Transformation and Plant Regeneration from Leaf Explants of Solanum tuberosum L. cv. ‘Shepody’

As part of a large-scale genomics project focused on understanding and improving the Shepody potato, we have increased the regeneration and transformation rates for this cultivar. Using combinations of auxins and trans-zeatin, leaf and stem explants were evaluated for callus induction and shoot formation. Several plant growth regulator combinations resulted in higher plant regeneration rates over a previous method. Using the best combination of auxin and cytokinin in combination with Agrobacterium-mediated transformation, we regenerated independent putative transformants from 59.5% of the total explants plated. We ran PCR on a sample of the plants to confirm transformation and 47.1% were nptII positive; giving a confirmed transformation rate of 28.0%.     

Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area

Protein–protein interactions play key roles in many cellular processes and their affinities and specificities are finely tuned to the functions they perform. Here, we present a study on the relationship between binding affinity and the size and chemical nature of protein–protein interfaces. Our analysis focuses on heterodimers and includes curated structural and thermodynamic data for 113 complexes. We observe a direct correlation between binding affinity and the amount of surface area buried at the interface. For a given amount of surface area buried, the binding affinity spans four orders of magnitude in terms of the dissociation constant (K_d). Across the entire dataset, we observe no obvious relationship between binding affinity and the chemical composition of the interface. We also calculate the free energy per unit surface area buried, or “surface energy density,” of each heterodimer. For interfacial surface areas between 500 and 2000 Ų, the surface energy density decreases as the buried surface area increases. As the buried surface area increases beyond about 2000 Ų, the surface energy density levels off to a constant value. We believe that these analyses and data will be useful for researchers with an interest in understanding, designing or inhibiting protein–protein interfaces.     

Ineffectiveness of plastic mesh for protecting artificial freshwater turtle nests from red fox (Vulpes vulpes) predation

Invasive mammalian predators are efficient at driving native animal declines. The red fox (Vulpes vulpes) kills millions of endemic reptiles in Australia each year. In areas of south-eastern Australia, the eastern long-necked turtle (Chelodina longicollis) and Murray River turtle (Emydura macquarii) have declined by more than 50%. High rates of nest predation by foxes limit the recruitment of young turtles in these populations, but previous methods of fox control have been ineffective at protecting turtle nests. Here, we tested the effectiveness of plastic mesh for protecting artificial turtle nests from predation by foxes, in the mid-Murray catchment, Victoria. We also tested whether protecting a large number of artificial nests in a given area encourages foxes to give up foraging following predictions from giving-up density theory. We made a series of plots, each containing 32 artificial turtle nests. In each plot, we covered a percentage (0%, 25%, 50%, 81% or 100%) of the nests with either 1 or 2 sheets of plastic mesh. We used remote cameras to photograph and identify any predator that attacked nests in the plots. The cameras also allowed us to estimate the amount of time a fox was visible on each plot, as a metric of how much effort foxes expended on protected nests. Nest survival rate was not increased by either 1 or 2 sheets of mesh, and increasing the number of protected nests on a plot did not reveal a giving-up density (GUD) value for fox foraging behaviour. Our study demonstrates that plastic mesh is not effective for protecting artificial turtle nests from foxes in this region.     

Theory of early warning signals of disease emergenceand leading indicators of elimination

Anticipating infectious disease emergence and documenting progress in disease elimination are important applications for the theory of critical transitions. A key problem is the development of theory relating the dynamical processes of transmission to observable phenomena. In this paper, we consider compartmental susceptible–infectious–susceptible (SIS) and susceptible–infectious–recovered (SIR) models that are slowly forced through a critical transition. We derive expressions for the behavior of several candidate indicators, including the autocorrelation coefficient, variance, coefficient of variation, and power spectra of SIS and SIR epidemics during the approach to emergence or elimination. We validated these expressions using individual-based simulations. We further showed that moving-window estimates of these quantities may be used for anticipating critical transitions in infectious disease systems. Although leading indicators of elimination were highly predictive, we found the approach to emergence to be much more difficult to detect. It is hoped that these results, which show the anticipation of critical transitions in infectious disease systems to be theoretically possible, may be used to guide the construction of online algorithms for processing surveillance data.     

Temporal and spatial dynamics in the apple flower microbiome in the presence of the phytopathogen Erwinia amylovora

Plant microbiomes have important roles in plant health and productivity. However, despite flowers being directly linked to reproductive outcomes, little is known about the microbiomes of flowers and their potential interaction with pathogen infection. Here, we investigated the temporal spatial dynamics of the apple stigma microbiome when challenged with a phytopathogen Erwinia amylovora, the causal agent of fire blight disease. We profiled the microbiome from the stigmas of individual flowers, greatly increasing the resolution at which we can characterize shifts in the composition of the microbiome. Individual flowers harbored unique microbiomes at the operational taxonomic unit level. However, taxonomic analysis of community succession showed a population gradually dominated by bacteria within the families Enterobacteriaceae and Pseudomonadaceae. Flowers inoculated with E. amylovora established large populations of the phytopathogen, with pathogen-specific gene counts of >3.0 × 10⁷ in 90% of the flowers. Yet, only 42% of inoculated flowers later developed fire blight symptoms. This reveals that pathogen abundance on the stigma is not sufficient to predict disease outcome. Our data demonstrate that apple flowers represent an excellent model in which to characterize how plant microbiomes establish, develop, and correlate with biological processes such as disease progression in an experimentally tractable plant organ.Subject terms: Microbial ecology, Non-coding RNAs