The Ustilago maydis Effector Pep1 Suppresses Plant Immunity by Inhibition of Host Peroxidase Activity

The corn smut Ustilago maydis establishes a biotrophic interaction with its host plant maize. This interaction requires efficient suppression of plant immune responses, which is attributed to secreted effector proteins. Previously we identified Pep1 (Protein essential during penetration-1) as a secreted effector with an essential role for U. maydis virulence. pep1 deletion mutants induce strong defense responses leading to an early block in pathogenic development of the fungus. Using cytological and functional assays we show that Pep1 functions as an inhibitor of plant peroxidases. At sites of Δpep1 mutant penetrations, H2O2 strongly accumulated in the cell walls, coinciding with a transcriptional induction of the secreted maize peroxidase POX12. Pep1 protein effectively inhibited the peroxidase driven oxidative burst and thereby suppresses the early immune responses of maize. Moreover, Pep1 directly inhibits peroxidases in vitro in a concentration-dependent manner. Using fluorescence complementation assays, we observed a direct interaction of Pep1 and the maize peroxidase POX12 in vivo. Functional relevance of this interaction was demonstrated by partial complementation of the Δpep1 mutant defect by virus induced gene silencing of maize POX12. We conclude that Pep1 acts as a potent suppressor of early plant defenses by inhibition of peroxidase activity. Thus, it represents a novel strategy for establishing a biotrophic interaction

Detection Rate of 18F-Fluorethylcholine-PET/CT in relation to PSA Value in PCA Patients Referred with Biochemical Relapse

Attempts to predict the likelihood of positive morphological imaging related with PSA value in patients referred with biochemical recurrence were the focus of many studies. Using nuclear medicine modalities, numerous studies likewise had been performed for the same purpose, however mostly using C-11-labeled choline. For this purpose, we selected 193 prostate cancer patients from our database between 2006 and 2010. They had been referred to our department to undergo 18F-fluorethylcholine (FECH)-PET/CT due to biochemical recurrence after potentially curative procedures. As a result, in 84 out of 193 patients, 18F-FECH-PET demonstrated positive findings with an overall detection rate of 44%. Statistically, there was a significant difference in PSA values in positive findings vs. negative findings (p70%) after radiation therapy alone. By contrast, patients after radical prostatectomy followed by salvage radiotherapy showed a low likelihood of local recurrence. In conclusion, PSA value was confirmed to have a determinant role in 18F-FECH-PET outcome. Moreover, there was a link between recurrence type and initial therapy, which—if prospectively confirmed—may play a guiding role in selecting the appropriate diagnostic methods

Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge

Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).Deutsche Forschungsgemeinschaft (Grant DFG-HA- 4364

Magnetic Resonance in Medicine 57:150–159 (2007) Automated Estimation of Tumor Probability in Prostate Magnetic Resonance Spectroscopic Imaging: Pattern Recognition vs Quantification

Despite its diagnostic value and technological availability, 1H NMR spectroscopic imaging (MRSI) has not found its way into clinical routine yet. Prerequisite for the clinical application is an automated and reliable method for the diagnostic evaluation of MRS images. In the present paper, different approaches to the estimation of tumor probability from MRSI in the prostate are assessed. Two approaches to feature extraction are compared: quantification (VARPRO, AMARES, QUEST) and subspace methods on spectral patterns (principal components, independent components, nonnegative matrix factorization, partial least squares). Linear as well as nonlinear classifiers (support vector machines, Gaussian processes, random forests) are applied and discussed. Quantification-based approaches are much more sensitive to the choice and parameterization of the quantification algorithm than to the choice of the classifier. Furthermore, linear methods based on magnitude spectra easil

CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods

Magnetic Resonance Spectroscopic Imaging (MRSI) measures relative concentrations of metabolites in vivo and can thus be used for the diagnosis of certain tumors

Impact of Stroma on the Growth, Microcirculation, Metabolism of Experimental Prostate Tumors

In prostate cancers (PCa), the formation of malignant stroma may substantially influence tumor phenotype and aggressiveness. Thus, the impact of the orthotopic and subcutaneous implantations of hormone-sensitive (H), hormone-insensitive (HI), anaplastic (AT1) Dunning PCa in rats on growth, microcirculation, metabolism was investigated. For this purpose, dynamic contrast-enhanced magnetic resonance imaging and 1H magnetic resonance spectroscopy ([1H]MRS) were applied in combination with histology. Consistent observations revealed that orthotopic H tumors grew significantly slower compared to subcutaneous ones, whereas the growth of HI and AT1 tumors was comparable at both locations. Histologic analysis indicated that glandular differentiation and a close interaction of tumor cells and smooth muscle cells (SMC) were associated with slow tumor growth. Furthermore, there was a significantly lower SMC density in subcutaneous H tumors than in orthotopic H tumors. Perfusion was observed to be significantly lower in orthotopic H tumors than in subcutaneous H tumors. Regional blood volume and permeability-surface area product showed no significant differences between tumor models and their implantation sites. Differences in growth between subcutaneous and orthotopic H tumors can be attributed to tumor-stroma interaction and perfusion. Here, SMC, may stabilize glandular structures and contribute to the maintenance of differentiated phenotype