Sequential delivery of host-induced virulence effectors by appressoria and intracellular hyphae of the phytopathogen Colletotrichum higginsianum.

Phytopathogens secrete effector proteins to manipulate their hosts for effective colonization. Hemibiotrophic fungi must maintain host viability during initial biotrophic growth and elicit host death for subsequent necrotrophic growth. To identify effectors mediating these opposing processes, we deeply sequenced the transcriptome of Colletotrichum higginsianum infecting Arabidopsis. Most effector genes are host-induced and expressed in consecutive waves associated with pathogenic transitions, indicating distinct effector suites are deployed at each stage. Using fluorescent protein tagging and transmission electron microscopy-immunogold labelling, we found effectors localised to stage-specific compartments at the host-pathogen interface. In particular, we show effectors are focally secreted from appressorial penetration pores before host invasion, revealing new levels of functional complexity for this fungal organ. Furthermore, we demonstrate that antagonistic effectors either induce or suppress plant cell death. Based on these results we conclude that hemibiotrophy in Colletotrichum is orchestrated through the coordinated expression of antagonistic effectors supporting either cell viability or cell death.

Genetic Dissection of a Prevalent Plasmid-Encoded Conjugation System in Lactococcus lactis.

Plasmid pNP40, which was first identified nearly 40 years ago in Lactococcus lactis subsp. lactis biovar diacetylactis DRC3, encodes functions such as heavy metal-, bacteriophage-, and nisin-resistance, as well as plasmid transfer ability by conjugation. Here, we report an optimized conjugation protocol for this plasmid, yielding a transfer frequency that is approximately 4,000-fold higher than those previously reported in literature, while we also observed high-frequency plasmid co-mobilization. Individual mutations in 18 genes that encompass the presumed conjugation cluster of pNP40 were generated using ssDNA recombineering to evaluate the role of each gene in the conjugation process. A possible transcriptional repressor of this conjugation cluster, the product of the traR gene, was identified in this manner. This mutational analysis, paired with bioinformatic predictions as based on sequence and structural similarities, allowed us to generate a preliminary model of the pNP40 conjugation machinery.

Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets.

BACKGROUND: Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. RESULTS: Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high.We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated. CONCLUSION: We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre-processing parameters lead to large differences in classification accuracy and are therefore of crucial importance. We advocate the evaluation over a range of parameter settings when comparing pre-processing methods. Our analysis also demonstrates that reliable classification results can be obtained with a combination of strict sample handling and a well-defined classification protocol on clinical samples.