Bohlin et al. BMC Genomics 2010, 11:464 http://www.biomedcentral.com/1471-2164/11/464 average, smaller genomes and are mostly AT rich, while soil bacteria tend to have larger genomes and higher % GC [14]. Higher AT content in intracellular bacteria may be attributed to a loss of repair genes; this loss will eventually lead to an increase in mutation rates from cytosine to thymine [15,16]. Genes not expressed will eventually lead to reduced genome sizes [15,16]. Higher GC content in soil bacteria may be due to the increased availability of nitrogen [9]. However, increased nitrogen in the soil does not explain why GC rich bacteria often have larger genomes. The base composition in GC rich genomes might reflect stronger selective forces than AT rich genomes [17-19]. This may indicate that GC rich microbes live in more complex environments than intra- cellular bacteria [20]. The reasons for stronger selective forces and GC richness is not known, but may be con- nected to the fact that considerably more energy is required to de-stack GC rich DNA sequences than AT rich DNA sequences [21]. Although GC content has been found to vary only slightly within prokaryotic genomes some regions differ more than others. A large region flanking the replication origin, for instance, is more GC rich than the average genomic GC content [22] whereas the region around replication terminus is more AT rich [5]. Surface pro- teins and RNA genes often have GC content that differs from the average genomic GC content [22], and protein coding regions have been found to be, on average, approximately 5% more GC rich than non-coding regions [18]. In addition to being more GC rich, coding regions have been found to be more homogeneous in terms of base composition than non-coding regions [18]. The GC heterogeneity in coding regions has, how- ever, been found to be associated with mean genomic AT content in non-coding regions [18,23]. In other words, GC content variability tends to increase with higher mean genomic AT content in non-coding regions. Horizontally transferred DNA may have a different fraction of GC than the host genome as a result of dif- ferent evolutionary pressures [6,24-26]. Since horizon- tally transferred DNA is often linked to pathogenesis in microbes [27], detection of such regions is of great importance. The GC content of foreign DNA will, how- ever, become progressively more similar to the host gen- ome in a process known as amelioration [24] making such regions more difficult to detect as time progress [25]. The conformation of base compositional patterns from foreign DNA to host DNA may be related to the finding that a particular subunit of the DNA polymerase III, the Pol III a subunit, appears to be driving genomic GC content in prokaryotes [28]. There is a considerable amount of research and docu- mentation related to mean genomic GC content in Page 2 of 8 prokaryotes demonstrating that this property is the result of many factors interacting in a highly complex manner [29]. On the other hand, analysis of genomic GC content variability within microbial chromosomes, has received much less attention. A more recent over- view of methodology used to analyze GC content varia- tion within genomes can be found in Bernaola-Galvan et. al., [30], and a study of how intra-genomic GC con- tent variation affects codon usage is described by Dau- bin et. al. [31]. In the present work, we introduce the GCVAR measure to examine GC content variability within prokaryotic genomes. The GCVAR metric gives a measure of how GC content varies within a given gen- ome with respect to the mean genomic GC content. A low GCVAR thus points to little GC content variation, or GC content homogeneity, within the genome, while a high GCVAR designates varying GC content, or GC content heterogeneity. To the best of our knowledge, no study has exam- ined the interplay between environmental factors and GC content homogeneity in prokaryotes. In the pre- sent study the aim was therefore to examine whether GC content homogeneity in prokaryotes, measured here using the GCVAR measure, could be related to specific factors in the environment such as tempera- ture and oxygen, as well as the broader properties implicated in phylogeny and GC content. To do this, regression analyses were performed using GCVAR as the response variable. The response variable was fitted to the following variables: oxygen requirement (a cate- gorical variable defined as either aerobic, anaerobic or facultative), phylum, genomic GC content, genome size, growth temperature (a categorical variable used to define psychrophiles, mesophiles and thermophiles), pathogenicity (a dichotomous variable describing whether the microbe is pathogenic or not) and habitat (a categorical variable describing the environment where the microbe is found, i.e. aquatic, host-asso- ciated, multiple, specialized and terrestrial). The data- set consisted of 488 genomes (526 chromosomes) with similar strains and species removed from the analysis to reduce phylogenetic bias. Results and Discussion GC distribution within genomes The histograms in Figure 1 shows the statistical distribu- tions of GC content differences, Di = GCi - GC (Equation (1), Methods section), within four AT-rich and four GC- rich genomes. The statistical distributions shown in Fig- ure 1 are based on the differences, or residuals, between the GC content of a 100 bp non-overlapping sliding window and mean genomic GC content for each of the 8 genomes. Figure 1 therefore shows the statistical distribu- tions of how GC contents differences are distributed