Examination of Apoptosis Signaling in Pancreatic Cancer by Computational Signal Transduction Analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction.

Methodology/Principal Findings

Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway.

Conclusions/Significance

Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.     

SCFFbxw5 targets kinesin‐13 proteins to facilitate ciliogenesis

Microtubule depolymerases of the kinesin‐13 family play important roles in various cellular processes and are frequently overexpressed in different cancer types. Despite the importance of their correct abundance, remarkably little is known about how their levels are regulated in cells. Using comprehensive screening on protein microarrays, we identified 161 candidate substrates of the multi‐subunit ubiquitin E3 ligase SCF^Fbxw5, including the kinesin‐13 member Kif2c/MCAK. In vitro reconstitution assays demonstrate that MCAK and its closely related orthologs Kif2a and Kif2b become efficiently polyubiquitylated by neddylated SCF^Fbxw5 and Cdc34, without requiring preceding modifications. In cells, SCF^Fbxw5 targets MCAK for proteasomal degradation predominantly during G₂. While this seems largely dispensable for mitotic progression, loss of Fbxw5 leads to increased MCAK levels at basal bodies and impairs ciliogenesis in the following G₁/G₀, which can be rescued by concomitant knockdown of MCAK, Kif2a or Kif2b. We thus propose a novel regulatory event of ciliogenesis that begins already within the G₂ phase of the preceding cell cycle.     

Plants,microorganisms, and soil temperatures contribute to a decrease in methane fluxes on a drained Arctic floodplain

As surface temperatures are expected to rise in the future, ice‐rich permafrost may thaw, altering soil topography and hydrology and creating a mosaic of wet and dry soil surfaces in the Arctic. Arctic wetlands are large sources of CH₄, and investigating effects of soil hydrology on CH₄ fluxes is of great importance for predicting ecosystem feedback in response to climate change. In this study, we investigate how a decade‐long drying manipulation on an Arctic floodplain influences CH₄‐associated microorganisms, soil thermal regimes, and plant communities. Moreover, we examine how these drainage‐induced changes may then modify CH₄ fluxes in the growing and nongrowing seasons. This study shows that drainage substantially lowered the abundance of methanogens along with methanotrophic bacteria, which may have reduced CH₄ cycling. Soil temperatures of the drained areas were lower in deep, anoxic soil layers (below 30 cm), but higher in oxic topsoil layers (0–15 cm) compared to the control wet areas. This pattern of soil temperatures may have reduced the rates of methanogenesis while elevating those of CH₄ oxidation, thereby decreasing net CH₄ fluxes. The abundance of Eriophorum angustifolium, an aerenchymatous plant species, diminished significantly in the drained areas. Due to this decrease, a higher fraction of CH₄ was alternatively emitted to the atmosphere by diffusion, possibly increasing the potential for CH₄ oxidation and leading to a decrease in net CH₄ fluxes compared to a control site. Drainage lowered CH₄ fluxes by a factor of 20 during the growing season, with postdrainage changes in microbial communities, soil temperatures, and plant communities also contributing to this reduction. In contrast, we observed CH₄ emissions increased by 10% in the drained areas during the nongrowing season, although this difference was insignificant given the small magnitudes of fluxes. This study showed that long‐term drainage considerably reduced CH₄ fluxes through modified ecosystem properties.     

Do natural enemies really make a difference? Field scale impacts of parasitoid wasps and hoverfly larvae on cereal aphid populations

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Causes and consequences of pattern diversification in a spatially self-organizing microbial community

Surface-attached microbial communities constitute a vast amount of life on our planet. They contribute to all major biogeochemical cycles, provide essential services to our society and environment, and have important effects on human health and disease. They typically consist of different interacting genotypes that arrange themselves non-randomly across space (referred to hereafter as spatial self-organization). While spatial self-organization is important for the functioning, ecology, and evolution of these communities, the underlying determinants of spatial self-organization remain unclear. Here, we performed a combination of experiments, statistical modeling, and mathematical simulations with a synthetic cross-feeding microbial community consisting of two isogenic strains. We found that two different patterns of spatial self-organization emerged at the same length and time scales, thus demonstrating pattern diversification. This pattern diversification was not caused by initial environmental heterogeneity or by genetic heterogeneity within populations. Instead, it was caused by nongenetic heterogeneity within populations, and we provide evidence that the source of this nongenetic heterogeneity is local differences in the initial spatial positionings of individuals. We further demonstrate that the different patterns exhibit different community-level properties; namely, they have different expansion speeds. Together, our results demonstrate that pattern diversification can emerge in the absence of initial environmental heterogeneity or genetic heterogeneity within populations and can affect community-level properties, thus providing novel insights into the causes and consequences of microbial spatial self-organization.Subject terms: Microbial ecology, Microbial ecology, Biofilms