Identifying cooperative transcriptional regulations using protein-protein interactions

Cancer-associated fibroblasts-derived exosomal miR-17-5p promotes colorectal cancer aggressive phenotype by initiating a RUNX3/MYC/TGF-β1 positive feedback loop

Cancer-associated fibroblasts (CAFs) are the main stromal cells in the tumour microenvironment (TME). We found that the distribution of CAFs was significantly increased with tumour progression and led to a poor prognosis. In vitro and in vivo assays revealed that CAFs enhanced colorectal cancer (CRC) metastasis. Based on extraction and identification of exosomes of CAFs and normal fibroblasts (NFs), CAFs-exo showed higher expression of miR-17-5p than NFs-exo and could deliver exosomal miR-17-5p from parental CAFs to CRC cells. Further exploration verified that miR-17-5p influenced CRC metastasis capacity and directly targeted 3'-untranslated regions (UTRs) of RUNX family transcription factor 3(RUNX3). Our findings further revealed that RUNX3 interacted with MYC proto-oncogene(MYC) and that both RUNX3 and MYC bound to the promoter of transforming growth factor beta1(TGF-β1) at base pairs 1005-1296, thereby activating the TGF-β signalling pathway and contributing to tumour progression. In addition, RUNX3/MYC/TGF-β1 signalling sustained autocrine TGF-β1 to activate CAFs, and activated CAFs released more exosomal miR-17-5p to CRC cells, forming a positive feedback loop for CRC progression. Taken together, these data provide a new understanding of the potential diagnostic value of exosomal miR-17-5p in CRC.

TransDetect Identifies a New Regulatory Module Controlling Phosphate Accumulation

Identifying transcription factor (TFs) cooperation controlling target gene expression is still an arduous challenge. The accuracy of current methods at genome scale significantly drops with the increase in number of genes, which limits their applicability to more complex genomes, like animals and plants. Here, we developed an algorithm, TransDetect, able to predict TF combinations controlling the expression level of a given gene. TransDetect was used to identify novel TF modules regulating the expression of Arabidopsis (Arabidopsis thaliana) phosphate transporter PHO1;H3 comprising MYB15, MYB84, bHLH35, and ICE1. These TFs were confirmed to interact between themselves and with the PHO1;H3 promoter. Phenotypic and genetic analyses of TF mutants enable the organization of these four TFs and PHO1;H3 in a new gene regulatory network controlling phosphate accumulation in zinc-dependent manner. This demonstrates the potential of TransDetect to extract directionality in nondynamic transcriptomes and to provide a blueprint to identify gene regulatory network involved in a given biological process.

Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets

In eukaryotic cells, transcriptional regulation of gene expression is usually achieved by cooperative transcription factors (TFs). Therefore, knowing cooperative TFs is the first step toward uncovering the molecular mechanisms of gene expression regulation. Many algorithms based on different rationales have been proposed to predict cooperative TF pairs in yeast. Although various types of rationales have been used in the existing algorithms, functional coherence is not yet used. This prompts us to develop a new algorithm based on functional coherence and similarity of the target gene sets to identify cooperative TF pairs in yeast. The proposed algorithm predicted 40 cooperative TF pairs. Among them, three (Pdc2-Thi2, Hot1-Msn1 and Leu3-Met28) are novel predictions, which have not been predicted by any existing algorithms. Strikingly, two (Pdc2-Thi2 and Hot1-Msn1) of the three novel predictions have been experimentally validated, demonstrating the power of the proposed algorithm. Moreover, we show that the predictions of the proposed algorithm are more biologically meaningful than the predictions of 17 existing algorithms under four evaluation indices. In summary, our study suggests that new algorithms based on novel rationales are worthy of developing for detecting previously unidentifiable cooperative TF pairs.

YCRD: Yeast Combinatorial Regulation Database

In eukaryotes, the precise transcriptional control of gene expression is typically achieved through combinatorial regulation using cooperative transcription factors (TFs). Therefore, a database which provides regulatory associations between cooperative TFs and their target genes is helpful for biologists to study the molecular mechanisms of transcriptional regulation of gene expression. Because there is no such kind of databases in the public domain, this prompts us to construct a database, called Yeast Combinatorial Regulation Database (YCRD), which deposits 434,197 regulatory associations between 2535 cooperative TF pairs and 6243 genes. The comprehensive collection of more than 2500 cooperative TF pairs was retrieved from 17 existing algorithms in the literature. The target genes of a cooperative TF pair (e.g. TF1-TF2) are defined as the common target genes of TF1 and TF2, where a TF’s experimentally validated target genes were downloaded from YEASTRACT database. In YCRD, users can (i) search the target genes of a cooperative TF pair of interest, (ii) search the cooperative TF pairs which regulate a gene of interest and (iii) identify important cooperative TF pairs which regulate a given set of genes. We believe that YCRD will be a valuable resource for yeast biologists to study combinatorial regulation of gene expression. YCRD is available at http://cosbi.ee.ncku.edu.tw/YCRD/ or http://cosbi2.ee.ncku.edu.tw/YCRD/.

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs

In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature. However, until now, there is still no database which collects the predicted yeast cooperative TFs from existing algorithms. This prompts us to construct Cooperative Transcription Factors Database (CoopTFD), which has a comprehensive collection of 2622 predicted cooperative TF pairs (PCTFPs) in yeast from 17 existing algorithms. For each PCTFP, our database also provides five types of validation information: (i) the algorithms which predict this PCTFP, (ii) the publications which experimentally show that this PCTFP has physical or genetic interactions, (iii) the publications which experimentally study the biological roles of both TFs of this PCTFP, (iv) the common Gene Ontology (GO) terms of this PCTFP and (v) the common target genes of this PCTFP. Based on the provided validation information, users can judge the biological plausibility of a PCTFP of interest. We believe that CoopTFD will be a valuable resource for yeast biologists to study the combinatorial regulation of gene expression controlled by cooperative TFs.

Database URL:http://cosbi.ee.ncku.edu.tw/CoopTFD/ or http://cosbi2.ee.ncku.edu.tw/CoopTFD/

cDREM: inferring dynamic combinatorial gene regulation

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

Background

Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.

Results

The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses.

Conclusions

Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast

Background

Transcriptional regulation of gene expression in eukaryotes is usually accomplished by cooperative transcription factors (TFs). Computational identification of cooperative TF pairs has become a hot research topic and many algorithms have been proposed in the literature. A typical algorithm for predicting cooperative TF pairs has two steps. (Step 1) Define the targets of each TF under study. (Step 2) Design a measure for calculating the cooperativity of a TF pair based on the targets of these two TFs. While different algorithms have distinct sophisticated cooperativity measures, the targets of a TF are usually defined using ChIP-chip data. However, there is an inherent weakness in using ChIP-chip data to define the targets of a TF. ChIP-chip analysis can only identify the binding targets of a TF but it cannot distinguish the true regulatory from the binding but non-regulatory targets of a TF.

Results

This work is the first study which aims to investigate whether the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. For this purpose, we propose four simple algorithms, all of which consist of two steps. (Step 1) Define the targets of a TF using (i) ChIP-chip data in the first algorithm, (ii) TF binding data in the second algorithm, (iii) TF perturbation data in the third algorithm, and (iv) the intersection of TF binding and TF perturbation data in the fourth algorithm. Compared with the first three algorithms, the fourth algorithm uses a more biologically relevant way to define the targets of a TF. (Step 2) Measure the cooperativity of a TF pair by the statistical significance of the overlap of the targets of these two TFs using the hypergeometric test. By adopting four existing performance indices, we show that the fourth proposed algorithm (PA4) significantly out performs the other three proposed algorithms. This suggests that the computational identification of cooperative TF pairs is indeed improved when using a more biologically relevant way to define the targets of a TF. Strikingly, the prediction results of our simple PA4 are more biologically meaningful than those of the 12 existing sophisticated algorithms in the literature, all of which used ChIP-chip data to define the targets of a TF. This suggests that properly defining the targets of a TF may be more important than designing sophisticated cooperativity measures. In addition, our PA4 has the power to predict several experimentally validated cooperative TF pairs, which have not been successfully predicted by any existing algorithms in the literature.

Conclusions

This study shows that the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. The main contribution of this study is not to propose another new algorithm but to provide a new thinking for the research of computational identification of cooperative TF pairs. Researchers should put more effort on properly defining the targets of a TF (i.e. Step 1) rather than totally focus on designing sophisticated cooperativity measures (i.e. Step 2). The lists of TF target genes, the Matlab codes and the prediction results of the four proposed algorithms could be downloaded from our companion website http://cosbi3.ee.ncku.edu.tw/TFI/

Identifying cooperative transcription factors in yeast using multiple data sources

BACKGROUND

Transcriptional regulation of gene expression is usually accomplished by multiple interactive transcription factors (TFs). Therefore, it is crucial to understand the precise cooperative interactions among TFs. Various kinds of experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction data have been used to identify cooperative TF pairs in existing methods. The nucleosome occupancy data is not yet used for this research topic despite that several researches have revealed the association between nucleosomes and TFBSs.

RESULTS

In this study, we developed a novel method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. TF-gene documented regulation and TFBS data were used to determine the target genes of a TF, and the genome-wide nucleosome occupancy data was used to assess the nucleosome occupancy on TFBSs. Our method identifies cooperative TF pairs based on two biologically plausible assumptions. If two TFs cooperate, then (i) they should have a significantly higher number of common target genes than random expectation and (ii) their binding sites (in the promoters of their common target genes) should tend to be co-depleted of nucleosomes in order to make these binding sites simultaneously accessible to TF binding. Each TF pair is given a cooperativity score by our method. The higher the score is, the more likely a TF pair has cooperativity. Finally, a list of 27 cooperative TF pairs has been predicted by our method. Among these 27 TF pairs, 19 pairs are also predicted by existing methods. The other 8 pairs are novel cooperative TF pairs predicted by our method. The biological relevance of these 8 novel cooperative TF pairs is justified by the existence of protein-protein interactions and co-annotation in the same MIPS functional categories. Moreover, we adopted three performance indices to compare our predictions with 11 existing methods' predictions. We show that our method performs better than these 11 existing methods in identifying cooperative TF pairs in yeast. Finally, the cooperative TF network constructed from the 27 predicted cooperative TF pairs shows that our method has the power to find cooperative TF pairs of different biological processes.

CONCLUSION

Our method is effective in identifying cooperative TF pairs in yeast. Many of our predictions are validated by the literature, and our method outperforms 11 existing methods. We believe that our study will help biologists to understand the mechanisms of transcriptional regulation in eukaryotic cells.

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms

Background

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation.

Results

We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast.

Conclusions

In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.

Integrating multiple resources to identify specific transcriptional cooperativity with a Bayesian approach

MOTIVATION

Limited cohort of transcription factors is capable to structure various gene-expression patterns. Transcriptional cooperativity (TC) is deemed to be the main mechanism of complexity and precision in regulatory programs. Although many data types generated from numerous experimental technologies are utilized in an attempt to understand combinational transcriptional regulation, complementary computational approach that can integrate diverse data resources and assimilate them into biological model is still under development.

RESULTS

We developed a novel Bayesian approach for integrative analysis of proteomic, transcriptomic and genomic data to identify specific TC. The model evaluation demonstrated distinguishable power of features derived from distinct data sources and their essentiality to model performance. Our model outperformed other classifiers and alternative methods. The application that contextualized TC within hepatocarcinogenesis revealed carcinoma associated alterations. Derived TC networks were highly significant in capturing validated cooperativity as well as revealing novel ones. Our methodology is the first multiple data integration approach to predict dynamic nature of TC. It is promising in identifying tissue- or disease-specific TC and can further facilitate the interpretation of underlying mechanisms for various physiological conditions.

CONTACT

tieliushi01@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional and topological characterization of transcriptional cooperativity in yeast

Background

Many cellular programs are regulated through the integration of specific transcriptional signals originated from external stimuli, being cooperation between transcription factors a key feature in this process. In this work, we studied how transcriptional cooperativity in yeast is aimed at integrating different regulatory inputs rather than controlling particular cellular functions from a organizational, evolutionary and functional point of view.

Findings

Our results showed that cooperative transcription factor pairs co-evolve and are essential for the life of the cell. When organized into a layered regulatory network, we observed that cooperative transcription factors were preferentially placed in the middle layers, which highlights a role in regulatory signal integration. We also observed significant co-activity and co-evolution between members of the same cooperative pairs, but a lack of common co-expression profile.

Conclusions

Our results suggest that transcriptional cooperativity has a specific role within the regulatory control scheme of the cell, focused in the amplification and integration of cellular signals rather than control of particular cellular functions. This information can be used for better characterization of regulatory interactions between transcription factors, aimed at determining the spatial and temporal control of gene expression.

Deletion of a subgroup of ribosome-related genes minimizes hypoxia-induced changes and confers hypoxia tolerance

Hypoxia is a widely occurring condition experienced by diverse organisms under numerous physiological and disease conditions. To probe the molecular mechanisms underlying hypoxia responses and tolerance, we performed a genome-wide screen to identify mutants with enhanced hypoxia tolerance in the model eukaryote, the yeast Saccharomyces cerevisiae. Yeast provides an excellent model for genomic and proteomic studies of hypoxia. We identified five genes whose deletion significantly enhanced hypoxia tolerance. They are RAI1, NSR1, BUD21, RPL20A, and RSM22, all of which encode functions involved in ribosome biogenesis. Further analysis of the deletion mutants showed that they minimized hypoxia-induced changes in polyribosome profiles and protein synthesis. Strikingly, proteomic analysis by using the iTRAQ profiling technology showed that a substantially fewer number of proteins were changed in response to hypoxia in the deletion mutants, compared with the parent strain. Computational analysis of the iTRAQ data indicated that the activities of a group of regulators were regulated by hypoxia in the wild-type parent cells, but such regulation appeared to be diminished in the deletion strains. These results show that the deletion of one of the genes involved in ribosome biogenesis leads to the reversal of hypoxia-induced changes in gene expression and related regulators. They suggest that modifying ribosomal function is an effective mechanism to minimize hypoxia-induced specific protein changes and to confer hypoxia tolerance. These results may have broad implications in understanding hypoxia responses and tolerance in diverse eukaryotes ranging from yeast to humans.

Prediction of human protein-protein interaction by a mixed Bayesian model and its application to exploring underlying cancer-related pathway crosstalk

Protein-protein interaction (PPI) prediction method has provided an opportunity for elucidating potential biological processes and disease mechanisms. We integrated eight features involving proteomic, genomic, phenotype and functional annotation datasets by a mixed model consisting of full connected Bayesian (FCB) model and naive Bayesian model to predict human PPIs, resulting in 40 447 PPIs which contain 2740 common PPIs with the human protein reference database (HPRD) by a likelihood ratio cutoff of 512. Then we applied them to exploring underlying pathway crosstalk where pathways were derived from the pathway interaction database. Two pathway crosstalk networks (PCNs) were constructed based on PPI sets. The PPI sets were derived from two different sources. One source was strictly the HPRD database while the other source was a combination of HPRD and PPIs predicted by our mixed Bayesian method. We demonstrated that PCNs based on the mixed PPI set showed much more underlying pathway interactions than the HPRD PPI set. Furthermore, we mapped cancer-causing mutated somatic genes to PPIs between significant pathway crosstalk pairs. We extracted highly connected clusters from over-represented subnetworks of PCNs, which were enriched for mutated gene interactions that acted as crosstalk links. Most of the pathways in top ranking clusters were shown to play important roles in cancer. The clusters themselves showed coherent function categories pertaining to cancer development.

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

Background

Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.

Results

We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.

Conclusions

The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.

Analysis of transcriptional synergy between upstream regions and introns in ribosomal protein genes of yeast

Transcriptional regulation in eukaryotic genes generally requires combinatorial binding on DNA of multiple transcription factors. Though many analyses have been performed for identification of combinatorial patterns in promoter sequences, there are few studies concerned with introns of genes. Here our study focuses on the transcriptional synergistic (cooperative) regulation between upstream promoters and introns of ribosomal protein (RP) genes in Saccharomyces cerevisiae yeast. We first extract some potential transcriptional regulatory motifs based on a statistical comparative analysis. 98% of these motifs are accordance with experimental analyses. Then by pairing these motifs each other, we identify some potential synergistic motif pairs between upstream regions and introns of yeast RP genes (RPGs). Among 48 detected motif pairs, 44 match the binding sites for interacting transcriptional factors known from experiments or predictions. Checking the positions of these motif pairs in yeast RPGs, it is found that both motifs of the detected motif pairs are enriched in specific regions of upstream regions and introns, respectively. Some motif pairs present distance and orientation preferences, which may be favorable for transcription factors to bind simultaneously to DNA. These results will be helpful to understand the mechanism of synergistic regulation in yeast RPGs.

Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.

A knowledge-based method to predict the cooperative relationship between transcription factors

Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.

Peroxisome proliferator-activated receptor-gamma regulates the expression of alveolar macrophage macrophage colony-stimulating factor

Macrophage CSF (M-CSF) regulates monocyte differentiation, activation, and foam cell formation. We have observed that it is elevated in human pulmonary alveolar proteinosis (PAP) and in the GM-CSF knockout mouse, a murine model for PAP. A potential regulator of M-CSF, peroxisome proliferator-activated receptor-gamma (PPARgamma), is severely deficient in both human PAP and the GM-CSF knockout mouse. To investigate the role of PPARgamma in alveolar macrophage homeostasis, we generated myeloid-specific PPARgamma knockout mice using the Lys-Cre method to knock out the floxed PPARgamma gene. Similar to the GM-CSF-deficient mouse, absence of alveolar macrophage PPARgamma resulted in development of lung pathology resembling PAP in 16-wk-old mice, along with excess M-CSF gene expression and secretion. In ex vivo wild-type alveolar macrophages, we observed that M-CSF itself is capable of inducing foam cell formation similar to that seen in PAP. Overexpression of PPARgamma prevented LPS-stimulated M-CSF production in RAW 264.7 cells, an effect that was abrogated by a specific PPARgamma antagonist, GW9662. Use of proteasome inhibitor, MG-132 or a PPARgamma agonist, pioglitazone, prevented LPS-mediated M-CSF induction. Using chromatin immunoprecipitation, we found that PPARgamma is capable of regulating M-CSF through transrepression of NF-kappaB binding at the promoter. Gel-shift assay experiments confirmed that pioglitazone is capable of blocking NF-kappaB binding. Taken together, these data suggest that M-CSF is an important mediator of alveolar macrophage homeostasis, and that transcriptional control of M-CSF production is regulated by NF-kappaB and PPARgamma.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background

The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks.

Results

Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes.

Conclusion

Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Prediction of synergistic transcription factors by function conservation

A new strategy is proposed for identifying synergistic transcription factors by function conservation, leading to the identification of 51 homotypic transcription-factor combinations.

Background

Previous methods employed for the identification of synergistic transcription factors (TFs) are based on either TF enrichment from co-regulated genes or phylogenetic footprinting. Despite the success of these methods, both have limitations.

Results

We propose a new strategy to identify synergistic TFs by function conservation. Rather than aligning the regulatory sequences from orthologous genes and then identifying conserved TF binding sites (TFBSs) in the alignment, we developed computational approaches to implement the novel strategy. These methods include combinatorial TFBS enrichment utilizing distance constraints followed by enrichment of overlapping orthologous genes from human and mouse, whose regulatory sequences contain the enriched TFBS combinations. Subsequently, integration of function conservation from both TFBS and overlapping orthologous genes was achieved by correlation analyses. These techniques have been used for genome-wide promoter analyses, which have led to the identification of 51 homotypic TF combinations; the validity of these approaches has been exemplified by both known TF-TF interactions and function coherence analyses. We further provide computational evidence that our novel methods were able to identify synergistic TFs to a much greater extent than phylogenetic footprinting.

Conclusion

Function conservation based on the concordance of combinatorial TFBS enrichment along with enrichment of overlapping orthologous genes has been proven to be a successful means for the identification of synergistic TFs. This approach avoids the limitations of phylogenetic footprinting as it does not depend upon sequence alignment. It utilizes existing gene annotation data, such as those available in GO, thus providing an alternative method for functional TF discovery and annotation.

High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae

A novel approach for identifying condition-specific regulatory modules in yeast reveals functionally distinct coregulated submodules.

We present an approach for identifying condition-specific regulatory modules by using separate units of gene expression profiles along with ChIP-chip and motif data from Saccharomyces cerevisiae. By investigating the unique and common features of the obtained condition-specific modules, we detected several important properties of transcriptional network reorganization. Our approach reveals the functionally distinct coregulated submodules embedded in a coexpressed gene module and provides an effective method for identifying various condition-specific regulatory events at high resolution.

Computational identification of combinatorial regulation and transcription factor binding sites

A number of computational methods have been used to unravel the core mechanisms governing the regulation of gene expression, but these techniques examine only portions of the genetic regulatory mechanism. For example, some studies have failed to include the combined action of multiple transcription factors (TFs) or the importance of TF binding constraints (i.e., the binding position and orientation), while others have examined combinations of only 2 or 3 TFs. Thus, we sought to develop a new method for identifying regulatory modules in yeast, using an algorithm that includes all combinations of TFs plus a number of binding constraints when identifying target genes. We successfully developed a computational method for using microarray and TF-DNA interaction data to identify regulatory modules. All possible combinations of yeast TFs and various binding constraints were tested to identify regulatory modules. Within the identified modules, target genes were found to have common binding constraints such as fixed binding regions and orientations for each TF. Moreover, targets showed similar mRNA expression profiles and high functional coherence. Our novel approach, which accounts for both combined actions of TFs and their binding constraints, can be used to identify target genes and reliably predict regulatory modules over a broad range of functional categories. Complete results and additional information are available online at http://bisl. kaist.ac.kr/~dhlee/comModule/index.html.

LICORN: learning cooperative regulation networks from gene expression data

MOTIVATION

One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels.

RESULTS

We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets.

AVAILABILITY

http://www.lri.fr/~elati/licorn.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data

MOTIVATION

Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability.

RESULTS

We describe a novel method for predicting protein-chemical interaction using SVM. We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions.

AVAILABILITY

Available on request from the authors.

Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast

Studies on various model systems have shown that a relatively small number of transcription factors can set up strikingly complex spatial and temporal patterns of gene expression. This is achieved mainly by means of combinatorial or differential gene regulation, i.e. regulation of a gene by two or more transcription factors simultaneously or under different conditions. While a number of specific molecular details of the mechanisms of combinatorial regulation have emerged, our understanding of the general principles of combinatorial regulation on a genomic scale is still limited. In this work, we approach this problem by using the largest assembled transcriptional regulatory network for yeast. A specific network transformation procedure was used to obtain the co-regulatory network describing the set of all significant associations among transcription factors in regulating common target genes. Analysis of the global properties of the co-regulatory network suggested the presence of two classes of regulatory hubs: (i) those that make many co-regulatory associations, thus serving as integrators of disparate cellular processes; and (ii) those that make few co-regulatory associations, and thereby specifically regulate one or a few major cellular processes. Investigation of the local structure of the co-regulatory network revealed a significantly higher than expected modular organization, which might have emerged as a result of selection by functional constraints. These constraints probably emerge from the need for extensive modular backup and the requirement to integrate transcriptional inputs of multiple distinct functional systems. We then explored the transcriptional control of three major regulatory systems (ubiquitin signaling, protein kinase and transcriptional regulation systems) to understand specific aspects of their upstream control. As a result, we observed that ubiquitin E3 ligases are regulated primarily by unique transcription factors, whereas E1 and E2 enzymes share common transcription factors to a much greater extent. This suggested that the deployment of E3s unique to specific functional contexts may be mediated significantly at the transcriptional level. Likewise, we were able to uncover evidence for much higher upstream transcription control of transcription factors themselves, in comparison to components of other regulatory systems. We believe that the results presented here might provide a framework for testing the role of co-regulatory associations in eukaryotic transcriptional control.

Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae

Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein-protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein-protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.