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Concerted Action of Evolutionarily Ancient and Novel SNARE Complexes in Flowering-Plant Cytokinesis
(2018)
Membrane vesicles delivered to the cell-division plane fuse with one another to form the partitioning membrane during plant cytokinesis, starting in the cell center. In Arabidopsis, this requires SNARE complexes involving the cytokinesis-specific Qa-SNARE KNOLLE. However, cytokinesis still occurs in knolle mutant embryos, suggesting contributions from KNOLLE-independent SNARE complexes. Here we show that Qa-SNARE SYP132, having counterparts in lower plants, functionally overlaps with the flowering plant-specific KNOLLE. SYP132 mutation causes cytokinesis defects, knolle syp132 double mutants consist of only one or a few multi-nucleate cells, and SYP132 has the same SNARE partners as KNOLLE. SYP132 and KNOLLE also have non-overlapping functions in secretion and in cellularization of the embryo-nourishing endosperm resulting from double fertilization unique to flowering plants. Evolutionarily ancient non-specialized SNARE complexes originating in algae were thus amended by the appearance of cytokinesis-specific SNARE complexes, meeting the high demand for membrane-fusion capacity during endosperm cellularization in angiosperms.
Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. alpha-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.
The numerical prediction of radiative transport is a challenging task due to the complexity of the radiative transport equation. We apply the lattice Boltzmann method (LBM), originally developed for fluid flow problems, to solve the radiative transport in volume. One model (meso RTLBM) is derived directly from a discretization of the radiative transport equation, yielding in a precise but numerical costly scheme. The second model (macro RTLBM) solves the Helmholtz equation, which is a proper approximation for highly scattering volumes. Both numerical algorithms are validated against Monte-Carlo data for a set of 35 optical parameters, which correspond to radiative transport ranging from ballistic to diffuse regimes. Together with a set of four benchmark simulations, the comprehensive validation concludes the overall quality and detects asymptotic trends for radiative transport LBM. Furthermore, an accuracy map is presented, which summarizes the error for all parameters. This graph allows to determine the validity range for both radiative transport LBM at a glance. Finally, comprehensive guidelines are formulated to facilitate the choice of the radiative transport LBM model.