Diversity among bacteria causing blotch disease on the commercial mushroom, agaricus bisporus /

A total of 251 bacterial isolates were isolated from blotched mushroom samples obtained from various mushroom farms in Canada. Out of 251 stored isolates, 170 isolates were tested for pathogenicity on Agaricus bisporus through mushroom rapid pitting test with three distinct pathotypes observed: dark brown, brovm and yellow/yellow-brown blotch. Phenotypic analysis of 83 isolates showed two distinct proteinase K resistant peptide profiles. Profile group A isolates exhibited peptides with masses of 45, 18, 16 and 14 kDa and fiirther biochemical tests identified them as Pseudomonasfluorescens III and V. Profile group B isolates lacked the 16-kDa peptide and the blotch causing bacterial isolates of this group was identified as Serratia liquefaciens and Cedecea davisae. Comparative genetic analysis using Amplified Fragment Length Polymorphism (AFLP) on 50 Pseudomonas sp. isolates (Group A) showed that various blotch symptoms were caused by isolates distributed throughout the Pseudomonas sp. clusters with the exception of the Pseudomonas tolaasii group and one non-pathogenic Pseudomonas fluorescens cluster. These results show that seven distinct Pseudomonas sp. genotypes (genetic clusters) have the ability to cause various symptoms of blotch and that AFLP can discriminate blotch causing from non-blotch causing Pseudomonasfluorescens. Therefore, a complex of diverse bacterial organisms causes bacterial blotch disease

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Novel glutathione disulfide transferase function of CC-glutaredoxins involved in disease resistance and flower development

Glutaredoxins are oxidoreductases capable of reducing protein disulfide bridges and glutathione mixed disulfides through the process of deglutathionylation and glutathionylation. Lately, redox-mediated modifications of functional cysteine residues of TGA1 and TGA8 transcription factors have been postulated. Namely, GRX480 and ROXY1 glutaredoxins have been previously shown to interact with TGA proteins and have been suggested to regulate redox state of these proteins. TGA1, together with TGA2, is involved in systemic acquired resistance (SAR) establishment in the plant Arabidopsis thaliana through PR1 (Pathogenesis related 1) gene activation. They both form an enhanceosome complex with the NPR1 protein (non-expressor of pathogenesis related gene 1) which leads to PR1 transcription. Although TGA1 is capable of activating PR1 transcription, the ability of the TGA1 NPR1 enhanceosome complex to assembly is based on the redox status of TGA1. We identified GRX480 as a glutathionylating enzyme that catalyzes the TGA1 glutathione disulfide transferase reaction with a Km of around 20μM GSSG (oxidized glutathione). Out of four cysteine residues found within TGA1, C172 and C266 were found to be glutathionylated by this enzyme. We also confirmed TGA1 glutathionylation in vivo and showed that this modification takes place while TGA1 is associated with the PR1 promoter enzymatically via GRX480. Furthermore, we show that glutathionylation via GRX480 abolishes TGA1's interaction with NPR1 and consequently prevents the TGA1-NPR1 transcription activation of PR1. When glutathionylated, TGA1 is recruited to the PR1 promoter and acts as a repressor. Therefore, glutathionylation is a mechanism that prevents TGA1 NPR1 interaction, allowing TGA1 to function as a repressor of PR1 transcription. Surprisingly, GRX480 was not able to deglutathionylate proteins demonstrating the irreversible nature of the reaction. Moreover, we demonstrate that other members of CC-class glutaredoxins, namely ROXY1 and ROXY2, can also catalyze protein glutathionylation. The TGA8 protein was previously shown to interact with NPR1 analogs, BOP1 and BOP2 proteins. However, unlike the case of TGA1 NPR1 interaction, here we demonstrate that TGA8-BOP1 interaction is not redox regulated and that TGA8 glutathionylation by ROXY1 and ROXY2 enzymes does not abolish this interaction in vitro. However, TGA8 glutathionylation results in TGA8 oligomer disassembly into smaller complexes and monomers. Our results suggest that CC-Grxs are unable to reduce mixed disulfides, instead they efficiently catalyze the opposite reaction which distinguishes them from traditional glutaredoxins. Therefore, they should not be classified as glutaredoxins but as protein glutathione disulfide transferases.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.