Identifying combinatorial regulation of transcription factors and binding motifs

NF-κB signaling is the major inflammatory pathway for inducing insulin resistance

Insulin resistance is major factor in the development of metabolic syndrome and type 2 diabetes (T2D). We extracted 430 genes from literature associated with both insulin resistance and inflammation. The highly significant pathways were Toll-like receptor signaling, PI3K-Akt signaling, cytokine-cytokine receptor interaction, pathways in cancer, TNF signaling, and NF-kappa B signaling. Among the 297 common genes in all datasets of various T2D patients' tissues including blood, muscle, liver, pancreas, and adipose tissues, 71% and 60% of these genes were differentially expressed in pancreas (GSE25724) and liver (GSE15653), respectively. A total of 169 genes contain highly conserved motifs for various transcription factors involved in immune response, thereby suggesting coordinated expression. Through co-expression analysis, we obtained three modules. The respective modules had 78, 158, and 55 genes, and TRAF2, HMGA1, and RGS5 as hub genes. Further, we used the BioNSi pathways simulation tool and identified the following five KEGG pathways perturbed in four or more tissues, namely Toll-like receptor signaling pathway, RIG-1-like receptor signaling pathway, pathways in cancer, NF-kappa B signaling pathway, and insulin resistance pathway. The genes NFKBIA and IKBKB are common to all these five pathways. In addition, using the NF-κB computational activation model, we identified that the reversal of NF-κB constitutive activation through overexpression of NFKB1 (P50 homodimer), PPARG, PIAS3 could reduce insulin resistance by almost half of its original value. To conclude, co-expression studies, gene expression network simulation, and NF-κB computational modeling substantiate the causal role of NF-κB pathway in insulin resistance. These results taken together with other published evidence suggests that the TNF-TRAF2-IKBKB-NF-κB axis could be explored as a potential target in combination with available metabolic targets in the management of insulin resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04202-4.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool-Liquid/Liquid Phase Separation, Oligomerization, GO Analysis: A Toolkit for Interaction Data-Based Domain Analysis

Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool-liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein-nucleic acid interaction (PNI) and protein-protein interaction (PPI) analysis based on domain-domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain-motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms.

Cold-responsive transcription factors in Arabidopsis and rice: A regulatory network analysis using array data and gene co-expression network

Plant growth and development can be influenced by cold stress. Responses of plants to cold are regulated in part by transcription factors (TFs) and microRNAs, which their determination would be necessary in comprehension of the corresponding molecular cues. Here, transcriptomes of Arabidopsis and rice were analyzed to computationally determine TFs and microRNAs that are differentially responsive to cold treatment, and their co-expression networks were established. Among 181 Arabidopsis and 168 rice differentially expressed TF genes, 37 (26 novel) were up- and 16 (8 novel) were downregulated. Common TF encoding genes were from ERF, MYB, bHLH, NFY, bZIP, GATA, HSF and WRKY families. NFY A4/C2/A10 were the significant hub TFs in both plants. Phytohormone responsive cis-elements such as ABRE, TGA, TCA and LTR were the common cis-elements in TF promoters. Arabidopsis had more responsive TFs compared to rice possibly due to its greater adaptation to ranges geographical latitudes. Rice had more relevant miRNAs probably because of its bigger genome size. The interacting partners and co-expressed genes were different for the common TFs so that of the downstream regulatory networks and the corresponding metabolic pathways. Identified cold-responsive TFs in (A + R) seemed to be more engaged in energy metabolism esp. photosynthesis, and signal transduction, respectively. At post-transcriptional level, miR5075 showed to target many identified TFs in rice. In comparison, the predictions showed that identified TFs are being targeted by diverse groups of miRNAs in Arabidopsis. Novel TFs, miRNAs and co-expressed genes were introduced as cold-responsive markers that can be harnessed in future studies and development of crop tolerant varieties.

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.

Causal Discovery in High-dimensional, Multicollinear Datasets

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

Prediction and Experimental Validation of a New Salinity-Responsive Cis-Regulatory Element (CRE) in a Tilapia Cell Line

Transcriptional regulation is a major mechanism by which organisms integrate gene x environment interactions. It can be achieved by coordinated interplay between cis-regulatory elements (CREs) and transcription factors (TFs). Euryhaline tilapia (Oreochromis mossambicus) tolerate a wide range of salinity and thus are an appropriate model to examine transcriptional regulatory mechanisms during salinity stress in fish. Quantitative proteomics in combination with the transcription inhibitor actinomycin D revealed 19 proteins that are transcriptionally upregulated by hyperosmolality in tilapia brain (OmB) cells. We searched the extended proximal promoter up to intron1 of each corresponding gene for common motifs using motif discovery tools. The top-ranked motif identified (STREME1) represents a binding site for the Forkhead box TF L1 (FoxL1). STREME1 function during hyperosmolality was experimentally validated by choosing two of the 19 genes, chloride intracellular channel 2 (clic2) and uridine phosphorylase 1 (upp1), that are enriched in STREME1 in their extended promoters. Transcriptional induction of these genes during hyperosmolality requires STREME1, as evidenced by motif mutagenesis. We conclude that STREME1 represents a new functional CRE that contributes to gene x environment interactions during salinity stress in tilapia. Moreover, our results indicate that FoxL1 family TFs are contribute to hyperosmotic induction of genes in euryhaline fish.

Cyclin/Forkhead-mediated coordination of cyclin waves: an autonomous oscillator rationalizing the quantitative model of Cdk control for budding yeast

Networks of interacting molecules organize topology, amount, and timing of biological functions. Systems biology concepts required to pin down 'network motifs' or 'design principles' for time-dependent processes have been developed for the cell division cycle, through integration of predictive computer modeling with quantitative experimentation. A dynamic coordination of sequential waves of cyclin-dependent kinases (cyclin/Cdk) with the transcription factors network offers insights to investigate how incompatible processes are kept separate in time during the eukaryotic cell cycle. Here this coordination is discussed for the Forkhead transcription factors in light of missing gaps in the current knowledge of cell cycle control in budding yeast. An emergent design principle is proposed where cyclin waves are synchronized by a cyclin/Cdk-mediated feed-forward regulation through the Forkhead as a transcriptional timer. This design is rationalized by the bidirectional interaction between mitotic cyclins and the Forkhead transcriptional timer, resulting in an autonomous oscillator that may be instrumental for a well-timed progression throughout the cell cycle. The regulation centered around the cyclin/Cdk-Forkhead axis can be pivotal to timely coordinate cell cycle dynamics, thereby to actuate the quantitative model of Cdk control.

Predict long-range enhancer regulation based on protein-protein interactions between transcription factors

Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.

Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades

The transcriptional network determines a cell’s internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of “telephone” should degrade this type of signal, with longer chains losing successively more information to noise. However, a previous modeling effort predicted that because the steps of these signaling cascades do not truly represent independent stages of data processing, the limits of the DPI could seemingly be surpassed, and the amount of transmitted information could actually increase with chain length. What that work did not examine was whether this regime of growing information transmission was attainable by a signaling system constrained by the mechanistic details of more complex protein-binding kinetics. Here we address this knowledge gap through the lens of information theory by examining a model that explicitly accounts for the binding of each transcription factor to DNA. We analyze this model by comparing stochastic simulations of the fully nonlinear kinetics to simulations constrained by the linear response approximations that displayed a regime of growing information. Our simulations show that even when molecular binding is considered, there remains a regime wherein the transmitted information can grow with cascade length, but ends after a critical number of links determined by the kinetic parameter values. This inflection point marks where correlations decay in response to an oversaturation of binding sites, screening informative transcription factor fluctuations from further propagation down the chain where they eventually become indistinguishable from the surrounding levels of noise.

The Regulation of Target Genes by Co-occupancy of Transcription Factors, c-Myc and Mxi1 with Max in the Mouse Cell Line

Background:

The regulatory function of transcription factors on genes is not only related to the location of binding genes and its related functions, but is also related to the methods of binding.

Objective:

It is necessary to study the regulation effects in different binding methods on target genes.

Methods:

In this study, we provided a reliable theoretical basis for studying gene expression regulation of co-binding transcription factors and further revealed the specific regulation of transcription factor co-binding in cancer cells.

Results:

Transcription factors tend to combine with other transcription factors in the regulatory region to form a competitive or synergistic relationship to regulate target genes accurately.

Conclusion:

We found that up-regulated genes in cancer cells were involved in the regulation of their own immune system related to the normal cells.

An osmolality/salinity-responsive enhancer 1 (OSRE1) in intron 1 promotes salinity induction of tilapia glutamine synthetase

Euryhaline tilapia (Oreochromis mossambicus) are fish that tolerate a wide salinity range from fresh water to > 3× seawater. Even though the physiological effector mechanisms of osmoregulation that maintain plasma homeostasis in fresh water and seawater fish are well known, the corresponding molecular mechanisms that control switching between hyper- (fresh water) and hypo-osmoregulation (seawater) remain mostly elusive. In this study we show that hyperosmotic induction of glutamine synthetase represents a prominent part of this switch. Proteomics analysis of the O. mossambicus OmB cell line revealed that glutamine synthetase is transcriptionally regulated by hyperosmolality. Therefore, the 5' regulatory sequence of O. mossambicus glutamine synthetase was investigated. Using an enhancer trapping assay, we discovered a novel osmosensitive mechanism by which intron 1 positively mediates glutamine synthetase transcription. Intron 1 includes a single, functional copy of an osmoresponsive element, osmolality/salinity-responsive enhancer 1 (OSRE1). Unlike for conventional enhancers, the hyperosmotic induction of glutamine synthetase by intron 1 is position dependent. But irrespective of intron 1 position, OSRE1 deletion from intron 1 abolishes hyperosmotic enhancer activity. These findings indicate that proper intron 1 positioning and the presence of an OSRE1 in intron 1 are required for precise enhancement of hyperosmotic glutamine synthetase expression.

A study on the effect of phthalate esters and their metabolites on idiopathic infertile males

Phthalate plasticisers in medical, cosmetic and consumer products might pose serious health implications in humans including infertility. We sought to investigate the correlation, if any, between the phthalates and their metabolites and sperm quality parameters, and male infertility. Phthalate esters (15) and their metabolites (5) were estimated in the blood serum and urine samples from the age-matched 152 infertile and 75 fertile males using gas chromatography (GC) and high-performance liquid chromatography (HPLC). Finally, the data were analysed to correlate phthalate exposure and semen quality parameters in the infertility group. The estimated levels of DEHP, DBP, DIBP, BEHIP, BPBG, DPP, DIOP, DIHP, DMP, DINP, BIOP, DMOP and DICHP were significantly higher in the infertile males compared to the fertile males (p < .05 or p < .01). However, these were not found to be associated with the semen quality parameters (sperm count, motility and sperm morphology). Similarly, HPLC data revealed that the associations between semen parameters (sperm count, sperm motility and sperm morphology) and phthalate metabolite (MEHP and MBP) concentrations in urine samples from the infertile males were mostly unremarkable or statistically nonsignificant. Conclusively, environmental exposure to phthalates and their impacts on male infertility were statistically insignificant in our study groups.

FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets

Detecting binding motifs of combinatorial transcription factors (TFs) from chromatin immunoprecipitation sequencing (ChIP-seq) experiments is an important and challenging computational problem for understanding gene regulations. Although a number of motif-finding algorithms have been presented, most are either time consuming or have sub-optimal accuracy for processing large-scale datasets. In this article, we present a fully parallelized algorithm for detecting combinatorial motifs from ChIP-seq datasets by using Fisher combined method and OpenMP parallel design. Large scale validations on both synthetic data and 350 ChIP-seq datasets from the ENCODE database showed that FisherMP has not only super speeds on large datasets, but also has high accuracy when compared with multiple popular methods. By using FisherMP, we successfully detected combinatorial motifs of CTCF, YY1, MAZ, STAT3 and USF2 in chromosome X, suggesting that they are functional co-players in gene regulation and chromosomal organization. Integrative and statistical analysis of these TF-binding peaks clearly demonstrate that they are not only highly coordinated with each other, but that they are also correlated with histone modifications. FisherMP can be applied for integrative analysis of binding motifs and for predicting cis-regulatory modules from a large number of ChIP-seq datasets.

From Pioneer to Repressor: Bimodal foxd3 Activity Dynamically Remodels Neural Crest Regulatory Landscape In Vivo

The neural crest (NC) is a transient embryonic stem cell-like population characterized by its multipotency and broad developmental potential. Here, we perform NC-specific transcriptional and epigenomic profiling of foxd3-mutant cells in vivo to define the gene regulatory circuits controlling NC specification. Together with global binding analysis obtained by foxd3 biotin-ChIP and single cell profiles of foxd3-expressing premigratory NC, our analysis shows that, during early steps of NC formation, foxd3 acts globally as a pioneer factor to prime the onset of genes regulating NC specification and migration by re-arranging the chromatin landscape, opening cis-regulatory elements and reshuffling nucleosomes. Strikingly, foxd3 then gradually switches from an activator to its well-described role as a transcriptional repressor and potentially uses differential partners for each role. Taken together, these results demonstrate that foxd3 acts bimodally in the neural crest as a switch from “permissive” to “repressive” nucleosome and chromatin organization to maintain multipotency and define cell fates.

•

FoxD3 primes neural crest specification by modulating distal enhancers

•

FoxD3 represses a number of neural crest migration and differentiation genes

•

In neural crest, FoxD3 acts to switch chromatin from “permissive” to “repressive”

•

Distinctive gene regulatory mechanisms underlie the bimodal action of FoxD3

Differentially Expressed MicroRNAs Link Cellular Physiology to Phenotypic Changes in Rice Under Stress Conditions

Plant microRNAs (miRNAs) and their target genes have important functional roles in nutrition deficiency and stress response. However, the underlying mechanisms relating relative expression of miRNAs and target mRNAs to morphological adjustments are not well defined. By combining miRNA expression profiles, corresponding target genes and transcription factors that bind to computationally identified over-represented cis-regulatory elements (CREs) common in miRNAs and target gene promoters, we implement a strategy that identifies a set of differentially expressed regulatory interactions which, in turn, relate underlying cellular mechanisms to some of the phenotypic changes observed. Integration of experimentally reported individual interactions with identified regulatory interactions explains how (i) during mineral deficiency osa-miR167 inhibits shoot growth but activates adventitious root growth by influencing free auxin content; (ii) during sulfur deficiency osa-miR394 is involved in adventitious root growth inhibition, sulfur and iron homeostasis, and auxin-mediated regulation of sulfur homeostasis; (iii) osa-miR399 contributes to cross-talk between cytokinin and phosphorus deficiency signaling; and (iv) a feed-forward loop involving the osa-miR166, trihelix and HD-ZIP III transcription factors may regulate leaf senescence during drought. This strategy not only identifies various regulatory interactions connecting phenotypic changes with cellular or molecular events triggered by stress, but also provides a framework to deepen our understanding of stress cellular physiology.

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

Motivation

Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites.

Results

We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values.

Availability and implementation

The software is available at https://bitbucket.org/wenxiu/sequence-shape.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

Node-independent elementary signaling modes: A measure of redundancy in Boolean signaling transduction networks

The redundancy of a system denotes the amount of duplicate components or mechanisms in it. For a network, especially one in which mass or information is being transferred from an origin to a destination, redundancy is related to the robustness of the system. Existing network measures of redundancy rely on local connectivity (e.g. clustering coefficients) or the existence of multiple paths. As in many systems there are functional dependencies between components and paths, a measure that not only characterizes the topology of a network, but also takes into account these functional dependencies, becomes most desirable.

We propose a network redundancy measure in a prototypical model that contains functionally dependent directed paths: a Boolean model of a signal transduction network. The functional dependencies are made explicit by using an expanded network and the concept of elementary signaling modes (ESMs). We define the redundancy of a Boolean signal transduction network as the maximum number of node-independent ESMs and develop a methodology for identifying all maximal node-independent ESM combinations. We apply our measure to a number of signal transduction network models and show that it successfully distills known properties of the systems and offers new functional insights. The concept can be easily extended to similar related forms, e.g. edge-independent ESMs.

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.

Regulatory Cross-Talks and Cascades in Rice Hormone Biosynthesis Pathways Contribute to Stress Signaling

Crosstalk among different hormone signaling pathways play an important role in modulating plant response to both biotic and abiotic stress. Hormone activity is controlled by its bio-availability, which is again influenced by its biosynthesis. Thus, independent hormone biosynthesis pathways must be regulated and co-ordinated to mount an integrated response. One of the possibilities is to use cis-regulatory elements to orchestrate expression of hormone biosynthesis genes. Analysis of CREs, associated with differentially expressed hormone biosynthesis related genes in rice leaf under Magnaporthe oryzae attack and drought stress enabled us to obtain insights about cross-talk among hormone biosynthesis pathways at the transcriptional level. We identified some master transcription regulators that co-ordinate different hormone biosynthesis pathways under stress. We found that Abscisic acid and Brassinosteroid regulate Cytokinin conjugation; conversely Brassinosteroid biosynthesis is affected by both Abscisic acid and Cytokinin. Jasmonic acid and Ethylene biosynthesis may be modulated by Abscisic acid through DREB transcription factors. Jasmonic acid or Salicylic acid biosynthesis pathways are co-regulated but they are unlikely to influence each others production directly. Thus, multiple hormones may modulate hormone biosynthesis pathways through a complex regulatory network, where biosynthesis of one hormone is affected by several other contributing hormones.

Modeling co-occupancy of transcription factors using chromatin features

Regulation of gene expression requires both transcription factor (TFs) and epigenetic modifications, and interplays between the two types of factors have been discovered. However study of relationships between chromatin features and TF–TF co-occupancy remains limited. Here, we revealed the relationship by first illustrating distinct profile patterns of chromatin features related to different binding events, including single TF binding and TF–TF co-occupancy of 71 TFs from five human cell lines. We further implemented statistical analyses to demonstrate the relationship by accurately predicting co-occupancy genome-widely using chromatin features including DNase I hypersensitivity, 11 histone modifications (HMs) and GC content. Remarkably, our results showed that the combination of chromatin features enables accurate predictions across the five cells. For individual chromatin features, DNase I enables high and consistent predictions. H3K27ac, H3K4me 2, H3K4me3 and H3K9ac are more reliable predictors than other HMs. Although the combination of 11 HMs achieves accurate predictions, their predictive ability varies considerably when a model obtained from one cell is applied to others, indicating relationship between HMs and TF–TF co-occupancy is cell type dependent. GC content is not a reliable predictor, but the addition of GC content to any other features enhances their predictive ability. Together, our results elucidate a strong relationship between TF–TF co-occupancy and chromatin features.

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.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome

Biochemical modifications to mRNA, especially N6-methyladenosine (m6A) and 5-methylcytosine (m5C), have been recently shown to be associated with crucial biological functions. Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets collected from 10 different experimental conditions (cell type/tissue or treatment) is performed, and the combinatorial epitranscriptome, which consists of 42 758 m6A sites, is extracted and divided into 3 clusters, in which the methylation sites are likely to be hyper- or hypo-methylated simultaneously (or co-methylated), indicating the sharing of a common methylation regulator. Four different clustering approaches are used, including K-means, hierarchical clustering (HC), Bayesian factor regression model (BFRM) and nonnegative matrix factorization (NMF) to unveil the co-methylation patterns. To validate whether the patterns are corresponding to enzymatic regulators, i.e., RNA methyltransferases or demethylases, the target sites of a known m6A regulator, fat mass and obesity-associated protein (FTO), are identified from an independent mouse MeRIP-Seq dataset and lifted to human. Our study shows that 3 out of the 4 clustering approaches used can successfully identify a group of methylation sites overlapping with FTO target sites at a significance level of 0.05 (after multiple hypothesis adjustment), among which, the result of NMF is the most significant (p-value 2.81×10(-06)). We defined a new approach evaluating the consistency between two clustering results which shows that clustering results of different methods are highly correlated strongly indicating the existence of co-methylation patterns. Consistent with recent studies, a number of cancer and neuronal disease-related bimolecular functions are enriched in the identified clusters, which are biological functions that can be regulated at the epitranscriptional level, indicating the pharmaceutical prospect of RNA N6-methyladenosine-related studies. This result successfully reveals the linkage between the global RNA co-methylation patterns embedded in the epitranscriptomic data under multiple experimental conditions and the latent enzymatic regulators, suggesting a promising direction towards a more comprehensive understanding of the epitranscriptome.

Topology and control of the cell-cycle-regulated transcriptional circuitry

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls.

REGNET: mining context-specific human transcription networks using composite genomic information

BACKGROUND

Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.

RESULTS

Using a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.

CONCLUSIONS

REGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet.

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/.

Extensive oscillatory gene expression during C. elegans larval development

Oscillations are a key to achieving dynamic behavior and thus occur in biological systems as diverse as the beating heart, defecating worms, and nascent somites. Here we report pervasive, large-amplitude, and phase-locked oscillations of gene expression in developing C. elegans larvae, caused by periodic transcription. Nearly one fifth of detectably expressed transcripts oscillate with an 8 hr period, and hundreds change >10-fold. Oscillations are important for molting but occur in all phases, implying additional functions. Ribosome profiling reveals that periodic mRNA accumulation causes rhythmic translation, potentially facilitating transient protein accumulation as well as coordinated production of stable, complex structures such as the cuticle. Finally, large-amplitude oscillations in RNA sampled from whole worms indicate robust synchronization of gene expression programs across cells and tissues, suggesting that these oscillations will be a powerful new model to study coordinated gene expression in an animal.

Characterization and identification of cis-regulatory elements in Arabidopsis based on single-nucleotide polymorphism information

Identifying regulatory elements and revealing their role in gene expression regulation remains a central goal of plant genome research. We exploited the detailed genomic sequencing information of a large number of Arabidopsis (Arabidopsis thaliana) accessions to characterize known and to identify novel cis-regulatory elements in gene promoter regions of Arabidopsis by relying on conservation as the hallmark signal of functional relevance. Based on the genomic layout and the obtained density profiles of single-nucleotide polymorphisms (SNPs) in sequence regions upstream of transcription start sites, the average length of promoter regions in Arabidopsis could be established at 500 bp. Genes associated with high degrees of variability of their respective upstream regions are preferentially involved in environmental response and signaling processes, while low levels of promoter SNP density are common among housekeeping genes. Known cis-elements were found to exhibit a decreased SNP density than sequence regions not associated with known motifs. For 15 known cis-element motifs, strong positional preferences relative to the transcription start site were detected based on their promoter SNP density profiles. Five novel candidate cis-element motifs were identified as consensus motifs of 17 sequence hexamers exhibiting increased sequence conservation combined with evidence of positional preferences, annotation information, and functional relevance for inducing correlated gene expression. Our study demonstrates that the currently available resolution of SNP data offers novel ways for the identification of functional genomic elements and the characterization of gene promoter sequences.

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.

Differential analysis of high-throughput quantitative genetic interaction data

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.

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.

GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group

Background

Sequence features in promoter regions are involved in regulating gene transcription initiation. Although numerous computational methods have been developed for predicting transcriptional start sites (TSSs) or transcription factor (TF) binding sites (TFBSs), they lack annotations for do not consider some important regulatory features such as CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, over-represented oligonucleotides, DNA stability, and GC content. Additionally, the combinatorial interaction of TFs regulates the gene group that is associated with same expression pattern. To investigate gene transcriptional regulation, an integrated system that annotates regulatory features in a promoter sequence and detects co-regulation of TFs in a group of genes is needed.

Results

This work identifies TSSs and regulatory features in a promoter sequence, and recognizes co-occurrence of cis-regulatory elements in co-expressed genes using a novel system. Three well-known TSS prediction tools are incorporated with orthologous conserved features, such as CpG islands, nucleotide composition, over-represented hexamer nucleotides, and DNA stability, to construct the novel Gene Promoter Miner (GPMiner) using a support vector machine (SVM). According to five-fold cross-validation results, the predictive sensitivity and specificity are both roughly 80%. The proposed system allows users to input a group of gene names/symbols, enabling the co-occurrence of TFBSs to be determined. Additionally, an input sequence can also be analyzed for homogeneity of experimental mammalian promoter sequences, and conserved regulatory features between homologous promoters can be observed through cross-species analysis. After identifying promoter regions, regulatory features are visualized graphically to facilitate gene promoter observations.

Conclusions

The GPMiner, which has a user-friendly input/output interface, has numerous benefits in analyzing human and mouse promoters. The proposed system is freely available at http://GPMiner.mbc.nctu.edu.tw/.

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.

Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms.

Commitment to a cellular transition precedes genome-wide transcriptional change

In budding yeast, commitment to cell division corresponds to activating the positive feedback loop of G1 cyclins controlled by the transcription factors SBF and MBF. This pair of transcription factors has over 200 targets, implying that cell-cycle commitment coincides with genome-wide changes in transcription. Here, we find that genes within this regulon have a well-defined distribution of transcriptional activation times. Combinatorial use of SBF and MBF results in a logical OR function for gene expression and partially explains activation timing. Activation of G1 cyclin expression precedes the activation of the bulk of the G1/S regulon, ensuring that commitment to cell division occurs before large-scale changes in transcription. Furthermore, we find similar positive feedback-first regulation in the yeasts S. bayanus and S. cerevisiae, as well as human cells. The widespread use of the feedback-first motif in eukaryotic cell-cycle control, implemented by nonorthologous proteins, suggests its frequent deployment at cellular transitions.

A novel method to identify cooperative functional modules: study of module coordination in the Saccharomyces cerevisiae cell cycle

BACKGROUND

Identifying key components in biological processes and their associations is critical for deciphering cellular functions. Recently, numerous gene expression and molecular interaction experiments have been reported in Saccharomyces cerevisiae, and these have enabled systematic studies. Although a number of approaches have been used to predict gene functions and interactions, tools that analyze the essential coordination of functional components in cellular processes still need to be developed.

RESULTS

In this work, we present a new approach to study the cooperation of functional modules (sets of functionally related genes) in a specific cellular process. A cooperative module pair is defined as two modules that significantly cooperate with certain functional genes in a cellular process. This method identifies cooperative module pairs that significantly influence a cellular process and the correlated genes and interactions that are essential to that process. Using the yeast cell cycle as an example, we identified 101 cooperative module associations among 82 modules, and importantly, we established a cell cycle-specific cooperative module network. Most of the identified module pairs cover cooperative pathways and components essential to the cell cycle. We found that 14, 36, 18, 15, and 20 cooperative module pairs significantly cooperate with genes regulated in early G1, late G1, S, G2, and M phase, respectively. Fifty-nine module pairs that correlate with Cdc28 and other essential regulators were also identified. These results are consistent with previous studies and demonstrate that our methodology is effective for studying cooperative mechanisms in the cell cycle.

CONCLUSIONS

In this work, we propose a new approach to identifying condition-related cooperative interactions, and importantly, we establish a cell cycle-specific cooperation module network. These results provide a global view of the cell cycle and the method can be used to discover the dynamic coordination properties of functional components in other cellular processes.

Simplified method to predict mutual interactions of human transcription factors based on their primary structure

Background

Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation.

Methodology

We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems.

Conclusions

The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.

The reconstruction of condition-specific transcriptional modules provides new insights in the evolution of yeast AP-1 proteins

AP-1 proteins are transcription factors (TFs) that belong to the basic leucine zipper family, one of the largest families of TFs in eukaryotic cells. Despite high homology between their DNA binding domains, these proteins are able to recognize diverse DNA motifs. In yeasts, these motifs are referred as YRE (Yap Response Element) and are either seven (YRE-Overlap) or eight (YRE-Adjacent) base pair long. It has been proposed that the AP-1 DNA binding motif preference relies on a single change in the amino acid sequence of the yeast AP-1 TFs (an arginine in the YRE-O binding factors being replaced by a lysine in the YRE-A binding Yaps). We developed a computational approach to infer condition-specific transcriptional modules associated to the orthologous AP-1 protein Yap1p, Cgap1p and Cap1p, in three yeast species: the model yeast Saccharomyces cerevisiae and two pathogenic species Candida glabrata and Candida albicans. Exploitation of these modules in terms of predictions of the protein/DNA regulatory interactions changed our vision of AP-1 protein evolution. Cis-regulatory motif analyses revealed the presence of a conserved adenine in 5' position of the canonical YRE sites. While Yap1p, Cgap1p and Cap1p shared a remarkably low number of target genes, an impressive conservation was observed in the YRE sequences identified by Yap1p and Cap1p. In Candida glabrata, we found that Cgap1p, unlike Yap1p and Cap1p, recognizes YRE-O and YRE-A motifs. These findings were supported by structural data available for the transcription factor Pap1p (Schizosaccharomyces pombe). Thus, whereas arginine and lysine substitutions in Cgap1p and Yap1p proteins were reported as responsible for a specific YRE-O or YRE-A preference, our analyses rather suggest that the ancestral yeast AP-1 protein could recognize both YRE-O and YRE-A motifs and that the arginine/lysine exchange is not the only determinant of the specialization of modern Yaps for one motif or another.

Combinatorial regulation of transcription factors and microRNAs

Background

Gene regulation is a key factor in gaining a full understanding of molecular biology. Cis-regulatory modules (CRMs), consisting of multiple transcription factor binding sites, have been confirmed as the main regulators in gene expression. In recent years, a novel regulator known as microRNA (miRNA) has been found to play an important role in gene regulation. Meanwhile, transcription factor and microRNA co-regulation has been widely identified. Thus, the relationships between CRMs and microRNAs have generated interest among biologists.

Results

We constructed new combinatorial regulatory modules based on CRMs and miRNAs. By analyzing their effect on gene expression profiles, we found that genes targeted by both CRMs and miRNAs express in a significantly similar way. Furthermore, we constructed a regulatory network composed of CRMs, miRNAs, and their target genes. Investigating its structure, we found that the feed forward loop is a significant network motif, which plays an important role in gene regulation. In addition, we further analyzed the effect of miRNAs in embryonic cells, and we found that mir-154, as well as some other miRNAs, have significant co-regulation effect with CRMs in embryonic development.

Conclusions

Based on the co-regulation of CRMs and miRNAs, we constructed a novel combinatorial regulatory network which was found to play an important role in gene regulation, particularly during embryonic development.

The transcriptional network activated by Cln3 cyclin at the G1-to-S transition of the yeast cell cycle

Background

The G1-to-S transition of the cell cycle in the yeast Saccharomyces cerevisiae involves an extensive transcriptional program driven by transcription factors SBF (Swi4-Swi6) and MBF (Mbp1-Swi6). Activation of these factors ultimately depends on the G1 cyclin Cln3.

Results

To determine the transcriptional targets of Cln3 and their dependence on SBF or MBF, we first have used DNA microarrays to interrogate gene expression upon Cln3 overexpression in synchronized cultures of strains lacking components of SBF and/or MBF. Secondly, we have integrated this expression dataset together with other heterogeneous data sources into a single probabilistic model based on Bayesian statistics. Our analysis has produced more than 200 transcription factor-target assignments, validated by ChIP assays and by functional enrichment. Our predictions show higher internal coherence and predictive power than previous classifications. Our results support a model whereby SBF and MBF may be differentially activated by Cln3.

Conclusions

Integration of heterogeneous genome-wide datasets is key to building accurate transcriptional networks. By such integration, we provide here a reliable transcriptional network at the G1-to-S transition in the budding yeast cell cycle. Our results suggest that to improve the reliability of predictions we need to feed our models with more informative experimental data.

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 protein-protein interactions in unbalanced data using the primary structure of proteins

BACKGROUND

Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.

RESULTS

This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.

CONCLUSIONS

Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.

A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data

The genome-wide DNA-protein-binding data, DNA sequence data and gene expression data represent complementary means to deciphering global and local transcriptional regulatory circuits. Combining these different types of data can not only improve the statistical power, but also provide a more comprehensive picture of gene regulation. In this paper, we propose a novel statistical model to augment protein-DNA-binding data with gene expression and DNA sequence data when available. We specify a hierarchical Bayes model and use Markov chain Monte Carlo simulations to draw inferences. Both simulation studies and an analysis of an experimental data set show that the proposed joint modeling method can significantly improve the specificity and sensitivity of identifying target genes as compared with conventional approaches relying on a single data source.