Phylo-VISTA: interactive visualization of multiple DNA sequence alignments

MOTIVATION: The power of multi-sequence comparison for biological discovery is well established. The need for new capabilities to visualize and compare cross-species alignment data is intensified by the growing number of genomic sequence datasets being generated for an ever-increasing number of organisms. To be efficient these visualization algorithms must support the ability to accommodate consistently a wide range of evolutionary distances in a comparison framework based upon phylogenetic relationships. RESULTS: We have developed Phylo-VISTA, an interactive tool for analyzing multiple alignments by visualizing a similarity measure for multiple DNA sequences. The complexity of visual presentation is effectively organized using a framework based upon interspecies phylogenetic relationships. The phylogenetic organization supports rapid, user-guided interspecies comparison. To aid in navigation through large sequence datasets, Phylo-VISTA leverages concepts from VISTA that provide a user with the ability to select and view data at varying resolutions. The combination of multiresolution data visualization and analysis, combined with the phylogenetic framework for interspecies comparison, produces a highly flexible and powerful tool for visual data analysis of multiple sequence alignments. AVAILABILITY: Phylo-VISTA is available at http://www-gsd.lbl.gov/phylovista. It requires an Internet browser with Java Plug-in 1.4.2 and it is integrated into the global alignment program LAGAN at http://lagan.stanford.edu     

ESM-1 silencing decreased cell survival,migration, and invasion and modulated cell cycle progression in hepatocellular carcinoma

Amino Acids - Endothelial cell-specific molecule-1 (ESM-1) is a secretory proteoglycan comprising a mature polypeptide of 165 amino acids and a single dermatan sulfate. The aim of this study was to...     

Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes

In order to explore microevolutionary trends in bacteria and archaea, we constructed a data set of 41 alignable tight genome clusters (ATGCs). We show that the ratio of the medians of nonsynonymous to synonymous substitution rates (dN/dS) that is used as a measure of the purifying selection pressure on protein sequences is a stable characteristic of the ATGCs. In agreement with previous findings, parasitic bacteria, notwithstanding the sometimes dramatic genome shrinkage caused by gene loss, are typically subjected to relatively weak purifying selection, presumably owing to relatively small effective population sizes and frequent bottlenecks. However, no evidence of genome streamlining caused by strong selective pressure was found in any of the ATGCs. On the contrary, a significant positive correlation between the genome size, as well as gene size, and selective pressure was observed, although a variety of free-living prokaryotes with very close selective pressures span nearly the entire range of genome sizes. In addition, we examined the connections between the sequence evolution rate and other genomic features. Although gene order changes much faster than protein sequences during the evolution of prokaryotes, a strong positive correlation was observed between the “rearrangement distance” and the amino acid distance, suggesting that at least some of the events leading to genome rearrangement are subjected to the same type of selective constraints as the evolution of amino acid sequences.     

Endothelial Transient Receptor Potential Conical Channel (TRPC)-3 Activation Induces Vasogenic Edema Formation in the Rat Piriform Cortex Following Status Epilepticus

Transient receptor potential canonical channel (TRPC) is a nonselective cation channel permeable to Ca²⁺, which express in many cell types, including neurons. However the alterations in TRPC receptor expressions in response to status epilepticus (SE) have not been explored. Therefore, the present study was designated to elucidate the roles of TRPC3 in neuronal death and vasogenic edema within the rat piriform cortex (PC) following SE. In non-SE animals, TRPC3 immunoreactivity was abundantly detected in the PC. Following SE, TRPC3 immunoreactivity was increased in neurons. Furthermore, TRPC3 expression was detected in endothelial cells that did not contain it in non-SE animals. Loss of SMI-71 (a blood–brain barrier antigen) immunoreactivity was also observed in TRPC3 positive endothelial cells. In addition, FJB positive neurons and vasogenic edema were noticeably detected in the PC. To directly determine whether TRPC3 activation is correlated to SE-induced vasogenic edema formation and neuronal damages in the PC, the effect of Pyr-3 (a TRPC3 antagonist) on SE-induced insults were investigated. Pyr-3 infusion effectively attenuated vasogenic edema in the PC as compared to the vehicle. Therefore, our findings indicate that TRPC3 activation/overexpression induced by SE may involve BBB disruption and neuronal damages in the rat PC following SE. Therefore, the present study was TRPC3 may play an important role in SE-induced vasogenic edema formation through BBB disruptions in the rat PC.     

Horizontal Transfer of Archaeal Genes into the Deinococcaceae: Detection by Molecular and Computer-Based Approaches

Members of the Deinococcaceae (e.g., Thermus, Meiothermus, Deinococcus) contain A/V-ATPases typically found in Archaea or Eukaryotes which were probably acquired by horizontal gene transfer. Two methods were used to quantify the extent to which archaeal or eukaryotic genes have been acquired by this lineage. Screening of a Meiothermus ruber library with probes made against Thermoplasma acidophilum DNA yielded a number of clones which hybridized more strongly than background. One of these contained the prolyl tRNA synthetase (RS) gene. Phylogenetic analysis shows the M. ruber and D. radiodurans prolyl RS to be more closely related to archaeal and eukaryal forms of this gene than to the typical bacterial type. Using a bioinformatics approach, putative open reading frames (ORFs) from the prerelease version of the D. radiodurans genome were screened for genes more closely related to archaeal or eukaryotic genes. Putative ORFs were searched against representative genomes from each of the three domains using automated BLAST. ORFs showing the highest matches against archaeal and eukaryotic genes were collected and ranked. Among the top-ranked hits were the A/V-ATPase catalytic and noncatalytic subunits and the prolyl RS genes. Using phylogenetic methods, ORFs were analyzed and trees assessed for evidence of horizontal gene transfer. Of the 45 genes examined, 20 showed topologies in which D. radiodurans homologues clearly group with eukaryotic or archaeal homologues, and 17 additional trees were found to show probable evidence of horizontal gene transfer. Compared to the total number of ORFs in the genome, those that can be identified as having been acquired from Archaea or Eukaryotes are relatively few (approximately 1%), suggesting that interdomain transfer is rare.     

Multi-class protein fold recognition using support vector machines and neural networks

MOTIVATION: Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. We study this approach with new multi-class classification methods and examined many issues important for a practical recognition system. RESULTS: Most current discriminative methods for protein fold prediction use the one-against-others method, which has the well-known 'False Positives' problem. We investigated two new methods: the unique one-against-others and the all-against-all methods. Both improve prediction accuracy by 14-110% on a dataset containing 27 SCOP folds. We used the Support Vector Machine (SVM) and the Neural Network (NN) learning methods as base classifiers. SVMs converges fast and leads to high accuracy. When scores of multiple parameter datasets are combined, majority voting reduces noise and increases recognition accuracy. We examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on the number of representatives in a fold. Overall, recognition systems achieve 56% fold prediction accuracy on a protein test dataset, where most of the proteins have below 25% sequence identity with the proteins used in training.