Bootstrap methods for estimating spatial synchrony of fluctuating populations

We describe and examine methods for estimating spatial correlations used in population ecology. We base our analyses on a hypothetical example of a species that has been censured at 30 different locations for 20 years. We assume that the population fluctuations can be described by a simple linear model on logarithmic scale. Stochastic simulations is utilized to check how seven different ways of resampling perform when the goal is to find nominal 95% confidence intervals for the spatial correlation in growth rates at given distances. It turns out that resampling of locations performs badly, with true coverage level as low as 30–40%, especially for small correlations at long distances. Resampling of timepoints performs much better, with coverage varying from 80 to 90%, depending on the strength of density regulation and whether the spatial correlation is estimated for the response variable or for the error terms in the model. Assuming that the underlying model is known, the best results are achieved for parametric bootstrapping, a result that strongly emphasize the importance of defining and estimating a proper population model when studying spatial processes.     

Contrasting patterns of density‐dependent selection at different life stages can create more than one fast–slow axis of life‐history variation

There has been much recent research interest in the existence of a major axis of life‐history variation along a fast–slow continuum within almost all major taxonomic groups. Eco‐evolutionary models of density‐dependent selection provide a general explanation for such observations of interspecific variation in the "pace of life." One issue, however, is that some large‐bodied long‐lived “slow” species (e.g., trees and large fish) often show an explosive “fast” type of reproduction with many small offspring, and species with “fast” adult life stages can have comparatively “slow” offspring life stages (e.g., mayflies). We attempt to explain such life‐history evolution using the same eco‐evolutionary modeling approach but with two life stages, separating adult reproductive strategies from offspring survival strategies. When the population dynamics in the two life stages are closely linked and affect each other, density‐dependent selection occurs in parallel on both reproduction and survival, producing the usual one‐dimensional fast–slow continuum (e.g., houseflies to blue whales). However, strong density dependence at either the adult reproduction or offspring survival life stage creates quasi‐independent population dynamics, allowing fast‐type reproduction alongside slow‐type survival (e.g., trees and large fish), or the perhaps rarer slow‐type reproduction alongside fast‐type survival (e.g., mayflies—short‐lived adults producing few long‐lived offspring). Therefore, most types of species life histories in nature can potentially be explained via the eco‐evolutionary consequences of density‐dependent selection given the possible separation of demographic effects at different life stages.     

Ultrastructure of the pericardium in chitons (Mollusca: Polyplacophora), in relation to filtration and contraction mechanisms

Summary The pericardium in Lepidopleurus asellus (Spengler), Tonicella marmorea (Fabricius), T. rubra L., Ischnochiton albus L., and Calleochiton laevis (Montagu), species taxonomically far apart, is described. It consists of a flat, simple epithelium facing the pericardial cavity, a basement membrane, a muscle layer with two types of muscle fibres, nerve processes, glio-interstitial cells, and fibrocytes, embedded in a loose collagen matrix. The epithelium in L. asellus and I. albus have convoluted lateral cell borders, and in L. asellus very long basal cell processes are seen. Type 1 muscle fibres resemble smooth molluscan muscle. Type 2 muscle fibres resemble cardiac muscle fibres in chitons. Nerve processes associated with glio-interstitial cells and cell processes, run free in the matrix. Synapses in type 1 fibres are covered with glio-interstitial cell processes, lacking in type 2 muscle fibres synapses.This work was supported by grants from the Norwegian Research Council for Science and the Humanities     

Lower Neighborhood Socioeconomic Status Associated with Reduced Diversity of the Colonic Microbiota in Healthy Adults

In the United States, there are persistent and widening socioeconomic gaps in morbidity and mortality from chronic diseases. Although most disparities research focuses on person-level socioeconomic-status, mounting evidence suggest that chronic diseases also pattern by the demographic characteristics of neighborhoods. Yet the biological mechanisms underlying these associations are poorly understood. There is increasing recognition that chronic diseases share common pathogenic features, some of which involve alterations in the composition, diversity, and functioning of the gut microbiota. This study examined whether socioeconomic-status was associated with alpha-diversity of the colonic microbiota. Forty-four healthy adults underwent un-prepped sigmoidoscopy, during which mucosal biopsies and fecal samples were collected. Subjects’ zip codes were geocoded, and census data was used to form a composite indicator of neighborhood socioeconomic-status, reflecting household income, educational attainment, employment status, and home value. In unadjusted analyses, neighborhood socioeconomic-status explained 12–18 percent of the variability in alpha-diversity of colonic microbiota. The direction of these associations was positive, meaning that as neighborhood socioeconomic-status increased, so did alpha-diversity of both the colonic sigmoid mucosa and fecal microbiota. The strength of these associations persisted when models were expanded to include covariates reflecting potential demographic (age, gender, race/ethnicity) and lifestyle (adiposity, alcohol use, smoking) confounds. In these models neighborhood socioeconomic-status continued to explain 11–22 percent of the variability in diversity indicators. Further analyses suggested these patterns reflected socioeconomic variations in evenness, but not richness, of microbial communities residing in the sigmoid. We also found indications that residence in neighborhoods of higher socioeconomic-status was associated with a greater abundance of Bacteroides and a lower abundance of Prevotella, suggesting that diet potentially underlies differences in microbiota composition. These findings suggest the presence of socioeconomic variations in colonic microbiota diversity. Future research should explore whether these variations contribute to disparities in chronic disease outcomes.     

Circadian Disorganization Alters Intestinal Microbiota

Intestinal dysbiosis and circadian rhythm disruption are associated with similar diseases including obesity, metabolic syndrome, and inflammatory bowel disease. Despite the overlap, the potential relationship between circadian disorganization and dysbiosis is unknown; thus, in the present study, a model of chronic circadian disruption was used to determine the impact on the intestinal microbiome. Male C57BL/6J mice underwent once weekly phase reversals of the light:dark cycle (i.e., circadian rhythm disrupted mice) to determine the impact of circadian rhythm disruption on the intestinal microbiome and were fed either standard chow or a high-fat, high-sugar diet to determine how diet influences circadian disruption-induced effects on the microbiome. Weekly phase reversals of the light:dark (LD) cycle did not alter the microbiome in mice fed standard chow; however, mice fed a high-fat, high-sugar diet in conjunction with phase shifts in the light:dark cycle had significantly altered microbiota. While it is yet to be established if some of the adverse effects associated with circadian disorganization in humans (e.g., shift workers, travelers moving across time zones, and in individuals with social jet lag) are mediated by dysbiosis, the current study demonstrates that circadian disorganization can impact the intestinal microbiota which may have implications for inflammatory diseases.     

Alcohol Induced Alterations to the Human Fecal VOC Metabolome

Studies have shown that excessive alcohol consumption impacts the intestinal microbiota composition, causing disruption of homeostasis (dysbiosis). However, this observed change is not indicative of the dysbiotic intestinal microbiota function that could result in the production of injurious and toxic products. Thus, knowledge of the effects of alcohol on the intestinal microbiota function and their metabolites is warranted, in order to better understand the role of the intestinal microbiota in alcohol associated organ failure. Here, we report the results of a differential metabolomic analysis comparing volatile organic compounds (VOC) detected in the stool of alcoholics and non-alcoholic healthy controls. We performed the analysis with fecal samples collected after passage as well as with samples collected directly from the sigmoid lumen. Regardless of the approach to fecal collection, we found a stool VOC metabolomic signature in alcoholics that is different from healthy controls. The most notable metabolite alterations in the alcoholic samples include: (1) an elevation in the oxidative stress biomarker tetradecane; (2) a decrease in five fatty alcohols with anti-oxidant property; (3) a decrease in the short chain fatty acids propionate and isobutyrate, important in maintaining intestinal epithelial cell health and barrier integrity; (4) a decrease in alcohol consumption natural suppressant caryophyllene; (5) a decrease in natural product and hepatic steatosis attenuator camphene; and (6) decreased dimethyl disulfide and dimethyl trisulfide, microbial products of decomposition. Our results showed that intestinal microbiota function is altered in alcoholics which might promote alcohol associated pathologies.     

Estimating similarity of communities: a parametric approach to spatio‐temporal analysis of species diversity

Several stochastic models with environmental noise generate spatio‐temporal Gaussian fields of log densities for the species in a community. Combinations of such models for many species often lead to lognormal species abundance distributions. In spatio‐temporal analysis it is often realistic to assume that the same species are expected to occur at different times and/or locations because extinctions are rare events. Spatial and temporal β‐diversity can then be analyzed by studying pairs of communities at different times or locations defined by a bivariate lognormal species abundance model in which a single correlation occurs. This correlation, which is a measure of similarity between two communities, can be estimated from samples even if the sampling intensities vary and are unknown, using the bivariate Poisson lognormal distribution. The estimators are approximately unbiased, although each specific correlation may be rather uncertain when the sampling effort is low with only a small fraction of the species represented in the samples. An important characteristic of this community correlation is that it relates to the classical Jaccard‐ or the Sørensen‐indices of similarity based on the number of species present or absent in two communities. However, these indices calculated from samples of species in a community do not necessarily reflect similarity of the communities because the observed number of species depends strongly on the sampling intensities. Thus, we propose that our community correlation should be considered as an alternative to these indices when comparing similarity of communities. We illustrate the application of the correlation method by computing the similarity between temperate bird communities.     

Therapeutic vaccination against autologous cancer stem cells with mRNA-transfected dendritic cells in patients with glioblastoma

Background

The growth and recurrence of several cancers appear to be driven by a population of cancer stem cells (CSCs). Glioblastoma, the most common primary brain tumor, is invariably fatal, with a median survival of approximately 1 year. Although experimental data have suggested the importance of CSCs, few data exist regarding the potential relevance and importance of these cells in a clinical setting.

Methods

We here present the first seven patients treated with a dendritic cell (DC)-based vaccine targeting CSCs in a solid tumor. Brain tumor biopsies were dissociated into single-cell suspensions, and autologous CSCs were expanded in vitro as tumorspheres. From these, CSC-mRNA was amplified and transfected into monocyte-derived autologous DCs. The DCs were aliquoted to 9–18 vaccines containing 10⁷ cells each. These vaccines were injected intradermally at specified intervals after the patients had received a standard 6-week course of post-operative radio-chemotherapy. The study was registered with the ClinicalTrials.gov identifier NCT00846456.

Results

Autologous CSC cultures were established from ten out of eleven tumors. High-quality RNA was isolated, and mRNA was amplified in all cases. Seven patients were able to be weaned from corticosteroids to receive DC immunotherapy. An immune response induced by vaccination was identified in all seven patients. No patients developed adverse autoimmune events or other side effects. Compared to matched controls, progression-free survival was 2.9 times longer in vaccinated patients (median 694 vs. 236 days, p = 0.0018, log-rank test).

Conclusion

These findings suggest that vaccination against glioblastoma stem cells is safe, well-tolerated, and may prolong progression-free survival.