Redox linked flavin sites in extracellular decaheme proteins involved in microbe-mineral electron transfer

Extracellular microbe-mineral electron transfer is a major driving force for the oxidation of organic carbon in many subsurface environments. Extracellular multi-heme cytochromes of the Shewenella genus play a major role in this process but the mechanism of electron exchange at the interface between cytochrome and acceptor is widely debated. The 1.8 Å x-ray crystal structure of the decaheme MtrC revealed a highly conserved CX8C disulfide that, when substituted for AX8A, severely compromised the ability of S. oneidensis to grow under aerobic conditions. Reductive cleavage of the disulfide in the presence of flavin mononucleotide (FMN) resulted in the reversible formation of a stable flavocytochrome. Similar results were also observed with other decaheme cytochromes, OmcA, MtrF and UndA. The data suggest that these decaheme cytochromes can transition between highly reactive flavocytochromes or less reactive cytochromes, and that this transition is controlled by a redox active disulfide that responds to the presence of oxygen

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself

Palaeosymbiosis Revealed by Genomic Fossils of Wolbachia in a Strongyloidean Nematode

Wolbachia are common endosymbionts of terrestrial arthropods, and are also found in nematodes: the animal-parasitic filaria, and the plant-parasite Radopholus similis. Lateral transfer of Wolbachia DNA to the host genome is common. We generated a draft genome sequence for the strongyloidean nematode parasite Dictyocaulus viviparus, the cattle lungworm. In the assembly, we identified nearly 1 Mb of sequence with similarity to Wolbachia. The fragments were unlikely to derive from a live Wolbachia infection: most were short, and the genes were disabled through inactivating mutations. Many fragments were co-assembled with definitively nematode-derived sequence. We found limited evidence of expression of the Wolbachia-derived genes. The D. viviparus Wolbachia genes were most similar to filarial strains and strains from the host promiscuous clade F. We conclude that D. viviparus was infected by Wolbachia in the past, and that clade F-like symbionts may have been the source of filarial Wolbachia infections

SIFTER search: a web server for accurate phylogeny-based protein function prediction

We are awash in proteins discovered through high-throughput sequencing projects. As only a minus-cule fraction of these have been experimentally char-acterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annota-tions throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible a