Condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome

Deciphering genetic regulatory codes remains a challenge. Here, we present an effective approach to identifying in vivo condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome. A resampling-based algorithm was adopted to cluster our microarray data of a stress response, which generated 35 tight clusters with unique expression patterns containing 811 genes of 5652 genes significantly altered. Database searches identified many known motifs within the 3-kb regulatory regions of 40 genes from 3 clusters and modules with six to nine motifs that were commonly shared by 60-100% of these genes. The upstream regulatory region contained the highest frequency of these common motifs. CisModule program predictions were comparable with the results from database searches and found four potentially novel motifs. This result indicates that these motifs and modules could be responsible for gene coregulation of the stress response in the lacrimal gland.     

AIMIE: a web-based environment for detection and interpretation of significant sequence motifs in prokaryotic genomes

Motivation: Genomes contain biologically significant informationthat extends beyond that encoded in genes. Some of this informationrelates to various short dispersed repeats distributed throughoutthe genome. The goal of this work was to combine tools for detectionof statistically significant dispersed repeats in DNA sequenceswith tools to aid development of hypotheses regarding theirpossible physiological functions in an easy-to-use web-basedenvironment. Results: Ab Initio Motif Identification Environment (AIMIE)was designed to facilitate investigations of dispersed sequencemotifs in prokaryotic genomes. We used AIMIE to analyze theEscherichia coli and Haemophilus influenzae genomes in orderto demonstrate the utility of the new environment. AIMIE detectedrepeated extragenic palindrome (REP) elements, CRISPR repeats,uptake signal sequences, intergenic dyad sequences and severalother over-represented sequence motifs. Distributional patternsof these motifs were analyzed using the tools included in AIMIE. Availability: AIMIE and the related software can be accessedat our web site http://www.cmbl.uga.edu/software.html. Contact: mrazek{at}uga.edu Associate Editor: Alex Bateman     

Insertion sequences in prokaryotic genomes

Insertion sequences (ISs) are small DNA segments that are often capable of moving neighbouring genes. Over 1500 different ISs have been identified to date. They can have large and spectacular effects in shaping and reshuffling the bacterial genome. Recent studies have provided dramatic examples of such IS activity, including massive IS expansion during the emergence of some pathogenic bacterial species and the intimate involvement of ISs in assembling genes into complex plasmid structures. However, a global understanding of their impact on bacterial genomes requires detailed knowledge of their distribution across the eubacterial and archaeal kingdoms, understanding their partition between chromosomes and extra-chromosomal elements (e.g. plasmids and viruses) and the factors which influence this, and appreciation of the different transposition mechanisms in action, the target preferences and the host factors that influence transposition. In addition, defective (non- autonomous) elements, which can be complemented by related active elements in the same cell, are often overlooked in genome annotations but also contribute to the evolution of genome organisation.     

Evolutionary patterns in prokaryotic genomes

Prokaryotic genomics is shifting towards comparative approaches to unravel how and why genomes change over time. Both phylogenetic and population genetics approaches are required to dissect the relative roles of selection and drift under these conditions. Lineages evolve adaptively by selection of changes in extant genomes and the way this occurs is being explored from a systemic and evolutionary perspective to understand how mutations relate with gene repertoire changes and how both are contextualized in cellular networks. Through an increased appreciation of genome dynamics in given ecological contexts, a more detailed picture of the genetic basis of prokaryotic evolution is emerging.     

Using hexamers to predict cis-regulatory motifs in Drosophila

Background  

Cis-regulatory modules (CRMs) are short stretches of DNA that help regulate gene expression in higher eukaryotes. They have been found up to 1 megabase away from the genes they regulate and can be located upstream, downstream, and even within their target genes. Due to the difficulty of finding CRMs using biological and computational techniques, even well-studied regulatory systems may contain CRMs that have not yet been discovered.     

Missing genes in the annotation of prokaryotic genomes

Background  

Protein-coding gene detection in prokaryotic genomes is considered a much simpler problem than in intron-containing eukaryotic genomes. However there have been reports that prokaryotic gene finder programs have problems with small genes (either over-predicting or under-predicting). Therefore the question arises as to whether current genome annotations have systematically missing, small genes.     

Solitary restriction endonucleases in prokaryotic genomes

Prokaryotic restriction-modification (R-M) systems defend the host cell from the invasion of a foreign DNA. They comprise two enzymatic activities: specific DNA cleavage activity and DNA methylation activity preventing cleavage. Typically, these activities are provided by two separate enzymes: a DNA methyltransferase (MTase) and a restriction endonuclease (RE). In the absence of a corresponding MTase, an RE of Type II R-M system is highly toxic for the cell. Genes of the R-M system are linked in the genome in the vast majority of annotated cases. There are only a few reported cases in which the genes of MTase and RE from one R-M system are not linked. Nevertheless, a few hundreds solitary RE genes are present in the Restriction Enzyme Database (http://rebase.neb.com) annotations. Using the comparative genomic approach, we analysed 272 solitary RE genes. For 57 solitary RE genes we predicted corresponding MTase genes located distantly in a genome. Of the 272 solitary RE genes, 99 are likely to be fragments of RE genes. Various explanations for the existence of the remaining 116 solitary RE genes are also discussed.     

Connected gene neighborhoods in prokaryotic genomes

A computational method was developed for delineating connected gene neighborhoods in bacterial and archaeal genomes. These gene neighborhoods are not typically present, in their entirety, in any single genome, but are held together by overlapping, partially conserved gene arrays. The procedure was applied to comparing the orders of orthologous genes, which were extracted from the database of Clusters of Orthologous Groups of proteins (COGs), in 31 prokaryotic genomes and resulted in the identification of 188 clusters of gene arrays, which included 1001 of 2890 COGs. These clusters were projected onto actual genomes to produce extended neighborhoods including additional genes, which are adjacent to the genes from the clusters and are transcribed in the same direction, which resulted in a total of 2387 COGs being included in the neighborhoods. Most of the neighborhoods consist predominantly of genes united by a coherent functional theme, but also include a minority of genes without an obvious functional connection to the main theme. We hypothesize that although some of the latter genes might have unsuspected roles, others are maintained within gene arrays because of the advantage of expression at a level that is typical of the given neighborhood. We designate this phenomenon ‘genomic hitchhiking’. The largest neighborhood includes 79 genes (COGs) and consists of overlapping, rearranged ribosomal protein superoperons; apparent genome hitchhiking is particularly typical of this neighborhood and other neighborhoods that consist of genes coding for translation machinery components. Several neighborhoods involve previously undetected connections between genes, allowing new functional predictions. Gene neighborhoods appear to evolve via complex rearrangement, with different combinations of genes from a neighborhood fixed in different lineages.     

Identification of replication origins in prokaryotic genomes

The availability of hundreds of complete bacterial genomes has created new challenges and simultaneously opportunities for bioinformatics. In the area of statistical analysis of genomic sequences, the studies of nucleotide compositional bias and gene bias between strands and replichores paved way to the development of tools for prediction of bacterial replication origins. Only a few (about 20) origin regions for eubacteria and archaea have been proven experimentally. One reason for that may be that this is now considered as an essentially bioinformatics problem, where predictions are sufficiently reliable not to run labor-intensive experiments, unless specifically needed. Here we describe the main existing approaches to the identification of replication origin (oriC) and termination (terC) loci in prokaryotic chromosomes and characterize a number of computational tools based on various skew types and other types of evidence. We also classify the eubacterial and archaeal chromosomes by predictability of their replication origins using skew plots. Finally, we discuss possible combined approaches to the identification of the oriC sites that may be used to improve the prediction tools, in particular, the analysis of DnaA binding sites using the comparative genomic methods.     

Causes of insertion sequences abundance in prokaryotic genomes

Insertion sequences (ISs) are the smallest and most frequent transposable elements in prokaryotes where they play an important evolutionary role by promoting gene inactivation and genome plasticity. Their genomic abundance varies by several orders of magnitude for reasons largely unknown and widely speculated. The current availability of hundreds of genomes renders testable many of these hypotheses, notably that IS abundance correlates positively with the frequency of horizontal gene transfer (HGT), genome size, pathogenicity, nonobligatory ecological associations, and human association. We thus reannotated ISs in 262 prokaryotic genomes and tested these hypotheses showing that when using appropriate controls, there is no empirical basis for IS family specificity, pathogenicity, or human association to influence IS abundance or density. HGT seems necessary for the presence of ISs, but cannot alone explain the absence of ISs in more than 20% of the organisms, some of which showing high rates of HGT. Gene transfer is also not a significant determinant of the abundance of IS elements in genomes, suggesting that IS abundance is controlled at the level of transposition and ensuing natural selection and not at the level of infection. Prokaryotes engaging in obligatory associations have fewer ISs when controlled for genome size, but this may be caused by some being sexually isolated. Surprisingly, genome size is the only significant predictor of IS numbers and density. Alone, it explains over 40% of the variance of IS abundance. Because we find that genome size and IS abundance correlate negatively with minimal doubling times, we conclude that selection for rapid replication cannot account for the few ISs found in small genomes. Instead, we show evidence that IS numbers are controlled by the frequency of highly deleterious insertion targets. Indeed, IS abundance increases quickly with genome size, which is the exact inverse trend found for the density of genes under strong selection such as essential genes. Hence, for ISs, the bigger the genome the better.     

Large and small rearrangements in the evolution of prokaryotic genomes

Relative frequencies of large and small genome rearrangements (inversions and transpositions) in the evolution of prokaryotic genomes can be evaluated using the ratio between the index S (the ratio of the number of identical pairs of neighboring genes in two genomes to the total number of genes in the sample of interest) and 1–6L/n, where L is the mean difference in intergenic distances and n is the number of genes in the sample. The S value uniformly decreases with the fixation of genome rearrangements, while the decrease rate of 1–6L/n is determined by the rearrangement size. Specifically, large inversions and transpositions lead to a dramatic decrease in the index value, while small rearrangements result in an insignificant decrease. The ratio between these indices was computed for twenty pairs of closely related species belonging to different groups of bacteria and archaea. The pairs examined strongly differed in the relative frequency of large and small rearrangements. However, computer simulation showed that the total variation can be reproduced with the same input parameters of the model. This means that the differences observed can be stochastic and can be interpreted without assuming different mechanisms and factors of genome rearrangements for different groups of prokaryotes. Relative frequencies of large and small rearrangements displayed no noticeable correlations with taxonomic position, total rate of rearrangement fixation, habitation conditions, and the abundance of transposons and repetitive sequences. It is suggested that, in some cases, phage activity increases the frequency of large genome rearrangements.