Active genetic elements present in the locus of enterocyte effacement in Escherichia coli O26 and their role in mobility

The locus of enterocyte effacement (LEE) is a large multigene chromosomal segment encoding gene products responsible for the generation of attaching and effacing lesions in many diarrheagenic Escherichia coli strains. A recently sequenced LEE harboring a pathogenicity island (PAI) from a Shiga toxin E. coli serotype 026 strain revealed a LEE PAI (designated LEE 026) almost identical to that obtained from a rabbit-specific enteropathogenic 015:H- strain. LEE 026 comprises 59,540 bp and is inserted at 94 min within the mature pheU tRNA locus. The LEE 026 PAI is flanked by two direct repeats of 137 and 136 bp (DR1 and DR2), as well as a gene encoding an integrase belonging to the P4 integrase family. We examined LEE 026 for horizontal gene transfer. By generating mini-LEE plasmids harboring only DR1 or DR2 with or without the integrase-like gene, we devised a simple assay to examine recombination processes between these sequences. Recombination was shown to be integrase dependent in a Delta recA E. coli K-12 strain background. Recombinant plasmids harboring a single direct repeat cloned either with or without the LEE 026 integrase gene were found to insert within the chromosomal pheU locus of E. coli K-12 strains with equal efficiency, suggesting that an endogenous P4-like integrase can substitute for this activity. An integrase with strong homology to the LEE 026 integrase was detected on the K-12 chromosome associated with the leuX tRNA locus at 97 min. Strains deleted for this integrase demonstrated a reduction in the insertion frequency of plasmids harboring only the DR into the pheU locus. These results provide strong evidence that LEE-harboring elements are indeed mobile and suggest that closely related integrases present on the chromosome of E. coli strains contribute to the dynamics of PAI mobility.

Ultraconserved elements in the human genome

There are 481 segments longer than 200 base pairs (bp) that are absolutely conserved (100% identity with no insertions or deletions) between orthologous regions of the human, rat, and mouse genomes. Nearly all of these segments are also conserved in the chicken and dog genomes, with an average of 95 and 99% identity, respectively. Many are also significantly conserved in fish. These ultraconserved elements of the human genome are most often located either overlapping exons in genes involved in RNA processing or in introns or nearby genes involved in the regulation of transcription and development. Along with more than 5000 sequences of over 100 bp that are absolutely conserved among the three sequenced mammals, these represent a class of genetic elements whose functions and evolutionary origins are yet to be determined, but which are more highly conserved between these species than are proteins and appear to be essential for the ontogeny of mammals and other vertebrates.

Evolutionary turnover of mammalian transcription start sites

Alignments of homologous genomic sequences are widely used to identify functional genetic elements and study their evolution. Most studies tacitly equate homology of functional elements with sequence homology. This assumption is violated by the phenomenon of turnover, in which functionally equivalent elements reside at locations that are nonorthologous at the sequence level. Turnover has been demonstrated previously for transcription-factor-binding sites. Here, we show that transcription start sites of equivalent genes do not always reside at equivalent locations in the human and mouse genomes. We also identify two types of partial turnover, illustrating evolutionary pathways that could lead to complete turnover. These findings suggest that the signals encoding transcription start sites are highly flexible and evolvable, and have cautionary implications for the use of sequence-level conservation to detect gene regulatory elements.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Identification of two evolutionarily conserved and functional regulatory elements in intron 2 of the human BRCA1 gene

Cross-species comparative genomics is a powerful strategy for identifying functional regulatory elements within noncoding DNA. In this paper, comparative analysis of human and mouse intronic sequences in the breast cancer susceptibility gene (BRCA1) revealed two evolutionarily conserved noncoding sequences (CNS) in intron 2, 5 kb downstream of the core BRCA1 promoter. The functionality of these elements was examined using homologous-recombination-based mutagenesis of reporter gene-tagged cosmids incorporating these regions and flanking sequences from the BRCA1 locus. This showed that CNS-1 and CNS-2 have differential transcriptional regulatory activity in epithelial cell lines. Mutation of CNS-1 significantly reduced reporter gene expression to 30% of control levels. Conversely mutation of CNS-2 increased expression to 200% of control levels. Regulation is at the level of transcription and shows promoter specificity. Both elements also specifically bind nuclear proteins in vitro. These studies demonstrate that the combination of comparative genomics and functional analysis is a successful strategy to identify novel regulatory elements and provide the first direct evidence that conserved noncoding sequences in BRCA1 regulate gene expression. (c) 2005 Elsevier Inc. All rights reserved.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.