Cell wall degrading enzymes and interaction between Trichoderma Aggressivum and Agaricus Bisporus

Agaricus bisporus is the most commonly cultivated mushroom in North America and has a great economic value. Green mould is a serious disease of A. bisporus and causes major reductions in mushroom crop production. The causative agent of green mould disease in North America was identified as Trichoderma aggressivum f. aggressivum. Variations in the disease resistance have been shown in the different commercial mushroom strains. The purpose of this study is to continue investigations of the interactions between T. aggressivum and A. bisporus during the development of green mould disease. The main focus of the research was to study the roles of cell wall degrading enzymes in green mould disease resistance and pathogenesis. First, we tried to isolate and sequence the N-acetylglucosaminidase from A. bisporus to understand the defensive mechanism of mushroom against the disease. However, the lack of genomic and proteomic information of A. bisporus limited our efforts. Next, T. aggressivum cell wall degrading enzymes that are thought to attack Agaricus and mediate the disease development were examined. The three cell wall degrading enzymes genes, encoding endochitinase (ech42), glucanase (fJ-1,3 glucanase) and protease (prb 1), were isolated and sequenced from T. aggressivum f. aggressivum. The sequence data showed significant homology with the corresponding genes from other fungi including Trichoderma species. The transcription levels of the three T. aggressivum cell wall degrading enzymes were studied during the in vitro co-cultivation with A. bisporus using R T -qPCR. The transcription levels of the three genes were significantly upregulated compared to the solitary culture levels but were upregulated to a lesser extent in co-cultivation with a resistant strain of A. bisporus than with a sensitive strain. An Agrobacterium tumefaciens transformation system was developed for T. aggressivum and was used to transform three silencing plasmids to construct three new T. aggressivum phenotypes, each with a silenced cell wall degrading enzyme. The silencing efficiency was determined by RT-qPCR during the individual in vitro cocultivation of each of the new phenotypes with A. bisporus. The results showed that the expression of the three enzymes was significantly decreased during the in vitro cocultivation when compared with the wild type. The phenotypes were co-cultivated with A. bisporus on compost with monitoring the green mould disease progression. The data indicated that prbi and ech42 genes is more important in disease progression than the p- 1,3 glucanase gene. Finally, the present study emphasises the role of the three cell wall degrading enzymes in green mould disease infection and may provide a promising tool for disease management.

Regulation of Systemic Acquired Resistance through the Interaction of Arabidopsis thaliana Transcription factors TGAI and TGA2 with NPRI

Arabidopsis thaliana is an established model plant system for studying plantpathogen interactions. The knowledge garnered from examining the mechanism of induced disease resistance in this model system can be applied to eliminate the cost and danger associated with current means of crop protection. A specific defense pathway, known as systemic acquired resistance (SAR), involves whole plant protection from a wide variety of bacterial, viral and fungal pathogens and remains induced weeks to months after being triggered. The ability of Arabidopsis to mount SAR depends on the accumulation of salicylic acid (SA), the NPRI (non-expressor of pathogenesis related gene 1) protein and the expression of a subset of pathogenesis related (PR) genes. NPRI exerts its effect in this pathway through interaction with a closely related class of bZIP transcription factors known as TGA factors, which are named for their recognition of the cognate DNA motif TGACG. We have discovered that one of these transcription factors, TGA2, behaves as a repressor in unchallenged Arabidopsis and acts to repress NPRI-dependent activation of PRJ. TGA1, which bears moderate sequence similarity to TGA2, acts as a transcriptional activator in unchallenged Arabidopsis, however the significance of this activity is J unclear. Once SAR has been induced, TGAI and TGA2 interact with NPRI to form complexes that are capable of activating transcription. Curiously, although TGAI is capable of transactivating, the ability of the TGAI-NPRI complex to activate transcription results from a novel transactivation domain in NPRI. This transactivation domain, which depends on the oxidation of cysteines 521 and 529, is also responsible for the transactivation ability of the TGA2-NPRI complex. Although the exact mechanism preventing TGA2-NPRI interaction in unchallenged Arabidopsis is unclear, the regulation of TGAI-NPRI interaction is based on the redox status of cysteines 260 and 266 in TGAl. We determined that a glutaredoxin, which is an enzyme capable of regulating a protein's redox status, interacts with the reduced form of TGAI and this interaction results .in the glutathionylation of TGAI and a loss of interaction with NPRl. Taken together, these results expand our understanding of how TGA transcription factors and NPRI behave to regulate events and gene expression during SAR. Furthermore, the regulation of the behavior of both TGAI and NPRI by their redox status and the involvement of a glutaredoxin in modulating TGAI-NPRI interaction suggests the redox regulation of proteins is a general mechanism implemented in SAR.

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.