BackgroundThe genetic diversity of crop species is the result of natural selection on the wild progenitor and human intervention by ancient and modern farmers and breeders. The genomes of modern cultivars, old cultivated landraces, ecotypes and wild relatives reflect the effects of these forces and provide insights into germplasm structural diversity, the geographical dimension to species diversity and the process of domestication of wild organisms. This issue is also of great practical importance for crop improvement because wild germplasm represents a rich potential source of useful under-exploited alleles or allele combinations. The aim of the present study was to analyse a major Pisum germplasm collection to gain a broad understanding of the diversity and evolution of Pisum and provide a new rational framework for designing germplasm core collections of the genus.Results3020 Pisum germplasm samples from the John Innes Pisum germplasm collection were genotyped for 45 retrotransposon based insertion polymorphism (RBIP) markers by the Tagged Array Marker (TAM) method. The data set was stored in a purpose-built Germinate relational database and analysed by both principal coordinate analysis and a nested application of the Structure program which yielded substantially similar but complementary views of the diversity of the genus Pisum. Structure revealed three Groups (1-3) corresponding approximately to landrace, cultivar and wild Pisum respectively, which were resolved by nested Structure analysis into 14 Sub-Groups, many of which correlate with taxonomic sub-divisions of Pisum, domestication related phenotypic traits and/or restricted geographical locations. Genetic distances calculated between these Sub-Groups are broadly supported by principal coordinate analysis and these, together with the trait and geographical data, were used to infer a detailed model for the domestication of Pisum.ConclusionsThese data provide a clear picture of the major distinct gene pools into which the genus Pisum is partitioned and their geographical distribution. The data strongly support the model of independent domestications for P. sativum ssp abyssinicum and P. sativum. The relationships between these two cultivated germplasms and the various sub-divisions of wild Pisum have been clarified and the most likely ancestral wild gene pools for domesticated P. sativum identified. Lastly, this study provides a framework for defining global Pisum germplasm which will be useful for designing core collections.
Host plant quality is a key determinant of the performance of larvae of herbivorous insects. The effects of nitrogen and dolomite fertilization on the quality of pedunculate oak, Quercus robur L. (Fagaceae) foliage, as a food for gypsy moth, Lymantria dispar L. (Lepidoptera: Lymantriidae) larvae were evaluated. The seedlings were divided into five fertilization treatments (nonfertilized control, commercial nutrient solution, commercial nutrient solution + (NH4)2SO4, commercial nutrient solution + KNO3, and commercial nutrient solution + dolomite). The experiment was performed in Petri dishes, in each of which a fresh leaf from one treatment and one larva were placed. Insect performance assays, survival, development, growth, and food utilization were evaluated for each fertilization treatment. Leaf samples were assayed for nitrogen and other main nutrients, soluble carbohydrates, and phenolic compounds. The fertilizer treatment with added ammonium improved gypsy moth performance, and the amount of food eaten was the lowest in this treatment. Utilization of elements from the food depended on the element and on the fertilization treatment. The insect bodies retained 50–64% of the nitrogen and 55–79% of the phosphorus. The results show that the efficiency of conversion of ingested food (ECI) and the efficiency of conversion of digested food (ECD) differ among the fertilization treatments, but it is not possible to define a general trend. Our results suggest that fertilization (especially ammonium) of host plants can increase herbivore performance, decrease the amount of food needed, and increase its utilization efficiency.
One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.
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