Yeast Transcriptional Regulatory Network Research Articles

Rapid reorganization of the transcriptional regulatory network after genome duplication in yeast

I study the reorganization of the yeast transcriptional regulatory network after whole-genome duplication (WGD). Individual transcription factors (TFs) were computationally removed from the regulatory network, and the resulting networks were analysed. TF gene pairs that survive in duplicate from WGD show detectable redundancy as a result of that duplication. However, in most other respects, these duplicated TFs are indistinguishable from other TFs in the genome, suggesting that the duplicate TFs produced by WGD were rapidly diverted to distinct functional roles in the regulatory network. Separately, I find that genes targeted by many TFs appear to be preferentially retained in duplicate after WGD, an effect I attribute to selection to maintain dosage balance in the regulatory network after WGD.

Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Information processing in the transcriptional regulatory network of yeast: Functional robustness

BackgroundGene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism.ResultsIn this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value.ConclusionOur study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties.

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper, we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extracting modules from known biological interaction networks. Using the yeast transcriptional regulatory network as a test case, we show that extracted modules have a biologically plausible connectivity because they preserve functional and structural properties of the original network. Our method was selected to generate the "gold standard" networks for the gene network reverse engineering challenge of the third DREAM conference (Dialogue on Reverse Engineering Assessment and Methods 2008, Cambridge, MA).

Backup machinery of yeast transcriptional regulatory network

AbstractSeveral studies have suggested the existence of backup machinery of transcriptional regulatory networks (TRNs). Here, we have quantified the backup machinery of yeast gene's TRNs under five different conditions in terms of alternate paths and have revealed that a statistically significant (p<0.0001) stronger backup is maintained for endogenous processes (ENPs) than exogenous processes (EXPs). A number of biologically important genes (SUC2, MF(ALPHA)2, CLN2 etc) are observed that maintain a higher backup. Hub and random transcription factor (TF) knockouts in TRNs have showed ENPs are more robust to deletion than EXPs. While higher average connectivity of TFs in EXPs than ENPs can't explain the higher robustness in ENPs, we have found that the later have a densely interconnectedness explaining their specialized architecture that may have evolved due to evolutionary pressure. Some non-hub TFs identified here are more likely to be essential, and if not essential, have a larger impact on fitness.

The information coded in the yeast response elements accounts for most of the topological properties of its transcriptional regulation network.

The regulation of gene expression in a cell relies to a major extent on transcription factors, proteins which recognize and bind the DNA at specific binding sites (response elements) within promoter regions associated with each gene. We present an information theoretic approach to modeling transcriptional regulatory networks, in terms of a simple “sequence-matching” rule and the statistics of the occurrence of binding sequences of given specificity in random promoter regions. The crucial biological input is the distribution of the amount of information coded in these cognate response elements and the length distribution of the promoter regions. We provide an analysis of the transcriptional regulatory network of yeast Saccharomyces cerevisiae, which we extract from the available databases, with respect to the degree distributions, clustering coefficient, degree correlations, rich-club coefficient and the k-core structure. We find that these topological features are in remarkable agreement with those predicted by our model, on the basis of the amount of information coded in the interaction between the transcription factors and response elements.

Intrinsic disorder in yeast transcriptional regulatory network

Intrinsic disorder has been shown to be important in mediating protein-protein and protein-DNA interactions. Proteins involved in regulatory functions are particularly enriched in intrinsic disorder. In this study we explored the role of intrinsic disorder in transcriptional regulatory network of yeast. Using disorder prediction programs we show that transcription factors (TFs) regulating large number of targets (transcriptional hubs) have significantly increased intrinsic disorder, though targets regulated by multiple TFs did not show increased intrinsic disorder. Intrinsic disorder may allow these transcriptional hubs to bind to diverse promoter regions of their targets in different contexts, and may also allow complex regulatory control of transcriptional hubs that are involved in coordinating different cellular processes.

Comprehensive Analysis of Combinatorial Regulation using the Transcriptional Regulatory Network of Yeast

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

Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Numerous studies in both prokaryotes and eukaryotes have shown that, under standard growth conditions, less than 20% of the protein-coding genes are essential for survival. This suggests that biological systems have evolved to have a high degree of robustness to mutational disruptions that can affect the majority of their genes. This mutational robustness could arise either due to redundancy, i.e. direct backup, or due to distributed architecture, i.e. indirect backup where multiple genes contribute to the functioning of a process in the system. Despite clear evidence for direct backup, the prevalence of indirect backup is poorly understood. In this study, we reveal the existence of a hidden distributed architecture behind the scale-free transcriptional regulatory network of yeast by applying a unique network transformation procedure and show that the network is tolerant even to mutations that disrupt regulatory hubs. Contrary to what is generally accepted, our observation that hubs can be lost or replaced in evolution suggests that this hidden distributed architecture behind scale-free networks protects the overall transcriptional program of the organism from mutations affecting major regulatory hubs. We show that the distributed architecture has been provided by an unexpectedly large number of coordinating partners for any regulatory protein. On the basis of these findings, we propose that the existence of such architecture can allow organisms to explore the adaptive landscape in changing environments by providing the plasticity required to reprogram levels of expression of specific genes that may enhance survival. Thus, an “over-engineered” backup system in the form of distributed architecture is likely to be a major determinant of the “evolvability” of the gene expression in organisms faced with environmental diversity.

Topological and causal structure of the yeast transcriptional regulatory network.

Interpretation of high-throughput biological data requires a knowledge of the design principles underlying the networks that sustain cellular functions. Of particular importance is the genetic network, a set of genes that interact through directed transcriptional regulation. Genes that exert a regulatory role encode dedicated transcription factors (hereafter referred to as regulating proteins) that can bind to specific DNA control regions of regulated genes to activate or inhibit their transcription. Regulated genes may themselves act in a regulatory manner, in which case they participate in a causal pathway. Looping pathways form feedback circuits. Because a gene can have several connections, circuits and pathways may crosslink and thus represent connected components. We have created a graph of 909 genetically or biochemically established interactions among 491 yeast genes. The number of regulating proteins per regulated gene has a narrow distribution with an exponential decay. The number of regulated genes per regulating protein has a broader distribution with a decay resembling a power law. Assuming in computer-generated graphs that gene connections fulfill these distributions but are otherwise random, the local clustering of connections and the number of short feedback circuits are largely underestimated. This deviation from randomness probably reflects functional constraints that include biosynthetic cost, response delay and differentiative and homeostatic regulation.

Estimating coarse gene network structure from large-scale gene perturbation data.

Large scale gene perturbation experiments generate information about the number of genes whose activity is directly or indirectly affected by a gene perturbation. From this information, one can numerically estimate coarse structural network features such as the total number of direct regulatory interactions and the number of isolated subnetworks in a transcriptional regulation network. Applied to the results of a large-scale gene knockout experiment in the yeast Saccharomyces cerevisiae, the results suggest that the yeast transcriptional regulatory network is very sparse, containing no more direct regulatory interactions than genes. The network comprises >100 independent subnetworks.

Puzzling out transcriptional networks on a genomic scale

In silico searches of promoter sequence motifs, combined with analysis of microarray data, have revealed potential synergistic interactions within the yeast transcriptional regulatory network