Compositional discordance between prokaryotic plasmids and host chromosomes

BACKGROUND: Most plasmids depend on the host replication machinery and possess partitioning genes. These properties confine plasmids to a limited range of hosts, yielding a close and presumably stable relationship between plasmid and host. Hence, it is anticipated that due to amelioration the dinucleotide composition of plasmids is similar to that of the genome of their hosts. However, plasmids are also thought to play a major role in horizontal gene transfer and thus are frequently exchanged between hosts, suggesting dinucleotide composition dissimilarity between plasmid and host genome. We compared the dinucleotide composition of a large collection of plasmids with that of their host genomes to shed more light on this enigma. RESULTS: The dinucleotide frequency, coined the genome signature, facilitates the identification of putative horizontally transferred DNA in complete genome sequences, since it was found to be typical for a certain genome, and similar between related species. By comparison of the genome signature of 230 plasmid sequences with that of the genome of each respective host, we found that in general the genome signature of plasmids is dissimilar from that of their host genome. CONCLUSION: Our results show that the genome signature of plasmids does not resemble that of their host genome. This indicates either absence of amelioration or a less stable relationship between plasmids and their host. We propose an indiscriminate lifestyle for plasmids preserving the genome signature discordance between these episomes and host chromosomes

Initial steps towards a production platform for DNA sequence analysis on the grid

ABSTRACT: BACKGROUND: Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. RESULTS: In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was signicantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations. All components are open source and can be transported to other grid infrastructures. CONCLUSIONS: The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl

Critical assessment of human metabolic pathway databases: a stepping stone for future integration

<p>Abstract</p> <p>Background</p> <p>Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.</p> <p>Results</p> <p>We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.</p> <p>Conclusions</p> <p>Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.</p

Fasting induces a biphasic adaptive metabolic response in murine small intestine

BACKGROUND: The gut is a major energy consumer, but a comprehensive overview of the adaptive response to fasting is lacking. Gene-expression profiling, pathway analysis, and immunohistochemistry were therefore carried out on mouse small intestine after 0, 12, 24, and 72 hours of fasting. RESULTS: Intestinal weight declined to 50% of control, but this loss of tissue mass was distributed proportionally among the gut's structural components, so that the microarrays' tissue base remained unaffected. Unsupervised hierarchical clustering of the microarrays revealed that the successive time points separated into distinct branches. Pathway analysis depicted a pronounced, but transient early response that peaked at 12 hours, and a late response that became progressively more pronounced with continued fasting. Early changes in gene expression were compatible with a cellular deficiency in glutamine, and metabolic adaptations directed at glutamine conservation, inhibition of pyruvate oxidation, stimulation of glutamate catabolism via aspartate and phosphoenolpyruvate to lactate, and enhanced fatty-acid oxidation and ketone-body synthesis. In addition, the expression of key genes involved in cell cycling and apoptosis was suppressed. At 24 hours of fasting, many of the early adaptive changes abated. Major changes upon continued fasting implied the production of glucose rather than lactate from carbohydrate backbones, a downregulation of fatty-acid oxidation and a very strong downregulation of the electron-transport chain. Cell cycling and apoptosis remained suppressed. CONCLUSION: The changes in gene expression indicate that the small intestine rapidly looses mass during fasting to generate lactate or glucose and ketone bodies. Meanwhile, intestinal architecture is maintained by downregulation of cell turnove

The transcriptomic signature of fasting murine liver

<p>Abstract</p> <p>Background</p> <p>The contribution of individual organs to the whole-body adaptive response to fasting has not been established. Hence, gene-expression profiling, pathway, network and gene-set enrichment analysis and immunohistochemistry were carried out on mouse liver after 0, 12, 24 and 72 hours of fasting.</p> <p>Results</p> <p>Liver wet weight had declined ~44, ~5, ~11 and ~10% per day after 12, 24, 48 and 72 hours of fasting, respectively. Liver structure and metabolic zonation were preserved. Supervised hierarchical clustering showed separation between the fed, 12–24 h-fasted and 72 h-fasted conditions. Expression profiling and pathway analysis revealed that genes involved in amino-acid, lipid, carbohydrate and energy metabolism responded most significantly to fasting, that the response peaked at 24 hours, and had largely abated by 72 hours. The strong induction of the urea cycle, in combination with increased expression of enzymes of the tricarboxylic-acid cycle and oxidative phosphorylation, indicated a strong stimulation of amino-acid oxidation peaking at 24 hours. At this time point, fatty-acid oxidation and ketone-body formation were also induced. The induction of genes involved in the unfolded-protein response underscored the cell stress due to enhanced energy metabolism. The continuous high expression of enzymes of the urea cycle, malate-aspartate shuttle, and the gluconeogenic enzyme Pepck and the re-appearance of glycogen in the pericentral hepatocytes indicate that amino-acid oxidation yields to glucose and glycogen synthesis during prolonged fasting.</p> <p>Conclusion</p> <p>The changes in liver gene expression during fasting indicate that, in the mouse, energy production predominates during early fasting and that glucose production and glycogen synthesis become predominant during prolonged fasting.</p

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust

Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data

High-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously. The resulting high dimensional experimental data poses significant challenges to transcriptomics and metabolomics data analysis methods, which may lead to spurious instead of biologically relevant results. One strategy to improve the results is the incorporation of prior biological knowledge in the analysis. This strategy is used to reduce the solution space and/or to focus the analysis on biological meaningful regions. In this article, we review a selection of these methods used in transcriptomics and metabolomics. We combine the reviewed methods in three groups based on the underlying mathematical model: exploratory methods, supervised methods and estimation of the covariance matrix. We discuss which prior knowledge has been used, how it is incorporated and how it modifies the mathematical properties of the underlying method

SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences

<p>Abstract</p> <p>Background</p> <p>Chromosome location is often used as a scaffold to organize genomic information in both the living cell and molecular biological research. Thus, ever-increasing amounts of data about genomic features are stored in public databases and can be readily visualized by genome browsers. To perform <it>in silico </it>experimentation conveniently with this genomics data, biologists need tools to process and compare datasets routinely and explore the obtained results interactively. The complexity of such experimentation requires these tools to be based on an e-Science approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential.</p> <p>Findings</p> <p>Here we apply an e-Science approach to develop SigWin-detector, a workflow-based tool that can detect significantly enriched windows of (genomic) features in a (DNA) sequence in a fast and reproducible way. For proof-of-principle, we utilize a biological use case to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGEs) in human transcriptome maps. We improved the original method for RIDGE detection by replacing the costly step of estimation by random sampling with a faster analytical formula for computing the distribution of the null hypothesis being tested and by developing a new algorithm for computing moving medians. SigWin-detector was developed using the WS-VLAM workflow management system and consists of several reusable modules that are linked together in a basic workflow. The configuration of this basic workflow can be adapted to satisfy the requirements of the specific <it>in silico </it>experiment.</p> <p>Conclusion</p> <p>As we show with the results from analyses in the biological use case on RIDGEs, SigWin-detector is an efficient and reusable Grid-based tool for discovering windows enriched for features of a particular type in any sequence of values. Thus, SigWin-detector provides the proof-of-principle for the modular e-Science based concept of integrative bioinformatics experimentation.</p