On motifs and functional modules in complex networks

BackgroundBiological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow.ResultsWe devised a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently find clusters or functional modules in complex biological networks as well as hubs and outliers. More specifically, we demonstrated that we can find functional modules in complex networks and classify nodes into various roles based on their structures. In this study, we showed the effectiveness of our methodology using the budding yeast (Saccharomyces cerevisiae) protein-protein interaction network. To validate our clustering results, we compared our clusters with the known functions of each protein. Our predicted functional modules achieved very high purity comparing with state-of-the-art approaches. Additionally the theoretical and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest approach for networks.ConclusionWe compare our algorithm with well-known modularity based clustering algorithm CNM. We successfully detect functional groups that are annotated with putative GO terms. Top-10 clusters with minimum p-value theoretically prove that newly proposed algorithm partitions network more accurately then CNM. Furthermore, manual interpretations of functional groups found by SCAN show superior performance over CNM.

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

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.

The machinery for cells to take decisions, when environmental conditions change, includes protein–DNA interactions defined by transcriptional factors and their targets around promoters. Properties of global regulators are revised attempting to reach diagnostic explicit criteria for their definition and eventual future computational identification. These include among others, the number of regulated genes, the number and type of co-regulators, the different σ-classes of promoters and the number of transcriptional factors they regulate, the size of the evolutionary family they belong to, and the variety of conditions where they exert their control. As a consequence, global versus local regulation can be identified, as shown for Escherichia coli and eventually in other genomes.

Streptomyces albidoflavus is a widely used strain for natural product discovery and production through heterologous biosynthetic gene clusters (BGCs). However, the transcriptional regulatory network (TRN) and its impact on secondary metabolism remain poorly understood. Here, we characterize the TRN using independent component analysis on 218 RNA sequencing (RNA-seq) transcriptomes across 88 unique growth conditions. We identify 78 independently modulated sets of genes (iModulons) that quantitatively describe the TRN across diverse conditions. Our analyses reveal (1) TRN adaptation to different growth conditions, (2) conserved and unique characteristics of the TRN across diverse lineages, (3) transcriptional activation of several endogenous BGCs, including surugamide, minimycin, and paulomycin, and (4) inferred functions of 40% of uncharacterized genes in the S.albidoflavus genome. These findings provide a comprehensive and quantitative understanding of the S.albidoflavus TRN, offering a knowledge base for further exploration and experimental validation.

Network motifs are patterns of complex networks occurring significantly more frequently than those in random networks. They have been considered as fundamental building blocks of complex networks. Therefore, the detection of network motifs in transcriptional regulation networks is a crucial step in understanding the mechanism of transcriptional regulation and network evolution. The search for network motifs is similar to solving subgraph searching problems, which has proven to be NP-complete. To quickly and effectively count subgraphs of a large biological network, we propose a novel graph canonization algorithm based on resolving sets. This method has been implemented in a command line interface (CLI) program sgip using the SeqAn library. Comparing to Babai’s algorithm, this approach has a tighter complexity bound, , on strongly regular graphs. Results on several simulated datasets and transcriptional regulation networks indicate that sgip outperforms nauty on many graph cases. The source code of sgip is freely accessible in https://github.com/seqan/seqan/tree/master/apps/sgip and the binary code in http://packages.seqan.de/sgip/.

A global transcriptional regulatory network was generated in the pathogenic bacterium Enterococcus faecalis in order to understand how this organism can activate and coordinate its expression at different copper concentrations. The topological evaluation of the network showed common patterns described in other organisms. Integrating microarray experiments allowed the identification of two sub-networks activated at low (0.05 mM CuSO4) and high (0.5 mM CuSO4) concentrations of copper. The analysis indicates the presence of specific functionally activated modules induced by copper levels, highlighting the regulons LysR and ArgR as global regulators and CopY, Fur and LexA as local regulators. Taking advantage of the fact that E. faecalis presented a homeostatic module, we produced an in vivo intervention by removing this system from the cell without affecting the connectivity of the global transcriptional network. This strategy led us to find that this bacterium can reconfigure its gene expression to maintain cellular homeostasis, activating new modules principally related to glucose metabolism and transcriptional processes. Finally, these results position E. faecalis as the most complete and controllable systemic model organism for copper homeostasis available to date.

Transcriptional Regulatory Network of Plant Heat Stress Response.

Cadmium (Cd) is a non-essential heavy metal with high toxicity to plants. Plants have acquired specialized mechanisms to sense, transport, and detoxify Cd. Recent studies have identified many transporters involved in Cd uptake, transport, and detoxification. However, the complex transcriptional regulatory networks involved in Cd response remain to be elucidated. Here, we provide an overview of current knowledge regarding transcriptional regulatory networks and post-translational regulation of the transcription factors involved in Cd response. An increasing number of reports indicate that epigenetic regulation and long non-coding and small RNAs are important in Cd-induced transcriptional responses. Several kinases play important roles in Cd signaling that activate transcriptional cascades. We also discuss the perspectives to reduce grain Cd content and improve crop tolerance to Cd stress, which provides a theoretical reference for food safety and the future research of plant varieties with low Cd accumulation.

Computational biology approaches for mapping transcriptional regulatory networks

Transposable elements are one of the main drivers of plant genome evolution. Transposon insertions can modify the gene coding capacity or the regulation of their expression, the latter being a more subtle effect, and therefore particularly useful for evolution. Transposons have been show to contain transcription factor binding sites that can be mobilized upon transposition with the potential to integrate new genes into transcriptional networks. Miniature inverted-repeat transposable elements (MITEs) are a type of noncoding DNA transposons that could be particularly suited as a vector to mobilize transcription factor binding sites and modify transcriptional networks during evolution. MITEs are small in comparison to other transposons and can be excised, which should make them less mutagenic when inserting into promoters. On the other hand, in spite of their cut-and-paste mechanisms of transposition, they can reach very high copy numbers in genomes. We have previously shown that MITEs have amplified and redistributed the binding motif of the E2F transcription factor in different Brassicas. Here, we show that MITEs have amplified and mobilized the binding motifs of the bZIP60 and PIF3 transcription factors in peach and Prunus mume, and the TCP15/23 binding motif in tomato. Our results suggest that MITEs could have rewired new genes into transcriptional regulatory networks that are responsible for important adaptive responses and breeding traits in plants, such as stress responses, flowering time, or fruit ripening. The results presented here therefore suggest a general impact of MITEs in the evolution of transcriptional regulatory networks in plants.

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.

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

ABSTRACTIn the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae.

Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory networks helps us to understand the complex cellular process. In this paper, we describe a statistical approach for constructing transcriptional regulatory networks using data of gene expression, promoter sequence, and transcription factor binding sites. Our simulation studies show that the overall and false positive error rates in the estimated transcriptional regulatory networks are expected to be small if the systematic noise in the constructed feature matrix is small. Our analysis based on 658 microarray experiments on yeast gene expression programs and 46 transcription factors suggests that the method is capable of identifying significant transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.