Identification of direct residue contacts in protein-protein interaction by message passing

Understanding the molecular determinants of specificity in protein-protein interaction is an outstanding challenge of postgenome biology. The availability of large protein databases generated from sequences of hundreds of bacterial genomes enables various statistical approaches to this problem. In this context covariance-based methods have been used to identify correlation between amino acid positions in interacting proteins. However, these methods have an important shortcoming, in that they cannot distinguish between directly and indirectly correlated residues. We developed a method that combines covariance analysis with global inference analysis, adopted from use in statistical physics. Applied to a set of >2,500 representatives of the bacterial two-component signal transduction system, the combination of covariance with global inference successfully and robustly identified residue pairs that are proximal in space without resorting to ad hoc tuning parameters, both for heterointeractions between sensor kinase (SK) and response regulator (RR) proteins and for homointeractions between RR proteins. The spectacular success of this approach illustrates the effectiveness of the global inference approach in identifying direct interaction based on sequence information alone. We expect this method to be applicable soon to interaction surfaces between proteins present in only 1 copy per genome as the number of sequenced genomes continues to expand. Use of this method could significantly increase the potential targets for therapeutic intervention, shed light on the mechanism of protein-protein interaction, and establish the foundation for the accurate prediction of interacting protein partners.Comment: Supplementary information available on http://www.pnas.org/content/106/1/67.abstrac

Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than forty identified network components, and accords well with previous experimental results at both the pathway and whole cell physiological level. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway or of K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited.Comment: 17 pages, 8 figure

Integrating Phosphorylation Network with Transcriptional Network Reveals Novel Functional Relationships

Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score) to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors

Some Perspectives on Network Modeling in Therapeutic Target Prediction

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities

Genomic Methods for Studying the Post-Translational Regulation of Transcription Factors

The spatiotemporal coordination of gene expression is a fundamental process in cellular biology. Gene expression is regulated, in large part, by sequence-specific transcription factors that bind to DNA regions in the proximity of each target gene. Transcription factor activity and specificity are, in turn, regulated post-translationally by protein-modifying enzymes. High-throughput methods exist to probe specific steps of this process, such as protein-protein and protein-DNA interactions, but few computational tools exist to integrate this information in a principled, model-oriented manner. In this work, I develop several computational tools for studying the functional implications of transcription factor modification. I establish the first publicly accessible database for known and predicted regulatory circuits that encompass modifying enzymes, transcription factors, and transcriptional targets. I also develop a model-based method for integrating heterogeneous genomic and proteomic data for the inference of modification-dependent transcriptional regulatory networks. The model-based method is thoroughly validated as a reliable and accurate computational genomic tool. Additionally, I propose and demonstrate fundamental improvements to computational proteomic methods for identifying modified protein forms. In summary, this work contributes critical methodological advances to the field of regulatory network inference

Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data

Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its ability to recover from trauma. We examined recovery from chemicals in rats and fish through changes in transcriptional networks. Transcriptional networks from time series experiments provide a good basis for organizing and studying the dynamic behavior of biological processes. The goal of this work was to identify networks affected by chemical exposure and track changes in these networks as animals recover. The top 1254 significantly changed genes based upon 1.5-fold change and P\u3c 0.05 across all the time points from the fish data and 937 significantly changed genes from rat data were chosen for network modeling using either a Mutual Information network (MIN) or a Graphical Gaussian Model (GGM) or a Dynamic Bayesian Network (DBN) approach. The top interacting genes were queried to find sub-networks, possible biological networks, biochemical pathways, and network topologies impacted after exposure to fadrozole. The methods were able to reconstruct transcriptional networks with few hub structures, some of which were found to be involved in major biological process and molecular function. The resulting network from rat experiment exhibited a clear hub (central in terms of connections and direction) connectivity structure. Genes such as Ania-7, Hnrpdl, Alad, Gapdh, etc. (all CNS related), GAT-2, Gabra6, Gabbrl, Gabbr2 (GABA, neurotransmitter transporters and receptors), SLC2A1 (glucose transporter), NCX3 (Na-Ca exchanger), Gnal (Olfactory related), skn-la were showed up in our network as the \u27hub\u27 genes while some of the known transcription factors Msx3, Cacngl, Brs3, NGF1 etc. were also matched with our network model. Aromatase in the fish experiment was a highly connected gene in a sub-network along with other genes involved in steroidogenesis. Many of the sub-networks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. Aromatase was a highly connected gene in a sub-network along with the genes LDLR, StAR, KRT18, HER1, CEBPB, ESR2A, and ACVRL1. Many of the subnetworks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. A credible transcriptional network was recovered from both the time series data and the static data. The network included transcription factors and genes with roles in brain function, neurotransmission and sex hormone synthesis. Examination of the dynamic changes in expression within this network over time provided insight into recovery from traumas and chemical exposures

TRANSWESD: inferring cellular networks with transitive reduction

Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic