Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins*

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Highlights

• •Method for the analysis of response curves from thermal proteome profiling (TPP).
• •NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control.
• •Increased proteome coverage and sensitivity to identify drug-binding proteins.

     

A Conservative Point Mutation in a Dynamic Antigen-binding Loop of Human Immunoglobulin λ6 Light Chain Promotes Pathologic Amyloid Formation

Immunoglobulin light chain (LC) amyloidosis (AL) is a life-threatening human disease wherein free mono-clonal LCs deposit in vital organs. To determine what makes some LCs amyloidogenic, we explored patient-based amyloidogenic and non-amyloidogenic recombinant LCs from the λ6 subtype prevalent in AL. Hydrogen-deuterium exchange mass spectrometry, structural stability, proteolysis, and amyloid growth studies revealed that the antigen-binding CDR1 loop is the least protected part in the variable domain of λ6 LC, particularly in the AL variant. N32T substitution in CRD1 is identified as a driver of amyloid formation. Substitution N32T increased the amyloidogenic propensity of CDR1 loop, decreased its protection in the native structure, and accelerated amyloid growth in the context of other AL substitutions. The destabilizing effects of N32T propagated across the molecule increasing its dynamics in regions ∼30 Å away from the substitution site. Such striking long-range effects of a conservative point substitution in a dynamic surface loop may be relevant to Ig function. Comparison of patient-derived and engineered proteins showed that N32T interactions with other substitution sites must contribute to amyloidosis. The results suggest that CDR1 is critical in amyloid formation by other λ6 LCs.     

Estimating Size and Trend of the North Interlake Woodland Caribou Population Using Fecal-DNA and Capture-Recapture Models

A critical step in recovery efforts for endangered and threatened species is the monitoring of population demographic parameters. As part of these efforts, we evaluated the use of fecal-DNA based capture–recapture methods to estimate population sizes and population rate of change for the North Interlake woodland caribou herd (Rangifer tarandus caribou), Manitoba, Canada. This herd is part of the boreal population of woodland caribou, listed as threatened under the federal Species at Risk Act (2003) and the provincial Manitoba Endangered Species Act (2006). Between 2004 and 2009 (9 surveys), we collected 1,080 fecal samples and identified 180 unique genotypes (102 females and 78 males). We used a robust design survey plan with 2 surveys in most years and analysed the data with Program MARK to estimate encounter rates (p), apparent survival rates (φ), rates of population change (λ), and population sizes (N). We estimated these demographic parameters for males and females and for 2 genetic clusters within the North Interlake. The population size estimates were larger for the Lower than the Upper North Interlake area and the proportion of males was lower in the Lower (33%) than the Upper North Interlake (49%). Population rate of change for the entire North Interlake area (2005–2009) using the robust design Pradel model was significantly <1.0 (λ = 0.90, 95% CI: 0.82–0.99) and varied between sex and area with the highest being for males in Lower North Interlake (λ = 0.98, 95% CI: 0.83–1.13) and the lowest being for females in Upper North Interlake (λ = 0.83, 95% CI: 0.69–0.97). The additivity of λ between sex and area is supported on the log scale and translates into males having a λ that is 0.09 greater than females and independent of sex, Lower North Interlake having a λ that is 0.06 greater than Upper North Interlake. Population estimates paralleled these declining trends, which correspond to trends observed in other fragmented populations of woodland caribou along the southern part of their range. The results of this study clearly demonstrate the applicability and success of non-invasive genetic sampling in monitoring populations of woodland caribou. © 2012 The Wildlife Society.     

Analysis of multivariate recurrent event data with time‐dependent covariates and informative censoring

Multivariate recurrent event data are usually encountered in many clinical and longitudinal studies in which each study subject may experience multiple recurrent events. For the analysis of such data, most existing approaches have been proposed under the assumption that the censoring times are noninformative, which may not be true especially when the observation of recurrent events is terminated by a failure event. In this article, we consider regression analysis of multivariate recurrent event data with both time‐dependent and time‐independent covariates where the censoring times and the recurrent event process are allowed to be correlated via a frailty. The proposed joint model is flexible where both the distributions of censoring and frailty variables are left unspecified. We propose a pairwise pseudolikelihood approach and an estimating equation‐based approach for estimating coefficients of time‐dependent and time‐independent covariates, respectively. The large sample properties of the proposed estimates are established, while the finite‐sample properties are demonstrated by simulation studies. The proposed methods are applied to the analysis of a set of bivariate recurrent event data from a study of platelet transfusion reactions.     

Modeling seasonality in space‐time infectious disease surveillance data

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one‐step‐ahead predictions if all three components allow for seasonal variation.     

Creating a community resource for protein science

Helen Berman is the recipient of the Protein Society 2012 Carl Branden Award In addition to being one of the early pioneers in protein crystallography, Carl Brändén made significant contributions to science education with his elegant and beautifully illustrated book Introduction to Protein Structure (Brändén and Tooze, New York: Garland, 1991). It is truly an honor to receive this award in their names. This award and the 40th anniversary of the Protein Data Bank (PDB; Berman et al., Structure 2012;20:391–396) have given me an opportunity to reflect on the various components that have contributed to building a resource for protein science and to try to quantify the impact of having PDB data openly available.     

Competing risks analysis of correlated failure time data

Summary .   We develop methods for competing risks analysis when individual event times are correlated within clusters. Clustering arises naturally in clinical genetic studies and other settings. We develop a nonparametric estimator of cumulative incidence, and obtain robust pointwise standard errors that account for within-cluster correlation. We modify the two-sample Gray and Pepe–Mori tests for correlated competing risks data, and propose a simple two-sample test of the difference in cumulative incidence at a landmark time. In simulation studies, our estimators are asymptotically unbiased, and the modified test statistics control the type I error. The power of the respective two-sample tests is differentially sensitive to the degree of correlation; the optimal test depends on the alternative hypothesis of interest and the within-cluster correlation. For purposes of illustration, we apply our methods to a family-based prospective cohort study of hereditary breast/ovarian cancer families. For women with BRCA1 mutations, we estimate the cumulative incidence of breast cancer in the presence of competing mortality from ovarian cancer, accounting for significant within-family correlation.     

Land-use intensification systematically alters the size structure of aquatic communities in the Neotropics

Land-use and land-cover transitions can affect biodiversity and ecosystem functioning in a myriad of ways, including how energy is transferred within food-webs. Size spectra (i.e. relationships between body size and biomass or abundance) provide a means to assess how food-webs respond to environmental stressors by depicting how energy is transferred from small to larger organisms. Here, we investigated changes in the size spectrum of aquatic macroinvertebrates along a broad land-use intensification gradient (from Atlantic Forest to mechanized agriculture) in 30 Brazilian streams. We expected to find a steeper size spectrum slope and lower total biomass in more disturbed streams due to higher energetic expenditure in physiologically stressful conditions, which has a disproportionate impact on large individuals. As expected, we found that more disturbed streams had fewer small organisms than pristine forest streams, but, surprisingly, they had shallower size spectrum slopes, which indicates that energy might be transferred more efficiently in disturbed streams. Disturbed streams were also less taxonomically diverse, suggesting that the potentially higher energy transfer in these webs might be channelled via a few efficient trophic links. However, because total biomass was higher in pristine streams, these sites still supported a greater number of larger organisms and longer food chains (i.e. larger size range). Our results indicate that land-use intensification decreases ecosystem stability and enhances vulnerability to population extinctions by reducing the possible energetic pathways while enhancing efficiency between the remaining food-web linkages. Our study represents a step forward in understanding how land-use intensification affects trophic interactions and ecosystem functioning in aquatic systems.     

Phylogenetic structure of the extinction and biotic factors explaining differential survival of terrestrial notosuchians at the Cretaceous–Palaeogene crisis

Although the clade Crocodylomorpha is represented by few extant species (Crocodylia), it has a rich fossil record. Hundreds of species, adapted to terrestrial, semi-aquatic and marine environments, have existed over more than 200 million years. Numerous studies have attempted to characterize the factors driving the diversification and extinction events of Crocodylomorpha, resulting in ambiguous and even contradictory conclusions, which points to the need for phylogenetically and temporally smaller-scaled studies. Here, we focus on differential survival at the Cretaceous–Palaeogene (K–Pg) crisis of Notosuchia, a diverse clade of mostly terrestrial Crocodylomorpha that achieved great diversity during the Cretaceous. More precisely, we tested the effect of body size and palaeotemperatures on notosuchian survival probability during the K–Pg crisis as well as the effect of diet on the evolution of their body size. We find that Notosuchia showed an evolutionary trend towards larger body sizes through time, associated with a shift from an omnivorous to a carnivorous diet. This may explain why sebecids were the only notosuchians to survive the K–Pg crisis. We also corroborate the conclusions of previous studies that detected a Lagerstätten effect occurring in the Adamantina Formation (Upper Cretaceous, Brazil, Bauru Group). This work confirms the value of more finely-scaled macroevolutionary studies for understanding the history of a rich and complex group such as Crocodylomorpha.