Transposable element derived DNaseI-hypersensitive sites in the human genome

BACKGROUND: Transposable elements (TEs) are abundant genomic sequences that have been found to contribute to genome evolution in unexpected ways. Here, we characterize the evolutionary and functional characteristics of TE-derived human genome regulatory sequences uncovered by the high throughput mapping of DNaseI-hypersensitive (HS) sites. RESULTS: Human genome TEs were found to contribute substantially to HS regulatory sequences characterized in CD4+ T cells: 23% of HS sites contain TE-derived sequences. While HS sites are far more evolutionarily conserved than non HS sites in the human genome, consistent with their functional importance, TE-derived HS sites are highly divergent. Nevertheless, TE-derived HS sites were shown to be functionally relevant in terms of driving gene expression in CD4+ T cells. Genes involved in immune response are statistically over-represented among genes with TE-derived HS sites. A number of genes with both TE-derived HS sites and immune tissue related expression patterns were found to encode proteins involved in immune response such as T cell specific receptor antigens and secreted cytokines as well as proteins with clinical relevance to HIV and cancer. Genes with TE-derived HS sites have higher average levels of sequence and expression divergence between human and mouse orthologs compared to genes with non TE-derived HS sites. CONCLUSION: The results reported here support the notion that TEs provide a specific genome-wide mechanism for generating functionally relevant gene regulatory divergence between evolutionary lineages. REVIEWERS: This article was reviewed by Wolfgang J. Miller (nominated by Jerzy Jurka), Itai Yanai and Mikhail S.Gelfand

Duplicated genes evolve slower than singletons despite the initial rate increase

BACKGROUND: Gene duplication is an important mechanism that can lead to the emergence of new functions during evolution. The impact of duplication on the mode of gene evolution has been the subject of several theoretical and empirical comparative-genomic studies. It has been shown that, shortly after the duplication, genes seem to experience a considerable relaxation of purifying selection. RESULTS: Here we demonstrate two opposite effects of gene duplication on evolutionary rates. Sequence comparisons between paralogs show that, in accord with previous observations, a substantial acceleration in the evolution of paralogs occurs after duplication, presumably due to relaxation of purifying selection. The effect of gene duplication on evolutionary rate was also assessed by sequence comparison between orthologs that have paralogs (duplicates) and those that do not (singletons). It is shown that, in eukaryotes, duplicates, on average, evolve significantly slower than singletons. Eukaryotic ortholog evolutionary rates for duplicates are also negatively correlated with the number of paralogs per gene and the strength of selection between paralogs. A tally of annotated gene functions shows that duplicates tend to be enriched for proteins with known functions, particularly those involved in signaling and related cellular processes; by contrast, singletons include an over-abundance of poorly characterized proteins. CONCLUSIONS: These results suggest that whether or not a gene duplicate is retained by selection depends critically on the pre-existing functional utility of the protein encoded by the ancestral singleton. Duplicates of genes of a higher biological import, which are subject to strong functional constraints on the sequence, are retained relatively more often. Thus, the evolutionary trajectory of duplicated genes appears to be determined by two opposing trends, namely, the post-duplication rate acceleration and the generally slow evolutionary rate owing to the high level of functional constraints

No simple dependence between protein evolution rate and the number of protein-protein interactions: only the most prolific interactors tend to evolve slowly

BACKGROUND: It has been suggested that rates of protein evolution are influenced, to a great extent, by the proportion of amino acid residues that are directly involved in protein function. In agreement with this hypothesis, recent work has shown a negative correlation between evolutionary rates and the number of protein-protein interactions. However, the extent to which the number of protein-protein interactions influences evolutionary rates remains unclear. Here, we address this question at several different levels of evolutionary relatedness. RESULTS: Manually curated data on the number of protein-protein interactions among Saccharomyces cerevisiae proteins was examined for possible correlation with evolutionary rates between S. cerevisiae and Schizosaccharomyces pombe orthologs. Only a very weak negative correlation between the number of interactions and evolutionary rate of a protein was observed. Furthermore, no relationship was found between a more general measure of the evolutionary conservation of S. cerevisiae proteins, based on the taxonomic distribution of their homologs, and the number of protein-protein interactions. However, when the proteins from yeast were assorted into discrete bins according to the number of interactions, it turned out that 6.5% of the proteins with the greatest number of interactions evolved, on average, significantly slower than the rest of the proteins. Comparisons were also performed using protein-protein interaction data obtained with high-throughput analysis of Helicobacter pylori proteins. No convincing relationship between the number of protein-protein interactions and evolutionary rates was detected, either for comparisons of orthologs from two completely sequenced H. pylori strains or for comparisons of H. pylori and Campylobacter jejuni orthologs, even when the proteins were classified into bins by the number of interactions. CONCLUSION: The currently available comparative-genomic data do not support the hypothesis that the evolutionary rates of the majority of proteins substantially depend on the number of protein-protein interactions they are involved in. However, a small fraction of yeast proteins with the largest number of interactions (the hubs of the interaction network) tend to evolve slower than the bulk of the proteins

Greedy Selection of Species for Ancestral State Reconstruction on Phylogenies: Elimination Is Better than Insertion

Accurate reconstruction of ancestral character states on a phylogeny is crucial in many genomics studies. We study how to select species to achieve the best reconstruction of ancestral character states on a phylogeny. We first show that the marginal maximum likelihood has the monotonicity property that more taxa give better reconstruction, but the Fitch method does not have it even on an ultrametric phylogeny. We further validate a greedy approach for species selection using simulation. The validation tests indicate that backward greedy selection outperforms forward greedy selection. In addition, by applying our selection strategy, we obtain a set of the ten most informative species for the reconstruction of the genomic sequence of the so-called boreoeutherian ancestor of placental mammals. This study has broad relevance in comparative genomics and paleogenomics since limited research resources do not allow researchers to sequence the large number of descendant species required to reconstruct an ancestral sequence

TreeVector: Scalable, Interactive, Phylogenetic Trees for the Web

Background: Phylogenetic trees are complex data forms that need to be graphically displayed to be human-readable. Traditional techniques of plotting phylogenetic trees focus on rendering a single static image, but increases in the production of biological data and large-scale analyses demand scalable, browsable, and interactive trees. Methodology/Principal Findings: We introduce TreeVector, a Scalable Vector Graphics–and Java-based method that allows trees to be integrated and viewed seamlessly in standard web browsers with no extra software required, and can be modified and linked using standard web technologies. There are now many bioinformatics servers and databases with a range of dynamic processes and updates to cope with the increasing volume of data. TreeVector is designed as a framework to integrate with these processes and produce user-customized phylogenies automatically. We also address the strengths of phylogenetic trees as part of a linked-in browsing process rather than an end graphic for print. Conclusions/Significance: TreeVector is fast and easy to use and is available to download precompiled, but is also open source. It can also be run from the web server listed below or the user’s own web server. It has already been deployed o

Effect of the Transposable Element Environment of Human Genes on Gene Length and Expression

Independent lines of investigation have documented effects of both transposable elements (TEs) and gene length (GL) on gene expression. However, TE gene fractions are highly correlated with GL, suggesting that they cannot be considered independently. We evaluated the TE environment of human genes and GL jointly in an attempt to tease apart their relative effects. TE gene fractions and GL were compared with the overall level of gene expression and the breadth of expression across tissues. GL is strongly correlated with overall expression level but weakly correlated with the breadth of expression, confirming the selection hypothesis that attributes the compactness of highly expressed genes to selection for economy of transcription. However, TE gene fractions overall, and for the L1 family in particular, show stronger anticorrelations with expression level than GL, indicating that GL may not be the most important target of selection for transcriptional economy. These results suggest a specific mechanism, removal of TEs, by which highly expressed genes are selectively tuned for efficiency. MIR elements are the only family of TEs with gene fractions that show a positive correlation with tissue-specific expression, suggesting that they may provide regulatory sequences that help to control human gene expression. Consistent with this notion, MIR fractions are relatively enriched close to transcription start sites and associated with coexpression in specific sets of related tissues. Our results confirm the overall relevance of the TE environment to gene expression and point to distinct mechanisms by which different TE families may contribute to gene regulation

Global similarity and local divergence in human and mouse gene co-expression networks

BACKGROUND: A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species. RESULTS: At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction (<10%) of coexpressed gene pair relationships are conserved between the two species. A series of controls for experimental and biological variance show that most of this divergence does not result from experimental noise. We further show that, while the expression divergence between species is genuinely rapid, expression does not evolve free from selective (functional) constraint. Indeed, the coexpression networks analyzed here are demonstrably functionally coherent as indicated by the functional similarity of coexpressed gene pairs, and this pattern is most pronounced in the conserved human-mouse intersection network. Numerous dense network clusters show evidence of dedicated functions, such as spermatogenesis and immune response, that are clearly consistent with the coherence of the expression patterns of their constituent gene members. CONCLUSION: The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection

Do human transposable element small RNAs serve primarily as genome defenders or genome regulators?

It is currently thought that small RNA (sRNA) based repression mechanisms are primarily employed to mitigate the mutagenic threat posed by the activity of transposable elements (TEs). This can be achieved by the sRNA guided processing of TE transcripts via Dicer-dependent (e.g., siRNA) or Dicer-independent (e.g., piRNA) mechanisms. For example, potentially active human L1 elements are silenced by mRNA cleavage induced by element encoded siRNAs, leading to a negative correlation between element mRNA and siRNA levels. On the other hand, there is emerging evidence that TE derived sRNAs can also be used to regulate the host genome. Here, we evaluated these two hypotheses for human TEs by comparing the levels of TE derived mRNA and TE sRNA across six tissues. The genome defense hypothesis predicts a negative correlation between TE mRNA and TE sRNA levels, whereas the genome regulatory hypothesis predicts a positive correlation. On average, TE mRNA and TE sRNA levels are positively correlated across human tissues. These correlations are higher than seen for human genes or for randomly permuted control data sets. Overall, Alu subfamilies show the highest positive correlations of element mRNA and sRNA levels across tissues, although a few of the youngest, and potentially most active, Alu subfamilies do show negative correlations. Thus, Alu derived sRNAs may be related to both genome regulation and genome defense. These results are inconsistent with a simple model whereby TE derived sRNAs reduce levels of standing TE mRNA via transcript cleavage, and suggest that human cells efficiently process TE transcripts into sRNA based on the available message levels. This may point to a widespread role for processed TE transcripts in genome regulation or to alternative roles of TE-to-sRNA processing including the mitigation of TE transcript cytotoxicity

A 150MG magnetic white dwarf in the cataclysmic variable RX J1554.2+2721

We report the detection of Zeeman-split Lalpha absorption pi and sigma+ lines in the far-ultraviolet Hubble Space Telescope/Space Telescope Imaging Spectrograph spectrum of the magnetic cataclysmic variable RX J1554.2+2721. Fitting the STIS data with magnetic white dwarf model spectra, we derive a field strength of B~144MG and an effective temperature of 17000K<Teff<23000K. This measurement makes RX J1554.2+2721 only the third cataclysmic variable containing a white dwarf with a field exceeding 100MG. Similar to the other high-field polar AR UMa, RX J1554.2+2721 is often found in a state of feeble mass transfer, which suggests that a considerable number of high-field polars may still remain undiscovered.Comment: 4 pages, accepted for ApJ Letter

Improved Microarray-Based Decision Support with Graph Encoded Interactome Data

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method