Search PubMed⌕ Search

Biomedical subjects

Trey Ideker

Publications and source records attributed to Trey Ideker.

At least 37 records · Page 2Linked to original sources
PubMed2006

Systematic identification of functional orthologs based on protein network comparison.

Annotating protein function across species is an important task that is often complicated by the presence of large paralogous gene families. Here, we report a novel strategy for identifying functionally related proteins that supplements sequence-based comparisons with information on conserved protein-protein interactions. First, the protein interaction networks of two species are aligned by assigning proteins to sequence homology clusters using the Inparanoid algorithm. Next, probabilistic inference is performed on the aligned networks to identify pairs of proteins, one from each species, that are likely to retain the same function based on conservation of their interacting partners. Applying this method to Drosophila melanogaster and Saccharomyces cerevisiae, we analyze 121 cases for which functional orthology assignment is ambiguous when sequence similarity is used alone. In 61 of these cases, the network supports a different protein pair than that favored by sequence comparisons. These results suggest that network analysis can be used to provide a key source of information for refining sequence-based homology searches.

Animals↗
PubMed2006

Discovering regulated networks during HIV-1 latency and reactivation.

Human immunodeficiency virus (HIV) affects millions of people across the globe. Despite the introduction of powerful anti-viral therapies, one factor confounding viral elimination is the ability of HIV to remain latent within the host genome. Here, we perform a network analysis of the viral reactivation process using human gene expression profiles and curated databases of both human-human and human-HIV protein interactions. Based on this analysis, we report the identification of active pathways in both the latent and early phases of reactivation. These active pathways suggest host functions that are altered and important for HIV pathogenesis.

Algorithms↗
PubMed2005

The Plasmodium protein network diverges from those of other eukaryotes.

Plasmodium falciparum is the pathogen responsible for over 90% of human deaths from malaria. Therefore, it has been the focus of a considerable research initiative, involving the complete DNA sequencing of the genome, large-scale expression analyses, and protein characterization of its life-cycle stages. The Plasmodium genome sequence is relatively distant from those of most other eukaryotes, with more than 60% of the 5,334 encoded proteins lacking any notable sequence similarity to other organisms. To systematically elucidate functional relationships among these proteins, a large two-hybrid study has recently mapped a network of 2,846 interactions involving 1,312 proteins within Plasmodium. This network adds to a growing collection of available interaction maps for a number of different organisms, and raises questions about whether the divergence of Plasmodium at the sequence level is reflected in the configuration of its protein network. Here we examine the degree of conservation between the Plasmodium protein network and those of model organisms. Although we find 29 highly connected protein complexes specific to the network of the pathogen, we find very little conservation with complexes observed in other organisms (three in yeast, none in the others). Overall, the patterns of protein interaction in Plasmodium, like its genome sequence, set it apart from other species.

Animals↗
PubMed2005

enoLOGOS: a versatile web tool for energy normalized sequence logos.

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at http://biodev.hgen.pitt.edu/enologos/.

Amino Acids↗
PubMed2005

VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data.

Microarrays are invaluable high-throughput tools used to snapshot the gene expression profiles of cells and tissues. Among the most basic and fundamental questions asked of microarray data is whether individual genes are significantly activated or repressed by a particular stimulus. We have previously presented two Bayesian statistical methods for this level of analysis, collectively known as variance-modeled posterior inference with regional exponentials (VAMPIRE). These methods each require a sophisticated modeling step followed by integration of a posterior probability density. We present here a publicly available, web-based platform that allows users to easily load data, associate related samples and identify differentially expressed features using the VAMPIRE statistical framework. In addition, this suite of tools seamlessly integrates a novel gene annotation tool, known as GOby, which identifies statistically overrepresented gene groups. Unlike other tools in this genre, GOby can localize enrichment while respecting the hierarchical structure of annotation systems like Gene Ontology (GO). By identifying statistically significant enrichment of GO terms, Kyoto Encyclopedia of Genes and Genomes pathways, and TRANSFAC transcription factor binding sites, users can gain substantial insight into the physiological significance of sets of differentially expressed genes. The VAMPIRE microarray suite can be accessed at http://genome.ucsd.edu/microarray.

Bayes Theorem↗
PubMed2005

Validation and refinement of gene-regulatory pathways on a network of physical interactions.

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4.

Computational Biology↗
PubMed2005

Conserved patterns of protein interaction in multiple species.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.

Amino Acid Sequence↗
PubMed2005

Systematic interpretation of genetic interactions using protein networks.

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.

Algorithms↗
PubMed2004

Integrating phenotypic and expression profiles to map arsenic-response networks.

BACKGROUND: Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known biochemical reactions in yeast, as well as the yeast network of 20,985 protein-protein/protein-DNA interactions. RESULTS: While the expression data unveiled no significant nodes in the metabolic network, the regulatory network revealed several important nodes as centers of arsenic-induced activity. The highest-scoring proteins included Fhl1, Msn2, Msn4, Yap1, Cad1 (Yap2), Pre1, Hsf1 and Met31. Contrary to the gene-expression analyses, the phenotypic-profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate, and serine, threonine and glutamate biosynthesis. We also carried out transcriptional profiling of specific deletion strains, confirming that the transcription factors Yap1, Arr1 (Yap8), and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact. CONCLUSIONS: By integrating phenotypic and transcriptional profiling and mapping the data onto the metabolic and regulatory networks, we have shown that arsenic is likely to channel sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Furthermore, we show that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that transcriptional and phenotypic profiling implicate distinct, but related, pathways.

Arsenic↗
PubMed2004

Hot spots for modulating toxicity identified by genomic phenotyping and localization mapping.

DNA repair and checkpoint pathways protect against carcinogen-induced toxicity. Here, we describe additional, equally protective pathways discovered by interrogating 4,733 yeast proteins for their ability to diminish toxicity induced by four known carcinogens. A computational mapping strategy for global phenotypic data was developed to build a systems toxicology model detailing recovery from carcinogen exposure and identifying protein complexes that modulate toxicity. Global phenotypic data were merged with global subcellular localization and protein interactome data to generate an integrated picture of cellular recovery after carcinogen exposure. Statistically validated results from this systems-wide integration demonstrate that, in addition to the nucleus, subnetworks of toxicity-modulating proteins were overrepresented in the vacuolar membrane, endosome, endoplasmic reticulum, and mitochondrion. In addition, we show that many proteins associated with RNA polymerase II, macromolecular trafficking, and vacuole function can now be counted among the many proteins that modulate carcinogen-induced toxicity.

Cell Nucleus↗
PubMed2004

PathBLAST: a tool for alignment of protein interaction networks.

PathBLAST is a network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species. PathBLAST is now available at http://www.pathblast.org/ as a web-based query. In this implementation, the user specifies a short protein interaction path for query against a target protein-protein interaction network selected from a network database. PathBLAST returns a ranked list of matching paths from the target network along with a graphical view of these paths and the overlap among them. Target protein-protein interaction networks are currently available for Helicobacter pylori, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. Just as BLAST enables rapid comparison of protein sequences between genomes, tools such as PathBLAST are enabling comparative genomics at the network level.

Animals↗
PubMed2004

A systems approach to discovering signaling and regulatory pathways--or, how to digest large interaction networks into relevant pieces.

In the post-genomic era, the first step in any study of protein function is a homology search against the complete genome sequence of the organism of interest. By analogy, if we also wish to elucidate the cadre of signaling and regulatory pathways in the cell, an extremely powerful first step is to construct a complete network of protein-protein and transcriptional interactions and then search through this network to identify critical pathways in a top-down fashion. Like genomic sequence, the molecular interaction network provides a broad foundation for more directed studies to follow. We illustrate this strategy using a large network of 12,232 interactions in yeast. A variety of applications are discussed, including screening the network to identify pathways responsible for gene expression changes observed in galactose-induced cells, and identifying groups of interacting proteins that are essential (by phenotypic assay) for the cellular response to DNA damage.

Cell Physiological Phenomena↗
PubMed2004

Physical network models.

We develop a new framework for inferring models of transcriptional regulation. The models, which we call physical network models, are annotated molecular interaction graphs. The attributes in the model correspond to verifiable properties of the underlying biological system such as the existence of protein-protein and protein-DNA interactions, the directionality of signal transduction in protein-protein interactions, as well as signs of the immediate effects of these interactions. Possible configurations of these variables are constrained by the available data sources. Some of the data sources, such as factor-binding data, involve measurements that are directly tied to the variables in the model. Other sources, such as gene knock-outs, are functional in nature and provide only indirect evidence about the variables. We associate each observed knock-out effect in the deletion mutant data with a set of causal paths (molecular cascades) that could in principle explain the effect, resulting in aggregate constraints about the physical variables in the model. The most likely settings of all the variables, specifying the most likely graph annotations, are found by a recursive application of the max-product algorithm. By testing our approach on datasets related to the pheromone response pathway in S. cerevisiae, we demonstrate that the resulting model is consistent with previous studies about the pathway. Moreover, we successfully predict gene knock-out effects with a high degree of accuracy in a cross-validation setting. When applying this approach genome-wide, we extract submodels consistent with previous studies. The approach can be readily extended to other data sources or to facilitate automated experimental design.

Computational Biology↗
PubMed2003

Conserved pathways within bacteria and yeast as revealed by global protein network alignment.

We implement a strategy for aligning two protein-protein interaction networks that combines interaction topology and protein sequence similarity to identify conserved interaction pathways and complexes. Using this approach we show that the protein-protein interaction networks of two distantly related species, Saccharomyces cerevisiae and Helicobacter pylori, harbor a large complement of evolutionarily conserved pathways, and that a large number of pathways appears to have duplicated and specialized within yeast. Analysis of these findings reveals many well characterized interaction pathways as well as many unanticipated pathways, the significance of which is reinforced by their presence in the networks of both species.

Bacteria↗
PubMed2003

Multiple pathways are co-regulated by the protein kinase Snf1 and the transcription factors Adr1 and Cat8.

ADR1 and CAT8 encode carbon source-responsive transcriptional regulators that cooperatively control expression of genes involved in ethanol utilization. These transcription factors are active only after the diauxic transition, when glucose is depleted and energy-generating metabolism has shifted to the aerobic oxidation of non-fermentable carbon sources. The Snf1 protein kinase complex is required for activation of their downstream target genes described previously. Using DNA microarrays, we determined the extent to which these three factors collaborate in regulating the expression of the yeast genome after glucose depletion. The expression of 108 genes is significantly decreased in the absence of ADR1. The importance of ADR1 during the diauxic transition is illustrated by the observation that expression of almost one-half of the 40 most highly glucose-repressed genes is ADR1-dependent. ADR1-dependent genes fall into a variety of functional classes with carbon metabolism containing the largest number of members. Most of the genes in this class are involved in the oxidation of different non-fermentable carbon sources. These microarray data show that ADR1 coordinates the biochemical pathways that generate acetyl-CoA and NADH from non-fermentable substrates. Only a small number of ADR1-dependent genes are also CAT8-dependent. However, nearly one-half of the ADR1-dependent genes are also dependent on the Snf1 protein kinase for derepression. Many more genes are SNF1-dependent than are either ADR1- or CAT8-dependent suggesting that SNF1 plays a broader role in gene expression than either ADR1 or CAT8. The largest class of SNF1-dependent genes encodes regulatory proteins that could extend SNF1 dependence to additional pathways.

Binding Sites↗
PubMed2003

Building with a scaffold: emerging strategies for high- to low-level cellular modeling.

Computational cellular models are becoming crucial for the analysis of complex biological systems. An important new paradigm for cellular modeling involves building a comprehensive scaffold of molecular interactions and then mining this scaffold to reveal a hierarchy of signaling, regulatory and metabolic pathways. We review the important trends that make this approach feasible and describe how they are spurring the development of models at multiple levels of abstraction. Pathway maps can be extracted from the scaffold using "high-level" computational models, which identify the key components, interactions and influences required for more detailed "low-level" models. Large-scale experimental measurements validate high-level models, whereas targeted experimental manipulations and measurements test low-level models.

Cell Physiological Phenomena↗