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Background: Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress. Results: In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork (functional module) of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurablemetabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration. Conclusions: The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner.
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.
Arboreal spiders in deciduous and coniferous trees were investigated on their distribution and diversity. Insecticidal knock-down was used to comprehensively sample spiders from 175 trees from 2001 to 2003 in the Białowieża forest and three remote forests in Poland. We identified 140 species from 9273 adult spiders. Spider communities were distinguished between deciduous and coniferous trees. The richest fauna was collected from Quercus where beta diversity was also highest. A tree-species-specific pattern was clearly observed for Alnus, Carpinus, Picea and Pinus trees and also for those tree species that were fogged in only four or three replicates, namely Betula and Populus. This hitherto unrecognised association was mainly due to the community composition of common species identified in a Dufrene-Legendre indicator species analysis. It was not caused by spatial or temporal autocorrelation. Explaining tree-species specificity for generalist predators like spiders is difficult and has to involve physical and ecological tree parameters like linkage with the abundance of prey species. However, neither did we find a consistent correlation of prey group abundances with spiders nor could differences in spider guild composition explain the observed pattern. Our results hint towards the importance of deterministic mechanisms structuring communities of generalist canopy spiders although the casual relationship is not yet understood.
The initial stages of the interaction between the host and Aspergillus fumigatus at the alveolar surface of the human lung are critical in the establishment of aspergillosis. Using an in vitro bilayer model of the alveolus, including both the epithelium (human lung adenocarcinoma epithelial cell line, A549) and endothelium (human pulmonary artery epithelial cells, HPAEC) on transwell membranes, it was possible to closely replicate the in vivo conditions. Two distinct sub-groups of dendritic cells (DC), monocyte-derived DC (moDC) and myeloid DC (mDC), were included in the model to examine immune responses to fungal infection at the alveolar surface. RNA in high quantity and quality was extracted from the cell layers on the transwell membrane to allow gene expression analysis using tailored custom-made microarrays, containing probes for 117 immune-relevant genes. This microarray data indicated minimal induction of immune gene expression in A549 alveolar epithelial cells in response to germ tubes of A. fumigatus. In contrast, the addition of DC to the system greatly increased the number of differentially expressed immune genes. moDC exhibited increased expression of genes including CLEC7A, CD209 and CCL18 in the absence of A. fumigatus compared to mDC. In the presence of A. fumigatus, both DC subgroups exhibited up-regulation of genes identified in previous studies as being associated with the exposure of DC to A. fumigatus and exhibiting chemotactic properties for neutrophils, including CXCL2, CXCL5, CCL20, and IL1B. This model closely approximated the human alveolus allowing for an analysis of the host pathogen interface that complements existing animal models of IA.
Freshly cut beech deadwood was enriched in the canopy and on the ground in three cultural landscapes in Germany (Swabian Alb, Hainich-Dun, Schorfheide-Chorin) in order to analyse the diversity, distribution and interaction of wood-inhabiting fungi and beetles. After two years of wood decay 83 MOTUs (Molecular Operational Taxonomic Units) from 28 wood samples were identified. Flight Interception Traps (FITs) installed adjacent to the deadwood enrichments captured 29.465 beetles which were sorted to 566 species. Geographical 'region' was the main factor determining both beetle and fungal assemblages. The proportions of species occurring in all regions were low. Statistic models suggest that assemblages of both taxa differed between stratum and management praxis but their strength varied among regions. Fungal assemblages in Hainich-Dun, for which the data was most comprehensive, discriminated unmanaged from extensively managed and age-class forests (even-aged timber management) while canopy communities differed not from those near the ground. In contrast, the beetle assemblages at the same sites showed the opposite pattern. We pursued an approach in the search for fungus-beetle associations by computing cross correlations and visualize significant links in a network graph. These correlations can be used to formulate hypotheses on mutualistic relationships for example in respect to beetles acting as vectors of fungal spores.
Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/or sequences covaried with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance- associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hairlike organs distributed across the fly's body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms.
Community-acquired (CA) Staphylococcus aureus cause various diseases even in healthy individuals. Enhanced virulence of CA-strains is partly attributed to increased production of toxins such as phenol-soluble modulins (PSM). The pathogen is internalized efficiently by mammalian host cells and intracellular S. aureus has recently been shown to contribute to disease. Upon internalization, cytotoxic S. aureus strains can disrupt phagosomal membranes and kill host cells in a PSM-dependent manner. However, PSM are not sufficient for these processes. Here we screened for factors required for intracellular S. aureus virulence. We infected escape reporter host cells with strains from an established transposon mutant library and detected phagosomal escape rates using automated microscopy. We thereby, among other factors, identified a non-ribosomal peptide synthetase (NRPS) to be required for efficient phagosomal escape and intracellular survival of S. aureus as well as induction of host cell death. By genetic complementation as well as supplementation with the synthetic NRPS product, the cyclic dipeptide phevalin, wild-type phenotypes were restored. We further demonstrate that the NRPS is contributing to virulence in a mouse pneumonia model. Together, our data illustrate a hitherto unrecognized function of the S. aureus NRPS and its dipeptide product during S. aureus infection.
Using Illumina 450K arrays, 1.85% of all analyzed CpG sites were significantly hypermethylated and 0.31% hypomethylated in fetal Down syndrome (DS) cortex throughout the genome. The methylation changes on chromosome 21 appeared to be balanced between hypo- and hyper-methylation, whereas, consistent with prior reports, all other chromosomes showed 3-11times more hyper- than hypo-methylated sites. Reduced NRSF/REST expression due to upregulation of DYRK1A (on chromosome 21q22.13) and methylation of REST binding sites during early developmental stages may contribute to this genome-wide excess of hypermethylated sites. Upregulation of DNMT3L (on chromosome 21q22.4) could lead to de novo methylation in neuroprogenitors, which then persists in the fetal DS brain where DNMT3A and DNMT3B become downregulated. The vast majority of differentially methylated promoters and genes was hypermethylated in DS and located outside chromosome 21, including the protocadherin gamma (PCDHG) cluster on chromosome 5q31, which is crucial for neural circuit formation in the developing brain. Bisulfite pyrosequencing and targeted RNA sequencing showed that several genes of PCDHG subfamilies A and B are hypermethylated and transcriptionally downregulated in fetal DS cortex. Decreased PCDHG expression is expected to reduce dendrite arborization and growth in cortical neurons. Since constitutive hypermethylation of PCDHG and other genes affects multiple tissues, including blood, it may provide useful biomarkers for DS brain development and pharmacologic targets for therapeutic interventions.
Normal human brain development is dependent on highly dynamic epigenetic processes for spatial and temporal gene regulation. Recent work identified wide-spread changes in DNA methylation during fetal brain development. We profiled CpG methylation in frontal cortex of 27 fetuses from gestational weeks 12-42, using Illumina 450K methylation arrays. Sites showing genome-wide significant correlation with gestational age were compared to a publicly available data set from gestational weeks 3-26. Altogether, we identified 2016 matching developmentally regulated differentially methylated positions (m-dDMPs): 1767 m-dDMPs were hypermethylated and 1149 hypomethylated during fetal development. M-dDMPs are underrepresented in CpG islands and gene promoters, and enriched in gene bodies. They appear to cluster in certain chromosome regions. M-dDMPs are significantly enriched in autism-associated genes and CpGs. Our results promote the idea that reduced methylation dynamics during fetal brain development may predispose to autism. In addition, m-dDMPs are enriched in genes with human-specific brain expression patterns and/or histone modifications. Collectively, we defined a subset of dDMPs exhibiting constant methylation changes from early to late pregnancy. The same epigenetic mechanisms involving methylation changes in cis-regulatory regions may have been adopted for human brain evolution and ontogeny.
Epigenetic alterations may contribute to the generation of cancer cells in a multi-step process of tumorigenesis following irradiation of normal body cells. Primary human fibroblasts with intact cell cycle checkpoints were used as a model to test whether X-ray irradiation with 2 and 4 Gray induces direct epigenetic effects (within the first cell cycle) in the exposed cells. ELISA-based fluorometric assays were consistent with slightly reduced global DNA methylation and hydroxymethylation, however the observed between-group differences were usually not significant. Similarly, bisulfite pyrosequencing of interspersed LINE-1 repeats and centromeric α-satellite DNA did not detect significant methylation differences between irradiated and non-irradiated cultures. Methylation of interspersed ALU repeats appeared to be slightly increased (one percentage point; p = 0.01) at 6 h after irradiation with 4 Gy. Single-cell analysis showed comparable variations in repeat methylation among individual cells in both irradiated and control cultures. Radiation-induced changes in global repeat methylation, if any, were much smaller than methylation variation between different fibroblast strains. Interestingly, α-satellite DNA methylation positively correlated with gestational age. Finally, 450K methylation arrays mainly targeting genes and CpG islands were used for global DNA methylation analysis. There were no detectable methylation differences in genic (promoter, 5' UTR, first exon, gene body, 3' UTR) and intergenic regions between irradiated and control fibroblast cultures. Although we cannot exclude minor effects, i.e. on individual CpG sites, collectively our data suggest that global DNA methylation remains rather stable in irradiated normal body cells in the early phase of DNA damage response.