Estimating genomic coexpression networks using first-order conditional independence

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.     

VarSAn: associating pathways with a set of genomic variants using network analysis

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn''s pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.     

Early Steps in Isoprenoid Biosynthesis: Multilevel Regulation of the Supply of Common Precursors in Plant Cells

Isoprenoids are produced in all organisms but are especially abundant and diverse in plants. Two separate pathways operate in plant cells to synthesize prenyl diphosphate precursors common to all isoprenoids. Cytosolic and mitochondrial precursors are produced by the mevalonic acid (MVA) pathway whereas the recently discovered methylerythritol phosphate (MEP) pathway is located in plastids. However, both pathways may participate in the synthesis of at least some isoprenoids under certain circumstances. Although genes encoding all the enzymes from both pathways have already been cloned, little is known about the regulatory mechanisms that control the supply of isoprenoid precursors. Genetic approaches are providing valuable information on the regulation of both pathways. Thus, recent data from overexpression experiments in transgenic plants show that several enzymes share control over the metabolic flux through the MEP pathway, whereas a single regulatory step has been proposed for the MVA pathway. Identification of Arabidopsis thaliana mutants that are resistant to the inhibition of the MVA and the MEP pathways is a promising approach to uncover mechanisms involved in the crosstalk between pathways. The characterization of some of these mutants impaired in light perception and signaling has recently provided genetic evidence for a role of light as a key factor to modulate the availability of isoprenoid precursors in Arabidopsis seedlings. The picture emerging from recent data supports that a complex regulatory network appears to be at work in plant cells to ensure the supply of isoprenoid precursors when needed.     

Metabolic flux ratio analysis by parallel 13C labeling of isoprenoid biosynthesis in Rhodobacter sphaeroides

Metabolic engineering for increased isoprenoid production often benefits from the simultaneous expression of the two naturally available isoprenoid metabolic routes, namely the 2-methyl-D-erythritol 4-phosphate (MEP) pathway and the mevalonate (MVA) pathway. Quantification of the contribution of these pathways to the overall isoprenoid production can help to obtain a better understanding of the metabolism within a microbial cell factory. Such type of investigation can benefit from ¹³C metabolic flux ratio studies. Here, we designed a method based on parallel labeling experiments (PLEs), using [1-¹³C]- and [4-¹³C]glucose as tracers to quantify the metabolic flux ratios in the glycolytic and isoprenoid pathways. By just analyzing a reporter isoprenoid molecule and employing only four equations, we could describe the metabolism involved from substrate catabolism to product formation. These equations infer ¹³C atom incorporation into the universal isoprenoid building blocks, isopentenyl-pyrophosphate (IPP) and dimethylallyl-pyrophosphate (DMAPP). Therefore, this renders the method applicable to the study of any of isoprenoid of interest. As proof of principle, we applied it to study amorpha-4,11-diene biosynthesis in the bacterium Rhodobacter sphaeroides. We confirmed that in this species the Entner-Doudoroff pathway is the major pathway for glucose catabolism, while the Embden-Meyerhof-Parnas pathway contributes to a lesser extent. Additionally, we demonstrated that co-expression of the MEP and MVA pathways caused a mutual enhancement of their metabolic flux capacity. Surprisingly, we also observed that the isoprenoid flux ratio remains constant under exponential growth conditions, independently from the expression level of the MVA pathway. Apart from proposing and applying a tool for studying isoprenoid biosynthesis within a microbial cell factory, our work reveals important insights from the co-expression of MEP and MVA pathways, including the existence of a yet unclear interaction between them.     

Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes,and Cytokine Signaling Genes in Crohn's Disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn''s disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.     

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general.     

Genome organization in Arabidopsis thaliana: a survey for genes involved in isoprenoid and chlorophyll metabolism

The isoprenoid biosynthetic pathway provides intermediates for the synthesis of a multitude of natural products which serve numerous biochemical functions in plants: sterols (isoprenoids with a C30 backbone) are essential components of membranes; carotenoids (C40) and chlorophylls (which contain a C20 isoprenoid side-chain) act as photosynthetic pigments; plastoquinone, phylloquinone and ubiquinone (all of which contain long isoprenoid side-chains) participate in electron transport chains; gibberellins (C20), brassinosteroids (C30) and abscisic acid (C15) are phytohormones derived from isoprenoid intermediates; prenylation of proteins (with C15 or C20 isoprenoid moieties) may mediate subcellular targeting and regulation of activity; and several monoterpenes (C10), sesquiterpenes (C15) and diterpenes (C20) have been demonstrated to be involved in plant defense. Here we present a comprehensive analysis of genes coding for enzymes involved in the metabolism of isoprenoid-derived compounds in Arabidopsis thaliana. By combining homology and sequence motif searches with knowledge regarding the phylogenetic distribution of pathways of isoprenoid metabolism across species, candidate genes for these pathways in A. thaliana were obtained. A detailed analysis of the vicinity of chromosome loci for genes of isoprenoid metabolism in A. thaliana provided evidence for the clustering of genes involved in common pathways. Multiple sequence alignments were used to estimate the number of genes in gene families and sequence relationship trees were utilized to classify their individual members. The integration of all these datasets allows the generation of a knowledge-based metabolic map of isoprenoid metabolic pathways in A. thaliana and provides a substantial improvement of the currently available gene annotation.     

An exception among diatoms: unique organization of genes involved in isoprenoid biosynthesis in Rhizosolenia setigera CCMP 1694

The marine diatom Rhizosolenia setigera is unique among this group of microalgae given that it is only one of a handful of diatom species that can produce highly branched isoprenoid (HBI) hydrocarbons. In our efforts to determine distinguishing molecular characteristics in R. setigera CCMP 1694 that could help elucidate the underlying mechanisms for its ability to biosynthesize HBIs, we discovered the occurrence of independent genes encoding for two isopentenyl diphosphate isomerases (RsIDI1 and RsIDI2) and one squalene synthase (RsSQS), enzymes that catalyze non‐consecutive steps in isoprenoid biosynthesis. These genes are peculiarly fused in all other genome‐sequenced diatoms to date, making their organization in R. setigera CCMP 1694 a clear distinguishing molecular feature. Phylogenetic and sequence analysis of RsIDI1, RsIDI2, and RsSQS revealed that such an arrangement of individually transcribed genes involved in isoprenoid biosynthesis could have arisen through a secondary gene fission event. We further demonstrate that inhibition of squalene synthase (SQS) shifts the flux of exogenous isoprenoid precursors towards HBI biosynthesis suggesting the competition for isoprenoid substrates in the form of farnesyl diphosphate between the sterol and HBI biosynthetic pathways in this diatom.     

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes.     

Toxoplasma gondii Relies on Both Host and Parasite Isoprenoids and Can Be Rendered Sensitive to Atorvastatin

Intracellular pathogens have complex metabolic interactions with their host cells to ensure a steady supply of energy and anabolic building blocks for rapid growth. Here we use the obligate intracellular parasite Toxoplasma gondii to probe this interaction for isoprenoids, abundant lipidic compounds essential to many cellular processes including signaling, trafficking, energy metabolism, and protein translation. Synthesis of precursors for isoprenoids in Apicomplexa occurs in the apicoplast and is essential. To synthesize longer isoprenoids from these precursors, T. gondii expresses a bifunctional farnesyl diphosphate/geranylgeranyl diphosphate synthase (TgFPPS). In this work we construct and characterize T. gondii null mutants for this enzyme. Surprisingly, these mutants have only a mild growth phenotype and an isoprenoid composition similar to wild type parasites. However, when extracellular, the loss of the enzyme becomes phenotypically apparent. This strongly suggests that intracellular parasite salvage FPP and/or geranylgeranyl diphosphate (GGPP) from the host. We test this hypothesis using inhibitors of host cell isoprenoid synthesis. Mammals use the mevalonate pathway, which is susceptible to statins. We document strong synergy between statin treatment and pharmacological or genetic interference with the parasite isoprenoid pathway. Mice can be cured with atorvastatin (Lipitor) from a lethal infection with the TgFPPs mutant. We propose a double-hit strategy combining inhibitors of host and parasite pathways as a novel therapeutic approach against Apicomplexan parasites.     

Reconstruction of a bacterial isoprenoid biosynthetic pathway in Saccharomyces cerevisiae

A eukaryotic mevalonate pathway transferred and expressed in Escherichia coli, and a mammalian hydrocortisone biosynthetic pathway rebuilt in Saccharomyces cerevisiae are examples showing that transferring metabolic pathways from one organism to another can have a powerful impact on cell properties. In this study, we reconstructed the E. coli isoprenoid biosynthetic pathway in S. cerevisiae. Genes encoding the seven enzymatic steps of the pathway were cloned and expressed in S. cerevisiae. mRNA from the seven genes was detected, and the pathway was shown able to sustain growth of yeast in conditions of inhibition of its constitutive isoprenoid biosynthetic pathway.     

Interrelation between the pathways of isoprenoid biosynthesis and carbon source catabolism in anaerobic and facultatively anaerobic bacteria

Data on the interrelation between the pathways of the carbon source catabolism and isoprenoid biosynthesis in anaerobic and facultatively anaerobic bacteria were obtained. Two pathways of isoprenoid biosynthesis (nonmevalonate and mevalonate) were revealed in the representatives of the genus Clostridium. The nonmevalonate pathway of isoprenoid biosynthesis and the glycolytic pathway of substrate oxidation are typical of glucose-grown bacteria, whereas the pentose phosphate cycle operates in xylose-grown bacteria. The mevalonate pathway of isoprenoid biosynthesis was revealed in strain Clostridium thermosaccharolyticum DSM 571 grown in the presence of mevinolin, as well as in a number of lactic acid bacteria. Mevinolin is known to react with the lactate dehydrogenase complex, preventing reduction of pyruvate. The nonmevalonate pathway of isoprenoid biosynthesis was revealed in Bifidobacterium bifidum. The role of different metabolic pathways in isoprenoid biosynthesis is discussed.     

Extending pathways based on gene lists using InterPro domain signatures

Background

High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.

Results

In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.

Conclusion

Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.