Anti-apoptotic machinery protects the necrotrophic fungus Botrytis cinerea from host-induced apoptotic-like cell death during plant infection

Necrotrophic fungi are unable to occupy living plant cells. How such pathogens survive first contact with living host tissue and initiate infection is therefore unclear. Here, we show that the necrotrophic grey mold fungus Botrytis cinerea undergoes massive apoptotic-like programmed cell death (PCD) following germination on the host plant. Manipulation of an anti-apoptotic gene BcBIR1 modified fungal response to PCD-inducing conditions. As a consequence, strains with reduced sensitivity to PCD were hyper virulent, while strains in which PCD was over-stimulated showed reduced pathogenicity. Similarly, reduced levels of PCD in the fungus were recorded following infection of Arabidopsis mutants that show enhanced susceptibility to B. cinerea. When considered together, these results suggest that Botrytis PCD machinery is targeted by plant defense molecules, and that the fungal anti-apoptotic machinery is essential for overcoming this host-induced PCD and hence, for establishment of infection. As such, fungal PCD machinery represents a novel target for fungicides and antifungal drugs.     

Optimization of probe coverage for high-resolution oligonucleotide aCGH

MOTIVATION: The resolution at which genomic alterations can be mapped by means of oligonucleotide aCGH (array-based comparative genomic hybridization) is limited by two factors: the availability of high-quality probes for the target genomic sequence and the array real-estate. Optimization of the probe selection process is required for arrays that are designed to probe specific genomic regions in very high resolution without compromising probe quality constraints. RESULTS: In this paper we describe a well-defined optimization problem associated with the problem of probe selection for high-resolution aCGH arrays. We propose the whenever possible in-cover as a formulation that faithfully captures the requirement of probe selection problem, and provide a fast randomized algorithm that solves the optimization problem in O(n logn) time, as well as a deterministic algorithm with the same asymptotic performance. We apply the method in a typical high-definition array design scenario and demonstrate its superiority with respect to alternative approaches. AVAILABILITY: Address requests to the authors.     

Instant domestication process of European chestnut cultivars

This study presents the results of the first genetic analysis of ancient chestnut trees (Castanea sativa Mill.) in Italy and in the Iberian Peninsula to better understand the effect of grafting on the domestication process of chestnut and to investigate the impacts of early selection and improvement on the genetic diversity retained. We evaluated 105 giant ancient trees from Italy, Spain and Portugal and compared them with the European Union (EU) database of chestnut cultivars by using a set of 24 simple sequence repeats (SSRs; microsatellite markers). We measured the perimeter (girth) at the diameter at breast height (DBH). Samples from both the canopy and the roots of each tree were analysed to distinguish which trees were self‐rooted and which were grafted. Diversity was compared using standard metrics and model‐based approaches based on the expected heterozygosity (He) at equilibrium. We could differentiate 91 new genotypes; 9.6% matched known chestnut cultivars. We found the first evidences of cultivation, that is, grafting to produce “instant domestication” in Galicia and in the Douro Valley in trees of 14‐m perimeter (15th century) and in the Basque Country (first report in that area) in a tree of 11.5‐m perimeter (16th century). In Italy, the cultivar “Marrone Fiorentino” was found in some giant trees with perimeters of 8 and 9 m (17th‐18th centuries) in the Toscana and Umbria. Those findings matched with written references in Portugal from the 16th century and from the 18th century in Spain. “Instant domestication” could be dated back to the 15th century and was related to the wild populations existing in the same areas where cultivars are being propagated, without a different genetic structure for wild chestnut trees and with a high diversity maintained through the initiation of domestication.     

SYCAMORE--a systems biology computational analysis and modeling research environment

SYCAMORE is a browser-based application that facilitates construction, simulation and analysis of kinetic models in systems biology. Thus, it allows e.g. database supported modelling, basic model checking and the estimation of unknown kinetic parameters based on protein structures. In addition, it offers some guidance in order to allow non-expert users to perform basic computational modelling tasks. AVAILABILITY: SYCAMORE is freely available for academic use at http://sycamore.eml.org. Commercial users may acquire a license. CONTACT: ursula.kummer@bioquant.uni-heidelberg.de.     

The advantage of functional prediction based on clustering of yeast genes and its correlation with non-sequence based classifications.

Sequence similarity is probably the most widely used tool to infer functional linkage between proteins. The fully sequenced, much researched, genome of Saccharomyces cerevisiae gives us on opportunity to compare and statistically quantify computational methods based on sequence similarity, which aim to detect such linkage. In addition, the amount of data regarding Saccharomyces Cerevisiae genes and proteins, which is not directly based on sequence is rapidly increasing. Consequently, it allows investigation of the connections and correlation between classification based on these types of data and that based solely on sequence similarity. In this work we start with a simple clustering algorithm to cluster genes based on the BLAST E-score of their similarity. We analyze how well one can infer function from these clusters and for how many of the genes that are currently unknown one can suggest a prediction. Given these parameters, we show that even a simple algorithm achieves better results than simply considering the BLAST output of matching genes. In the second part of the paper, we show that there is a highly significant correlation (p-value < 10(-4) for the vast majority of the experiments) between the aforementioned clusters and other types of classifications. Namely, we show that a pair of genes being clustered together is correlated with these genes having similar expression patterns in DNA array experiments and with the encoded proteins being involved in protein-protein interactions. Although this correlation is highly significant, it is, of course, not strong enough to be, by itself, a tool for predicting co-regulation of genes or interaction of proteins. We discuss possible explanations for this correlation. Furthermore, the statistical evaluation of these results should be considered when developing tools that are aimed at making such predictions.