(Mix-)Tapes – die ewige Leier

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Hypnagogic Pop

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Nutrient status not secondary metabolites drives herbivory and pathogen infestation across differently mycorrhized tree monocultures and mixtures.

14 páginas, 3 figuras, 2 tablasResearch aimed at understanding the mechanisms underlying the relationship between tree diversity and antagonist infestation is often neglecting resource-use complementarity among plant species. We investigated the effects of tree species identity, species richness, and mycorrhizal type on leaf herbivory and pathogen infestation. We used a tree sapling experiment manipulating the two most common mycorrhizal types, arbuscular mycorrhiza and ectomycorrhiza, via respective tree species in monocultures and two-species mixtures. We visually assessed leaf herbivory and pathogen infestation rates, and measured concentrations of a suite of plant metabolites (amino acids, sugars, and phenolics), leaf elemental concentrations (carbon, nitrogen, and phosphorus), and tree biomass. Tree species and mycorrhizal richness had no significant effect on herbivory and pathogen infestation, whereas species identity and mycorrhizal type had. Damage rates were higher in arbuscular mycorrhizal (AM) than in ectomycorrhizal (EM) trees. Our structural equation model (SEM) indicated that elemental, but not metabolite concentrations, determined herbivory and pathogen infestation, suggesting that the investigated chemical defence strategies may not have been involved in the effects found in our study with tree saplings. Other chemical and physical defence strategies as well as species identity as its determinant may have played a more crucial role in the studied saplings. Furthermore, the SEM indicated a direct positive effect of AM trees on herbivory rates, suggesting that other dominant mechanisms, not considered here, were involved as well. We found differences in the attribution of elemental concentrations between the two rates. This points to the fact that herbivory and pathogen infestation are driven by distinct mechanisms. Our study highlights the importance of biotic contexts for understanding the mechanisms underlying the effects of biodiversity on tree-antagonist interactionsWe thank Nicole M. van Dam, Henriette Uthe, Fredd Vergara, Martin Volf, and Alexander Weinhold for their valuable advice on metabolite analyses as well as Beate Rothe and Michael Reichelt for their help with chemical analyses. Moreover, comments by two anonymous reviewers helped to improve the paper. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 677232). Further support came from the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and the EcoMetEoR platform, funded by the German Research Foundation (FZT 118). AMM acknowledges support from the program for attracting talent to Salamanca from Fundación Salamanca Ciudad de Cultura y Saberes and Ayuntamiento de Salamanca.Peer reviewe

Expression analysis of secreted and cell surface genes of five transformed human cell lines and derivative xenograft tumors

BACKGROUND: Since the early stages of tumorigenesis involve adhesion, escape from immune surveillance, vascularization and angiogenesis, we devised a strategy to study the expression profiles of all publicly known and putative secreted and cell surface genes. We designed a custom oligonucleotide microarray containing probes for 3531 secreted and cell surface genes to study 5 diverse human transformed cell lines and their derivative xenograft tumors. The origins of these human cell lines were lung (A549), breast (MDA MB-231), colon (HCT-116), ovarian (SK-OV-3) and prostate (PC3) carcinomas. RESULTS: Three different analyses were performed: (1) A PCA-based linear discriminant analysis identified a 54 gene profile characteristic of all tumors, (2) Application of MANOVA (Pcorr < .05) to tumor data revealed a larger set of 149 differentially expressed genes. (3) After MANOVA was performed on data from individual tumors, a comparison of differential genes amongst all tumor types revealed 12 common differential genes. Seven of the 12 genes were identified by all three analytical methods. These included late angiogenic, morphogenic and extracellular matrix genes such as ANGPTL4, COL1A1, GP2, GPR57, LAMB3, PCDHB9 and PTGER3. The differential expression of ANGPTL4 and COL1A1 and other genes was confirmed by quantitative PCR. CONCLUSION: Overall, a comparison of the three analyses revealed an expression pattern indicative of late angiogenic processes. These results show that a xenograft model using multiple cell lines of diverse tissue origin can identify common tumorigenic cell surface or secreted molecules that may be important biomarker and therapeutic discoveries