Heterologous prion-forming proteins interact to cross-seed aggregation in Saccharomyces cerevisiae

AbstractThe early stages of protein misfolding remain incompletely understood, as most mammalian proteinopathies are only detected after irreversible protein aggregates have formed. Cross-seeding, where one aggregated protein templates the misfolding of a heterologous protein, is one mechanism proposed to stimulate protein aggregation and facilitate disease pathogenesis. Here, we demonstrate the existence of cross-seeding as a crucial step in the formation of the yeast prion [PSI+], formed by the translation termination factor Sup35. We provide evidence for the genetic and physical interaction of the prion protein Rnq1 with Sup35 as a predominant mechanism leading to self-propagating Sup35 aggregation. We identify interacting sites within Rnq1 and Sup35 and determine the effects of breaking and restoring a crucial interaction. Altogether, our results demonstrate that single-residue disruption can drastically reduce the effects of cross-seeding, a finding that has important implications for human protein misfolding disorders.</jats:p

PAYOFFS TO FARM MANAGEMENT: HOW IMPORTANT IS CROP MARKETING?

In production agriculture, good management is demonstrated by profits that are persistenly greater than those of similar neighboring farms. This research examined the effects of management practices on risk-adjusted profit per acre for Kansas farms over 1990-1999. The management practices were price, cost, yield, planting intensity, and technology adoption (less-tillage). Cost management, planting intensity, and technology adoption had the greatest effect on profit per acre, and cash price management was found to have the smallest impact. If producers wish to have continuously high profits, their efforts are best spent in management practices over which they have the most control.farm management, marketing, risk, technology adoption, Farm Management, Marketing,

USING SATELLITE IMAGERY IN KANSAS CROP YIELD AND NET FARM INCOME FORECASTS

Remotely sensed data have been used in the past to predict crop yields. This research attempts to incorporate remotely sensed data into a net farm income projection model. Using in-sample regressions, satellite imagery appears to increase prediction accuracy in the time periods prior to USDA's first crop production estimate for wheat and corn. Remotely sensed data improved model performance more in the western regions of the state than in the eastern regions. However, in a jackknife out-of-sample framework, the satellite imagery appeared to statistically improve only 8 of the 81 models (9 crop reporting districts by 9 forecasting horizons) estimated. Moreover, 41 of the 81 models were statistically better without the satellite imagery data. This indicates that perhaps the functional form of net farm income has not been well-specified since additional information should generally not cause a model to deteriorate.net farm income, remote sensing, satellite imagery, Crop Production/Industries,

Predicting the effects of sand erosion on collector surfaces in CSP plants

This paper presents a methodology to predict the optical performance and physical topography of the glass collector surfaces of any given CSP plant in the presence of sand and dust storms, providing that local climate conditions are known and representative sand and dust particles samples are available. Using existing meteorological data for a defined CSP plant in Egypt, plus sand and dust samples from two desert locations in Libya, we describe how to derive air speed, duration, and sand concentrations to use within the Global CSP Laboratory sand erosion simulation rig at Cranfield University. This then allows us to predict the optical performance of parabolic trough collector glass after an extended period by the use of accelerated ageing. However the behavior of particles in sandstorms is complex and has prompted a theoretical analysis of sand particle dynamics which is also described in this paper

The [RNQ+] prion: A model of both functional and pathological amyloid

The formation of fibrillar amyloid is most often associated with protein conformational disorders such as prion diseases, Alzheimer disease and Huntington disease. Interestingly, however, an increasing number of studies suggest that amyloid structures can sometimes play a functional role in normal biology. Several proteins form self-propagating amyloids called prions in the budding yeast Saccharomyces cerevisiae. These unique elements operate by creating a reversible, epigenetic change in phenotype. While the function of the non-prion conformation of the Rnq1 protein is unclear, the prion form, [RNQ(+)], acts to facilitate the de novo formation of other prions to influence cellular phenotypes. The [RNQ(+)] prion itself does not adversely affect the growth of yeast, but the overexpression of Rnq1p can form toxic aggregated structures that are not necessarily prions. The [RNQ(+)] prion is also involved in dictating the aggregation and toxicity of polyglutamine proteins ectopically expressed in yeast. Thus, the [RNQ(+)] prion provides a tractable model that has the potential to reveal significant insight into the factors that dictate how amyloid structures are initiated and propagated in both physiological and pathological contexts

USING SATELLITE IMAGERY IN PREDICTING KANSAS FARMLAND VALUES

Can remotely sensed imagery improve hedonic land price models? A remotely sensed variable was added to a hedonic farmland value model as a proxy for land productivity. Land cover data were used to obtain urban and recreational effects as well. The urban and recreational effects were statistically significant but economically small. The remotely sensed productivity variable was statistically significant and economically large, indicating that knowing the "greenness" of the land increased the explanatory power of the hedonic price model. Thus, depending upon the cost of this information, including remotely sensed imagery in traditional hedonic land price models is economically beneficial.Land Economics/Use,

Extensive diversity of prion strains is defined by differential chaperone interactions and distinct amyloidogenic regions

Amyloidogenic proteins associated with a variety of unrelated diseases are typically capable of forming several distinct self-templating conformers. In prion diseases, these different structures, called prion strains (or variants), confer dramatic variation in disease pathology and transmission. Aggregate stability has been found to be a key determinant of the diverse pathological consequences of different prion strains. Yet, it remains largely unclear what other factors might account for the widespread phenotypic variation seen with aggregation-prone proteins. Here, we examined a set of yeast prion variants of the [RNQ+] prion that differ in their ability to induce the formation of another yeast prion called [PSI+]. Remarkably, we found that the [RNQ+] variants require different, non-contiguous regions of the Rnq1 protein for both prion propagation and [PSI+] induction. This included regions outside of the canonical prion-forming domain of Rnq1. Remarkably, such differences did not result in variation in aggregate stability. Our analysis also revealed a striking difference in the ability of these [RNQ+] variants to interact with the chaperone Sis1. Thus, our work shows that the differential influence of various amyloidogenic regions and interactions with host cofactors are critical determinants of the phenotypic consequences of distinct aggregate structures. This helps reveal the complex interdependent factors that influence how a particular amyloid structure may dictate disease pathology and progression

Invasive Wild pigs as primary nest predators for Wild turkeys

Depredation of wild turkey (Meleagris gallopavo) nests is a leading cause of reduced recruitment for the recovering and iconic game species. invasive wild pigs (Sus scrofa) are known to depredate nests, and have been expanding throughout the distributed range of wild turkeys in north America. We sought to gain better insight on the magnitude of wild pigs depredating wild turkey nests. We constructed simulated wild turkey nests throughout the home ranges of 20 GPS-collared wild pigs to evaluate nest depredation relative to three periods within the nesting season (i.e., early, peak, and late) and two nest densities (moderate = 12.5-25 nests/km2, high = 25-50 nests/km2) in south-central Texas, USA during March–June 2016. Overall, the estimated probability of nest depredation by wild pigs was 0.3, equivalent to native species of nest predators in the study area (e.g., gray fox [Urocyon cinereoargenteus], raccoon [Procyon lotor], and coyote [Canis latrans]). female wild pigs exhibited a constant rate of depredation regardless of nesting period or density of nests. However, male wild pigs increased their rate of depredation in areas with higher nest densities. Management efforts should remove wild pigs to reduce nest failure in wild turkey populations especially where recruitment is low