Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.

A role for Rho in smooth muscle phenotypic regulation

The role of the small GTP-binding protein Rho in the process of smooth muscle cell (SMC) phenotypic modulation was investigated using cultured rabbit aortic SMCs. Both Rho transcription and Rho protein expression were high for the first 3 days of culture (contractile state cells), with expression decreasing after change to the synthetic state and peaking upon return to the contractile phenotype. Activation of Rho (indicated by translocation to the membrane) also peaked upon return to the contractile state and was low in synthetic state SMCs. Transient transfection of synthetic state rabbit SMCs with constitutively active Rho (val14rho) caused a dramatic decrease in cell size and reorganization of cytoskeletal proteins to resemble those of the contractile phenotype; alpha-actin and myosin adopted a tightly packed, highly organized arrangement, whereas vimentin localized to the immediate perinuclear region and focal adhesions were enlarged. Conversely, specific inhibition of endogenous Rho, by expression of C3 transferase, resulted in the complete loss of actin and myosin filaments without affecting the distribution of vimentin. Focal adhesions were reduced in number. Thus, Rho plays a key role in regulating SMC phenotypic expression.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

Importance of tissue transglutaminase in repair of extracellular matrices and cell death of dermal fibroblasts after exposure to a solarium ultraviolet A source

Investigations were undertaken to study the role of the protein cross-linking enzyme tissue transglutaminase in changes associated with the extracellular matrix and in the cell death of human dermal fibroblasts following exposure to a solarium ultraviolet A source consisting of 98.8% ultraviolet A and 1.2% ultraviolet B. Exposure to nonlethal ultraviolet doses of 60 to 120 kJ per m2 resulted in increased tissue transglutaminase activity when measured either in cell homogenates, "in situ" by incorporation of fluorescein-cadaverine into the extracellular matrix or by changes in the epsilon(gamma-glutamyl) lysine cross-link. This increase in enzyme activity did not require de novo protein synthesis. Incorporation of fluorescein-cadaverine into matrix proteins was accompanied by the cross-linking of fibronectin and tissue transglutaminase into nonreducible high molecular weight polymers. Addition of exogenous tissue transglutaminase to cultured cells mimicking extensive cell leakage of the enzyme resulted in increased extracellular matrix deposition and a decreased rate of matrix turnover. Exposure of cells to 180 kJ per m2 resulted in 40% to 50% cell death with dying cells showing extensive tissue transglutaminase cross-linking of intracellular proteins and increased cross-linking of the surrounding extracellular matrix, the latter probably occurring as a result of cell leakage of tissue transglutaminase. These cells demonstrated negligible caspase activation and DNA fragmentation but maintained their cell morphology. In contrast, exposure of cells to 240 kJ per m2 resulted in increased cell death with caspase activation and some DNA fragmentation. These cells could be partially rescued from death by addition of caspase inhibitors. These data suggest that changes in cross-linking both in the intracellular and extracellular compartments elicited by tissue transglutaminase following exposure to ultraviolet provides a rapid tissue stabilization process following damage, but as such may be a contributory factor to the scarring process that results.

Integration of the Manakov-PMD Equation with Precomputed M(w) Matrices for PMD Simulation

A novel direct integration technique of the Manakov-PMD equation for the simulation of polarisation mode dispersion (PMD) in optical communication systems is demonstrated and shown to be numerically as efficient as the commonly used coarse-step method. The main advantage of using a direct integration of the Manakov-PMD equation over the coarse-step method is a higher accuracy of the PMD model. The new algorithm uses precomputed M(w) matrices to increase the computational speed compared to a full integration without loss of accuracy. The simulation results for the probability distribution function (PDF) of the differential group delay (DGD) and the autocorrelation function (ACF) of the polarisation dispersion vector for varying numbers of precomputed M(w) matrices are compared to analytical models and results from the coarse-step method. It is shown that the coarse-step method achieves a significantly inferior reproduction of the statistical properties of PMD in optical fibres compared to a direct integration of the Manakov-PMD equation.