Insulin amyloid fibrillation at above 100 degrees C: new insights into protein folding under extreme temperatures

To investigate the folding behavior of amyloidogenic proteins under extreme temperatures, the kinetics of fibrillation and accompanying secondary structure transitions of bovine insulin were studied for temperatures ranging up to 140 degrees C. The presence of extreme heat stress had traditionally been associated with irreversible denaturation of protein while the initial steps of such a denaturation process may be common with a fibril formation pathway of amyloidogenic proteins. The present work demonstrates the ability of insulin to form amyloid fibrils at above 100 degrees C. Amyloid formation was gradually replaced by random coil generation after approximately 80 degrees C until no amyloid was detected at 140 degrees C. The morphology of insulin amyloid fibrils underwent sharp changes with increasing the temperature. The dependence of amyloid formation rate on incubation temperature followed non-Arrhenius kinetics, which is explained by temperature-dependent enthalpy change for amyloid formation. The intermediate stage of amyloid formation and random coil generation consisted of a partially folded intermediate common to both pathways. The fully unfolded monomers in random coil conformation showed partial reversibility through this intermediate by reverting back to the amyloid pathway when formed at 140 degrees C and incubated at 100 degrees C. This study highlights the non-Arrhenius kinetics of amyloid fibrillation under extreme temperatures, and elucidates its intermediate stage common with random coil formation.     

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.     

A survey of flagellate diversity at four deep-sea hydrothermal vents in the Eastern Pacific Ocean using structural and molecular approaches

Eighteen strains of flagellated protists representing nine species were isolated and cultured from four deep-sea hydrothermal vents: Juan de Fuca Ridge (2,200 m), Guaymas Basin (2,000 m), 21 degrees N (2,550 m) and 9 degrees N (2,000 m). Light and electron microscopy were used to identify flagellates to genus and, when possible, species. The small subunit ribosomal RNA genes of each vent species and related strains from shallow-waters and the American Type Culture Collection were sequenced then used for comparative analysis with database sequences to place taxa in an rDNA tree. The hydrothermal vent flagellates belonged to six different taxonomic orders: the Ancyromonadida, Bicosoecida, Cercomonadida, Choanoflagellida, Chrysomonadida, and Kinetoplastida. Comparative analysis of vent isolate and database sequences resolved systematic placement of some well-known species with previously uncertain taxonomic affinities, such as Ancyromonas sigmoides, Caecitellus parvulus, and Massisteria marina. Many of these vent isolates are ubiquitous members of marine, freshwater, and terrestrial ecosystems worldwide, suggesting a global distribution of these flagellate species.     

Extreme value analysis in biometrics

We review some approaches of extreme value analysis in the context of biometrical applications. The classical extreme value analysis is based on iid random variables. Two different general methods are applied, which will be discussed together with biometrical examples. Different estimation, testing, goodness‐of‐fit procedures for applications are discussed. Furthermore, some non‐classical situations are considered where the data are possibly dependent, where a non‐stationary behavior is observed in the data or where the observations are not univariate. A few open problems are also stated.     

Genetic basis of natural variation in body pigmentation in Drosophila melanogaster

Body pigmentation in insects and other organisms is typically variable within and between species and is often associated with fitness. Regulatory variants with large effects at bab1, t and e affect variation in abdominal pigmentation in several populations of Drosophila melanogaster. Recently, we performed a genome wide association (GWA) analysis of variation in abdominal pigmentation using the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP). We confirmed the large effects of regulatory variants in bab1, t and e; identified 81 additional candidate genes; and validated 17 candidate genes (out of 28 tested) using RNAi knockdown of gene expression and mutant alleles. However, these analyses are imperfect proxies for the effects of segregating variants. Here, we describe the results of an extreme quantitative trait locus (xQTL) GWA analysis of female body pigmentation in an outbred population derived from light and dark DGRP lines. We replicated the effects on pigmentation of 28 genes implicated by the DGRP GWA study, including bab1, t and e and 7 genes previously validated by RNAi and/or mutant analyses. We also identified many additional loci. The genetic architecture of Drosophila pigmentation is complex, with a few major genes and many other loci with smaller effects.     

Climate change increases risk of fusarium ear blight on wheat in central China

To estimate potential impact of climate change on wheat fusarium ear blight (FEB), simulated weather for the A1B climate change scenario was input into a model for estimating FEB in central China. In this article, a logistic weather‐based regression model for estimating incidence of wheat FEB in central China was developed, using up to 10 years (2001–2010) of disease, anthesis date and weather data available for 10 locations in Anhui and Hubei provinces. In the model, the weather variables were defined with respect to the anthesis date for each location in each year. The model suggested that incidence of FEB is related to number of days of rainfall in a 30‐day period after anthesis and that high temperatures before anthesis increase the incidence of disease. Validation was done to test whether this relationship was satisfied for another five locations in Anhui province with FEB data for 4–5 years but no nearby weather data, using simulated weather data obtained employing the regional climate modelling system PRECIS. How climate change may affect wheat anthesis date and FEB in central China was investigated for period 2020–2050 using wheat growth model Sirius and climate data simulated using PRECIS. The projection suggested that wheat anthesis dates will generally be earlier and FEB incidence will increase substantially for most locations.     

Weather-Related Indices of Autumn– Winter Dabbling Duck Abundance in Middle North America

ABSTRACT Research on effects of key weather stimuli influencing waterfowl migration during autumn and winter is limited. We investigated relationships between changes in relative abundances of mallard (Anas platyrhynchos) and other dabbling ducks (Anas spp.) and weather variables at midlatitude locations in North America. We used waterfowl survey data from Missouri Conservation Areas and temperature and snow cover data from the Historical Climatology Network to evaluate competing models to explain changes in relative abundance of ducks in Missouri, USA, during autumn-winter, 1995–2005. We found that a cumulative weather severity index model (CumulativeWSI; calculated as mean daily temp - degrees C + no. of consecutive days with mean temp ≤ 0° C + snow depth + no. of consecutive days with snow cover) had the greatest weight of evidence in explaining changes in relative abundance of ducks. We concluded the CumulativeWSI reflected current and cumulative effects of ambient temperatures on energy expenditure by ducks, and snow cover and wetland icing, on food availability for ducks. The CumulativeWSI may be useful in determining potential changes in autumn-winter distributions of North American waterfowl given different climate change projections and associated changes in habitat conservation needs. Future investigations should address interactions between CumulativeWSI and landscape habitat quality, regional waterfowl populations, hunter harvest, and other anthropogenic influences to increase understanding of waterfowl migration during autumn-winter.     

Near edge X-ray absorption fine structure spectroscopy (NEXAFS) of pigment-protein complexes: peridinin-chlorophyll a protein (PCP) of Amphidinium carterae

Peridinin-chlorophyll a protein (PCP) is a unique water soluble antenna complex that employs the carotenoid peridinin as the main light-harvesting pigment. In the present study the near edge X-ray absorption fine structure (NEXAFS) spectrum of PCP was recorded at the carbon K-edge. Additionally, the NEXAFS spectra of the constituent pigments, chlorophyll a and peridinin, were measured. The energies of the lowest unoccupied molecular levels of these pigments appearing in the carbon NEXAFS spectrum were resolved. Individual contributions of the pigments and the protein to the measured NEXAFS spectrum of PCP were determined using a “building block” approach combining NEXAFS spectra of the pigments and the amino acids constituting the PCP apoprotein. The results suggest that absorption changes of the pigments in the carbon near K-edge region can be resolved following excitation using a suitable visible pump laser pulse. Consequently, it may be possible to study excitation energy transfer processes involving “optically dark” states of carotenoids in pigment-protein complexes by soft X-ray probe optical pump double resonance spectroscopy (XODR).     

Synchrony in a Wild Turkey Population and its Relationship to Spring Weather

Abstract: Synchrony is an important component of wildlife population dynamics because it describes spatial pattern in temporal population fluctuations. The strength and spatial extent of synchrony can provide information about the extrinsic and intrinsic forces that shape population structure. Wild turkey (Meleagris gallopavo silvestris) populations undergo annual fluctuations, possibly due to variation in weather during the reproductive season. To determine if spring weather plays a role in synchronizing wild turkey populations, we used a modified Mantel-type spatial autocorrelation procedure to measure the synchrony in fall wild turkey harvest data collected in 443 townships from 1990 to 1995 and compared this to the pattern of synchrony in spring weather variables (May rainfall and temp) over the same period. We measured correlation using Spearman correlation coefficients between the total fall harvests from 1990 to 1995 for each pair of townships, and sorted pairs into 6 50-km distance intervals. We calculated a mean correlation coefficient for each interval and estimated its P-value using resampling. We found moderately significant synchrony in the fall harvest (r_s = 0.12-0.34, P < 0.008) among township pairs <150 km apart, but no significant synchrony beyond this distance. In contrast, both May temperature (r = 0.82-0.90, P < 0.001) and rainfall (r = 0.49-0.76, P < 0.001) were strongly synchronized across all 6 distance intervals. Visual inspection of time series in the wild turkey fall harvest suggests that populations may be synchronized in some years when weather promotes high reproductive success (i.e., a synchronized growth peak) and asynchronous in other years. Knowledge of the spatial dynamics of wild turkey populations will aid wildlife managers in estimating population change, setting harvest quotas, and managing habitat.     

Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.