Large, activated B cells are the primary B-cell target of 8-bromoguanosine and 8-mercaptoguanosine

Previous studies have documented the ability of 8-bromoguanosine (8-BrGuo) and 8-mercaptoguanosine (8-MGuo) to induce polyclonal proliferation and differentiation of B cells from a variety of mouse strains. In the present study, we have defined the cellular target of this mitogenic activity. Using B cells fractionated according to size, we have found that large B cells are responsive to 8-BrGuo- and 8-MGuo-induced proliferation and differentiation whereas small, resting B cells are relatively unresponsive to these compounds. Addition of splenic adherent cells to the small B-cell fraction partially restored the proliferative but not the differentiative responses to 8-BrGuo and 8-MGuo. Although small B cells alone did not proliferate or differentiate in response to 8-BrGuo and 8-MGuo, cell surface expression of Ia antigens increased following incubation with these compounds. Thus, the biological activity of 8-BrGuo and 8-MGuo appears to be dictated by the cell type upon which it is acting. Small B cells are activated as evidenced by increased levels of surface Ia whereas large B cells are not only activated but are also induced to proliferate and differentiate.     

On tight junction structure: Forssman glycolipid does not flow between MDCK cells in an intact epithelial monolayer

One model of tight junction structure suggests that lipids might flow from cell to cell within shared exoplasmic membrane leaflets. We tested this proposal by co-culturing two clones of MDCK epithelial cells, which differed in their content of Forssman glycolipid, and then staining by immunofluorescence with rabbit anti-Forssman Ig. In co-cultures grown on glass cover slips and on nitrocellulose filters, positive Forssman staining was restricted to sharply demarcated clusters of cells formed by the Forssman-positive clone. Integrity of tight junctions between the two clones was indicated on cover slips by the presence of individual domes (hemicysts) composed of both clones and on filters by the generation of transepithelial potential differences. These results suggest that glycolipids in the exoplasmic leaflet of cells in a tight epithelium do not flow to adjacent cells.     

No change in DNA damage or repair of single- and double-strand breaks as human diploid fibroblasts age in vitro

Using the in vitro human diploid fibroblast model, we tested theories of aging which hypothesize that either accumulation of DNA damage or decreased DNA repair capacity is causally related to cellular senescence. Between population doubling level (PDL) 32 and 71, fetal lung-derived normal diploid human fibroblasts (IMR 90) were assayed for both DNA single-strand breaks (SSBs, spontaneous and induced by 6 Gy) and DNA double-strand breaks (DSBs, spontaneous and induced by 100 Gy). After gamma-irradiation cells were kept on ice unless undergoing repair incubation at 37 degrees C for 7.5-120 min or 18-24 h. To assay DNA strand breaks we used the filter elution technique in conjunction with a fluorometric determination of DNA which is not biased in favor of proliferating aging cells as are radioactive labelling methods. We found no change with in vitro age in the accumulation of spontaneous SSBs or DSBs, nor in the kinetics or completeness of DNA strand rejoining after gamma-irradiation. Cells at varying PDLs rejoined approx. 90% of SSBs and DSBs after 60 min repair incubation and 100% after 18-24 h repair incubation. We conclude that aging and senescence as measured by proliferative lifespan in IMR 90 cells are neither accompanied nor caused by accumulation of DNA strand breaks or by diminished capacity to rejoin gamma-radiation-induced SSBs or DSBs in DNA.     

Evidence for Directional Selection at a Novel Major Histocompatibility Class I Marker in Wild Common Frogs (Rana temporaria) Exposed to a Viral Pathogen (Ranavirus)

Whilst the Major Histocompatibility Complex (MHC) is well characterized in the anuran Xenopus, this region has not previously been studied in another popular model species, the common frog (Rana temporaria). Nor, to date, have there been any studies of MHC in wild amphibian host-pathogen systems. We characterise an MHC class I locus in the common frog, and present primers to amplify both the whole region, and specifically the antigen binding region. As no more than two expressed haplotypes were found in over 400 clones from 66 individuals, it is likely that there is a single class I locus in this species. This finding is consistent with the single class I locus in Xenopus, but contrasts with the multiple loci identified in axolotls, providing evidence that the diversification of MHC class I into multiple loci likely occurred after the Caudata/Anura divergence (approximately 350 million years ago) but before the Ranidae/Pipidae divergence (approximately 230 mya). We use this locus to compare wild populations of common frogs that have been infected with a viral pathogen (Ranavirus) with those that have no history of infection. We demonstrate that certain MHC supertypes are associated with infection status (even after accounting for shared ancestry), and that the diseased populations have more similar supertype frequencies (lower F_ST) than the uninfected. These patterns were not seen in a suite of putatively neutral microsatellite loci. We interpret this pattern at the MHC locus to indicate that the disease has imposed selection for particular haplotypes, and hence that common frogs may be adapting to the presence of Ranavirus, which currently kills tens of thousands of amphibians in the UK each year.     

A Bayesian Model of Category-Specific Emotional Brain Responses

Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories—fear, anger, disgust, sadness, or happiness—is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.     

Sample Variability Influences on the Precision of Predictive Bioassessment

The rapid bioassessment technique we investigate (AUSRIVAS) requires a nationally standardized sampling protocol that uses a single collection of macroinvertebrates (without replication) taken from 10 m of specific habitats (e.g. stream edge and/or riffle) and sub-samples of 200 animals. The macroinvertebrate data are run through predictive models that provide an assessment of biological condition based on a comparison of the animals found in the collection (the observed) and those expected to be there given the site-specific characteristics of the stream (the O/E taxa score). The important questions are related to the conclusions regarding river condition that can be drawn from the biological assessment. Rapid bioassessment studies are generally of two types: those for assessment of individual sites and those where many sites are selected to collectively assess the potential impacts of some human activity such as forestry or agriculture. We wanted to identify the effects of sample variability on the outputs of this predictive bioassessment technique. We found that a single collection of benthic macroinvertebrates was sufficient for bioassessment when taken from a site that had a large area of nearly uniform substrate and was in good condition. Also, collections taken from a larger and smaller area of substrate (1.75, 3.5 or 7 m²) gave the same bioassessment. In other sites, not in such good condition, the variability in bioassessment from different collections could result in different interpretations of biological condition. For all sites, regardless of condition, much of the variation in bioassessment was derived from sub-sampling the macroinvertebrates. We develop a statistical sub-sampling and solver algorithm that provides a measure of variability and a statistically valid probability of impairment for a single site, without the need to actually collect the hundreds of replicated collections needed for this study. We found that assessment at impaired sites, where only 1 collection and 1 sub-sample are taken (a common situation in rapid assessment), the 95% confidence level for O/E taxa scores is estimated to be as much as ±0.22. At sites in reference condition, the 95% confidence interval may be much narrower (~±0.1 O/E units). Therefore, assessments of sites at, or near, reference condition will be more precise than for impaired sites. Power analysis revealed that where single sites are being assessed we recommend a sample collected from 3.5 m² of habitat, but replicate collections should be taken at a site (rather than one only) and we recommend replicate sub-samples of each collection (total of six sub-samples from a site). However, this would remove a ‘rapid’ component of the bioassessment. We recommend the addition of sub-sampling and solver algorithms to the predictive models such as AUSRIVAS to provide a statistical measure of probability of impairment. An adaptive sub-sampling regime could then be used to optimize sampling effort. For example, a single sub-sample may be sufficient for screening or the agency could use the sub-sample and solver algorithms to sub-sample the parent sample for a more precise estimate of the biological condition. Replication should be maximized at the spatial scale required for reporting: site, or regional. But as a general rule, catchment or land-use scale studies should maximize replicate sites, and site-scale assessments should maximize replication within sites.     

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.