Co-culture of human fibroblasts,smooth muscle and endothelial cells promotes osteopontin induction in hypoxia

Arteriovenous fistulas (AVFs) are the preferred vascular access for haemodialysis of patients suffering from end-stage renal disease, a worldwide public health problem. However, they are prone to a high rate of failure due to neointimal hyperplasia and stenosis. This study aimed to determine if osteopontin (OPN) was induced in hypoxia and if OPN could be responsible for driving AVF failure. Identification of new factors that participate in remodelling of AVFs is a challenge. Three cell lines representing the cells of the three layers of the walls of arteries and veins, fibroblasts, smooth muscle cells and endothelial cells, were tested in mono- and co-culture in vitro for OPN expression and secretion in normoxia compared to hypoxia after silencing the hypoxia-inducible factors (HIF-1α, HIF-2α and HIF-1/2α) with siRNA or after treatment with an inhibitor of NF-kB. None of the cells in mono-culture showed OPN induction in hypoxia, whereas cells in co-culture secreted OPN in hypoxia. The changes in oxygenation that occur during AVF maturation up-regulate secretion of OPN through cell-cell interactions between the different cell layers that form AVF, and in turn, these promote endothelial cell proliferation and could participate in neointimal hyperplasia.     

Single-cell determination of iron content in magnetotactic bacteria: implications for the iron biogeochemical cycle

Magnetotactic bacteria (MTB) are ubiquitous aquatic microorganisms that mineralize dissolved iron into intracellular magnetic crystals. After cell death, these crystals are trapped into sediments that remove iron from the soluble pool. MTB may significantly impact the iron biogeochemical cycle, especially in the ocean where dissolved iron limits nitrogen fixation and primary productivity. A thorough assessment of their impact has been hampered by a lack of methodology to measure the amount of, and variability in, their intracellular iron content. We quantified the iron mass contained in single MTB cells of Magnetospirillum magneticum strain AMB-1 using a time-resolved inductively coupled plasma-mass spectrometry methodology. Bacterial iron content depends on the external iron concentration, and reaches a maximum value of ~10⁻⁶ ng of iron per cell. From these results, we calculated the flux of dissolved iron incorporation into environmental MTB populations and conclude that MTB may mineralize a significant fraction of dissolved iron into crystals.     

Population studies of the wild tomato species Solanum chilense reveal geographically structured major gene-mediated pathogen resistance

Natural plant populations encounter strong pathogen pressure and defence-associated genes are known to be under selection dependent on the pressure by the pathogens. Here, we use populations of the wild tomato Solanum chilense to investigate natural resistance against Cladosporium fulvum, a well-known ascomycete pathogen of domesticated tomatoes. Host populations used are from distinct geographical origins and share a defined evolutionary history. We show that distinct populations of S. chilense differ in resistance against the pathogen. Screening for major resistance gene-mediated pathogen recognition throughout the whole species showed clear geographical differences between populations and complete loss of pathogen recognition in the south of the species range. In addition, we observed high complexity in a homologues of Cladosporium resistance (Hcr) locus, underlying the recognition of C. fulvum, in central and northern populations. Our findings show that major gene-mediated recognition specificity is diverse in a natural plant-pathosystem. We place major gene resistance in a geographical context that also defined the evolutionary history of that species. Data suggest that the underlying loci are more complex than previously anticipated, with small-scale gene recombination being possibly responsible for maintaining balanced polymorphisms in the populations that experience pathogen pressure.     

Multi-proxy analysis of waterlogged preserved Late Neolithic canine excrements

Vegetation History and Archaeobotany - Multi-proxy analysis of the coprolites which were found during excavations at two Late Neolithic (fourth millennium bc) pile-dwelling sites (Črnelnik and...     

Structure of the Catalytic Domain of EZH2 Reveals Conformational Plasticity in Cofactor and Substrate Binding Sites and Explains Oncogenic Mutations

Polycomb repressive complex 2 (PRC2) is an important regulator of cellular differentiation and cell type identity. Overexpression or activating mutations of EZH2, the catalytic component of the PRC2 complex, are linked to hyper-trimethylation of lysine 27 of histone H3 (H3K27me3) in many cancers. Potent EZH2 inhibitors that reduce levels of H3K27me3 kill mutant lymphoma cells and are efficacious in a mouse xenograft model of malignant rhabdoid tumors. Unlike most SET domain methyltransferases, EZH2 requires PRC2 components, SUZ12 and EED, for activity, but the mechanism by which catalysis is promoted in the PRC2 complex is unknown. We solved the 2.0 Å crystal structure of the EZH2 methyltransferase domain revealing that most of the canonical structural features of SET domain methyltransferase structures are conserved. The site of methyl transfer is in a catalytically competent state, and the structure clarifies the structural mechanism underlying oncogenic hyper-trimethylation of H3K27 in tumors harboring mutations at Y641 or A677. On the other hand, the I-SET and post-SET domains occupy atypical positions relative to the core SET domain resulting in incomplete formation of the cofactor binding site and occlusion of the substrate binding groove. A novel CXC domain N-terminal to the SET domain may contribute to the apparent inactive conformation. We propose that protein interactions within the PRC2 complex modulate the trajectory of the post-SET and I-SET domains of EZH2 in favor of a catalytically competent conformation.     

A high-throughput media design approach for high performance mammalian fed-batch cultures

An innovative high-throughput medium development method based on media blending was successfully used to improve the performance of a Chinese hamster ovary fed-batch medium in shaking 96-deepwell plates. Starting from a proprietary chemically-defined medium, 16 formulations testing 43 of 47 components at 3 different levels were designed. Media blending was performed following a custom-made mixture design of experiments considering binary blends, resulting in 376 different blends that were tested during both cell expansion and fed-batch production phases in one single experiment. Three approaches were chosen to provide the best output of the large amount of data obtained. A simple ranking of conditions was first used as a quick approach to select new formulations with promising features. Then, prediction of the best mixes was done to maximize both growth and titer using the Design Expert software. Finally, a multivariate analysis enabled identification of individual potential critical components for further optimization. Applying this high-throughput method on a fed-batch, rather than on a simple batch, process opens new perspectives for medium and feed development that enables identification of an optimized process in a short time frame.     

Nanomoulding of Functional Materials,a Versatile Complementary Pattern Replication Method to Nanoimprinting

We describe a nanomoulding technique which allows low-cost nanoscale patterning of functional materials, materials stacks and full devices. Nanomoulding combined with layer transfer enables the replication of arbitrary surface patterns from a master structure onto the functional material. Nanomoulding can be performed on any nanoimprinting setup and can be applied to a wide range of materials and deposition processes. In particular we demonstrate the fabrication of patterned transparent zinc oxide electrodes for light trapping applications in solar cells.     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.