Revealing pancrustacean relationships: Phylogenetic analysis of ribosomal protein genes places Collembola (springtails) in a monophyletic Hexapoda and reinforces the discrepancy between mitochondrial and nuclear DNA markers

Background  

In recent years, several new hypotheses on phylogenetic relations among arthropods have been proposed on the basis of DNA sequences. One of the challenged hypotheses is the monophyly of hexapods. This discussion originated from analyses based on mitochondrial DNA datasets that, due to an unusual positioning of Collembola, suggested that the hexapod body plan evolved at least twice. Here, we re-evaluate the position of Collembola using ribosomal protein gene sequences.     

Potentiometric monitoring of the redox state of brain structures of freely-moving rats in sleep-wake cycles

Variations of brain tissue redox state potential (E) of freely-moving white rats (300-350 g) in cycles of wakefulness (W), slow-wave sleep (SWS), and paradoxical sleep (PS) were measured by platinum electrodes symmetrically implanted into the frontal and occipital cortices and hippocampus. In addition, EMG of neck muscles and general motor activity of animals were recorded. The common reference electrode was implanted in the nasal bone. It was shown that in some brain sites (called active), episodes of W and PS were accompanied by a rise of E, and during transitions from W and PS to SWS, E dropped. The value of E varied in the range of 100 mV. It is suggested that transitions from W and PS to SWS are accompanied by shifts in the balance between the main energy sources. Oxidative phosphorylation prevails in W and PS, whereas aerobic glycolysis is the main source of energy during SWS. We think that this suggestion is supported both by a decrease in E in SWS and its oscillations typical of glucolytic processes [Aon et al., 1992]. Recent literature data [Bitter et al., 1996] suggest that astroglia is the main compartment for aerobic glycolysis.     

Ranking non-synonymous single nucleotide polymorphisms based on disease concepts

As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.