Identifying cooperativity among transcription factors controlling the cell cycle in yeast

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

A systematic study of HIF1A cofactors in hypoxic cancer cells

Hypoxia inducible factor 1 alpha (HIF1A) is a transcription factor (TF) that forms highly structural and functional protein-protein interactions with other TFs to promote gene expression in hypoxic cancer cells. However, despite the importance of these TF-TF interactions, we still lack a comprehensive view of many of the TF cofactors involved and how they cooperate. In this study, we systematically studied HIF1A cofactors in eight cancer cell lines using the computational motif mining tool, SIOMICS, and discovered 201 potential HIF1A cofactors, which included 21 of the 29 known HIF1A cofactors in public databases. These 201 cofactors were statistically and biologically significant, with 19 of the top 37 cofactors in our study directly validated in the literature. The remaining 18 were novel cofactors. These discovered cofactors can be essential to HIF1A's regulatory functions and may lead to the discovery of new therapeutic targets in cancer treatment.

TFSyntax: a database of transcription factors binding syntax in mammalian genomes

In mammals, transcriptional factors (TFs) drive gene expression by binding to regulatory elements in a cooperative manner. Deciphering the rules of such cooperation is crucial to obtain a full understanding of cellular homeostasis and development. Although this is a long-standing topic, there is no comprehensive database for biologists to access the syntax of TF binding sites. Here we present TFSyntax (https://tfsyntax.zhaopage.com), a database focusing on the arrangement of TF binding sites. TFSyntax maps the binding motif of 1299 human TFs and 890 mouse TFs across 382 cells and tissues, representing the most comprehensive TF binding map to date. In addition to location, TFSyntax defines motif positional preference, density and colocalization within accessible elements. Powered by a series of functional modules based on web interface, users can freely search, browse, analyze, and download data of interest. With comprehensive characterization of TF binding syntax across distinct tissues and cell types, TFSyntax represents a valuable resource and platform for studying the mechanism of transcriptional regulation and exploring how regulatory DNA variants cause disease.

Anthocyanins accumulation analysis of correlated genes by metabolome and transcriptome in green and purple peppers (Capsicum annuum)

BACKGROUND

In order to clarify the the molecular mechanism of anthocyanin accumulation in green and purple fruits of pepper using metabolomics and transcriptomics,to identify different anthocyanin metabolites,and to analyze the differentially expressed genes involved in anthocyanin biosynthesis..

RESULTS

We analyzed the anthocyanin metabolome and transcriptome data of the fruits of 2 purple pepper and 1 green pepper. A total of 5 anthocyanin metabolites and 2224 differentially expressed genes were identified between the green and purple fruits of pepper. Among the 5 anthocyanin metabolites,delphin chloride was unique to purple pepper fruits,which is the mainly responsible for the purple fruit color of pepper. A total of 59 unigenes encoding 7 enzymes were identified as candidate genes involved in anthocyanin biosynthesis in pepper fruit. The six enzymes (PAL,C4H,CHI,DFR,ANS,UFGT) had higher expression levels except the F3H gene in purple compared with green fruits. In addition,seven transcription factors were also found in this study. These transcription factors may contribute to anthocyanin metabolite biosynthesis in the fruits of pepper. One of differentially expressed gene novel.2098 was founded. It was not annotated in NCBI. Though blast analysis we preliminarily considered that this gene related to MYB transcription factor and was involved in anthocyanin biosynthesis in pepper fruit.

CONCLUSIONS

Overall, the results of this study provide useful information for understanding anthocyanin accumulation and the molecular mechanism of anthocyanin biosynthesis in peppers.

Bayesian regularization for a nonstationary Gaussian linear mixed effects model

In omics experiments, estimation and variable selection can involve thousands of proteins/genes observed from a relatively small number of subjects. Many regression regularization procedures have been developed for estimation and variable selection in such high-dimensional problems. However, approaches have predominantly focused on linear regression models that ignore correlation arising from long sequences of repeated measurements on the outcome. Our work is motivated by the need to identify proteomic biomarkers that improve the prediction of rapid lung-function decline for individuals with cystic fibrosis (CF) lung disease. We extend four Bayesian penalized regression approaches for a Gaussian linear mixed effects model with nonstationary covariance structure to account for the complicated structure of longitudinal lung function data while simultaneously estimating unknown parameters and selecting important protein isoforms to improve predictive performance. Different types of shrinkage priors are evaluated to induce variable selection in a fully Bayesian framework. The approaches are studied with simulations. We apply the proposed method to real proteomics and lung-function outcome data from our motivating CF study, identifying a set of relevant clinical/demographic predictors and a proteomic biomarker for rapid decline of lung function. We also illustrate the methods on CD4 yeast cell-cycle genomic data, confirming that the proposed method identifies transcription factors that have been highlighted in the literature for their importance as cell cycle transcription factors.

Robust approach for variable selection with high dimensional longitudinal data analysis

This article proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates p n increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging p n " are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.

Reciprocal stabilization of transcription factor binding integrates two signaling pathways to regulate fission yeast fbp1 transcription

Transcriptional regulation, a pivotal biological process by which cells adapt to environmental fluctuations, is achieved by the binding of transcription factors to target sequences in a sequence-specific manner. However, how transcription factors recognize the correct target from amongst the numerous candidates in a genome has not been fully elucidated. We here show that, in the fission-yeast fbp1 gene, when transcription factors bind to target sequences in close proximity, their binding is reciprocally stabilized, thereby integrating distinct signal transduction pathways. The fbp1 gene is massively induced upon glucose starvation by the activation of two transcription factors, Atf1 and Rst2, mediated via distinct signal transduction pathways. Atf1 and Rst2 bind to the upstream-activating sequence 1 region, carrying two binding sites located 45 bp apart. Their binding is reciprocally stabilized due to the close proximity of the two target sites, which destabilizes the independent binding of Atf1 or Rst2. Tup11/12 (Tup-family co-repressors) suppress independent binding. These data demonstrate a previously unappreciated mechanism by which two transcription-factor binding sites, in close proximity, integrate two independent-signal pathways, thereby behaving as a hub for signal integration.

Lack of CpG islands in human unitary pseudogenes and its implication

CpG islands (CGIs) are aggregation of CpG dinucleotides in the promoters of mammalian genes. These CGIs are present in almost all the housekeeping genes and some tissue-specific genes in the mammalian genome. Extensive research has been done on the prevalence and role of CGIs in protein-coding genes. However, little is known about CGIs in pseudogenes. In the current research project, we focused on CGIs in three main classes of pseudogenes e.g., duplicated pseudogenes (DPGs), processed pseudogenes (PPGs), and unitary pseudogenes (UPGs). We discovered a predominant absence of CGIs in the promoters of all three pseudogenes. We also compared the CGI profile of these pseudogenes with their parent genes and found that unitary pseudogenes (UPGs) differ from the DPGs and PPGs in the sense that in the latter, lack of CGIs is a consequential event while in UPGs, this lack of CGIs in their promoters is not a result of pseudogenization process. We also discussed the implication of the results obtained from this comparison. To our knowledge, this is the first-ever study highlighting this aspect of UPGs throwing new insights into the evolution of genome in general and especially in the context of pseudogenes.

New insights on key genes involved in drought stress response of barley: gene networks reconstruction, hub, and promoter analysis

Background

Barley (Hordeum vulgare L.) is one of the most important cereals worldwide. Although this crop is drought-tolerant, water deficiency negatively affects its growth and production. To detect key genes involved in drought tolerance in barley, a reconstruction of the related gene network and discovery of the hub genes would help. Here, drought-responsive genes in barley were collected through analysis of the available microarray datasets (− 5 ≥ Fold change ≥ 5, adjusted p value ≤ 0.05). Protein-protein interaction (PPI) networks were reconstructed.

Results

The hub genes were identified by Cytoscape software using three Cyto-hubba algorithms (Degree, Closeness, and MNC), leading to the identification of 17 and 16 non-redundant genes at vegetative and reproductive stages, respectively. These genes consist of some transcription factors such as HvVp1, HvERF4, HvFUS3, HvCBF6, DRF1.3, HvNAC6, HvCO5, and HvWRKY42, which belong to AP2, NAC, Zinc-finger, and WRKY families. In addition, the expression pattern of four hub genes was compared between the two studied cultivars, i.e., “Yousef” (drought-tolerant) and “Morocco” (susceptible). The results of real-time PCR revealed that the expression patterns corresponded well with those determined by the microarray. Also, promoter analysis revealed that some TF families, including AP2, NAC, Trihelix, MYB, and one modular (composed of two HD-ZIP TFs), had a binding site in 85% of promoters of the drought-responsive genes and of the hub genes in barley.

Conclusions

The identified hub genes, especially those from AP2 and NAC families, might be among key TFs that regulate drought-stress response in barley and are suggested as promising candidate genes for further functional analysis.

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.

Ultra high-dimensional semiparametric longitudinal data analysis

As ultra high-dimensional longitudinal data are becoming ever more apparent in fields such as public health and bioinformatics, developing flexible methods with a sparse model is of high interest. In this setting, the dimension of the covariates can potentially grow exponentially as exp ( n 1 / 2 ) with respect to the number of clusters n. We consider a flexible semiparametric approach, namely, partially linear single-index models, for ultra high-dimensional longitudinal data. Most importantly, we allow not only the partially linear covariates but also the single-index covariates within the unknown flexible function estimated nonparametrically to be ultra high dimensional. Using penalized generalized estimating equations, this approach can capture correlation within subjects, can perform simultaneous variable selection and estimation with a smoothly clipped absolute deviation penalty, and can capture nonlinearity and potentially some interactions among predictors. We establish asymptotic theory for the estimators including the oracle property in ultra high dimension for both the partially linear and nonparametric components, and we present an efficient algorithm to handle the computational challenges. We show the effectiveness of our method and algorithm via a simulation study and a yeast cell cycle gene expression data.

Contribution of anthocyanin pathways to fruit flesh coloration in pitayas

BACKGROUND

Color formation in Hylocereus spp. (pitayas) has been ascribed to the accumulation of betalains. However, several studies have reported the presence of anthocyanins in pitaya fruit and their potential role in color formation has not yet been explored. In this study, we profiled metabolome and transcriptome in fruit of three cultivars with contrasting flesh colors (red, pink and white) to investigate their nutritional quality and the mechanism of color formation involving anthocyanins.

RESULTS

Results revealed that pitaya fruit is enriched in amino acid, lipid, carbohydrate, polyphenols, vitamin and other bioactive components with significant variation among the three cultivars. Anthocyanins were detected in the fruit flesh and accumulation levels of Cyanidin 3-glucoside, Cyanidin 3-rutinoside, Delphinidin 3-O-(6-O-malonyl)-beta-glucoside-3-O-beta-glucoside and Delphinidin 3-O-beta-D-glucoside 5-O-(6-coumaroyl-beta-D-glucoside) positively correlated with the reddish coloration. Transcriptome data showed that the white cultivar tends to repress the anthocyanin biosynthetic pathway and divert substrates to other competing pathways. This perfectly contrasted with observations in the red cultivar. The pink cultivar however seems to keep a balance between the anthocyanin biosynthetic pathway and the competing pathways. We identified several active transcription factors of the MYB and bHLH families which can be further investigated as potential regulators of the anthocyanin biosynthetic genes.

CONCLUSIONS

Collectively, our results suggest that anthocyanins partly contribute to color formation in pitaya fruit. Future studies aiming at manipulating the biosynthetic pathways of anthocyanins and betalains will better clarify the exact contribution of each pathway in color formation in pitayas. This will facilitate efforts to improve pitaya fruit quality and appeal.

The Molecular Mechanism of Perillaldehyde Inducing Cell Death in Aspergillus flavus by Inhibiting Energy Metabolism Revealed by Transcriptome Sequencing

Perillaldehyde (PAE), an essential oil in Perilla plants, serves as a safe flavor ingredient in foods, and shows an effectively antifungal activity. Reactive oxygen species (ROS) accumulation in Aspergillus flavus plays a critical role in initiating a metacaspase-dependent apoptosis. However, the reason for ROS accumulation in A. flavus is not yet clear. Using transcriptome sequencing of A. flavus treated with different concentrations of PAE, our data showed that the ROS accumulation might have been as a result of an inhibition of energy metabolism with less production of reducing power. By means of GO and KEGG enrichment analysis, we screened four key pathways, which were divided into two distinct groups: a downregulated group that was made up of the glycolysis and pentose phosphate pathway, and an upregulated group that consisted of MAPK signaling pathway and GSH metabolism pathway. The inhibition of dehydrogenase gene expression in two glycometabolism pathways might play a crucial role in antifungal mechanism of PAE. Also, in our present study, we systematically showed a gene interaction network of how genes of four subsets are effected by PAE stress on glycometabolism, oxidant damage repair, and cell cycle control. This research may contribute to explaining an intrinsic antifungal mechanism of PAE against A. flavus.

Molecular Analysis of UV-C Induced Resveratrol Accumulation in Polygonum cuspidatum Leaves

Resveratrol is one of the most studied plant secondary metabolites owing to its numerous health benefits. It is accumulated in some plants following biotic and abiotic stress pressures, including UV-C irradiation. Polygonum cuspidatum represents the major natural source of concentrated resveratrol but the underlying mechanisms as well as the effects of UV-C irradiation on resveratrol content have not yet been documented. Herein, we found that UV-C irradiation significantly increased by 2.6-fold and 1.6-fold the resveratrol content in irradiated leaf samples followed by a dark incubation for 6 h and 12 h, respectively, compared to the untreated samples. De novo transcriptome sequencing and assembly resulted into 165,013 unigenes with 98 unigenes mapped to the resveratrol biosynthetic pathway. Differential expression analysis showed that P.cuspidatum strongly induced the genes directly involved in the resveratrol synthesis, including phenylalanine ammonia-lyase, cinnamic acid 4-hydroxylase, 4-coumarate-CoA ligase and stilbene synthase (STS) genes, while strongly decreased the chalcone synthase (CHS) genes after exposure to UV-C. Since CHS and STS share the same substrate, P. cuspidatum tends to preferentially divert the substrate to the resveratrol synthesis pathway under UV-C treatment. We identified several members of the MYB, bHLH and ERF families as potential regulators of the resveratrol biosynthesis genes.

Synergistic action of the transcription factors Krüppel homolog 1 and Hairy in juvenile hormone/Methoprene-tolerant-mediated gene-repression in the mosquito Aedes aegypti

Arthropod-specific juvenile hormones control numerous essential functions in development and reproduction. In the dengue-fever mosquito Aedes aegypti, in addition to its role in immature stages, juvenile hormone III (JH) governs post-eclosion (PE) development in adult females, a phase required for competence acquisition for blood feeding and subsequent egg maturation. During PE, JH through its receptor Methoprene-tolerant (Met) regulate the expression of many genes, causing either activation or repression. Met-mediated gene repression is indirect, requiring involvement of intermediate repressors. Hairy, which functions downstream of Met in the JH gene-repression hierarchy, is one such factor. Krüppel-homolog 1, a zinc-finger transcriptional factor, is directly regulated by Met and has been implicated in both activation and repression of JH-regulated genes. However, the interaction between Hairy and Kr-h1 in the JH-repression hierarchy is not well understood. Our RNAseq-based transcriptomic analysis of the Kr-h1-depleted mosquito fat body revealed that 92% of Kr-h1 repressed genes are also repressed by Met, supporting the existence of a hierarchy between Met and Kr-h1 as previously demonstrated in various insects. Notably, 130 genes are co-repressed by both Kr-h1 and Hairy, indicating regulatory complexity of the JH-mediated PE gene repression. A mosquito Kr-h1 binding site in genes co-regulated by this factor and Hairy was identified computationally. Moreover, this was validated using electrophoretic mobility shift assays. A complete phenocopy of the effect of Met RNAi depletion on target genes could only be observed after Kr-h1 and Hairy double RNAi knockdown, suggesting a synergistic action between these two factors in target gene repression. This was confirmed using a cell-culture-based luciferase reporter assay. Taken together, our results indicate that Hairy and Kr-h1 not only function as intermediate downstream factors, but also act together in a synergistic fashion in the JH/Met gene repression hierarchy.

Juvenile hormone (JH) plays an essential role in preparing Aedes aegypti female mosquitoes for blood feeding, egg development, and pathogen transmission. JH acting through its receptor Methoprene-tolerant (Met) regulates the expression of large gene cohorts. JH mediated gene repression, unlike activation that is directly mediated by Met, is indirect and requires intermediate transcriptional repressors Hairy and Krüppel-homolog 1 (Kr-h1). Here, we demonstrate that Hairy and Kr-h1 can act synergistically in the JH-Met gene repression pathway in Aedes female mosquitoes. These interact directly with regulatory regions of the genes that have both Hairy and Kr-h1 binding sites. Thus, this study has significantly advanced our understanding of the complexity of the JH-mediated gene expression pathway. This research yields valuable information about the JH control of reproductive development of the mosquito A. aegypti, one of the most important vectors of human diseases.

Differential Regulation of Anthocyanins in Green and Purple Turnips Revealed by Combined De Novo Transcriptome and Metabolome Analysis

Purple turnip Brassica rapa ssp. rapa is highly appreciated by consumers but the metabolites and molecular mechanisms underlying the root skin pigmentation remain open to study. Herein, we analyzed the anthocyanin composition in purple turnip (PT) and green turnip (GT) at five developmental stages. A total of 21 anthocyanins were detected and classified into the six major anthocynanin aglycones. Distinctly, PT contains 20 times higher levels of anthocyanins than GT, which explain the difference in the root skin pigmentation. We further sequenced the transcriptomes and analyzed the differentially expressed genes between the two turnips. We found that PT essentially diverts dihydroflavonols to the biosynthesis of anthocyanins over flavonols biosynthesis by strongly down-regulating one flavonol synthase gene, while strikingly up-regulating dihydroflavonol 4-reductase (DFR), anthocyanidin synthase and UDP-glucose: flavonoid-3-O-glucosyltransferase genes as compared to GT. Moreover, a nonsense mutation identified in the coding sequence of the DFR gene may lead to a nonfunctional protein, adding another hurdle to the accumulation of anthocyanin in GT. We also uncovered several key members of MYB, bHLH and WRKY families as the putative main drivers of transcriptional changes between the two turnips. Overall, this study provides new tools for modifying anthocyanin content and improving turnip nutritional quality.

p.E95K mutation in Indian hedgehog causing brachydactyly type A1 impairs IHH/Gli1 downstream transcriptional regulation

Background

Brachydactyly type A1 (BDA1, OMIM 112500) is a rare inherited malformation characterized primarily by shortness or absence of middle bones of fingers and toes. It is the first recorded disorder of the autosomal dominant Mendelian trait. Indian hedgehog (IHH) gene is closely associated with BDA1, which was firstly mapped and identified in Chinese families in 2000. Previous studies have demonstrated that BDA1-related mutant IHH proteins affected interactions with its receptors and impaired IHH signaling. However, how the altered signaling pathway affects downstream transcriptional regulation remains unclear.

Results

Based on the mouse C3H10T1/2 cell model for IHH signaling activation, two recombinant human IHH-N proteins, including a wild type protein (WT, amino acid residues 28–202) and a mutant protein (MT, p.E95k), were analyzed. We identified 347, 47 and 4 Gli1 binding sites in the corresponding WT, MT and control group by chromatin immunoprecipitation and the overlapping of these three sets was poor. The putative cis regulated genes in WT group were enriched in sensory perception and G-protein coupled receptor-signaling pathway. On the other hand, putative cis regulated genes were enriched in Runx2-related pathways in MT group. Differentially expressed genes in WT and MT groups indicated that the alteration of mutant IHH signaling involved cell-cell signaling and cellular migration. Cellular assay of migration and proliferation validated that the mutant IHH signaling impaired these two cellular functions.

Conclusions

In this study, we performed integrated genome-wide analyses to characterize differences of IHH/Gli1 downstream regulation between wild type IHH signaling and the E95K mutant signaling. Based on the cell model, our results demonstrated that the E95K mutant signaling altered Gli1-DNA binding pattern, impaired downstream gene expressions, and leaded to weakened cellular proliferation and migration. This study may help to deepen the understanding of pathogenesis of BDA1 and the role of IHH signaling in chondrogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12863-018-0697-5) contains supplementary material, which is available to authorized users.

Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data

Background

Interactions among transcription factors (TFs) and histone modifications (HMs) play an important role in the precise regulation of gene expression. The context specificity of those interactions and further its dynamics in normal and disease remains largely unknown. Recent development in genomics technology enables transcription profiling by RNA-seq and protein’s binding profiling by ChIP-seq. Integrative analysis of the two types of data allows us to investigate TFs and HMs interactions both from the genome co-localization and downstream target gene expression.

Results

We propose a integrative pipeline to explore the co-localization of 55 TFs and 11 HMs and its dynamics in human GM12878 and K562 by matched ChIP-seq and RNA-seq data from ENCODE. We classify TFs and HMs into three types based on their binding enrichment around transcription start site (TSS). Then a set of statistical indexes are proposed to characterize the TF-TF and TF-HM co-localizations. We found that Rad21, SMC3, and CTCF co-localized across five cell lines. High resolution Hi-C data in GM12878 shows that they associate most of the Hi-C peak loci with a specific CTCF-motif “anchor” and supports that CTCF, SMC3, and RAD2 co-localization serves important role in 3D chromatin structure. Meanwhile, 17 TF-TF pairs are highly dynamic between GM12878 and K562. We then build SVM models to correlate high and low expression level of target genes with TF binding and HM strength. We found that H3k9ac, H3k27ac, and three TFs (ELF1, TAF1, and POL2) are predictive with the accuracy about 85~92%.

Conclusion

We propose a pipeline to analyze the co-localization of TF and HM and their dynamics across cell lines from ChIP-seq, and investigate their regulatory potency by RNA-seq. The integrative analysis of two level data reveals new insight for the cooperation of TFs and HMs and is helpful in understanding cell line specificity of TF/HM interactions.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5278-5) contains supplementary material, which is available to authorized users.

Multivalency regulates activity in an intrinsically disordered transcription factor

The transcription factor ASCIZ (ATMIN, ZNF822) has an unusually high number of recognition motifs for the product of its main target gene, the hub protein LC8 (DYNLL1). Using a combination of biophysical methods, structural analysis by NMR and electron microscopy, and cellular transcription assays, we developed a model that proposes a concerted role of intrinsic disorder and multiple LC8 binding events in regulating LC8 transcription. We demonstrate that the long intrinsically disordered C-terminal domain of ASCIZ binds LC8 to form a dynamic ensemble of complexes with a gradient of transcriptional activity that is inversely proportional to LC8 occupancy. The preference for low occupancy complexes at saturating LC8 concentrations with both human and Drosophila ASCIZ indicates that negative cooperativity is an important feature of ASCIZ-LC8 interactions. The prevalence of intrinsic disorder and multivalency among transcription factors suggests that formation of heterogeneous, dynamic complexes is a widespread mechanism for tuning transcriptional regulation.

A novel statistical approach for identification of the master regulator transcription factor

BACKGROUND

Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.

METHODS

We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.

RESULTS

In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software .

CONCLUSION

We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.

Network-Guided Key Gene Discovery for a Given Cellular Process

Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function. Here we review and compare the current approaches in predicting key genes, which have no chances to stand out by classic differential expression analysis, from gene-regulatory, protein-protein interaction, or gene expression correlation networks. We elaborate these network-based approaches mainly in the context of immunology and infection, and urge more usage of correlation network-based predictions. Such network-based key gene discovery approaches driven by information-enriched 'omics' data should be very useful for systematic key gene discoveries for any given biochemical process or cellular function, and also valuable for novel drug target discovery and novel diagnostic, prognostic and therapeutic-efficiency marker prediction for a specific disease or disorder.

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/

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

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/

Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition

Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

A Computational Method for Identifying Yeast Cell Cycle Transcription Factors

The eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes. Here, we describe a computational method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor-binding site (TFBS), and cell cycle gene expression data. For each identified cell cycle TF, our method also assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. Moreover, our method can identify novel cell cycle-regulated genes as a by-product.

TRRUST: a reference database of human transcriptional regulatory interactions

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.

Coherent regulation in yeast's cell-cycle network

We define a measure of coherent activity for gene regulatory networks, a property that reflects the unity of purpose between the regulatory agents with a common target. We propose that such harmonious regulatory action is desirable under a demand for energy efficiency and may be selected for under evolutionary pressures. We consider two recent models of the cell-cycle regulatory network of the yeast, Saccharomyces cerevisiae as a case study and calculate their degree of coherence. A comparison with random networks of similar size and composition reveals that the yeast's cell-cycle regulation is wired to yield an exceptionally high level of coherent regulatory activity. We also investigate the mean degree of coherence as a function of the network size, connectivity and the fraction of repressory/activatory interactions.

Loregic: a method to characterize the cooperative logic of regulatory factors

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

Gene expression is controlled by various gene regulatory factors. Those factors work cooperatively forming a complex regulatory circuit genome wide. Corruptions of regulatory cooperativity may lead to abnormal gene expression activity such as cancer. Traditional experimental methods, however, can only identify small-scale regulatory activity. Thus, to systematically understand the cooperativity between and among different types of regulatory factors, we need the efficient and systematic computational methods. Regulatory circuits have been suggested to behave analogously to the electronic circuits in which a wide variety of electronic elements work coordinately to function correctly. Recently, an increasing amount of next generation sequencing data provides a great resource to study regulatory activity. Thus, we developed a general-purpose computational method using logic-circuit models from electronics and applied it to a human leukemia dataset, identifying the genome-wide cooperativity of transcription factors and microRNAs.

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.

Profiling the transcription factor regulatory networks of human cell types

Neph et al. (2012) (Circuitry and dynamics of human transcription factor regulatory networks. Cell, 150: 1274-1286) reported the transcription factor (TF) regulatory networks of 41 human cell types using the DNaseI footprinting technique. This provides a valuable resource for uncovering regulation principles in different human cells. In this paper, the architectures of the 41 regulatory networks and the distributions of housekeeping and specific regulatory interactions are investigated. The TF regulatory networks of different human cell types demonstrate similar global three-layer (top, core and bottom) hierarchical architectures, which are greatly different from the yeast TF regulatory network. However, they have distinguishable local organizations, as suggested by the fact that wiring patterns of only a few TFs are enough to distinguish cell identities. The TF regulatory network of human embryonic stem cells (hESCs) is dense and enriched with interactions that are unseen in the networks of other cell types. The examination of specific regulatory interactions suggests that specific interactions play important roles in hESCs.

PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps

Background

The progress in mapping RNA-protein and RNA-RNA interactions at the transcriptome-wide level paves the way to decipher possible combinatorial patterns embedded in post-transcriptional regulation of gene expression.

Results

Here we propose an innovative computational tool to extract clusters of mRNA trans-acting co-regulators (RNA binding proteins and non-coding RNAs) from pairwise interaction annotations. In addition the tool allows to analyze the binding site similarity of co-regulators belonging to the same cluster, given their positional binding information. The tool has been tested on experimental collections of human and yeast interactions, identifying modules that coordinate functionally related messages.

Conclusions

This tool is an original attempt to uncover combinatorial patterns using all the post-transcriptional interaction data available so far. PTRcombiner is available at http://disi.unitn.it/~passerini/software/PTRcombiner/.

Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression

During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min during three cell cycle periods of the yeast Saccharomyces cerevisiae. A novel statistical model identified 479 genes that show periodic changes in mRNA synthesis and generally also periodic changes in their mRNA degradation rates. Peaks of mRNA degradation generally follow peaks of mRNA synthesis, resulting in sharp and high peaks of mRNA levels at defined times during the cell cycle. Whereas the timing of mRNA synthesis is set by upstream DNA motifs and their associated transcription factors (TFs), the synthesis rate of a periodically expressed gene is apparently set by its core promoter.

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.

JunD/AP1 regulatory network analysis during macrophage activation in a rat model of crescentic glomerulonephritis

Background

Function and efficiency of a transcription factor (TF) are often modulated by interactions with other proteins or TFs to achieve finely tuned regulation of target genes. However, complex TF interactions are often not taken into account to identify functionally active TF-targets and characterize their regulatory network. Here, we have developed a computational framework for integrated analysis of genome-wide ChIP-seq and gene expression data to identify the functional interacting partners of a TF and characterize the TF-driven regulatory network. We have applied this methodology in a rat model of macrophage dependent crescentic glomerulonephritis (Crgn) where we have previously identified JunD as a TF gene responsible for enhanced macrophage activation associated with susceptibility to Crgn in the Wistar-Kyoto (WKY) strain.

Results

To evaluate the regulatory effects of JunD on its target genes, we analysed data from two rat strains (WKY and WKY.LCrgn2) that show 20-fold difference in their JunD expression in macrophages. We identified 36 TFs interacting with JunD/Jun and JunD/ATF complexes (i.e., AP1 complex), which resulted in strain-dependent gene expression regulation of 1,274 target genes in macrophages. After lipopolysaccharide (LPS) stimulation we found that 2.4 fold more JunD/ATF-target genes were up-regulated as compared with JunD/Jun-target genes. The enriched 314 genes up-regulated by AP1 complex during LPS stimulation were most significantly enriched for immune response (P = 6.9 × 10^-4) and antigen processing and presentation functions (P = 2.4 × 10^-5), suggesting a role for these genes in macrophage LPS-stimulated activation driven by JunD interaction with Jun/ATF.

Conclusions

In summary, our integrated analyses revealed a large network of TFs interacting with JunD and their regulated targets. Our data also suggest a previously unappreciated contribution of the ATF complex to JunD-mediated mechanisms of macrophage activation in a rat model of crescentic glomerulonephritis.

Statistical significance of combinatorial regulations

More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the "limitless arity multiple-testing procedure" (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data.

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147 bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin.

Analysing microarray data in drug discovery using systems biology

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.

Role of the repressor Oaf3p in the recruitment of transcription factors and chromatin dynamics during the oleate response

Cellular responses to environmental stimuli are mediated by the co-ordinated activity of multiple control mechanisms, which result in the dynamics of cell function. Communication between different levels of regulation is central for this adaptability. The present study focuses on the interplay between transcriptional regulators and chromatin modifiers to co-operatively regulate transcription in response to a fatty acid stimulus. The genes involved in the β-oxidation of fatty acids are highly induced in response to fatty acid exposure by four gene-specific transcriptional regulators, Oaf (oleate-activated transcription factor) 1p, Pip2p (peroxisome induction pathway 2), Oaf3p and Adr1p (alcohol dehydrogenase regulator 1). In the present study, we examine the interplay of these factors with Htz1p (histone variant H2A.Z) in regulating POT1 (peroxisomal oxoacyl thiolase 1) encoding peroxisomal thiolase and PIP2 encoding the autoregulatory oleate-specific transcriptional activator. Temporal resolution of ChIP (chromatin immunoprecipitation) data indicates that Htz1p is required for the timely removal of the transcriptional repressor Oaf3p during oleate induction. Adr1p plays an important role in the assembly of Htz1p-containing nucleosomes on the POT1 and PIP2 promoters. We also investigated the function of the uncharacterized transcriptional inhibitor Oaf3p. Deletion of OAF3 led to faster POT1 mRNA accumulation than in the wild-type. Most impressively, a highly protected nucleosome structure on the POT1 promoter during activation was observed in the OAF3 mutant cells in response to oleate induction.

A novel unbiased measure for motif co-occurrence predicts combinatorial regulation of transcription

Background

Multiple transcription factors (TFs) are involved in the generation of gene expression patterns, such as tissue-specific gene expression and pleiotropic immune responses. However, how combinations of TFs orchestrate diverse gene expression patterns is poorly understood. Here we propose a new measure for regulatory motif co-occurrence and a new methodology to systematically identify TF pairs significantly co-occurring in a set of promoter sequences.

Results

Initial analyses suggest that non-CpG promoters have a higher potential for combinatorial regulation than CpG island-associated promoters, and that co-occurrences are strongly influenced by motif similarity. We applied our method to large-scale gene expression data from various tissues, and showed how our measure for motif co-occurrence is not biased by motif over-representation. Our method identified, amongst others, the binding motifs of HNF1 and FOXP1 to be significantly co-occurring in promoters of liver/kidney specific genes. Binding sites tend to be positioned proximally to each other, suggesting interactions exist between this pair of transcription factors. Moreover, the binding sites of several TFs were found to co-occur with NF-κB and IRF sites in sets of genes with similar expression patterns in dendritic cells after Toll-like receptor stimulation. Of these, we experimentally verified that CCAAT enhancer binding protein alpha positively regulates its target promoters synergistically with NF-κB.

Conclusions

Both computational and experimental results indicate that the proposed method can clarify TF interactions that could not be observed by currently available prediction methods.

One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data

We present One Hand Clapping (OHC), a method for the detection of condition-specific interactions between transcription factors (TFs) from genome-wide gene activity measurements. OHC is based on a mapping between transcription factors and their target genes. Given a single case–control experiment, it uses a linear regression model to assess whether the common targets of two arbitrary TFs behave differently than expected from the genes targeted by only one of the TFs. When applied to osmotic stress data in S. cerevisiae, OHC produces consistent results across three types of expression measurements: gene expression microarray data, RNA Polymerase II ChIP-chip binding data and messenger RNA synthesis rates. Among the eight novel, condition-specific TF pairs, we validate the interaction between Gcn4p and Arr1p experimentally. We apply OHC to a large gene activity dataset in S. cerevisiae and provide a compendium of condition-specific TF interactions.

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.

Deriving transcriptional programs and functional processes from gene expression databases

MOTIVATION

A system-wide approach to revealing the underlying molecular state of a cell is a long-standing biological challenge. Developed over the last decade, gene expression profiles possess the characteristics of such an assay. They have the capacity to reveal both underlying molecular events as well as broader phenotypes such as clinical outcomes. To interpret these profiles, many gene sets have been developed that characterize biological processes. However, the full potential of these gene sets has not yet been achieved. Since the advent of gene expression databases, many have posited that they can reveal properties of activities that are not evident from individual datasets, analogous to how the expression of a single gene generally cannot reveal the activation of a biological process.

RESULTS

To address this issue, we have developed a high-throughput method to mine gene expression databases for the regulation of gene sets. Given a set of genes, we scored it against each gene expression dataset by looking for enrichment of co-regulated genes relative to an empirical null distribution. After validating the method, we applied it to address two biological problems. First, we deciphered the E2F transcriptional network. We confirmed that true transcriptional targets exhibit a distinct regulatory profile across a database. Second, we leveraged the patterns of regulation across a database of gene sets to produce an automatically generated catalog of biological processes. These demonstrations revealed the power of a global analysis of the data contained within gene expression databases, and the potential for using them to address biological questions.

De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae

MOTIVATION

Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data.

RESULTS

The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.