Autoradiography of muscarinic cholinergic receptors in cortical and subcortical brain regions of C57BL/6 and DBA/2 mice

Mice of the inbred strains C57BL/6 and DBA/2 show strain-dependent behavioural differences which have been correlated with variations in brain cholinergic systems. In the present study, the density of muscarinic cholinergic receptors in both strains of mice was determined by autoradiographic methods using [³H]quinuclidinyl benzilate (QNB) and [³H]pirenzepine as ligands. C57BL/6 mice showed a significantly lower [³H]QNB binding level in the frontal cortex by one third as compared to DBA/2 mice. In the striatum and the cholinergic pontomesencephalic nucleus laterodorsalis tegmenti the [³H]QNB binding was lower in C57BL/6 by 28% and 31%, respectively. The [³H]pirenzepine binding level was found to be significantly higher in C57BL/6 temporal cortex (by 22%). These results are discussed in relation to interstrain differences in cholinergic cell density and in the activity of cholinergic enzymes.     

Kinetic and structural studies on the interactions of Torpedo californica acetylcholinesterase with two donepezil-like rigid analogues

Acetylcholinesterase inhibitors were introduced for the symptomatic treatment of Alzheimer’s disease (AD). Among the currently approved inhibitors, donepezil (DNP) is one of the most preferred choices in AD therapy. The X-ray crystal structures of Torpedo californica AChE in complex with two novel rigid DNP-like analogs, compounds 1 and 2, have been determined. Kinetic studies indicated that compounds 1 and 2 show a mixed-type inhibition against TcAChE, with K_i values of 11.12?±?2.88 and 29.86?±?1.12?nM, respectively. The DNP rigidification results in a likely entropy-enthalpy compensation with solvation effects contributing primarily to AChE binding affinity. Molecular docking evidenced the molecular basis for the binding of compounds 1 and 2 to the active site of β-secretase-1. Overall, these simplified DNP derivatives may represent new structural templates for the design of lead compounds for a more effective therapeutic strategy against AD by foreseeing a dual AChE and BACE-1 inhibitory activity.     

Disentangling genetic and epigenetic determinants of ultrafast adaptation

A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical–experimental framework for disclosing the presence of such adaptation‐speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation–accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic‐adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data‐driven individual‐based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation‐speeding mechanisms in general.     

A molecular phylogenetic framework for the subfamily Ocenebrinae (Gastropoda,Muricidae)

The Ocenebrinae is a subfamily of marine predatory gastropods known as oyster and mussel drills. Their current phylogenetic framework is traditionally based on shell and radular characters, but a consensus on relationships among genera is still lacking. We investigated the molecular phylogeny of Ocenebrinae using 50 species and DNA data from one nuclear (28S) and two mitochondrial (COI and 16S) genes, the largest data set so far assembled for this subfamily. We found support for the monophyly of the Ocenebrinae, and species were divided into four major lineages. Within groups, genera had similar geographic distributions, suggesting that except in a few cases, species diversification within clades occurred without range expansions. We discuss the phylogenetic distribution of a labral tooth and a sealed siphonal canal, two characteristic ocenebrine features. We also show that Ocinebrina species in the north‐eastern Pacific are not monophyletic with north‐eastern Atlantic and Mediterranean species, and that the Ocinebrina edwardsii species complex belongs to Ocenebra.     

A novel harmony search-K means hybrid algorithm for clustering gene expression data

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.