Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

Weirauch, Matthew T.; Yang, Ally; Albu, Mihai; Coté, Atina G.; Montenegro‐Montero, Alejandro; Drewe, Philipp; Najafabadi, Hamed S.; Lambert, Samuel A.; Mann, Ishminder; Cook, Kate B.; Zheng, Hong; Goity, Alejandra; Bakel, Harm van; Lozano, Jean Claude; Galli, Mary; Lewsey, Mathew G.; Huang, Eryong; Mukherjee, Tuhin; Rothenberg, Marc E.; Reece‐Hoyes, John S.; Govindarajan, Sridhar; Shaulsky, Gad; Walhout, Albertha J.M.; Bouget, François Yves; Rätsch, Gunnar; Larrondo, Luis F.; Ecker, Joseph R.; Hughes, Timothy R.

doi:10.1016/j.cell.2014.08.009

Cited by 1,667 publications

(2,007 citation statements)

References 70 publications

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“…athamap.de/) or in a collection of plant protein-binding microarray datasets (Materials and Methods and SI Appendix, Fig. S5) (68,69). If a significantly similar TFBS existed in the TF-TFBS interaction datasets, its corresponding TF was used to find the maize homologous TFs.…”

Section: Expression Dynamics Of Tf Gene Families During Germination Andmentioning

confidence: 99%

Transcriptome dynamics of developing maize leaves and genomewide prediction ofciselements and their cognate transcription factors

Chen²,

Chang³

et al. 2015

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

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Maize is a major crop and a model plant for studying C4 photosynthesis and leaf development. However, a genomewide regulatory network of leaf development is not yet available. This knowledge is useful for developing C3 crops to perform C4 photosynthesis for enhanced yields. Here, using 22 transcriptomes of developing maize leaves from dry seeds to 192 h post imbibition, we studied gene up-and down-regulation and functional transition during leaf development and inferred sets of strongly coexpressed genes. More significantly, we developed a method to predict transcription factor binding sites (TFBSs) and their cognate transcription factors (TFs) using genomic sequence and transcriptomic data. The method requires not only evolutionary conservation of candidate TFBSs and sets of strongly coexpressed genes but also that the genes in a gene set share the same Gene Ontology term so that they are involved in the same biological function. In addition, we developed another method to predict maize TF-TFBS pairs using known TF-TFBS pairs in Arabidopsis or rice. From these efforts, we predicted 1,340 novel TFBSs and 253 new TF-TFBS pairs in the maize genome, far exceeding the 30 TF-TFBS pairs currently known in maize. In most cases studied by both methods, the two methods gave similar predictions. In vitro tests of 12 predicted TF-TFBS interactions showed that our methods perform well. Our study has significantly expanded our knowledge on the regulatory network involved in maize leaf development. maize transcriptomes | coexpressed genes | cis binding site M aize (Zea mays) is a major crop and a model plant for studying C4 photosynthesis and leaf development. However, the regulatory network that controls maize leaf development is still not well understood. In fact, the number of known maize transcription factor (TF)-binding sites (TFBSs) is far smaller than that for Arabidopsis thaliana (1-3).To understand better the regulation of maize leaf development, several recent studies used next-generation sequencing (NGS) technologies to survey transcriptomic differences among maize leaf cell types and developmental stages. The first large-scale study was by Li et al. (7) investigated the transcriptomes of Kranz (i.e., the foliar leaf blade) and non-Kranz (the husk leaf sheath) maize leaves to identify cohorts of genes associated with procambium initiation and vascular patterning. Recently, Wang et al. (8) conducted comparative transcriptomic and metabolomic analyses of developing leaves in maize and rice and identified putative structural and regulatory components important for C4 and C3 photosynthesis. These studies provided insights into the regulatory mechanisms underlying the development of Kranz leaf anatomy in maize.In the present study, we obtained nine transcriptomes of the second leaf from 84-192 h post imbibition at 12-h (6:00 AM and 6:00 PM) or 24-h (6:00 PM only) intervals. Together with the 13 SignificanceMaize is a major crop and a model plant for studying C4 leaf development. However, its regulatory network of leaf ...

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Section: Expression Dynamics Of Tf Gene Families During Germination Andmentioning

confidence: 99%

Transcriptome dynamics of developing maize leaves and genomewide prediction ofciselements and their cognate transcription factors

Chen²,

Chang³

et al. 2015

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

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show abstract

“…Alternative methods are needed for capturing genome-wide binding data for many organisms. In vitro TFBS identification methods such as Protein Binding Microarrays (PBM) and High Throughput Systematic Evolution of Ligands by Exponential Enrichment (HT-SELEX) have achieved the highest throughput for deducing TF binding specificities in vitro [9][10][11] , but these methods use short synthetic oligonucleotides lacking secondary DNA modifications and genomic context, both important determinants of selective TF binding in vivo [12][13][14][15][16] . The DAP-seq technique 15 described here employs an in vitro-expressed affinitytagged TF in combination with high-throughput sequencing of a genomic DNA library, allowing for the generation of genome-wide binding site maps reflective of both local sequence context and DNA methylation status.…”

Section: Introductionmentioning

confidence: 99%

“…Successful DAPseq experiments will typically give over 5% reads in peaks and produce peaks with a significant enrichment over background. Enriched motifs such as those produced by the MEME motif discovery software can be compared to comprehensive databases of known TF binding sites 9 . Step 39-41, protein washes: ~20 min…”

mentioning

confidence: 99%

Mapping genome-wide transcription-factor binding sites using DAP-seq

Bartlett¹,

O’Malley²,

Huang³

et al. 2017

Nat Protoc

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285

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In order to enable low-cost, high-throughput generation of cistrome and epicistrome maps for any organism, we developed DNA affinity purification sequencing (DAP-seq), a transcription factor (TF) binding site discovery assay that couples affinity-purified TFs with next-generation sequencing of a genomic DNA library. The method is fast, inexpensive, and more easily scaled than ChIP-seq. DNA libraries are constructed using native genomic DNA from any source of interest, preserving cell-and tissue-specific chemical modifications that are known to impact TF binding (such as DNA methylation) and providing increased specificity compared to in silico motif prediction methods such as protein binding microarrays (PBM) and systematic evolution of ligands by exponential enrichment (SELEX). The resulting DNA library is incubated with an affinity-tagged in vitro expressed TF, and TF-DNA complexes are purified using magnetic separation of the affinity tag. Bound genomic DNA is eluted from the TF and sequenced using next-generation sequencing. Sequence reads are mapped to a reference genome, identifying genome-wide binding locations for each TF assayed, from which sequence motifs can then be

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“…With RSAT, a tool specifically designed to detect regulatory signals in non‐coding sequences, we could retrieve the exact positions of experimentally determined motifs (known for most TFs but unavailable for ERF9, WRKY6, WRKY28, ZAT6, and MYB51) in the different promoters (Weirauch et al , 2014; Medina‐Rivera et al , 2015; Source Data for Appendix Fig S25). We could observe that the overall effect of the co‐regulation of two TFs depended on three factors.…”

Section: Discussionmentioning

confidence: 99%

“…The position‐specific scoring matrices (PSSMs) from the DNA‐binding motifs of the TFs were retrieved from the CIS‐BP database (http://cisbp.ccbr.utoronto.ca/; Weirauch et al , 2014). For five genes, ERF9 , WRKY6 , WRKY28 , ZAT6, and MYB51 , the binding motif was not yet experimentally determined and was left out of the analysis.…”

Section: Methodsmentioning

confidence: 99%

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress

Broeck

Dubois

Vermeersch

et al. 2017

Molecular Systems Biology

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Plants have established different mechanisms to cope with environmental fluctuations and accordingly fine‐tune their growth and development through the regulation of complex molecular networks. It is largely unknown how the network architectures change and what the key regulators in stress responses and plant growth are. Here, we investigated a complex, highly interconnected network of 20 Arabidopsis transcription factors (TFs) at the basis of leaf growth inhibition upon mild osmotic stress. We tracked the dynamic behavior of the stress‐responsive TFs over time, showing the rapid induction following stress treatment, specifically in growing leaves. The connections between the TFs were uncovered using inducible overexpression lines and were validated with transient expression assays. This study resulted in the identification of a core network, composed of ERF6, ERF8, ERF9, ERF59, and ERF98, which is responsible for most transcriptional connections. The analyses highlight the biological function of this core network in environmental adaptation and its redundancy. Finally, a phenotypic analysis of loss‐of‐function and gain‐of‐function lines of the transcription factors established multiple connections between the stress‐responsive network and leaf growth.

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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

Cited by 1,667 publications

References 70 publications

Transcriptome dynamics of developing maize leaves and genomewide prediction ofciselements and their cognate transcription factors

Transcriptome dynamics of developing maize leaves and genomewide prediction ofciselements and their cognate transcription factors

Mapping genome-wide transcription-factor binding sites using DAP-seq

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress

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