Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues

Yu, Xueping; Lin, Jimmy; Zack, Donald J.; Qian, Jiang

doi:10.1093/nar/gkl595

Cited by 135 publications

(152 citation statements)

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“…Taken together, the fact that only 39 root and 40 shoot pCREs are necessary to predict organ salt up-regulation implies that the remaining 1,854 pCREs we have identified are not as important. Alternatively, it is possible that the importance of some of these seemingly uninformative pCREs may be revealed only in combination, as demonstrated in previous studies of the regulation of gene expression under stress conditions (Harbison et al, 2004;Zou et al, 2011) and tissue-specific expression (Yu et al, 2006;Hu and Gallo, 2010).…”

Section: The Most Informative Pcres and Their Propertiesmentioning

confidence: 99%

Predictive Models of Spatial Transcriptional Response to High Salinity

et al. 2017

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ORCID IDs: 0000-0003-0863-0384 (S.U.); 0000-0001-6470-235X (S.-H.S.). Plants are exposed to a variety of environmental conditions, and their ability to respond to environmental variation depends on the proper regulation of gene expression in an organ-, tissue-, and cell type-specific manner. Although our knowledge of how stress responses are regulated is accumulating, a genome-wide model of how plant transcription factors (TFs) and cis-regulatory elements control spatially specific stress response has yet to emerge. Using Arabidopsis (Arabidopsis thaliana) as a model, we identified a set of 1,894 putative cis-regulatory elements (pCREs) that are associated with high-salinity (salt) up-regulated genes in the root or the shoot. We used these pCREs to develop computational models that can better predict salt up-regulated genes in the root and shoot compared with models based on known TF binding motifs. In addition, we incorporated TF binding sites identified via large-scale in vitro assays, chromatin accessibility, evolutionary conservation, and pCRE combinatorial relationships in machine learning models and found that only consideration of pCRE combinations led to better performance in salt up-regulation prediction in the root and shoot. Our results suggest that the plant organ transcriptional response to high salinity is regulated by a core set of pCREs and provide a genome-wide view of the cis-regulatory code of plant spatial transcriptional responses to environmental stress.

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Section: The Most Informative Pcres and Their Propertiesmentioning

confidence: 99%

Predictive Models of Spatial Transcriptional Response to High Salinity

et al. 2017

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“…Their homeodomain belongs to the K50 (lysine at position 50) paired-like class, similar to that of Drosophila bicoid protein, which, based on structure and functional studies, prefers to bind to DNA motifs with TAATCC or TAAGCT sequences (Treisman et al, 1989;Furukawa et al, 1997;Baird-Titus et al, 2006). These sequence motifs are widely present in the promoter or enhancer regions of many photoreceptor genes, including the opsin genes Furukawa et al, 1997;Yu et al, 2006).…”

Section: Factors Specifying the Photoreceptor Lineage -Otx2 And Crxmentioning

confidence: 99%

Regulation of photoreceptor gene expression by Crx-associated transcription factor network

2008

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Rod and cone photoreceptors in the mammalian retina are special types of neurons that are responsible for phototransduction, the first step of vision. Development and maintenance of photoreceptors require precisely regulated gene expression. This regulation is mediated by a network of photoreceptor transcription factors centered on Crx, an Otx-like homeodomain transcription factor. The cell type (subtype) specificity of this network is governed by factors that are preferentially expressed by rods or cones or both, including the rod-determining factors neural retina leucine zipper protein (Nrl) and the orphan nuclear receptor Nr2e3; and cone-determining factors, mostly nuclear receptor family members. The best-documented of these include thyroid hormone receptor β2 (Trβ2), retinoid related orphan receptor Rorβ, and retinoid X receptor Rxrγ. The appropriate function of this network also depends on general transcription factors and co-factors that are ubiquitously expressed, such as the Sp zinc finger transcription factors and STAGA coactivator complexes. These cell type-specific and general transcription regulators form complex interactomes; mutations that interfere with any of the interactions can cause photoreceptor development defects or degeneration. In this manuscript, we review recent progress on the roles of various photoreceptor transcription factors and interactions in photoreceptor subtype development. We also provide evidence of auto-, para-, and feedback regulation among these factors at the transcriptional level. These protein-protein and protein-promoter interactions provide precision and specificity in controlling photoreceptor subtype-specific gene expression, development and survival. Understanding these interactions may provide insights to more effective therapeutic interventions for photoreceptor diseases.

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“…Computational approaches have been successful in predicting binding sites by analyzing motifs in promoter regions in the context of expression data, but these are noisy and do not consider condition-specific binding events (5). Coassociation of TF binding sites can also be used to predict factors that work together (6,7), but such approaches lack functional information about specific interactions and typically require large numbers of coassociated sites.…”

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confidence: 99%

Cooperative transcription factor associations discovered using regulatory variation

Karczewski

Tatonetti

Landt

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Regulation of gene expression at the transcriptional level is achieved by complex interactions of transcription factors operating at their target genes. Dissecting the specific combination of factors that bind each target is a significant challenge. Here, we describe in detail the Allele Binding Cooperativity test, which uses variation in transcription factor binding among individuals to discover combinations of factors and their targets. We developed the ALPHABIT (a large-scale process to hunt for allele binding interacting transcription factors) pipeline, which includes statistical analysis of binding sites followed by experimental validation, and demonstrate that this method predicts transcription factors that associate with NFκB. Our method successfully identifies factors that have been known to work with NFκB (E2A, STAT1, IRF2), but whose global coassociation and sites of cooperative action were not known. In addition, we identify a unique coassociation (EBF1) that had not been reported previously. We present a general approach for discovering combinatorial models of regulation and advance our understanding of the genetic basis of variation in transcription factor binding.functional genomics | systems biology O ver the last several decades, it has become increasingly clear that the control of gene expression is due to the complex interactions of different transcription factors (TFs) working together to regulate RNA polymerase activity at promoters (1, 2). Although one factor may be a global regulator of a single process, it may function with other TFs to achieve precise regulation at different loci. Identifying the factors that work together to regulate each gene is a fundamental problem in biology.A variety of approaches have been used to measure TF coassociation. In vitro and in vivo biochemical assays have been used to detect protein-protein interactions, but these are plagued by trade-offs between sensitivity and specificity (3, 4). Moreover, in vitro assays do not always reflect the events that occur in vivo. Computational approaches have been successful in predicting binding sites by analyzing motifs in promoter regions in the context of expression data, but these are noisy and do not consider condition-specific binding events (5). Coassociation of TF binding sites can also be used to predict factors that work together (6, 7), but such approaches lack functional information about specific interactions and typically require large numbers of coassociated sites.We have recently suggested a unique approach, the Allele Binding Cooperativity (ABC) test, which uses binding variation among individuals to identify TF coassociation (8, 9). We hypothesized that variation in TF binding can occur because of sequence variation in associated TF binding sites and motifs. By searching for motifs in the binding regions for a factor of interest, the covariance of the associated motif can be correlated with binding of the factor across individuals. We demonstrated this phenomenon in a preliminary proof-of-concept, but di...

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Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues

Cited by 135 publications

References 46 publications

Predictive Models of Spatial Transcriptional Response to High Salinity

Predictive Models of Spatial Transcriptional Response to High Salinity

Regulation of photoreceptor gene expression by Crx-associated transcription factor network

Cooperative transcription factor associations discovered using regulatory variation

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