Genome analysis at different ploidy levels allows cloning of the powdery mildew resistance gene Pm3b from hexaploid wheat

In wheat, race-specific resistance to the fungal pathogen powdery mildew (Blumeria graminis f. sp. tritici) is controlled by the Pm genes. There are 10 alleles conferring resistance at the Pm3 locus (Pm3a to Pm3j) on chromosome 1AS of hexaploid bread wheat (Triticum aestivum L.). The genome of hexaploid wheat has a size of 1.6 x 1010 bp and contains more than 80% of repetitive sequences, making positional cloning difficult. Here, we demonstrate that the combined analysis of genomes from wheat species with different ploidy levels can be exploited for positional cloning in bread wheat. We have mapped the Pm3b gene in hexaploid wheat to a genetic interval of 0.97 centimorgan (cM). The diploid T. monococcum and the tetraploid T. turgidum ssp. durum provided models for the A genome of hexaploid wheat and allowed to establish a physical contig spanning the Pm3 locus. Although the haplotypes at the Pm3 locus differed markedly between the three species, a large resistance gene-like family specific to wheat group 1 chromosomes was consistently found at the Pm3 locus. A candidate gene for Pm3b was identified using partial sequence conservation between resistant line Chul and T. monococcum cv. DV92. A susceptible Pm3b mutant, carrying a single-base pair deletion in the coding region of the candidate gene was isolated. When tested in a single cell transformation assay, the Pm3b candidate gene conferred race-specific resistance to powdery mildew. These results demonstrate that the candidate gene, a member of the coiled-coil nucleotide binding site leucine-rich repeat (NBS-LRR) type of disease resistance genes, is the Pm3b gene.     

A mathematical model to study the effects of drugs administration on tumor growth dynamics

A mathematical model for describing the cancer growth dynamics in response to anticancer agents administration in xenograft models is discussed. The model consists of a system of ordinary differential equations involving five parameters (three for describing the untreated growth and two for describing the drug action). Tumor growth in untreated animals is modelled by an exponential growth followed by a linear growth. In treated animals, tumor growth rate is decreased by an additional factor proportional to both drug concentration and proliferating cells. The mathematical analysis conducted in this paper highlights several interesting properties of this tumor growth model. It suggests also effective strategies to design in vivo experiments in animals with potential saving of time and resources. For example, the drug concentration threshold for the tumor eradication, the delay between drug administration and tumor regression, and a time index that measures the efficacy of a treatment are derived and discussed. The model has already been employed in several drug discovery projects. Its application on a data set coming from one of these projects is discussed in this paper.     

Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic-virtual plant model

Background and Aims

The relationship between Septoria tritici, a splash-dispersed disease, and its host is complex because of the interactions between the dynamic plant architecture and the vertical progress of the disease. The aim of this study was to test the capacity of a coupled virtual wheat–Septoria tritici epidemic model (Septo3D) to simulate disease progress on the different leaf layers for contrasted sowing density treatments.

Methods

A field experiment was performed with winter wheat ‘Soissons’ grown at three contrasted densities. Plant architecture was characterized to parameterize the wheat model, and disease dynamic was monitored to compare with simulations. Three simulation scenarios, differing in the degree of detail with which plant variability of development was represented, were defined.

Key Results

Despite architectural differences between density treatments, few differences were found in disease progress; only the lower-density treatment resulted in a slightly higher rate of lesion development. Model predictions were consistent with field measurements but did not reproduce the higher rate of lesion progress in the low density. The canopy reconstruction scenario in which inter-plant variability was taken into account yielded the best agreement between measured and simulated epidemics. Simulations performed with the canopy represented by a population of the same average plant deviated strongly from the observations.

Conclusions

It was possible to compare the predicted and measured epidemics on detailed variables, supporting the hypothesis that the approach is able to provide new insights into the processes and plant traits that contribute to the epidemics. On the other hand, the complex and dynamic responses to sowing density made it difficult to test the model precisely and to disentangle the various aspects involved. This could be overcome by comparing more contrasted and/or simpler canopy architectures such as those resulting from quasi-isogenic lines differing by single architectural traits.     

Modelling the combined effect of temperature,pH and aw on the growth rate of Monascus ruber,a heat-resistant fungus isolated from green table olives

AIMS: Growth modes predicting the effect of pH (3.5-5.0), NaCl (2-10%), i.e. aw (0.937-0.970) and temperature (20-40 degrees C) on the colony growth rate of Monascus ruber, a fungus isolated from thermally-processed olives of the Conservolea variety, were developed on a solid culture medium. METHODS AND RESULTS: Fungal growth was measured as colony diameter on a daily basis. The primary predictive model of Baranyi was used to fit the growth data and estimate the maximum specific growth rates. Combined secondary predictive models were developed and comparatively evaluated based on polynomial, Davey, gamma concept and Rosso equations. The data-set was fitted successfully in all models. However, models with biological interpretable parameters (gamma concept and Rosso equation) were highly rated compared with the polynomial equation and Davey model and gave realistic cardinal pHs, temperatures and aw. CONCLUSIONS: The combined effect of temperature, pH and aw on growth responses of M. ruber could be satisfactorily predicted under the current experimental conditions, and the models examined could serve as tools for this purpose. SIGNIFICANCE AND IMPACT OF THE STUDY: The results can be successfully employed by the industry to predict the extent of fungal growth on table olives.     

Resonance effect of millimeter waves in the power range from 10-19 to 3 × 10-3 W/cm2 on Escherichia coli cells at different concentrations

The effect of millimeter waves (MMWs) on the genome conformational state (GCS) of E. coli AB1157 cells was studied by the method of anomalous viscosity time dependencies (AVTD) in the frequency range of 51.64-51.85 GHz. The 51.755 GHz resonance frequency of the cell reaction to MMWs did not depend on power density (PD) in the range from 10^-19 to 3 × 10^-3 W/cm². The half-width of the resonant reaction of cells showed a sigmoid dependence on PD, changing from 3 MHz to 100 MHz. The PD dependence of the half-width had the same shape for different concentrations of exposed cells (4 × 10⁷ and 4 × 10⁸ cells/ml), whereas the magnitude of the 51.755 GHz resonance effect differed significantly and depended on the PD of MMW exposure. Sharp narrowing of the 51.755 GHz resonance in the PD range from 10^-4 to 10^-7 W/cm² was followed by an emergence of new resonance frequencies. The PD dependence of the MMW effect at one of these resonance frequencies (51.674 GHz) differed markedly from the corresponding dependence at the 51.755 GHz resonance, the power window occurring in the range from 10^-16 to 10^-8 W/cm². The results obtained were explained in the framework of a model of electron-conformational interactions. The frequency-time parameters of this model appeared to be in good agreement with experimental data. © 1996 Wiley-Liss, Inc.     

Predicting the start of the birch pollen season at London, Derby and Cardiff, United Kingdom, using a multiple regression model, based on data from 1987 to 1997

Recent changes in weather in North-West Europehave been reflected in the start dates ofpollen seasons. It is therefore necessary toupdate previous models, such as the oneproduced by Jones (1995), so that the modelwill be weighted by current weather patterns. Birch pollen data, collected over a period ofeleven years (1987 to 1997 inclusive) fromthree pollen counting stations in the UK,London, Derby and Cardiff, were analysed todetermine the start dates using the Sum75method. The start dates of the birch pollenseasons of the eleven-year period were thentested for significance against ten-dayaggregated variables of temperature andrainfall for each site. The significantvariables were entered into multiple regressionmodels until the most valid equation for eachsite was found. The models were then tested onthree years not included in their data sets. The models showed mean differences betweenactual and predicted start dates, for theeleven years used, of 1.5, 3 and 5 days atDerby, Cardiff and London respectively. Forthe test years the mean difference was 1, 4.5and 7.5 days at Derby, Cardiff and Londonrespectively. The most powerful model was forDerby where the corresponding meteorologicalstation is at 0.5 km distance and the weakestwas for London where the correspondingmeteorological station is much further away at21 km distance. Weather variables from earlyFebruary to mid March were found to be the mostinfluential on the start dates of the birchpollen season at the three sites.     

Application of a substrate inhibition model to estimate the effect of fructose concentration on the growth of diverse <Emphasis Type="Italic">Saccharomyces</Emphasis><Emphasis Type="Italic">cerevisiae</Emphasis> strains

In this study, we performed an analysis of the ability of four Saccharomyces cerevisiae and one S. bayanus var. uvarum strains, isolated from different industrial processes, to ferment increasing amounts of fructose (from 0 to 70%, w/v). Overall yeast growth was estimated by integration of the area under optical density vs. time curves. Subsequently, this parameter was modeled by means of a substrate inhibition model. All strains showed a similar behavior against fructose concentration in spite of their different origins, but with slight differences. The optimum fructose concentrations to stimulate yeast growth were obtained between 4.33 and 6.05%, while the maximum concentrations above which growth was completely inhibited were attained between 59.56 and 63.85%. Statistically, model parameters calculated for wine yeast strains were significantly different than those obtained for yeasts from Agave and table olive fermentations, except for the maximum inhibitory concentration. The methodology used in this work could be useful for the industry in general as a first procedure to select yeast strains with higher fructose preferences or tolerances, and especially for winemakers, where the risk of spoilage increases by the presence of a marked residual fructose concentration in the finished wine.