Mitochondrial Lysyl-tRNA Synthetase Independent Import of tRNA Lysine into Yeast Mitochondria

Aminoacyl tRNA synthetases play a central role in protein synthesis by charging tRNAs with amino acids. Yeast mitochondrial lysyl tRNA synthetase (Msk1), in addition to the aminoacylation of mitochondrial tRNA, also functions as a chaperone to facilitate the import of cytosolic lysyl tRNA. In this report, we show that human mitochondrial Kars (lysyl tRNA synthetase) can complement the growth defect associated with the loss of yeast Msk1 and can additionally facilitate the in vitro import of tRNA into mitochondria. Surprisingly, the import of lysyl tRNA can occur independent of Msk1 in vivo. This suggests that an alternative mechanism is present for the import of lysyl tRNA in yeast

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

Mining Biological Pathways Using WikiPathways Web Services

WikiPathways is a platform for creating, updating, and sharing biological pathways [1]. Pathways can be edited and downloaded using the wiki-style website. Here we present a SOAP web service that provides programmatic access to WikiPathways that is complementary to the website. We describe the functionality that this web service offers and discuss several use cases in detail. Exposing WikiPathways through a web service opens up new ways of utilizing pathway information and assisting the community curation process

New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size

<p>Abstract</p> <p>Background</p> <p>As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism <it>Saccharomyces cerevisiae</it>. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.</p> <p>Results</p> <p>We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in <it>S. cerevisiae</it>.</p> <p>Conclusions</p> <p>Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.</p

Mitochondrial targeting of recombinant RNAs modulates the level of a heteroplasmic mutation in human mitochondrial DNA associated with Kearns Sayre Syndrome

Mitochondrial mutations, an important cause of incurable human neuromuscular diseases, are mostly heteroplasmic: mutated mitochondrial DNA is present in cells simultaneously with wild-type genomes, the pathogenic threshold being generally >70% of mutant mtDNA. We studied whether heteroplasmy level could be decreased by specifically designed oligoribonucleotides, targeted into mitochondria by the pathway delivering RNA molecules in vivo. Using mitochondrially imported RNAs as vectors, we demonstrated that oligoribonucleotides complementary to mutant mtDNA region can specifically reduce the proportion of mtDNA bearing a large deletion associated with the Kearns Sayre Syndrome in cultured transmitochondrial cybrid cells. These findings may be relevant to developing of a new tool for therapy of mtDNA associated diseases

On the Role of Inhibition Processes in Modeling Control Strategies for Composting Plants

We introduce a mathematical model for the composting process in biocells where several chemical phenomena, like the aerobic biodegradation, the hydrolysis of insoluble substrate and the biomass decay, occur. We investigate the best aeration strategies in presence of inhibition processes due to high concentrations of oxygen. Optimal stategries are obtained as result of a suitable optimal control problem. The dynamics exhibits an enhanced level of the oxygen concentration that guarantees the aerobic feature of the biodegradation process. Then, a nonlinear bioeconomic term is included in the objective functional to take into account of the external operational cost. The role of the economic cost in the control policy is analyzed and discussed

A New U.S.-U.S.S.R. Seismological Program

On July 9, 1986, a team of researchers from the University of California, San Diego; University of Nevada, Reno; and the University of Colorado, Boulder established the first of three seismic stations to be located in the vicinity of the Soviet nuclear test site in eastern Kazakhstan (KTS) (see cover). Under an agreement reached between the Soviet Academy of Sciences and the Natural Resources Defense Council, a nonprofit U.S. environmental organization, these stations, which are configured to meet the specifications of the proposed new global seismographic network [Incorporated Research Institutions for Seismology (IRIS), 1984], will be complemented by three similarly equipped stations to be installed in the vicinity of the U.S. nuclear test site in southern Nevada (NTS). The stations are to be operated cooperatively by Soviet and U.S. personnel (Figure 1)

Incorporating functional inter-relationships into protein function prediction algorithms

<p>Abstract</p> <p>Background</p> <p>Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.</p> <p>Results</p> <p>We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the <it>k</it>-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of predictions made over a set of base classes. Finally, we provide qualitative and quantitative evidence that this incorporation of functional inter-relationships enables the discovery of interesting biology in the form of novel functional annotations for several yeast proteins, such as Sna4, Rtn1 and Lin1.</p> <p>Conclusion</p> <p>We implemented and evaluated a methodology for incorporating interrelationships between functional classes into a standard classification-based protein function prediction algorithm. Our results show that this incorporation can help improve the accuracy of such algorithms, and help uncover novel biology in the form of previously unknown functional annotations. The complete source code, a sample data set and the additional files for this paper are available free of charge for non-commercial use at <url>http://www.cs.umn.edu/vk/gaurav/functionalsimilarity/</url>.</p

Comprehensive Structural and Substrate Specificity Classification of the Saccharomyces cerevisiae Methyltransferome

Methylation is one of the most common chemical modifications of biologically active molecules and it occurs in all life forms. Its functional role is very diverse and involves many essential cellular processes, such as signal transduction, transcriptional control, biosynthesis, and metabolism. Here, we provide further insight into the enzymatic methylation in S. cerevisiae by conducting a comprehensive structural and functional survey of all the methyltransferases encoded in its genome. Using distant homology detection and fold recognition, we found that the S. cerevisiae methyltransferome comprises 86 MTases (53 well-known and 33 putative with unknown substrate specificity). Structural classification of their catalytic domains shows that these enzymes may adopt nine different folds, the most common being the Rossmann-like. We also analyzed the domain architecture of these proteins and identified several new domain contexts. Interestingly, we found that the majority of MTase genes are periodically expressed during yeast metabolic cycle. This finding, together with calculated isoelectric point, fold assignment and cellular localization, was used to develop a novel approach for predicting substrate specificity. Using this approach, we predicted the general substrates for 24 of 33 putative MTases and confirmed these predictions experimentally in both cases tested. Finally, we show that, in S. cerevisiae, methylation is carried out by 34 RNA MTases, 32 protein MTases, eight small molecule MTases, three lipid MTases, and nine MTases with still unknown substrate specificity