A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data

1. Early Embryonic Cell Fate Decisions in the Mouse Yojiro Yamanaka and Amy Ralston Abstract Introduction Lineage Establishment and the Pre?stem Cell Program: Formation of the Blastocyst Lineage Maintenance and the Stem Cell Program: Beyond the Blastocyst The Second Lineage Decision: Subdividing the ICM Cell Signaling Regulates PE/EPI Specification Establishment and Modulation of Pluripotency in the EPI Lineage Conclusion 2. Nuclear Architecture in Stem Cells Kelly J. Morris, Mita Chotalia and Ana Pombo Abstract Introduction Functional Compartmentalization of the ES Cell Nucleus Stem Cell Features of Other Nucleoplasmic Subcompartments Chromatin Features Characteristic of ES Cell Nuclei Conclusion 3. Epigenetic Regulation of Pluripotency Eleni M. Tomazou and Alexander Meissner Abstract Introduction Epigenetic Modifications The Epigenome of ES Cells Conclusion 4. Autosomal Lyonization of Replication Domains During Early Mammalian Development Ichiro Hiratani and David M. Gilbert Abstract Introduction Replication Timing Program: An Elusive Measure of Genome Organization An Evolutionarily Conserved Epigenetic Fingerprint Replication Timing as a Quantitative Index of 3?Dimensional Genome Organization Replication Timing Reveals An Epigenetic Transition: Autosomal Lyonization at the Epiblast Stage Replication Timing and Cellular Reprogramming: Further Support for Autosomal Lyonization Maintenance and Alteration of Replication Timing Program and Its Potential Roles Conclusion 5. PRESERVATION OF GENOMIC INTEGRITY IN MOUSE EMBRYONIC STEM CELLS Peter J. Stambrook and Elisia D. Tichy Abstract Introduction and Historical Perspective Mutation Frequencies in Somatic Cells Protection of the Mouse ES Cell Genome Conclusion 6. Transcriptional Regulation in Embryonic Stem Cells Jian?Chien Dominic Heng and Huck?Hui Ng Abstract Introduction Embryonic Stem Cells as a Model to Study Transcriptional Regulation Transcription Factors Governing ESC Pluripotency TranscriptionalRegulatory Network Technologies for Dissecting the Transcriptional Regulatory Network The Core Transcriptional Regulatory Network: Oct4, Sox2 and Nanog Expanded Transcriptional Regulatory Network Enhanceosomes: Transcription Factor Complex Integration of Signaling Pathways to Transcriptional Network Interface Between Transcriptional and Epigenetic Regulation Conclusion 7. ALTERNATIVE SPLICING IN STEM CELL SELF?RENEWAL AND DIFERENTIATION David A. Nelles and Gene W. Yeo Abstract Introduction Introduction to Alternative Splicing Alternative Splicing of Genes Implicated in Stemness and Differentiation Genome?Wide Methods to Identify and Detect Alternative Splicing Events Regulation of Alternative Splicing by RNA Binding Proteins Conclusion and Perspectives 8. MicroRNA Regulation of Embryonic Stem Cell Self?Renewal and Differentiation Collin Melton and Robert Blelloch Abstract Introduction: The Self?Renewal Program Embryonic Stem Cells miRNA Biogenesis and Function ESCC miRNAs Promote Self?Renewal miRNAs Induced during ESC Differentiation Suppress the Self?Renewal Program Regulatory Networks Controlling miRNA Expression miRNAs Can Promote or Inhibit Dedifferentiation to iPS Cells miRNAs in Somatic Stem Cells miRNAs in Cancer Cells Conclusion 9. TELOMERES AND TELOMERASE IN ADULT STEM CELLS and PLURIPOTENT EMBRYONIC STEM CELLS Rosa M. Marion and Maria A. Blasco Abstract Introduction Regulation of Telomeres and Telomerase Role of Telomeres and Telomerase in Adult SC Compartments Telomeres and Telomerase Regulation During Reprogramming by SCNT Telomeres and Telomerase Regulation During iPS Cell Generation Telomerase Activation is Essential for the Good Quality of the Resulting iPS Cells Regulation of Telomere Reprogramming Conclusion 10. X Chromosome Inactivation and Embryonic Stem Cells Tahsin Stefan Barakat and Joost Gribnau Abstract Introduction Cis Acting Factors in XCI Trans Acting Factors in XCI Counting and Choice Silencing and Maintenance of

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.

Carotid intima-media thickness (CIMT) is a surrogate indicator for atherosclerosis and has been shown to predict cardiovascular risk in multiple large studies. Identification of molecular markers for carotid atheroma plaque formation can be critical for early intervention and prevention of atherosclerosis. This study performed transcription factor (TF) network analysis of global gene expression data focusing on two TF genes, ZNF385D and HAND2, whose polymorphisms have been recently reported to show association with CIMT. Genome-wide gene expression data were measured from pieces of carotid endarterectomy collected from 34 hypertensive patients (atheroma plaque of stages IV and above according to the Stary classification) each paired with one sample of distant macroscopically intact tissue (stages I and II). Transcriptional regulation networks or the regulons were reconstructed for ZNF385D (5644 target genes) and HAND2 (781 target genes) using network inference. Their association with the progression of carotid atheroma was examined using gene-set enrichment analysis with extremely high statistical significance for regulons of both ZNF385D and HAND2 (p < 6.95 × 10−7) suggesting the involvement of expression quantitative loci (eQTL). Functional annotation of the regulon genes found heavy involvement in the immune system’s response to inflammation and infection in the development of atherosclerosis. Detailed examination of the regulation and correlation patterns suggests that activities of the two TF genes could have high clinical and interventional impacts on impairing carotid atheroma plaque formation and preventing carotid atherosclerosis.

Hematopoiesis is an extensively studied model system for cell differentiation. Cell-type-specific gene expression patterns are observed during hematopoiesis. Gene expression is governed by regulatory networks composed of cell-type-specific transcription factors. Resolving the transcriptional regulatory network for cell-type-specific gene expression provides a promising means of understanding the mechanisms underlying cell fate decisions. In this study, transcriptional regulatory networks in hematopoietic stem and progenitor cells were predicted based on gene expression profiles and distributions of transcription factor binding motifs in the promoter regions of cell-type-specific transcription factors. In particular, structural changes that occur when pluripotent stem cells progress to lineage-committed progenitors were evaluated. Marked changes in the regulatory circuit of transcription throughout the differentiation process could be elucidated by network analysis. Modular structures were a frequently described feature of biological networks observed in estimated networks. Within a module, most transcription factors were found to be regulated by a small number of regulators acting as downstream targets. Certain regulators within these modules coincide with known key regulators of hematopoietic cell differentiation. In addition to the modular structure, a twolayered structure was clearly observed in progenitor regulatory networks. Transcription factors could be distinctly divided into regulators within the regulatory layer and into targets in the output layer according to their degree of distribution. The restriction of mutual regulation between transcription factors was remarkable in that it allowed for alterations in network structures between hematopoietic stem cells and progenitors. Thus, using this approach, the relationships among transcription factors could be revealed by a reduction in mutual regulation to form a modular structure within the regulatory network

The determination of transcriptional regulatory networks is key to the understanding of biological systems. However, the experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while current computational methods to infer these networks (typically from gene-expression data) achieve only modest accuracy. The latter can be attributed in part to the limitations of a single-organism approach. Computational biology has long used comparative and, more generally, evolutionary approaches to extend the reach and accuracy of its analyses. We therefore use an evolutionary approach to the inference of regulatory networks, which enables us to study evolutionary models for these networks as well as to improve the accuracy of inferred networks. Since the regulatory networks evolve along with the genomes, we consider that the regulatory networks for a family of organisms are related to each other through the same phylogenetic tree. These relationships contain information that can be used to improve the accuracy of inferred networks. Advances in the study of evolution of regulatory networks provide evidence to establish evolutionary models for regulatory networks, which is an important component of our evolutionary approach. We use two network evolutionary models, a basic model that considers only the gains and losses of regulatory connections during evolution, and an extended model that also takes into account the duplications and losses of genes. With the network evolutionary models, we design refinement algorithms to make use of the phylogenetic relationships to refine noisy regulatory networks for a family of organisms. These refinement algorithms include: RefineFast and RefineML, which are two-step iterative algorithms, and ProPhyC and ProPhyCC, which are based on a probabilistic phylogenetic model. For each algorithm we first design it with the basic network evolutionary model and then generalize it to the extended evolutionary model. All these algorithms are computationally efficient and are supported by extensive experimental results showing that they yield substantial improvement in the quality of the input noisy networks. In particular, ProPhyC and ProPhyCC further improve the performance of RefineFast and RefineML. Besides the four refinement algorithms mentioned above, we also design an algorithm based on transfer learning theory called tree transfer learning (TTL). TTL differs from the previous four refinement algorithms in the sense that it takes the gene-expression data for the family of organisms as input, instead of their inferred noisy networks. TTL then learns the network structures for all the organisms at once, meanwhile taking advantage of the phylogenetic relationships. Although this approach outperforms an inference algorithm used alone, it does not perform better than ProPhyC, which indicates that the ProPhyC framework makes good use of the phylogenetic information.

This study was designed to explore the effects of tobacco smoke on gene expression through bioinformatics analyses. Gene expression profile GSE17913 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in buccal mucosa tissues between 39 active smokers and 40 never smokers were identified. Gene Ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by protein-protein interaction (PPI) network, transcriptional regulatory network as well as miRNA-target regulatory network construction. In total, 88 up-regulated DEGs and 106 down-regulated DEGs were identified. Among these DEGs, cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) and CYP1B1 were enriched in the Metabolism of xenobiotics by cytochrome P450 pathway. In the PPI network, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta (YWHAZ), and CYP1A1 were hub genes. In the transcriptional regulatory network, transcription factors of MYC associated factor X (MAX) and upstream transcription factor 1 (USF1) regulated many overlapped DEGs. In addition, protein tyrosine phosphatase, receptor type, D (PTPRD) was regulated by multiple miRNAs in the miRNA-DEG regulatory network. CYP1A1, CYP1B1, YWHAZ and PTPRD, and TF of MAX and USF1 may have the potential to be used as biomarkers and therapeutic targets in tobacco smoke-related pathological changes.

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

A compendium of 934 transcription factor genes in C. elegans and identified by computational searches and extensive manual.

The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.

Splicing regulatory networks are essential components of eukaryotic gene expression programs, yet little is known about how they are integrated with transcriptional regulatory networks into coherent gene expression programs. Here we define the MER1 splicing regulatory network and examine its role in the gene expression program during meiosis in budding yeast. Mer1p splicing factor promotes splicing of just four pre-mRNAs. All four Mer1p-responsive genes also require Nam8p for splicing activation by Mer1p; however, other genes require Nam8p but not Mer1p, exposing an overlapping meiotic splicing network controlled by Nam8p. MER1 mRNA and three of the four Mer1p substrate pre-mRNAs are induced by the transcriptional regulator Ume6p. This unusual arrangement delays expression of Mer1p-responsive genes relative to other genes under Ume6p control. Products of Mer1p-responsive genes are required for initiating and completing recombination and for activation of Ndt80p, the activator of the transcriptional network required for subsequent steps in the program. Thus, the MER1 splicing regulatory network mediates the dependent relationship between the UME6 and NDT80 transcriptional regulatory networks in the meiotic gene expression program. This study reveals how splicing regulatory networks can be interlaced with transcriptional regulatory networks in eukaryotic gene expression programs.

BackgroundGene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding.ResultsOur approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented.ConclusionMultiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN.

Author SummaryConserved metabolic machineries direct energy production and investment in most life forms. However, variation in the transcriptional regulation of the genes that encode this machinery has been observed and shown to contribute to phenotypic differences between species. Here, we show that the regulatory circuits governing the expression of central metabolic components (in this case the ribosomes) in different yeast species have an unexpected level of evolutionary plasticity. Most transcription factors involved in the regulation of expression of ribosomal genes have in fact been reused in new ways during the evolutionary time separating S. cerevisiae and C. albicans to generate global changes in transcriptional network structures and new ribosomal regulatory complexes.

The integration of transcriptional regulatory and metabolic networks is a crucial step in the process of predicting metabolic behaviors that emerge from either genetic or environmental changes. Here, we present a guide to PROM (probabilistic regulation of metabolism), an automated method for the construction and simulation of integrated metabolic and transcriptional regulatory networks that enables large-scale phenotypic predictions for a wide range of model organisms.

Transcriptional regulation and expression network responding to cadmium stress in a Cd-tolerant perennial grass Poa Pratensis