Photosystem I gene cassettes are present in marine virus genomes.

Cyanobacteria of the Synechococcus and Prochlorococcus genera are important contributors to photosynthetic productivity in the open oceans. Recently, core photosystem II (PSII) genes were identified in cyanophages and proposed to function in photosynthesis and in increasing viral fitness by supplementing the host production of these proteins. Here we show evidence for the presence of photosystem I (PSI) genes in the genomes of viruses that infect these marine cyanobacteria, using pre-existing metagenomic data from the global ocean sampling expedition as well as from viral biomes. The seven cyanobacterial core PSI genes identified in this study, psaA, B, C, D, E, K and a unique J and F fusion, form a cluster in cyanophage genomes, suggestive of selection for a distinct function in the virus life cycle. The existence of this PSI cluster was confirmed with overlapping and long polymerase chain reaction on environmental DNA from the Northern Line Islands. Potentially, the seven proteins encoded by the viral genes are sufficient to form an intact monomeric PSI complex. Projection of viral predicted peptides on the cyanobacterial PSI crystal structure suggested that the viral-PSI components might provide a unique way of funnelling reducing power from respiratory and other electron transfer chains to the PSI.

Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways.

BACKGROUND: Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways. Here we present a computational approach for solving these two challenges. First, we describe a comprehensive, scalable, information theory based computational pipeline that calculates pathway alignment information and then compiles it in a novel manner that allows further analysis. This approach can be used for building phylogenies and for pointing out specific differences that can then be analyzed in depth. Second, we describe a new approach for comparing the evolution of metabolic pathways. This approach can be used for detecting co-evolutionary relationships between metabolic pathways. RESULTS: We demonstrate the advantages of our approach by applying our pipeline to data from the MetaCyc repository (which includes a total of 205 organisms and 660 metabolic pathways). Our analysis revealed several surprising biological observations. For example, we show that the different habitats in which Archaea organisms reside are reflected by a pathway based phylogeny. In addition, we discover two striking clusters of metabolic pathways, each cluster includes pathways that have very similar evolution. CONCLUSION: We demonstrate that distance measures that are based on the topology and the content of metabolic networks are useful for studying evolution and co-evolution.

Constraint-based functional similarity of metabolic genes: going beyond network topology.

MOTIVATION: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. RESULTS: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of functional gene similarity, one based on the response of the metabolic network to gene knockouts and the other based on the metabolic flux activity across a variety of growth media. We applied these measures to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to Gene Ontology (GO) annotations and gene expression data, we show that our computational method captures functional similarities between metabolic genes that go beyond those obtained by the topological analysis of metabolic networks alone, thus revealing dynamic characteristics of gene function. Interestingly, the measure based on the network response to different growth environments markedly outperforms the measure based on its response to gene knockouts, though both have some added synergistic value in depicting the functional relationships between metabolic genes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Translation towards personalized medicine in Multiple Sclerosis.

In recent years the realization that the concept 'one drug fits all' - does not work, created the need to shift gears from 'treating the disease' to 'treating the patient', and implementation of 'Personalized Medicine' where treatment is tailored to the individual. In chronic and progressive diseases, such as Multiple Sclerosis (MS), the need for tailored therapeutics is especially imperative, as the consequences of an ineffective medication might be irreversible dysfunction. In recent years accumulating evidence indicates that MS is not a single disease and that patients with different disease subtypes respond differently to a medication. Environment and genetics are among the factors that determine disease subtype and activity, and the patient's response to medication. Additional factors include demographic characteristics such as gender and age, as well as chrono-biological indicators. During the last few years, advances and availability of new technologies have brought genome-wide gene expression profiling studies to many medical fields, including MS. Genomic technologies have also stimulated pharmacogenetics studies, that aim to identify genetic factors that affect response to treatment. However, pharmacogenetics information is still immature to allow its translation to clinical practice in MS. Notably, one of the major limitations in obtaining reproducible data across MS pharmacogenetics studies has been the lack of a consensus as to the appropriate method for determining clinical response. In light of the rapid advances in technology and progress in applying individualized treatment strategies in other diseases, 'Personalized Medicine' for MS seems feasible within the coming years.

An integrative method for accurate comparative genome mapping.

We present MAGIC, an integrative and accurate method for comparative genome mapping. Our method consists of two phases: preprocessing for identifying "maximal similar segments," and mapping for clustering and classifying these segments. MAGIC's main novelty lies in its biologically intuitive clustering approach, which aims towards both calculating reorder-free segments and identifying orthologous segments. In the process, MAGIC efficiently handles ambiguities resulting from duplications that occurred before the speciation of the considered organisms from their most recent common ancestor. We demonstrate both MAGIC's robustness and scalability: the former is asserted with respect to its initial input and with respect to its parameters' values. The latter is asserted by applying MAGIC to distantly related organisms and to large genomes. We compare MAGIC to other comparative mapping methods and provide detailed analysis of the differences between them. Our improvements allow a comprehensive study of the diversity of genetic repertoires resulting from large-scale mutations, such as indels and duplications, including explicitly transposable and phagic elements. The strength of our method is demonstrated by detailed statistics computed for each type of these large-scale mutations. MAGIC enabled us to conduct a comprehensive analysis of the different forces shaping prokaryotic genomes from different clades, and to quantify the importance of novel gene content introduced by horizontal gene transfer relative to gene duplication in bacterial genome evolution. We use these results to investigate the breakpoint distribution in several prokaryotic genomes.

On the repeat-annotated phylogenetic tree reconstruction problem.

A new problem in phylogenetic inference is presented, based on recent biological findings indicating a strong association between reversals (i.e., inversions) and repeats. These biological findings are formalized here in a new mathematical model, called repeat-annotated phylogenetic trees (RAPT). We show that, under RAPT, the evolutionary process--including both the tree-topology as well as internal node genome orders--is uniquely determined, a property that is of major significance both in theory and in practice. Furthermore, the repeats are employed to provide linear-time algorithms for reconstructing both the genomic orders and the phylogeny, which are NP-hard problems under the classical model of sorting by reversals (SBR).

A high-throughput approach for associating MicroRNAs with their activity conditions.

Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by redirecting their stabilities and marking them for cleavage. In Arabidopsis thaliana, microRNAs have been shown to regulate development and are believed to impact expression both under various conditions, such as stress and stimuli, as well as in specific tissue types. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic techniques. Our new tool, miRNAXpress, at first computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act. Thus, the program comprises two main modules that work in tandem to compute the desired output. The first is an efficient target prediction engine that predicts mRNA targets of query microRNAs by evaluating the optimal duplex that could be formed between the two: given a short query RNA, a long target RNA, and a predefined energy cut-off threshold, the program finds and reports all putative binding sites of the query RNA in the target RNA with hybridization energy bounded by the predefined threshold. The second module realizes an association operation that is computed by a method which relies on an efficient t-test to compute the associations. The calculation of the matrix of microRNAs and their potential targets is the computationally intensive part of the work done by miRNAXpress, and therefore an efficient algorithm for this portion facilitates the entire process. Thus, the target prediction engine is based on an efficient approximate hybridization search algorithm whose efficiency is the result of utilizing the sparsity of the search space without sacrificing the optimality of the results. The time complexity of this algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more. Thus miRNAXpress is a novel tool for associating between microRNAs and the conditions in which they act. We employed it to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered. For example, mir159C activity is possibly a factor in the misresponse of nph4 mutants to phototropic stimulations.

Genomic sorting with length-weighted reversals.

Current algorithmic studies of genome rearrangement ignore the length of reversals (or inversions); rather, they only count their number. We introduce a new cost model in which the lengths of the reversed sequences play a role, allowing more flexibility in accounting for mutation phenomena. Our focus is on sorting unsigned (unoriented) permutations by reversals; since this problem remains difficult (NP-hard) in our new model, the best we can hope for are approximation results. We propose an efficient, novel algorithm that takes (a monotonic function f of) length into account as an optimization criterion and study its properties. Our results include an upper bound of O(fn lg2n) for any additive cost measure f on the cost of sorting any n-element permutation, and a guaranteed approximation ratio of O(lg2n) times optimal for sorting a given permutation. Our work poses some interesting questions to both biologists and computer scientists and suggests some new bioinformatic insights that are currently being studied.

Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function.

It has been shown that the distribution of genes in eukaryotic genomes is not random; however, formerly reported relations between gene function and genomic organization were relatively weak. Previous studies have demonstrated that codon usage bias is related to all stages of gene expression and to protein function. Here we apply a novel tool for assessing functional relatedness, codon usage frequency similarity (CUFS), which measures similarity between genes in terms of codon and amino acid usage. By analyzing chromosome conformation capture data, describing the three-dimensional (3D) conformation of the DNA, we show that the functional similarity between genes captured by CUFS is directly and very strongly correlated with their 3D distance in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, mouse and human. This emphasizes the importance of three-dimensional genomic localization in eukaryotes and indicates that codon usage is tightly linked to genome architecture.

The effective application of a discrete transition model to explore cell-cycle regulation in yeast.

BACKGROUND: Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as "black-boxes". Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background. RESULTS: We present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle. CONCLUSION: The model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.

Interferon-beta induces distinct gene expression response patterns in human monocytes versus T cells.

BACKGROUND: Monocytes, which are key players in innate immunity, are outnumbered by neutrophils and lymphocytes among peripheral white blood cells. The cytokine interferon-ß (IFN-ß) is widely used as an immunomodulatory drug for multiple sclerosis and its functional pathways in peripheral blood mononuclear cells (PBMCs) have been previously described. The aim of the present study was to identify novel, cell-specific IFN-ß functions and pathways in tumor necrosis factor (TNF)-α-activated monocytes that may have been missed in studies using PBMCs. METHODOLOGY/PRINCIPAL FINDINGS: Whole genome gene expression profiles of human monocytes and T cells were compared following in vitro priming to TNF-α and overnight exposure to IFN-ß. Statistical analyses of the gene expression data revealed a cell-type-specific change of 699 transcripts, 667 monocyte-specific transcripts, 21 T cell-specific transcripts and 11 transcripts with either a difference in the response direction or a difference in the magnitude of response. RT-PCR revealed a set of differentially expressed genes (DEGs), exhibiting responses to IFN-ß that are modulated by TNF-α in monocytes, such as RIPK2 and CD83, but not in T cells or PBMCs. Known IFN-ß promoter response elements, such as ISRE, were enriched in T cell DEGs but not in monocyte DEGs. The overall directionality of the gene expression regulation by IFN-ß was different in T cells and monocytes, with up-regulation more prevalent in T cells, and a similar extent of up and down-regulation recorded in monocytes. CONCLUSIONS: By focusing on the response of distinct cell types and by evaluating the combined effects of two cytokines with pro and anti-inflammatory activities, we were able to present two new findings First, new IFN-ß response pathways and genes, some of which were monocytes specific; second, a cell-specific modulation of the IFN-ß response transcriptome by TNF-α.

Pathway-based functional analysis of metagenomes.

Metagenomic data enables the study of microbes and viruses through their DNA as retrieved directly from the environment in which they live. Functional analysis of metagenomes explores the abundance of gene families, pathways, and systems, rather than their taxonomy. Through such analysis, researchers are able to identify those functional capabilities most important to organisms in the examined environment. Recently, a statistical framework for the functional analysis of metagenomes was described that focuses on gene families. Here we describe two pathway level computational models for functional analysis that take into account important, yet unaddressed issues such as pathway size, gene length, and overlap in gene content among pathways. We test our models over carefully designed simulated data and propose novel approaches for performance evaluation. Our models significantly improve over the current approach with respect to pathway ranking and the computations of relative abundance of pathways in environments.

Comparative metagenomics of microbial traits within oceanic viral communities.

Viral genomes often contain genes recently acquired from microbes. In some cases (for example, psbA) the proteins encoded by these genes have been shown to be important for viral replication. In this study, using a unique search strategy on the Global Ocean Survey (GOS) metagenomes in combination with marine virome and microbiome pyrosequencing-based datasets, we characterize previously undetected microbial metabolic capabilities concealed within the genomes of uncultured marine viral communities. A total of 34 microbial gene families were detected on 452 viral GOS scaffolds. The majority of auxiliary metabolic genes found on these scaffolds have never been reported in phages. Host genes detected in viruses were mainly divided between genes encoding for different energy metabolism pathways, such as electron transport and newly identified photosystem genes, or translation and post-translation mechanism related. Our findings suggest previously undetected ways, in which marine phages adapt to their hosts and improve their fitness, including translation and post-translation level control over the host rather than the already known transcription level control.

A computational approach for genome-wide mapping of splicing factor binding sites.

Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns. Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements. The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.

Seeded tree alignment.

The optimal transformation of one tree into another by means of elementary edit operations is an important algorithmic problem that has several interesting applications to computational biology. Here we introduce a constrained form of this problem in which a partial mapping of a set of nodes (the "seeds") in one tree to a corresponding set of nodes in the other tree is given, and present efficient algorithms for both ordered and unordered trees. Whereas ordered tree matching based on seeded nodes has applications in pattern matching of RNA structures, unordered tree matching based on seeded nodes has applications in co-speciation and phylogeny reconciliation. The latter involves the solution of the planar tanglegram layout problem, for which a polynomial-time algorithm is given here.

Faithful modeling of transient expression and its application to elucidating negative feedback regulation.

Modeling and analysis of genetic regulatory networks is essential both for better understanding their dynamic behavior and for elucidating and refining open issues. We hereby present a discrete computational model that effectively describes the transient and sequential expression of a network of genes in a representative developmental pathway. Our model system is a transcriptional cascade that includes positive and negative feedback loops directing the initiation and progression through meiosis in budding yeast. The computational model allows qualitative analysis of the transcription of early meiosis-specific genes, specifically, Ime2 and their master activator, Ime1. The simulations demonstrate a robust transcriptional behavior with respect to the initial levels of Ime1 and Ime2. The computational results were verified experimentally by deleting various genes and by changing initial conditions. The model has a strong predictive aspect, and it provides insights into how to distinguish among and reason about alternative hypotheses concerning the mode by which negative regulation through Ime1 and Ime2 is accomplished. Some predictions were validated experimentally, for instance, showing that the decline in the transcription of IME1 depends on Rpd3, which is recruited by Ime1 to its promoter. Finally, this general model promotes the analysis of systems that are devoid of consistent quantitative data, as is often the case, and it can be easily adapted to other developmental pathways.

Computer modelling of antifolate inhibition of folate metabolism using hybrid functional petri nets.

Antifolates are used in the treatment of various human malignancies and exert their cytotoxic activity by inhibiting folate-dependent enzymes resulting in disruption of DNA synthesis and cell death. Here we devised a computerized hybrid functional petri nets (HFPN) modelling of folate metabolism under physiological and antifolate inhibitory conditions. This HFPN modelling proved valid as a good agreement was found between the simulated steady-state concentrations of various reduced folates and those published for cell extracts; consistently, the simulation derived total folate pool size (11.3 microM) was identical to that published for cell extracts. In silico experiments were conducted to characterize the inhibitory profile of four distinct antifolates including methotrexate (MTX), tomudex, and LY309887, which inhibit dihydrofolate reductase (DHFR), thymidylate synthase (TS) and glycineamide ribonucleotide transformylase (GARTFase), respectively, as well as pemetrexed which has the capacity to inhibit all three enzymes. In order to assess the inhibitory activity of antifolates on purines and pyrimidines, the biosynthesis rates of IMP (20.53 microM/min) and dTMP (23.8 microM/min) were first simulated. Whereas the biochemical inhibitory profile of MTX was characterized by increased dihydrofolate and decreased tetrahydrofolate (THF) concentrations, the remaining antifolates did not decrease THF levels. Furthermore, MTX was 766- and 10-fold more potent in decreasing the production rates of IMP and dTMP, respectively, than pemetrexed. LY309887 indirectly decreased the rate of dTMP production by reducing the levels of 5-CH2-THF, a folate cofactor for TS. Surprisingly, pemetrexed failed to inhibit DHFR even at high concentrations. This HFPN-based simulation offers an inexpensive, user-friendly, rapid and reliable means of pre-clinical evaluation of the inhibitory profiles of antifolates.

Alignment of metabolic pathways.

MOTIVATION: Several genome-scale efforts are underway to reconstruct metabolic networks for a variety of organisms. As the resulting data accumulates, the need for analysis tools increases. A notable requirement is a pathway alignment finder that enables both the detection of conserved metabolic pathways among different species as well as divergent metabolic pathways within a species. When comparing two pathways, the tool should be powerful enough to take into account both the pathway topology as well as the nodes' labels (e.g. the enzymes they denote), and allow flexibility by matching similar--rather than identical--pathways. RESULTS: MetaPathwayHunter is a pathway alignment tool that, given a query pathway and a collection of pathways, finds and reports all approximate occurrences of the query in the collection, ranked by similarity and statistical significance. It is based on a novel, efficient graph matching algorithm that extends the functionality of known techniques. The program also supports a visualization interface with which the alignment of two homologous pathways can be graphically displayed. We employed this tool to study the similarities and differences in the metabolic networks of the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae, as represented in highly curated databases. We reaffirmed that most known metabolic pathways common to both the species are conserved. Furthermore, we discovered a few intriguing relationships between pathways that provide insight into the evolution of metabolic pathways. We conclude with a description of biologically meaningful meta-queries, demonstrating the power and flexibility of our new tool in the analysis of metabolic pathways.

Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction.

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.