Spatial patterns of transcriptional activity in the chromosome of Escherichia coli

BACKGROUND: Although genes on the chromosome are organized in a fixed order, the spatial correlations in transcription have not been systematically evaluated. We used a combination of genomic and signal processing techniques to investigate the properties of transcription in the genome of Escherichia coli K12 as a function of the position of genes on the chromosome. RESULTS: Spectral analysis of transcriptional series revealed the existence of statistically significant patterns in the spatial series of transcriptional activity. These patterns could be classified into three categories: short-range, of up to 16 kilobases (kb); medium-range, over 100-125 kb; and long-range, over 600-800 kb. We show that the significant similarities in gene activities extend beyond the length of an operon and that local patterns of coexpression are dependent on DNA supercoiling. Unlike short-range patterns, the formation of medium and long-range transcriptional patterns does not strictly depend on the level of DNA supercoiling. The long-range patterns appear to correlate with the patterns of distribution of DNA gyrase on the bacterial chromosome. CONCLUSIONS: Localization of structural components in the transcriptional signal revealed an asymmetry in the distribution of transcriptional patterns along the bacterial chromosome. The demonstration that spatial patterns of transcription could be modulated pharmacologically and genetically, along with the identification of molecular correlates of transcriptional patterns, offer for the first time strong evidence of physiologically determined higher-order organization of transcription in the bacterial chromosome

Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.</p> <p>Results</p> <p>Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium <it>E. coli </it>to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.</p

Genomics and proteomics: a signal processor's tour

The theory and methods of signal processing are becoming increasingly important in molecular biology. Digital filtering techniques, transform domain methods, and Markov models have played important roles in gene identification, biological sequence analysis, and alignment. This paper contains a brief review of molecular biology, followed by a review of the applications of signal processing theory. This includes the problem of gene finding using digital filtering, and the use of transform domain methods in the study of protein binding spots. The relatively new topic of noncoding genes, and the associated problem of identifying ncRNA buried in DNA sequences are also described. This includes a discussion of hidden Markov models and context free grammars. Several new directions in genomic signal processing are briefly outlined in the end

Biophotonic Tools in Cell and Tissue Diagnostics.

In order to maintain the rapid advance of biophotonics in the U.S. and enhance our competitiveness worldwide, key measurement tools must be in place. As part of a wide-reaching effort to improve the U.S. technology base, the National Institute of Standards and Technology sponsored a workshop titled "Biophotonic tools for cell and tissue diagnostics." The workshop focused on diagnostic techniques involving the interaction between biological systems and photons. Through invited presentations by industry representatives and panel discussion, near- and far-term measurement needs were evaluated. As a result of this workshop, this document has been prepared on the measurement tools needed for biophotonic cell and tissue diagnostics. This will become a part of the larger measurement road-mapping effort to be presented to the Nation as an assessment of the U.S. Measurement System. The information will be used to highlight measurement needs to the community and to facilitate solutions

A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given

Natural variation in Drosophila melanogaster

This work is dedicated to studying natural variation in D. melanogaster at the DNA sequence and gene expression level. In addition I present a new version of the DNA polymorphism analysis program VariScan, which includes significant improvements. In CHAPTER 1 I describe a genome scan of single nucleotide polymorphism in two natural D. melanogaster populations (from Africa and Europe) on the third chromosome. Together with polymorphism data previously published for the X chromosome of the same populations, this allows a comparative study of the polymorphism patterns of the X chromosome and an autosome. The frequency spectrum of mutations and the patterns of linkage disequilibrium are investigated. The observed patterns indicate that there is a significant difference in the behavior of the two chromosomes, as has already been suggested by previous studies. To uncover the reasons for this a coalescent based maximum likelihood method is applied that incorporates the effects of demographic history and unequal sex ratios. For the African population the differential behavior of the chromosomes can be explained by its demographic history and an excess of females. In Europe, a population bottleneck and an excess of males alone cannot explain the patterns we observe. The additional action of positive selection in this population is proposed as a possible explanation. In CHAPTER 2 I investigate the variation in gene expression of the two aforementioned populations. Whole-genome microarrays are used to study levels of expression for 88% of all known genes in eight adult males from both populations. The observed levels of expression variation are equal in Africa and Europe, despite the fact that DNA sequence variation is much higher in Africa. This is evidence for the action of stabilizing selection governing levels of expression polymorphism. Supporting this view, genes involved in many different functions, and are therefore on strong selective constraint, show less variation than do genes with only few functions. The experimental design allows the search for genes which differ in their expression patterns between Europe and Africa and might therefore have undergone adaptive evolution. Detected candidates include genes putatively involved in insecticide resistance and food choice. Surprisingly, many genes over-expressed in Africa are involved in the formation and function of the flying apparatus. In CHAPTER 3 I present version 2 of the program VariScan. This program was designed to analyse patterns of DNA sequence polymorphism on a chromosomal scale. The functionality of the core analysis tool, the wavelet decomposition, is described. In addition, multiple improvements to the previous version are presented. The program now supports the “pairwise deletion” option. This is essential for analysing data at the chromosome scale, since such data often contains incomplete information. It is now possible to add outgroup information, which allows the calculation of additional statistics. Furthermore, the separate analysis of different predefined chromosomal regions is added as an option. To increase the user friendliness, a graphical user interface is now included as part of the software package. Finally, VariScan is applied to published and computer-generated data and the ability of the wavelet-based analysis to uncover chromosomal regions with interesting DNA polymorphism patterns is demonstrated

Wavelet-Based Cancer Drug Recommender System

A natureza molecular do cancro serve de base para estudos sistemáticos de genomas cancerígenos, fornecendo valiosos insights e permitindo o desenvolvimento de tratamentos clínicos. Acima de tudo, estes estudos estão a impulsionar o uso clínico de informação genómica na escolha de tratamentos, de outro modo não expectáveis, em pacientes com diversos tipos de cancro, possibilitando a medicina de precisão. Com isso em mente, neste projeto combinamos técnicas de processamento de imagem, para aprimoramento de dados, e sistemas de recomendação para propor um ranking personalizado de drogas anticancerígenas. O sistema é implementado em Python e testado usando uma base de dados que contém registos de sensibilidade a drogas, com mais de 310.000 IC50 que, por sua vez, descrevem a resposta de mais de 300 drogas anticancerígenas em 987 linhas celulares cancerígenas. Após várias tarefas de pré-processamento, são realizadas duas experiências. A primeira experiência usa as imagens originais de microarrays de DNA e a segunda usa as mesmas imagens, mas submetidas a uma transformada wavelet. As experiências confirmam que as imagens de microarrays de DNA submetidas a transformadas wavelet melhoram o desempenho do sistema de recomendação, otimizando a pesquisa de linhas celulares cancerígenas com perfil semelhante ao da nova linha celular. Além disso, concluímos que as imagens de microarrays de DNA com transformadas de wavelet apropriadas, não apenas fornecem informações mais ricas para a pesquisa de utilizadores similares, mas também comprimem essas imagens com eficiência, otimizando os recursos computacionais. Tanto quanto é do nosso conhecimento, este projeto é inovador no que diz respeito ao uso de imagens de microarrays de DNA submetidas a transformadas wavelet, para perfilar linhas celulares num sistema de recomendação personalizado de drogas anticancerígenas