Investigating the global genomic diversity of Escherichia coli using a multi-genome DNA microarray platform with novel gene prediction strategies

<p>Abstract</p> <p>Background</p> <p>The gene content of a diverse group of 183 unique <it>Escherichia coli </it>and <it>Shigella </it>isolates was determined using the Affymetrix GeneChip^®<it>E. coli </it>Genome 2.0 Array, originally designed for transcriptome analysis, as a genotyping tool. The probe set design utilized by this array provided the opportunity to determine the gene content of each strain very accurately and reliably. This array constitutes 10,112 independent genes representing four individual <it>E. coli </it>genomes, therefore providing the ability to survey genes of several different pathogen types. The entire ECOR collection, 80 EHEC-like isolates, and a diverse set of isolates from our FDA strain repository were included in our analysis.</p> <p>Results</p> <p>From this study we were able to define sets of genes that correspond to, and therefore define, the EHEC pathogen type. Furthermore, our sampling of 63 unique strains of O157:H7 showed the ability of this array to discriminate between closely related strains. We found that individual strains of O157:H7 differed, on average, by 197 probe sets. Finally, we describe an analysis method that utilizes the power of the probe sets to determine accurately the presence/absence of each gene represented on this array.</p> <p>Conclusions</p> <p>These elements provide insights into understanding the microbial diversity that exists within extant <it>E. coli </it>populations. Moreover, these data demonstrate that this novel microarray-based analysis is a powerful tool in the field of molecular epidemiology and the newly emerging field of microbial forensics.</p

The effects of stimulus complexity on the preattentive processing of self-generated and nonself voices: an ERP study

The ability to differentiate one's own voice from the voice of somebody else plays a critical role in successful verbal self-monitoring processes and in communication. However, most of the existing studies have only focused on the sensory correlates of self-generated voice processing, whereas the effects of attentional demands and stimulus complexity on self-generated voice processing remain largely unknown. In this study, we investigated the effects of stimulus complexity on the preattentive processing of self and nonself voice stimuli. Event-related potentials (ERPs) were recorded from 17 healthy males who watched a silent movie while ignoring prerecorded self-generated (SGV) and nonself (NSV) voice stimuli, consisting of a vocalization (vocalization category condition: VCC) or of a disyllabic word (word category condition: WCC). All voice stimuli were presented as standard and deviant events in four distinct oddball sequences. The mismatch negativity (MMN) ERP component peaked earlier for NSV than for SGV stimuli. Moreover, when compared with SGV stimuli, the P3a amplitude was increased for NSV stimuli in the VCC only, whereas in the WCC no significant differences were found between the two voice types. These findings suggest differences in the time course of automatic detection of a change in voice identity. In addition, they suggest that stimulus complexity modulates the magnitude of the orienting response to SGV and NSV stimuli, extending previous findings on self-voice processing.This work was supported by Grant Numbers IF/00334/2012, PTDC/PSI-PCL/116626/2010, and PTDC/MHN-PCN/3606/2012, funded by the Fundacao para a Ciencia e a Tecnologia (FCT, Portugal) and the Fundo Europeu de Desenvolvimento Regional through the European programs Quadro de Referencia Estrategico Nacional and Programa Operacional Factores de Competitividade, awarded to A.P.P., and by FCT Doctoral Grant Number SFRH/BD/77681/2011, awarded to T.C.info:eu-repo/semantics/publishedVersio

Array CGH Phylogeny: How accurate are Comparative Genomic Hybridization-based trees?

<p>Abstract</p> <p>Background</p> <p>Array-based Comparative Genomic Hybridization (CGH) data have been used to infer phylogenetic relationships. However, the reliability of array CGH analysis to determine evolutionary relationships has not been well established. In most CGH work, all species and strains are compared to a single reference species, whose genome was used to design the array. In the accompanying work, we critically evaluated CGH-based phylogeny using simulated competitive hybridization data. This work showed that a limited number of conditions, principally the tree topology and placement of the reference taxon in the tree, had a strong effect on the ability to recover the correct tree topology. Here, we add to our simulation study by testing the use of CGH as a phylogenetic tool with experimental CGH data from competitive hybridizations between <it>N. crassa </it>and other <it>Neurospora </it>species. In the discussion, we add to our empirical study of <it>Neurospora </it>by reanalyzing of data from a previous CGH phylogenetic analysis of the yeast <it>sensu stricto </it>complex.</p> <p>Results</p> <p>Array ratio data for <it>Neurospora </it>and related species were normalized with loess, robust spline, and linear ratio based methods, and then used to construct Neighbor-Joining and parsimony trees. These trees were compared to published phylogenetic analyses for <it>Neurospora </it>based on multilocus sequence analysis (MLSA). For the <it>Neurospora </it>dataset, the best combination of methods resulted in recovery of the MLSA tree topology less than half the time. Our reanalysis of a yeast dataset found that trees identical to established phylogeny were recovered only by pruning taxa - including the reference taxon - from the analysis.</p> <p>Conclusion</p> <p>Our results indicate that CGH data can be problematic for phylogenetic analysis. Success fluctuates based on the methods utilized to construct the tree and the taxa included. Selective pruning of the taxa improves the results - an impractical approach for normal phylogenetic analysis. From the more successful methods we make suggestions on the normalization and post-normalization methods that work best in estimating genetic distance between taxa.</p