Candidate metastasis suppressor genes uncovered by array comparative genomic hybridization in a mouse allograft model of prostate cancer.

Background The purpose of this study was to identify candidate metastasis suppressor genes from a mouse allograft model of prostate cancer (NE-10). This allograft model originally developed metastases by twelve weeks after implantation in male athymic nude mice, but lost the ability to metastasize after a number of in vivo passages. We performed high resolution array comparative genomic hybridization on the metastasizing and non-metastasizing allografts to identify chromosome imbalances that differed between the two groups of tumors. Results This analysis uncovered a deletion on chromosome 2 that differed between the metastasizing and non-metastasizing tumors. Bioinformatics filters were employed to mine this region of the genome for candidate metastasis suppressor genes. Of the 146 known genes that reside within the region of interest on mouse chromosome 2, four candidate metastasis suppressor genes (Slc27a2, Mall, Snrpb, and Rassf2) were identified. Quantitative expression analysis confirmed decreased expression of these genes in the metastasizing compared to non-metastasizing tumors. Conclusion This study presents combined genomics and bioinformatics approaches for identifying potential metastasis suppressor genes. The genes identified here are candidates for further studies to determine their functional role in inhibiting metastases in the NE-10 allograft model and human prostate cancer.

A myosin from a higher plant has structural similarities to class V myosins

In plant cells, myosin is believed to be the molecular motor responsible for actin-based motility processes such as cytoplasmic streaming and directed vesicle transport. In an effort to characterize plant myosin, a cDNA encoding a myosin heavy chain was isolated from Arabidopsis thaliana. The predicted product of the MYA1 gene is 173 kDa and is structurally similar to the class V myosins. It is composed of the highly-conserved NH2-terminal "head" domain, a putative calmodulin-binding "neck" domain an alpha-helical coiled-coil domain, and a COOH-terminal domain. Northern blot analysis shows that the Arabidopsis MYA1 gene is expressed in all the major plant tissues (flower, leaf, root, and stem). We suggest that the MYA1 myosin may be involved in a general intracellular transport process in plant cells.

Modelling cell separation during plant organ abscission

A simple mathematical model is presented to describe the cell separation process that plants undertake in order to deliberately shed organs. The focus here is on modelling the production of the enzyme polygalacturonase, which breaks down pectin that provides natural cell-to-cell adhesion in the localised abscission zone. A coupled system of three ordinary differential equations is given for a single cell, and then extended to hold for a layer of cells in the abscission zone. Simple observations are made based on the results of this preliminary model and, furthermore, a number of opportunities for applied mathematicians to make contributions in this subject area are discussed.

Sensitivity of Dot-ELISA on Nitrocellulose Membranes in Comparison with ELISA on Polystyrene Plates for the Detection of Four Plant Viruses

ABSTR.4CT Senitivity of dot-immunobindinding ELf SA on nitrocellulose membrane (DotELISA)was compared with double-antibody sandwich ELISA (DAS-ELlSA) on polystyrene plates for the detection of bean yellow mosaic virus (BYMV), broad bean stain virus (WMV-2). Dot-ELISA was 2 and 1O times more sensitive than DAS-ELISA for the detection of BBSV and WMV-2, respectively, whereas DAS-ELISA was more sensitive than Dot-ELiSA for {he detection of BYMV. Both techniques were equally sensitive for the detection of BYDV. Using one day instead uf the two-day procedure, the four viruses were still detectable and the ralative sensitivity of both techniques remained the same.

Can hybridization cause local extinction : the case for demographic swamping of the Australian native, Senecio pinnatifolius, by the invasive, S. madagascariensis?

The outcome of interspecific hybridization between native and invasive species depends on the relative frequencies of parental taxa and viability of hybrid progeny. We investigated individual and population level consequences of hybridization between the Australian native, Senecio pinnatifolius, and the exotic S. madagascariensis, with AFLP markers and used this information to simulate the expected outcome of hybridization.A high frequency (range 8.3-75.6 %) of hybrids was detected in open pollinated seeds of both species, but mature hybrids were absent from sympatric populations indicating that sympatric populations represent tension zones. A hybridization advantage was observed for S. madagascariensis,where significantly more progeny than expected were sired based on proportional representation of the two species in sympatric populations. Simulations indicated S. pinnatifolius could be replaced in sympatric populations if hybridization was density dependent.For this native-exotic pair, prezygotic isolating barriers are weak, but low hybrid viability maintains a strong postzygotic barrier to introgression. Due to asymmetric hybridization, S. pinnatifolius appears under threat from demographic swamping, and local extinction is possible where it occurs in sympatry with S. madagascariensis.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.