Chromatin architecture and gene expression in Escherichia coli

Two recent genome-scale analyses underscore the importance of DNA topology and chromatin structure in regulating transcription in Escherichia coli

Prediction of highly expressed genes in microbes based on chromatin accessibility

BACKGROUND: It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI) values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. RESULTS: We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. CONCLUSION: This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches

Functional Associations by Response Overlap (FARO), a Functional Genomics Approach Matching Gene Expression Phenotypes

The systematic comparison of transcriptional responses of organisms is a powerful tool in functional genomics. For example, mutants may be characterized by comparing their transcript profiles to those obtained in other experiments querying the effects on gene expression of many experimental factors including treatments, mutations and pathogen infections. Similarly, drugs may be discovered by the relationship between the transcript profiles effectuated or impacted by a candidate drug and by the target disease. The integration of such data enables systems biology to predict the interplay between experimental factors affecting a biological system. Unfortunately, direct comparisons of gene expression profiles obtained in independent, publicly available microarray experiments are typically compromised by substantial, experiment-specific biases. Here we suggest a novel yet conceptually simple approach for deriving ‘Functional Association(s) by Response Overlap’ (FARO) between microarray gene expression studies. The transcriptional response is defined by the set of differentially expressed genes independent from the magnitude or direction of the change. This approach overcomes the limited comparability between studies that is typical for methods that rely on correlation in gene expression. We apply FARO to a compendium of 242 diverse Arabidopsis microarray experimental factors, including phyto-hormones, stresses and pathogens, growth conditions/stages, tissue types and mutants. We also use FARO to confirm and further delineate the functions of Arabidopsis MAP kinase 4 in disease and stress responses. Furthermore, we find that a large, well-defined set of genes responds in opposing directions to different stress conditions and predict the effects of different stress combinations. This demonstrates the usefulness of our approach for exploiting public microarray data to derive biologically meaningful associations between experimental factors. Finally, our results indicate that FARO is more powerful in associating mutants in common pathways than existing methods such as co-expression analysis

An environmental signature for 323 microbial genomes based on codon adaptation indices

BACKGROUND: Codon adaptation indices (CAIs) represent an evolutionary strategy to modulate gene expression and have widely been used to predict potentially highly expressed genes within microbial genomes. Here, we evaluate and compare two very different methods for estimating CAI values, one corresponding to translational codon usage bias and the second obtained mathematically by searching for the most dominant codon bias. RESULTS: The level of correlation between these two CAI methods is a simple and intuitive measure of the degree of translational bias in an organism, and from this we confirm that fast replicating bacteria are more likely to have a dominant translational codon usage bias than are slow replicating bacteria, and that this translational codon usage bias may be used for prediction of highly expressed genes. By analyzing more than 300 bacterial genomes, as well as five fungal genomes, we show that codon usage preference provides an environmental signature by which it is possible to group bacteria according to their lifestyle, for instance soil bacteria and soil symbionts, spore formers, enteric bacteria, aquatic bacteria, and intercellular and extracellular pathogens. CONCLUSION: The results and the approach described here may be used to acquire new knowledge regarding species lifestyle and to elucidate relationships between organisms that are far apart evolutionarily

Characterization of probiotic Escherichia coli isolates with a novel pan-genome microarray

pan-genome microarra

Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing

Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification

Improving comparability between microarray probe signals by thermodynamic intensity correction

Signals from different oligonucleotide probes against the same target show great variation in intensities. However, detection of differences along a sequence e.g. to reveal intron/exon architecture, transcription boundary as well as simple absent/present calls depends on comparisons between different probes. It is therefore of great interest to correct for the variation between probes. Much of this variation is sequence dependent. We demonstrate that a thermodynamic model for hybridization of either DNA or RNA to a DNA microarray, which takes the sequence-dependent probe affinities into account significantly reduces the signal fluctuation between probes targeting the same gene transcript. For a test set of tightly tiled yeast genes, the model reduces the variance by up to a factor ∼1/3. As a consequence of this reduction, the model is shown to yield a more accurate determination of transcription start sites for a subset of yeast genes. In another application, we identify present/absent calls for probes hybridized to the sequenced Escherichia coli strain O157:H7 EDL933. The model improves the correct calls from 85 to 95% relative to raw intensity measures. The model thus makes applications which depend on comparisons between probes aimed at different sections of the same target more reliable