Impairment of mitochondrial tRNAIle processing by a novel mutation associated with chronic progressive external ophthalmoplegia

We report a sporadic case of chronic progressive external ophthalmoplegia associated with ragged red fibers. The patient presented with enlarged mitochondria with deranged internal architecture and crystalline inclusions. Biochemical studies showed reduced activities of complex I, III and IV in skeletal muscle. Molecular genetic analysis of all mitochondrial tRNAs revealed a G to A transition at nt 4308; the G is a highly conserved nucleotide that participates in a GC base-pair in the T-stem of mammalian mitochondrial tRNA(Ile). The mutation was detected at a high level (approx. 50%) in muscle but not in blood. The mutation co-segregated with the phenotype, as the mutation was absent from blood and muscle in the patient's healthy mother. Functional characterization of the mutation revealed a six-fold reduced rate of tRNA(Ile) precursor 3' end maturation in vitro by tRNAse Z. Furthermore, the mutated tRNA(Ile) displays local structural differences from wild-type. These results suggest that structural perturbations reduce efficiency of tRNA(Ile) precursor 3' end processing and contribute to the molecular pathomechanism of this mutation.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.     

Effects of vanadium-containing compounds on membrane lipids and on microdomains used in receptor-mediated signaling

There is increasing evidence for the involvement of plasma membrane microdomains in insulin receptor function. Moreover, disruption of these structures, which are typically enriched in sphingomyelin and cholesterol, results in insulin resistance. Treatment strategies for insulin resistance include the use of vanadium (V) compounds which have been shown in animal models to enhance insulin responsiveness. One possible mechanism for insulin-enhancing effects might involve direct effects of V compounds on membrane lipid organization. These changes in lipid organization promote the partitioning of insulin receptors and other receptors into membrane microdomains where receptors are optimally functional. To explore this possibility, we have used several strategies involving V complexes such as [VO(2)(dipic)](-) (pyridin-2,6-dicarboxylatodioxovanadium(V)), decavanadate (V(10)O(28)(6-), V(10)), BMOV (bis(maltolato)oxovanadium(IV)), and [VO(saltris)](2) (2-salicylideniminato-2-(hydroxymethyl)-1,3-dihydroxypropane-oxovanadium(V)). Our strategies include an evaluation of interactions between V-containing compounds and model lipid systems, an evaluation of the effects of V compounds on lipid fluidity in erythrocyte membranes, and studies of the effects of V-containing compounds on signaling events initiated by receptors known to use membrane microdomains as signaling platforms.