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1.  AthaMap, integrating transcriptional and post-transcriptional data 
Nucleic Acids Research  2008;37(Database issue):D983-D986.
The AthaMap database generates a map of predicted transcription factor binding sites (TFBS) for the whole Arabidopsis thaliana genome. AthaMap has now been extended to include data on post-transcriptional regulation. A total of 403 173 genomic positions of small RNAs have been mapped in the A. thaliana genome. These identify 5772 putative post-transcriptionally regulated target genes. AthaMap tools have been modified to improve the identification of common TFBS in co-regulated genes by subtracting post-transcriptionally regulated genes from such analyses. Furthermore, AthaMap was updated to the TAIR7 genome annotation, a graphic display of gene analysis results was implemented, and the TFBS data content was increased. AthaMap is freely available at http://www.athamap.de/.
doi:10.1093/nar/gkn709
PMCID: PMC2686474  PMID: 18842622
2.  ‘MicroRNA Targets’, a new AthaMap web-tool for genome-wide identification of miRNA targets in Arabidopsis thaliana 
BioData Mining  2012;5:7.
Background
The AthaMap database generates a genome-wide map for putative transcription factor binding sites for A. thaliana. When analyzing transcriptional regulation using AthaMap it may be important to learn which genes are also post-transcriptionally regulated by inhibitory RNAs. Therefore, a unified database for transcriptional and post-transcriptional regulation will be highly useful for the analysis of gene expression regulation.
Methods
To identify putative microRNA target sites in the genome of A. thaliana, processed mature miRNAs from 243 annotated miRNA genes were used for screening with the psRNATarget web server. Positional information, target genes and the psRNATarget score for each target site were annotated to the AthaMap database. Furthermore, putative target sites for small RNAs from seven small RNA transcriptome datasets were used to determine small RNA target sites within the A. thaliana genome.
Results
Putative 41,965 genome wide miRNA target sites and 10,442 miRNA target genes were identified in the A. thaliana genome. Taken together with genes targeted by small RNAs from small RNA transcriptome datasets, a total of 16,600 A. thaliana genes are putatively regulated by inhibitory RNAs. A novel web-tool, ‘MicroRNA Targets’, was integrated into AthaMap which permits the identification of genes predicted to be regulated by selected miRNAs. The predicted target genes are displayed with positional information and the psRNATarget score of the target site. Furthermore, putative target sites of small RNAs from selected tissue datasets can be identified with the new ‘Small RNA Targets’ web-tool.
Conclusions
The integration of predicted miRNA and small RNA target sites with transcription factor binding sites will be useful for AthaMap-assisted gene expression analysis. URL: http://www.athamap.de/
doi:10.1186/1756-0381-5-7
PMCID: PMC3410767  PMID: 22800758
Arabidopsis thaliana; AthaMap; MicroRNAs; Small RNAs; Post-transcriptional regulation
3.  AthaMap web tools for the analysis and identification of co-regulated genes 
Nucleic Acids Research  2006;35(Database issue):D857-D862.
The AthaMap database generates a map of cis-regulatory elements for the whole Arabidopsis thaliana genome. This database has been extended by new tools to identify common cis-regulatory elements in specific regions of user-provided gene sets. A resulting table displays all cis-regulatory elements annotated in AthaMap including positional information relative to the respective gene. Further tables show overviews with the number of individual transcription factor binding sites (TFBS) present and TFBS common to the whole set of genes. Over represented cis-elements are easily identified. These features were used to detect specific enrichment of drought-responsive elements in cold-induced genes. For identification of co-regulated genes, the output table of the colocalization function was extended to show the closest genes and their relative distances to the colocalizing TFBS. Gene sets determined by this function can be used for a co-regulation analysis in microarray gene expression databases such as Genevestigator or PathoPlant. Additional improvements of AthaMap include display of the gene structure in the sequence window and a significant data increase. AthaMap is freely available at .
doi:10.1093/nar/gkl1006
PMCID: PMC1761422  PMID: 17148485
4.  AthaMap-assisted transcription factor target gene identification in Arabidopsis thaliana 
The AthaMap database generates a map of potential transcription factor binding sites (TFBS) and small RNA target sites in the Arabidopsis thaliana genome. The database contains sites for 115 different transcription factors (TFs). TFBS were identified with positional weight matrices (PWMs) or with single binding sites. With the new web tool ‘Gene Identification’, it is possible to identify potential target genes for selected TFs. For these analyses, the user can define a region of interest of up to 6000 bp in all annotated genes. For TFBS determined with PWMs, the search can be restricted to high-quality TFBS. The results are displayed in tables that identify the gene, position of the TFBS and, if applicable, individual score of the TFBS. In addition, data files can be downloaded that harbour positional information of TFBS of all TFs in a region between −2000 and +2000 bp relative to the transcription or translation start site. Also, data content of AthaMap was increased and the database was updated to the TAIR8 genome release.
Database URL: http://www.athamap.de/gene_ident.php
doi:10.1093/database/baq034
PMCID: PMC3011983  PMID: 21177332
5.  AthaMap web tools for database-assisted identification of combinatorial cis-regulatory elements and the display of highly conserved transcription factor binding sites in Arabidopsis thaliana 
Nucleic Acids Research  2005;33(Web Server issue):W397-W402.
The AthaMap database generates a map of cis-regulatory elements for the Arabidopsis thaliana genome. AthaMap contains more than 7.4 × 106 putative binding sites for 36 transcription factors (TFs) from 16 different TF families. A newly implemented functionality allows the display of subsets of higher conserved transcription factor binding sites (TFBSs). Furthermore, a web tool was developed that permits a user-defined search for co-localizing cis-regulatory elements. The user can specify individually the level of conservation for each TFBS and a spacer range between them. This web tool was employed for the identification of co-localizing sites of known interacting TFs and TFs containing two DNA-binding domains. More than 1.8 × 105 combinatorial elements were annotated in the AthaMap database. These elements can also be used to identify more complex co-localizing elements consisting of up to four TFBSs. The AthaMap database and the connected web tools are a valuable resource for the analysis and the prediction of gene expression regulation at .
doi:10.1093/nar/gki395
PMCID: PMC1160156  PMID: 15980498
6.  Internet Resources for Gene Expression Analysis in Arabidopsis thaliana 
Current Genomics  2008;9(6):375-380.
The number of online databases and web-tools for gene expression analysis in Arabidopsis thaliana has increased tremendously during the last years. These resources permit the database-assisted identification of putative cis-regulatory DNA sequences, their binding proteins, and the determination of common cis-regulatory motifs in coregulated genes. DNA binding proteins may be predicted by the type of cis-regulatory motif. Further questions of combinatorial control based on the interaction of DNA binding proteins and the colocalization of cis-regulatory motifs can be addressed. The database-assisted spatial and temporal expression analysis of DNA binding proteins and their target genes may help to further refine experimental approaches. Signal transduction pathways upstream of regulated genes are not yet fully accessible in databases mainly because they need to be manually annotated. This review focuses on the use of the AthaMap and PathoPlant® databases for gene expression regulation analysis and discusses similar and complementary online databases and web-tools. Online databases are helpful for the development of working hypothesis and for designing subsequent experiments.
doi:10.2174/138920208785699535
PMCID: PMC2691667  PMID: 19506727
Bioinformatics; databases; gene expression; plants; transcription; web-server.
7.  PathoPlant®: a platform for microarray expression data to analyze co-regulated genes involved in plant defense responses 
Nucleic Acids Research  2006;35(Database issue):D841-D845.
Plants react to pathogen attack by expressing specific proteins directed toward the infecting pathogens. This involves the transcriptional activation of specific gene sets. PathoPlant®, a database on plant–pathogen interactions and signal transduction reactions, has now been complemented by microarray gene expression data from Arabidopsis thaliana subjected to pathogen infection and elicitor treatment. New web tools enable identification of plant genes regulated by specific stimuli. Sets of genes co-regulated by multiple stimuli can be displayed as well. A user-friendly web interface was created for the submission of gene sets to be analyzed. This results in a table, listing the stimuli that act either inducing or repressing on the respective genes. The search can be restricted to certain induction factors to identify, e.g. strongly up- or down-regulated genes. Up to three stimuli can be combined with the option of induction factor restriction to determine similarly regulated genes. To identify common cis-regulatory elements in co-regulated genes, a resulting gene list can directly be exported to the AthaMap database for analysis. PathoPlant is freely accessible at .
doi:10.1093/nar/gkl835
PMCID: PMC1669748  PMID: 17099232
8.  AGRIS: Arabidopsis Gene Regulatory Information Server, an information resource of Arabidopsis cis-regulatory elements and transcription factors 
BMC Bioinformatics  2003;4:25.
Background
The gene regulatory information is hardwired in the promoter regions formed by cis-regulatory elements that bind specific transcription factors (TFs). Hence, establishing the architecture of plant promoters is fundamental to understanding gene expression. The determination of the regulatory circuits controlled by each TF and the identification of the cis-regulatory sequences for all genes have been identified as two of the goals of the Multinational Coordinated Arabidopsis thaliana Functional Genomics Project by the Multinational Arabidopsis Steering Committee (June 2002).
Results
AGRIS is an information resource of Arabidopsis promoter sequences, transcription factors and their target genes. AGRIS currently contains two databases, AtTFDB (Arabidopsis thaliana transcription factor database) and AtcisDB (Arabidopsis thaliana cis-regulatory database). AtTFDB contains information on approximately 1,400 transcription factors identified through motif searches and grouped into 34 families. AtTFDB links the sequence of the transcription factors with available mutants and, when known, with the possible genes they may regulate. AtcisDB consists of the 5' regulatory sequences of all 29,388 annotated genes with a description of the corresponding cis-regulatory elements. Users can search the databases for (i) promoter sequences, (ii) a transcription factor, (iii) a direct target genes for a specific transcription factor, or (vi) a regulatory network that consists of transcription factors and their target genes.
Conclusion
AGRIS provides the necessary software tools on Arabidopsis transcription factors and their putative binding sites on all genes to initiate the identification of transcriptional regulatory networks in the model dicotyledoneous plant Arabidopsis thaliana. AGRIS can be accessed from .
doi:10.1186/1471-2105-4-25
PMCID: PMC166152  PMID: 12820902
9.  The Innate Immune Database (IIDB) 
BMC Immunology  2008;9:7.
Background
As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens.
Description
We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser.
Conclusion
We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at .
doi:10.1186/1471-2172-9-7
PMCID: PMC2268913  PMID: 18321385
10.  Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology 
PLoS ONE  2012;7(8):e42779.
Transcription factor-DNA interactions, central to cellular regulation and control, are commonly described by position weight matrices (PWMs). These matrices are frequently used to predict transcription factor binding sites in regulatory regions of DNA to complement and guide further experimental investigation. The DNA sequence preferences of transcription factors, encoded in PWMs, are dictated primarily by select residues within the DNA binding domain(s) that interact directly with DNA. Therefore, the DNA binding properties of homologous transcription factors with identical DNA binding domains may be characterized by PWMs derived from different species. Accordingly, we have implemented a fully automated domain-level homology searching method for identical DNA binding sequences.
By applying the domain-level homology search to transcription factors with existing PWMs in the JASPAR and TRANSFAC databases, we were able to significantly increase coverage in terms of the total number of PWMs associated with a given species, assign PWMs to transcription factors that did not previously have any associations, and increase the number of represented species with PWMs over an order of magnitude. Additionally, using protein binding microarray (PBM) data, we have validated the domain-level method by demonstrating that transcription factor pairs with matching DNA binding domains exhibit comparable DNA binding specificity predictions to transcription factor pairs with completely identical sequences.
The increased coverage achieved herein demonstrates the potential for more thorough species-associated investigation of protein-DNA interactions using existing resources. The PWM scanning results highlight the challenging nature of transcription factors that contain multiple DNA binding domains, as well as the impact of motif discovery on the ability to predict DNA binding properties. The method is additionally suitable for identifying domain-level homology mappings to enable utilization of additional information sources in the study of transcription factors. The domain-level homology search method, resulting PWM mappings, web-based user interface, and web API are publicly available at http://dodoma.systemsbiology.netdodoma.systemsbiology.net.
doi:10.1371/journal.pone.0042779
PMCID: PMC3428306  PMID: 22952610
11.  Whole-Genome rVISTA: a tool to determine enrichment of transcription factor binding sites in gene promoters from transcriptomic data 
Bioinformatics  2013;29(16):2059-2061.
Summary: We have developed a web-based query tool, Whole-Genome rVISTA (WGRV), that determines enrichment of transcription factors (TFs) and associated target genes in sets of co-regulated genes. WGRV enables users to query databases containing pre-computed genome coordinates of evolutionarily conserved transcription factor binding sites in the proximal promoters (from 100 bp to 5 kb upstream) of human, mouse and Drosophila genomes. TF binding sites are based on position-weight matrices from the TRANSFAC Professional database. For a given set of co-regulated genes, WGRV returns statistically enriched and evolutionarily conserved binding sites, mapped by the regulatory VISTA (rVISTA) algorithm. Users can then retrieve a list of genes from the query set containing the enriched TF binding sites and their location in the query set promoters. Results are exported in a BED format for rapid visualization in the UCSC genome browser. Flat files of mapped conserved sites and their genomic coordinates are also available for analysis with stand-alone software.
Availability: http://genome.lbl.gov/cgi-bin/WGRVistaInputCommon.pl.
Contact: azambon@ucsd.edu or ildubchak@lbl.gov
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt318
PMCID: PMC3722517  PMID: 23736530
12.  PlantPAN: Plant promoter analysis navigator, for identifying combinatorial cis-regulatory elements with distance constraint in plant gene groups 
BMC Genomics  2008;9:561.
Background
The elucidation of transcriptional regulation in plant genes is important area of research for plant scientists, following the mapping of various plant genomes, such as A. thaliana, O. sativa and Z. mays. A variety of bioinformatic servers or databases of plant promoters have been established, although most have been focused only on annotating transcription factor binding sites in a single gene and have neglected some important regulatory elements (tandem repeats and CpG/CpNpG islands) in promoter regions. Additionally, the combinatorial interaction of transcription factors (TFs) is important in regulating the gene group that is associated with the same expression pattern. Therefore, a tool for detecting the co-regulation of transcription factors in a group of gene promoters is required.
Results
This study develops a database-assisted system, PlantPAN (Plant Promoter Analysis Navigator), for recognizing combinatorial cis-regulatory elements with a distance constraint in sets of plant genes. The system collects the plant transcription factor binding profiles from PLACE, TRANSFAC (public release 7.0), AGRIS, and JASPER databases and allows users to input a group of gene IDs or promoter sequences, enabling the co-occurrence of combinatorial transcription factor binding sites (TFBSs) within a defined distance (20 bp to 200 bp) to be identified. Furthermore, the new resource enables other regulatory features in a plant promoter, such as CpG/CpNpG islands and tandem repeats, to be displayed. The regulatory elements in the conserved regions of the promoters across homologous genes are detected and presented.
Conclusion
In addition to providing a user-friendly input/output interface, PlantPAN has numerous advantages in the analysis of a plant promoter. Several case studies have established the effectiveness of PlantPAN. This novel analytical resource is now freely available at .
doi:10.1186/1471-2164-9-561
PMCID: PMC2633311  PMID: 19036138
13.  High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions 
PLoS Computational Biology  2010;6(9):e1000916.
Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.
Author Summary
Transcription factors (TFs) are proteins that bind sites in the non-coding DNA and regulate the expression of targeted genes. Being able to predict the genome-wide binding locations of TFs is an important step in deciphering gene regulatory networks. Historically, there was very limited experimental data on the DNA-binding preferences of most TFs. Computational biologists used known sites to estimate simple binding site motifs, called position-specific scoring matrices, and scan the genome for additional potential binding locations, but this approach often led to many false positive predictions. Here we introduce a machine learning approach to leverage new high resolution data on the binding preferences of TFs, namely, protein binding microarray (PBM) experiments which measure the in vitro binding affinities of TFs with respect to an array of double-stranded DNA probes, and chromatin immunoprecipitation experiments followed by next generation sequencing (ChIP-seq) which measure in vivo genome-wide binding of TFs in a given cell type. We show that by training statistical models on high resolution PBM and ChIP-seq data, we can more accurately represent the subtle DNA binding preferences of TFs and predict their genome-wide binding locations. These results will enable advances in the computational analysis of transcriptional regulation in mammalian genomes.
doi:10.1371/journal.pcbi.1000916
PMCID: PMC2936517  PMID: 20838582
14.  MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes 
BMC Bioinformatics  2005;6:79.
Background
Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.
Results
We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method.
Conclusion
The search engine, available at , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.
doi:10.1186/1471-2105-6-79
PMCID: PMC1131891  PMID: 15799782
15.  PlnTFDB: an integrative plant transcription factor database 
BMC Bioinformatics  2007;8:42.
Background
Transcription factors (TFs) are key regulatory proteins that enhance or repress the transcriptional rate of their target genes by binding to specific promoter regions (i.e. cis-acting elements) upon activation or de-activation of upstream signaling cascades. TFs thus constitute master control elements of dynamic transcriptional networks. TFs have fundamental roles in almost all biological processes (development, growth and response to environmental factors) and it is assumed that they play immensely important functions in the evolution of species. In plants, TFs have been employed to manipulate various types of metabolic, developmental and stress response pathways. Cross-species comparison and identification of regulatory modules and hence TFs is thought to become increasingly important for the rational design of new plant biomass. Up to now, however, no computational repository is available that provides access to the largely complete sets of transcription factors of sequenced plant genomes.
Description
PlnTFDB is an integrative plant transcription factor database that provides a web interface to access large (close to complete) sets of transcription factors of several plant species, currently encompassing Arabidopsis thaliana (thale cress), Populus trichocarpa (poplar), Oryza sativa (rice), Chlamydomonas reinhardtii and Ostreococcus tauri. It also provides an access point to its daughter databases of a species-centered representation of transcription factors (OstreoTFDB, ChlamyTFDB, ArabTFDB, PoplarTFDB and RiceTFDB). Information including protein sequences, coding regions, genomic sequences, expressed sequence tags (ESTs), domain architecture and scientific literature is provided for each family.
Conclusion
We have created lists of putatively complete sets of transcription factors and other transcriptional regulators for five plant genomes. They are publicly available through . Further data will be included in the future when the sequences of other plant genomes become available.
doi:10.1186/1471-2105-8-42
PMCID: PMC1802092  PMID: 17286856
16.  The Regulatory Code for Transcriptional Response Diversity and Its Relation to Genome Structural Properties in A. thaliana 
PLoS Genetics  2007;3(2):e11.
Regulation of gene expression via specific cis-regulatory promoter elements has evolved in cellular organisms as a major adaptive mechanism to respond to environmental change. Assuming a simple model of transcriptional regulation, genes that are differentially expressed in response to a large number of different external stimuli should harbor more distinct regulatory elements in their upstream regions than do genes that only respond to few environmental challenges. We tested this hypothesis in Arabidopsis thaliana using the compendium of gene expression profiling data available in AtGenExpress and known cis-element motifs mapped to upstream gene promoter regions and studied the relation of the observed breadth of differential gene expression response with several fundamental genome architectural properties. We observed highly significant positive correlations between the density of cis-elements in upstream regions and the number of conditions in which a gene was differentially regulated. The correlation was most pronounced in regions immediately upstream of the transcription start sites. Multistimuli response genes were observed to be associated with significantly longer upstream intergenic regions, retain more paralogs in the Arabidopsis genome, are shorter, have fewer introns, and are more likely to contain TATA-box motifs in their promoters. In abiotic stress time series data, multistimuli response genes were found to be overrepresented among early-responding genes. Genes involved in the regulation of transcription, stress response, and signaling processes were observed to possess the greatest regulatory capacity. Our results suggest that greater gene expression regulatory complexity appears to be encoded by an increased density of cis-regulatory elements and provide further evidence for an evolutionary adaptation of the regulatory code at the genomic layout level. Larger intergenic spaces preceding multistimuli response genes may have evolved to allow greater regulatory gene expression potential.
Author Summary
The induction or repression of specific genes has evolved in living organisms as a mechanism to respond to environmental changes. At the molecular level, this process is mediated via molecular switches, so-called regulatory elements, generally located in the genomic region adjacent to the gene they control, the gene promoter. Upon environmental change, specific proteins bind to such regulatory elements, thereby turning on or off the associated genes. As this molecular response is often specific to the external signal, genes that respond to a large number of different external stimuli should harbor more distinct regulatory elements in their promoter regions than should genes responding only to few environmental challenges. In analyzing data for the plant Arabidopsis thaliana, we observed that indeed an increased number of regulatory elements is associated with a broader range of responses. Several other genome structural properties, such as gene size, the occurrence of similar genes in the Arabidopsis genome, and the distance between genes, were also observed to be correlated with a broader breadth of response. The results suggest that greater regulatory complexity appears encoded by an increased density of regulatory elements and provide further evidence for an evolutionary adaptation of the regulatory code at the genomic architectural level.
doi:10.1371/journal.pgen.0030011
PMCID: PMC1796623  PMID: 17291162
17.  De-Novo Discovery of Differentially Abundant Transcription Factor Binding Sites Including Their Positional Preference 
PLoS Computational Biology  2011;7(2):e1001070.
Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open-source Java framework Jstacs and as a stand-alone application at http://www.jstacs.de/index.php/Dispom.
Author Summary
Binding of transcription factors to promoters of genes, and subsequent enhancement or repression of transcription, is one of the main steps of transcriptional gene regulation. Direct or indirect wet-lab experiments allow the identification of approximate regions potentially bound or regulated by a transcription factor. Subsequently, de-novo motif discovery tools can be used for detecting the precise positions of binding sites. Many traditional tools focus on motifs over-represented in the target regions, which often turn out to be similarly over-represented in the entire genome. In contrast, several recent tools focus on differentially abundant motifs in target regions compared to a control set. As binding sites are often located at some preferred distance to the transcription start site, it is favorable to include this information into de-novo motif discovery. Here, we present Dispom a novel approach for learning differentially abundant motifs and their positional preferences simultaneously, which predicts binding sites with increased accuracy compared to many popular de-novo motif discovery tools. When applying Dispom to promoters of auxin-responsive genes of Arabidopsis thaliana, we find a binding motif slightly different from the canonical auxin-response element, which exhibits a strong positional preference and which is considerably more specific to auxin-responsive genes.
doi:10.1371/journal.pcbi.1001070
PMCID: PMC3037384  PMID: 21347314
18.  Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana 
A protein interactome focused towards cell proliferation was mapped comprising 857 interactions among 393 proteins, leading to many new insights in plant cell cycle regulation.A comprehensive view on heterodimeric cyclin-dependent kinase (CDK)/cyclin complexes in plants is obtained, in relation with their regulators.Over 100 new candidate cell cycle proteins were predicted.
The basic underlying mechanisms that govern the cell cycle are conserved among all eukaryotes. Peculiar for plants, however, is that their genome contains a collection of cell cycle regulatory genes that is intriguingly large (Vandepoele et al, 2002; Menges et al, 2005) compared to other eukaryotes. Arabidopsis thaliana (Arabidopsis) encodes 71 genes in five regulatory classes versus only 15 in yeast and 23 in human.
Despite the discovery of numerous cell cycle genes, little is known about the protein complex machinery that steers plant cell division. Therefore, we applied tandem affinity purification (TAP) approach coupled with mass spectrometry (MS) on Arabidopsis cell suspension cultures to isolate and analyze protein complexes involved in the cell cycle. This approach allowed us to successfully map a first draft of the basic cell cycle complex machinery of Arabidopsis, providing many new insights into plant cell division.
To map the interactome, we relied on a streamlined platform comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, TAP adapted for plant cells, and matrix-assisted laser desorption ionization (MALDI) tandem-MS for the identification of purified proteins (Van Leene et al, 2007, 2008Van Leene et al, 2007, 2008). Complexes for 102 cell cycle proteins were analyzed using this approach, leading to a non-redundant data set of 857 interactions among 393 proteins (Figure 1A). Two subspaces were identified in this data set, domain I1, containing interactions confirmed in at least two independent experimental repeats or in the reciprocal purification experiment, and domain I2 consisting of uniquely observed interactions.
Several observations underlined the quality of both domains. All tested reverse purifications found the original interaction, and 150 known or predicted interactions were confirmed, meaning that also a huge stack of new interactions was revealed. An in-depth computational analysis revealed enrichment for many cell cycle-related features among the proteins of the network (Figure 1B), and many protein pairs were coregulated at the transcriptional level (Figure 1C). Through integration of known cell cycle-related features, more than 100 new candidate cell cycle proteins were predicted (Figure 1D). Besides common qualities of both interactome domains, their real significance appeared through mutual differences exposing two subspaces in the cell cycle interactome: a central regulatory network of stable complexes that are repeatedly isolated and represent core regulatory units, and a peripheral network comprising transient interactions identified less frequently, which are involved in other aspects of the process, such as crosstalk between core complexes or connections with other pathways. To evaluate the biological relevance of the cell cycle interactome in plants, we validated interactions from both domains by a transient split-luciferase assay in Arabidopsis plants (Marion et al, 2008), further sustaining the hypothesis-generating power of the data set to understand plant growth.
With respect to insights into the cell cycle physiology, the interactome was subdivided according to the functional classes of the baits and core protein complexes were extracted, covering cyclin-dependent kinase (CDK)/cyclin core complexes together with their positive and negative regulation networks, DNA replication complexes, the anaphase-promoting complex, and spindle checkpoint complexes. The data imply that mitotic A- and B-type cyclins exclusively form heterodimeric complexes with the plant-specific B-type CDKs and not with CDKA;1, whereas D-type cyclins seem to associate with CDKA;1. Besides the extraction of complexes previously shown in other organisms, our data also suggested many new functional links; for example, the link coupling cell division with the regulation of transcript splicing. The association of negative regulators of CDK/cyclin complexes with transcription factors suggests that their role in reallocation is not solely targeted to CDK/cyclin complexes. New members of the Siamese-related inhibitory proteins were identified, and for the first time potential inhibitors of plant-specific mitotic B-type CDKs have been found in plants. New evidence that the E2F–DP–RBR network is not only active at G1-to-S, but also at the G2-to-M transition is provided and many complexes involved in DNA replication or repair were isolated. For the first time, a plant APC has been isolated biochemically, identifying three potential new plant-specific APC interactors, and finally, complexes involved in the spindle checkpoint were isolated mapping many new but specific interactions.
Finally, to get a general view on the complex machinery, modules of interacting cyclins and core cell cycle regulators were ranked along the cell cycle phases according to the transcript expression peak of the cyclins, showing an assorted set of CDK–cyclin complexes with high regulatory differentiation (Figure 4). Even within the same subfamily (e.g. cyclin A3, B1, B2, D3, and D4), cyclins differ not only in their functional time frame but also in the type and number of CDKs, inhibitors, and scaffolding proteins they bind, further indicating their functional diversification. According to our interaction data, at least 92 different variants of CDK–cyclin complexes are found in Arabidopsis.
In conclusion, these results reflect how several rounds of gene duplication (Sterck et al, 2007) led to the evolution of a large set of cyclin paralogs and a myriad of regulators, resulting in a significant jump in the complexity of the cell cycle machinery that could accommodate unique plant-specific features such as an indeterminate mode of postembryonic development. Through their extensive regulation and connection with a myriad of up- and downstream pathways, the core cell cycle complexes might offer the plant a flexible toolkit to fine-tune cell proliferation in response to an ever-changing environment.
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.
doi:10.1038/msb.2010.53
PMCID: PMC2950081  PMID: 20706207
Arabidopsis thaliana; cell cycle; interactome; protein complex; protein interactions
19.  K-SPMM: a database of murine spermatogenic promoters modules & motifs 
BMC Bioinformatics  2006;7:238.
Background
Understanding the regulatory processes that coordinate the cascade of gene expression leading to male gamete development has proven challenging. Research has been hindered in part by an incomplete picture of the regulatory elements that are both characteristic of and distinctive to the broad population of spermatogenically expressed genes.
Description
K-SPMM, a database of murine Spermatogenic Promoters Modules and Motifs, has been developed as a web-based resource for the comparative analysis of promoter regions and their constituent elements in developing male germ cells. The system contains data on 7,551 genes and 11,715 putative promoter regions in Sertoli cells, spermatogonia, spermatocytes and spermatids. K-SPMM provides a detailed portrait of promoter site components, ranging from broad distributions of transcription factor binding sites to graphical illustrations of dimeric modules with respect to individual transcription start sites. Binding sites are identified through their similarities to position weight matrices catalogued in either the JASPAR or the TRANSFAC transcription factor archives. A flexible search function allows sub-populations of promoters to be identified on the basis of their presence in any of the four cell-types, their association with a list of genes or their component transcription-factor families.
Conclusion
This system can now be used independently or in conjunction with other databases of gene expression as a powerful aid to research networks of co-regulation. We illustrate this with respect to the spermiogenically active protamine locus in which binding sites are predicted that align well with biologically foot-printed protein binding domains.
Availability
doi:10.1186/1471-2105-7-238
PMCID: PMC1463010  PMID: 16670029
20.  AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana 
BMC Genomics  2012;13:85.
Background
Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks.
Description For systems biology applications, this work develops a novel database called Arabidopsis thaliana Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in Arabidopsis. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in Arabidopsis and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future.
Conclusions
The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at http://AtPAN.itps.ncku.edu.tw/.
doi:10.1186/1471-2164-13-85
PMCID: PMC3314555  PMID: 22397531
21.  rVISTA 2.0: evolutionary analysis of transcription factor binding sites 
Nucleic Acids Research  2004;32(Web Server issue):W217-W221.
Identifying and characterizing the transcription factor binding site (TFBS) patterns of cis-regulatory elements represents a challenge, but holds promise to reveal the regulatory language the genome uses to dictate transcriptional dynamics. Several studies have demonstrated that regulatory modules are under positive selection and, therefore, are often conserved between related species. Using this evolutionary principle, we have created a comparative tool, rVISTA, for analyzing the regulatory potential of noncoding sequences. Our ability to experimentally identify functional noncoding sequences is extremely limited, therefore, rVISTA attempts to fill this great gap in genomic analysis by offering a powerful approach for eliminating TFBSs least likely to be biologically relevant. The rVISTA tool combines TFBS predictions, sequence comparisons and cluster analysis to identify noncoding DNA regions that are evolutionarily conserved and present in a specific configuration within genomic sequences. Here, we present the newly developed version 2.0 of the rVISTA tool, which can process alignments generated by both the zPicture and blastz alignment programs or use pre-computed pairwise alignments of several vertebrate genomes available from the ECR Browser and GALA database. The rVISTA web server is closely interconnected with the TRANSFAC database, allowing users to either search for matrices present in the TRANSFAC library collection or search for user-defined consensus sequences. The rVISTA tool is publicly available at http://rvista.dcode.org/.
doi:10.1093/nar/gkh383
PMCID: PMC441521  PMID: 15215384
22.  TOBFAC: the database of tobacco transcription factors 
BMC Bioinformatics  2008;9:53.
Background
Regulation of gene expression at the level of transcription is a major control point in many biological processes. Transcription factors (TFs) can activate and/or repress the transcriptional rate of target genes and vascular plant genomes devote approximately 7% of their coding capacity to TFs. Global analysis of TFs has only been performed for three complete higher plant genomes – Arabidopsis (Arabidopsis thaliana), poplar (Populus trichocarpa) and rice (Oryza sativa). Presently, no large-scale analysis of TFs has been made from a member of the Solanaceae, one of the most important families of vascular plants. To fill this void, we have analysed tobacco (Nicotiana tabacum) TFs using a dataset of 1,159,022 gene-space sequence reads (GSRs) obtained by methylation filtering of the tobacco genome. An analytical pipeline was developed to isolate TF sequences from the GSR data set. This involved multiple (typically 10–15) independent searches with different versions of the TF family-defining domain(s) (normally the DNA-binding domain) followed by assembly into contigs and verification. Our analysis revealed that tobacco contains a minimum of 2,513 TFs representing all of the 64 well-characterised plant TF families. The number of TFs in tobacco is higher than previously reported for Arabidopsis and rice.
Results
TOBFAC: the database of tobacco transcription factors, is an integrative database that provides a portal to sequence and phylogeny data for the identified TFs, together with a large quantity of other data concerning TFs in tobacco. The database contains an individual page dedicated to each of the 64 TF families. These contain background information, domain architecture via Pfam links, a list of all sequences and an assessment of the minimum number of TFs in this family in tobacco. Downloadable phylogenetic trees of the major families are provided along with detailed information on the bioinformatic pipeline that was used to find all family members. TOBFAC also contains EST data, a list of published tobacco TFs and a list of papers concerning tobacco TFs. The sequences and annotation data are stored in relational tables using a PostgrelSQL relational database management system. The data processing and analysis pipelines used the Perl programming language. The web interface was implemented in JavaScript and Perl CGI running on an Apache web server. The computationally intensive data processing and analysis pipelines were run on an Apple XServe cluster with more than 20 nodes.
Conclusion
TOBFAC is an expandable knowledgebase of tobacco TFs with data currently available for over 2,513 TFs from 64 gene families. TOBFAC integrates available sequence information, phylogenetic analysis, and EST data with published reports on tobacco TF function. The database provides a major resource for the study of gene expression in tobacco and the Solanaceae and helps to fill a current gap in studies of TF families across the plant kingdom. TOBFAC is publicly accessible at .
doi:10.1186/1471-2105-9-53
PMCID: PMC2246155  PMID: 18221524
23.  Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control 
Combining translating ribosome affinity purification with RNA-seq for cell-specific profiling of translating RNAs in developing flowers.Cell type comparisons of cell type-specific hormone responses, promoter motifs, coexpressed cognate binding factor candidates, and splicing isoforms.Widespread post-transcriptional regulation at both the intron splicing and translational stages.A new class of noncoding RNAs associated with polysomes.
What constitutes a differentiated cell type? How much do cell types differ in their transcription of genes? The development and functions of tissues rely on constant interactions among distinct and nonequivalent cell types. Answering these questions will require quantitative information on transcriptomes, proteomes, protein–protein interactions, protein–nucleic acid interactions, and metabolomes at cellular resolution. The systems approaches emerging in biology promise to explain properties of biological systems based on genome-wide measurements of expression, interaction, regulation, and metabolism. To facilitate a systems approach, it is essential first to capture such components in a global manner, ideally at cellular resolution.
Recently, microarray analysis of transcriptomes has been extended to a cellular level of resolution by using laser microdissection or fluorescence-activated sorting (for review, see Nelson et al, 2008). These methods have been limited by stresses associated with cellular separation and isolation procedures, and biases associated with mandatory RNA amplification steps. A newly developed method, translating ribosome affinity purification (TRAP; Zanetti et al, 2005; Heiman et al, 2008; Mustroph et al, 2009), circumvents these problems by epitopetagging a ribosomal protein in specific cellular domains to selectively purify polysomes. We combined TRAP with deep sequencing, which we term TRAP-seq, to provide cell-level spatiotemporal maps for Arabidopsis early floral development at single-base resolution.
Flower development in Arabidopsis has been studied extensively and is one of the best understood aspects of plant development (for review, see Krizek and Fletcher, 2005). Genetic analysis of homeotic mutants established the ABC model, in which three classes of regulatory genes, A, B and C, work in a combinatorial manner to confer organ identities of four whorls (Coen and Meyerowitz, 1991). Each class of regulatory gene is expressed in a specific and evolutionarily conserved domain, and the action of the class A, B and C genes is necessary for specification of organ identity (Figure 1A).
Using TRAP-seq, we purified cell-specific translating mRNA populations, which we and others call the translatome, from the A, B and C domains of early developing flowers, in which floral patterning and the specification of floral organs is established. To achieve temporal specificity, we used a floral induction system to facilitate collection of early stage flowers (Wellmer et al, 2006). The combination of TRAP-seq with domain-specific promoters and this floral induction system enabled fine spatiotemporal isolation of translating mRNA in specific cellular domains, and at specific developmental stages.
Multiple lines of evidence confirmed the specificity of this approach, including detecting the expression in expected domains but not in other domains for well-studied flower marker genes and known physiological functions (Figures 1B–D and 2A–C). Furthermore, we provide numerous examples from flower development in which a spatiotemporal map of rigorously comparable cell-specific translatomes makes possible new views of the properties of cell domains not evident in data obtained from whole organs or tissues, including patterns of transcription and cis-regulation, new physiological differences among cell domains and between flower stages, putative hormone-active centers, and splicing events specific for flower domains (Figure 2A–D). Such findings may provide new targets for reverse genetics studies and may aid in the formulation and validation of interaction and pathway networks.
Beside cellular heterogeneity, the transcriptome is regulated at several steps through the life of mRNA molecules, which are not directly available through traditional transcriptome profiling of total mRNA abundance. By comparing the translatome and transcriptome, we integratively profiled two key posttranscriptional control points, intron splicing and translation state. From our translatome-wide profiling, we (i) confirmed that both posttranscriptional regulation control points were used by a large portion of the transcriptome; (ii) identified a number of cis-acting features within the coding or noncoding sequences that correlate with splicing or translation state; and (iii) revealed correlation between each regulation mechanism and gene function. Our transcriptome-wide surveys have highlighted target genes transcripts of which are probably under extensive posttranscriptional regulation during flower development.
Finally, we reported the finding of a large number of polysome-associated ncRNAs. About one-third of all annotated ncRNA in the Arabidopsis genome were observed co-purified with polysomes. Coding capacity analysis confirmed that most of them are real ncRNA without conserved ORFs. The group of polysome-associated ncRNA reported in this study is a potential new addition to the expanding riboregulator catalog; they could have roles in translational regulation during early flower development.
Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post-transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type-specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell-specific transcripts and uncovered many new ones, from which we inferred cell type-specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady-state overall transcripts, we found evidence for widespread post-transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development.
doi:10.1038/msb.2010.76
PMCID: PMC2990639  PMID: 20924354
Arabidopsis; flower; intron; transcriptome; translation
24.  TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes 
Nucleic Acids Research  2005;34(Database issue):D108-D110.
The TRANSFAC® database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel® on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC®. The list of databases which are linked to the common GENE table of TRANSFAC® and TRANSCompel® has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel® contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC®, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC® 7.0 and TRANSCompel® 7.0, are accessible under .
doi:10.1093/nar/gkj143
PMCID: PMC1347505  PMID: 16381825
25.  Identifying the genetic determinants of transcription factor activity 
Genome-wide messenger RNA expression levels are highly heritable. However, the molecular mechanisms underlying this heritability are poorly understood.The influence of trans-acting polymorphisms is often mediated by changes in the regulatory activity of one or more sequence-specific transcription factors (TFs). We use a method that exploits prior information about the DNA-binding specificity of each TF to estimate its genotype-specific regulatory activity. To this end, we perform linear regression of genotype-specific differential mRNA expression on TF-specific promoter-binding affinity.Treating inferred TF activity as a quantitative trait and mapping it across a panel of segregants from an experimental genetic cross allows us to identify trans-acting loci (‘aQTLs') whose allelic variation modulates the TF. A few of these aQTL regions contain the gene encoding the TF itself; several others contain a gene whose protein product is known to interact with the TF.Our method is strictly causal, as it only uses sequence-based features as predictors. Application to budding yeast demonstrates a dramatic increase in statistical power, compared with existing methods, to detect locus-TF associations and trans-acting loci. Our aQTL mapping strategy also succeeds in mouse.
Genetic sequence variation naturally perturbs mRNA expression levels in the cell. In recent years, analysis of parallel genotyping and expression profiling data for segregants from genetic crosses between parental strains has revealed that mRNA expression levels are highly heritable. Expression quantitative trait loci (eQTLs), whose allelic variation regulates the expression level of individual genes, have successfully been identified (Brem et al, 2002; Schadt et al, 2003). The molecular mechanisms underlying the heritability of mRNA expression are poorly understood. However, they are likely to involve mediation by transcription factors (TFs). We present a new transcription-factor-centric method that greatly increases our ability to understand what drives the genetic variation in mRNA expression (Figure 1). Our method identifies genomic loci (‘aQTLs') whose allelic variation modulates the protein-level activity of specific TFs. To map aQTLs, we integrate genotyping and expression profiling data with quantitative prior information about DNA-binding specificity of transcription factors in the form of position-specific affinity matrices (Bussemaker et al, 2007). We applied our method in two different organisms: budding yeast and mouse.
In our approach, the inferred TF activity is explicitly treated as a quantitative trait, and genetically mapped. The decrease of ‘phenotype space' from that of all genes (in the eQTL approach) to that of all TFs (in our aQTL approach) increases the statistical power to detect trans-acting loci in two distinct ways. First, as each inferred TF activity is derived from a large number of genes, it is far less noisy than mRNA levels of individual genes. Second, the number of trait/marker combinations that needs to be tested for statistical significance in parallel is roughly two orders of magnitude smaller than for eQTLs. We identified a total of 103 locus-TF associations, a more than six-fold improvement over the 17 locus-TF associations identified by several existing methods (Brem et al, 2002; Yvert et al, 2003; Lee et al, 2006; Smith and Kruglyak, 2008; Zhu et al, 2008). The total number of distinct genomic loci identified as an aQTL equals 31, which includes 11 of the 13 previously identified eQTL hotspots (Smith and Kruglyak, 2008).
To better understand the mechanisms underlying the identified genetic linkages, we examined the genes within each aQTL region. First, we found four ‘local' aQTLs, which encompass the gene encoding the TF itself. This includes the known polymorphism in the HAP1 gene (Brem et al, 2002), but also novel predictions of trans-acting polymorphisms in RFX1, STB5, and HAP4. Second, using high-throughput protein–protein interaction data, we identified putative causal genes for several aQTLs. For example, we predict that a polymorphism in the cyclin-dependent kinase CDC28 antagonistically modulates the functionally distinct cell cycle regulators Fkh1 and Fkh2. In this and other cases, our approach naturally accounts for post-translational modulation of TF activity at the protein level.
We validated our ability to predict locus-TF associations in yeast using gene expression profiles of allele replacement strains from a previous study (Smith and Kruglyak, 2008). Chromosome 15 contains an aQTL whose allelic status influences the activity of no fewer than 30 distinct TFs. This locus includes IRA2, which controls intracellular cAMP levels. We used the gene expression profile of IRA2 replacement strains to confirm that the polymorphism within IRA2 indeed modulates a subset of the TFs whose activity was predicted to link to this locus, and no other TFs.
Application of our approach to mouse data identified an aQTL modulating the activity of a specific TF in liver cells. We identified an aQTL on mouse chromosome 7 for Zscan4, a transcription factor containing four zinc finger domains and a SCAN domain. Even though we could not detect a candidate causal gene for Zscan4p because of lack of information about the mouse genome, our result demonstrates that our method also works in higher eukaryotes.
In summary, aQTL mapping has a greatly improved sensitivity to detect molecular mechanisms underlying the heritability of gene expression. The successful application of our approach to yeast and mouse data underscores the value of explicitly treating the inferred TF activity as a quantitative trait for increasing statistical power of detecting trans-acting loci. Furthermore, our method is computationally efficient, and easily applicable to any other organism whenever prior information about the DNA-binding specificity of TFs is available.
Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci (‘aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.
doi:10.1038/msb.2010.64
PMCID: PMC2964119  PMID: 20865005
gene expression; gene regulatory networks; genetic variation; quantitative trait loci; transcription factors

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