The density distribution of gene loci over the genetic map of Escherichia coli: its structural, functional and evolutionary implications

Ranges of control in the transcriptional regulation of Escherichia coli

BACKGROUND

The positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression.

RESULTS

Here we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances.

CONCLUSIONS

These findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).

Locational distribution of gene functional classes in Arabidopsis thaliana

Background

We are interested in understanding the locational distribution of genes and their functions in genomes, as this distribution has both functional and evolutionary significance. Gene locational distribution is known to be affected by various evolutionary processes, with tandem duplication thought to be the main process producing clustering of homologous sequences. Recent research has found clustering of protein structural families in the human genome, even when genes identified as tandem duplicates have been removed from the data. However, this previous research was hindered as they were unable to analyse small sample sizes. This is a challenge for bioinformatics as more specific functional classes have fewer examples and conventional statistical analyses of these small data sets often produces unsatisfactory results.

Results

We have developed a novel bioinformatics method based on Monte Carlo methods and Greenwood's spacing statistic for the computational analysis of the distribution of individual functional classes of genes (from GO). We used this to make the first comprehensive statistical analysis of the relationship between gene functional class and location on a genome. Analysis of the distribution of all genes except tandem duplicates on the five chromosomes of A. thaliana reveals that the distribution on chromosomes I, II, IV and V is clustered at P = 0.001. Many functional classes are clustered, with the degree of clustering within an individual class generally consistent across all five chromosomes. A novel and surprising result was that the locational distribution of some functional classes were significantly more evenly spaced than would be expected by chance.

Conclusion

Analysis of the A. thaliana genome reveals evidence of unexplained order in the locational distribution of genes. The same general analysis method can be applied to any genome, and indeed any sequential data involving classes.

From genes to genomes: universal scale-invariant properties of microbial chromosome organisation

The availability of complete genome sequences for a large variety of organisms is a major advance in understanding genome structure and function. One attribute of genome structure is chromosome organisation in terms of gene localisation and orientation. For example, bacterial operons, i.e. clusters of co-oriented genes that form transcription units, enable functionally related genes to be expressed simultaneously. The description of genome organisation was pioneered with the study of the distribution of genes of the Escherichia coli partial genetic map before the full genome sequence was known. Deploying powerful techniques from circular statistics and signal processing, we revisit the issue of gene localisation and orientation using 89 complete microbial chromosomes from the eubacterial and archaeal domains. We demonstrate that there is no characteristic size pertinent to the description of chromosome structure, e.g. there does not exist any single length appropriate to describe gene clustering. Our results show that, for all 89 chromosomes, gene positions and gene orientations share a common form of scale-invariant correlations known as "long-range correlations" that we can reveal for distances from the gene length, up to the chromosome size. This observation indicates that genes tend to assemble and to co-orient over any scale of observation greater than a few kilobases. This unexpected property of chromosome structure can be portrayed as an operon-like organisation at all scales and implies that a complete scale range extending over more than three orders of magnitudes of chromosome segment lengths is necessary to properly describe prokaryotic genome organisation. We propose that this pattern results from the effects of the superhelical context on gene expression coupled with the structure and dynamics of the nucleoid, possibly accommodating the diverse gene expression profiles needed during the different stages of cellular life.

Gene density over the chromosome of Escherichia coli: frequency distribution, spatial clustering, and symmetry

Published studies of gene density (the number of genetic loci per unit of length on the linkage map) for Escherichia coli report a nonrandom frequency distribution and indicate notable symmetry in spatial clustering of gene density. We reexamined these results and found that gene density is a random variable with a frequency distribution that is lognormal. That is, the logarithm of gene density is a normally distributed random variable. Furthermore, comparison of the observed E. coli map and computer-generated random maps showed that symmetries in the spatial clustering of gene density are not exceptional; these features arise naturally among genes (or loci) whose density has this frequency distribution. These results are discussed along with other related examples that illustrate the emerging importance of statistical inference in molecular genetics.