an OSKM interaction network highlights intermediate path nodes as candidates for improvement intervention. Overall, the results suggest three strategies to improve reprogramming efficiency employing: 1) anti-inflammatory modulation of innate immune response, 2) pre-selection of cells expressing pluripotency-associated surface antigens, 3) activation of specific interaction paths that amplify the pluripotency signal.
    PLoS ONE 08/2011; 6(8):e24351. DOI:10.1371/journal.pone.0024351 · 3.23 Impact Factor
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