An Important Role for Purifying Selection in Archaeal Genome Evolution

Expansion of archaeal genomes associated with relaxed purifying selection.In eukaryotes, relaxed purifying selection is associated with genome expansion, which is consistent with the accumulation of introns and mobile elements that are often deleterious (3).

While our bacterial data set reproduced the previous observations showing that strong purifying selection was associated with genome expansion (7, 8), our archaeal data set revealed a eukaryote-like pattern based on a genome-wide dN/dS ratio analysis (Fig. 1A and B; see Tables S1 and S2 in the supplemental material). This finding predicts an enrichment of noncoding sequences in larger archaeal genomes, as also observed in eukaryotic genomes (see below for coding density analysis).

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FIG 1

Association between genome size and the dN/dS ratio for archaeal (A; n = 21) and bacterial (B; n = 28) genome pairs and association between coding density and genome size in Archaea (C; n = 49) and Bacteria (D; n = 78). Note that the stronger the purifying selection becomes, the lower the dN/dS ratio is. Spearman rank correlation coefficients (Rs) and two-sided significance (P) values are indicated in each panel. Genome sizes are shown in megabase pairs (Mb) in panels C and D but are in protein coding sequences (CDS) in panels A and B, as only protein coding sequences were used for dN/dS ratio analysis. Regardless, the same trends were reproduced when genome size measured in base pairs instead of protein coding sequences was used.

At least a couple of observations indicated that our archaeal and bacterial data sets were comparable and likely representative. First, both archaeal and bacterial mean dN/dS ratios were between 0.05 and 0.20 (Fig. 1A and B) and a similar range has also been observed in eukaryotic lineages (11, 12). Second, most archaeal and bacterial genome pairs had an average nucleotide identity (ANI) of 75 to 95% (Fig. 2), an empirical range that delineated closely related prokaryotic species belonging to the same genus (13). This minimized the potential bias of dN/dS ratio computations caused by uneven variation in evolutionary distances between genomes (7). Nevertheless, while the bacterial samplings both here and in previous studies seem to have captured substantial taxonomic diversities of bacterial genomes, the archaeal samplings need further improvements. Sampling of archaeal genomes across more phyla will be necessary to begin to address their full genome size range. This is because the archaeal genome size narrowly ranges from 0.5 to 6 Mb compared to the 0.6- to 9-Mb range of bacterial genomes (Tables S3 and S4). Of the 29 prokaryotic phyla taxonomically established, only 2 belong to Archaea, where most complete archaeal genomes currently come from, and an estimated ~300 archaeal phyla still wait to be sampled (14).

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FIG 2

Association between the dN/dS ratio and genomic distance as measured by percent ANI. Closed circles represent bacterial pairs (n = 28), and open triangles represent archaeal pairs (n = 21).

Coding density in Archaea shows a trend similar to that seen in eukaryotes.Coding density or gene density, as measured by the proportion of a genome sequence that is composed of coding or gene sequence, has been shown to correlate negatively and neutrally with eukaryotic and bacterial genome sizes, respectively (7, 15, 16). This observation made in eukaryotes is consistent with a genomic structure showing that noncoding sequences, primarily spliceosomal introns and mobile genetic elements, are overrepresented in larger eukaryotic genomes (3). The observation regarding Bacteria is also consistent with the dynamic balance between DNA loss and DNA gain (1, 8, 9). While our bacterial data set confirmed previous observations, our archaeal data set again revealed a eukaryote-like pattern (Fig. 1C and D and Tables S3 and S4). That is to say, a strong negative correlation was observed between coding density and archaeal genome size, indicating that an insertion bias enriching noncoding sequences was also evident during the expansion of archaeal genomes. It remains elusive what category of noncoding sequences is overrepresented in larger archaeal genomes, as the nature of most archaeal noncoding sequences is poorly characterized (17). Regardless of the category, it would probably be different from that observed in eukaryotes, as spliceosomal introns have never been found in Archaea and the distribution of known mobile elements in archaeal genomes is similar to that in their bacterial counterparts (18). However, the presence of novel mobile elements in Archaea could not be ruled out. Compared to eukaryotes, one common feature is still shared by Archaea and Bacteria, both of which had a similar and narrow range of coding density, i.e., about 70 to 95% in our data set. In contrast, the eukaryotic coding density roughly ranged from 1 to 80% (15). Therefore, there could be an unknown force(s) that prevents noncoding regions in archaeal genomes from enriching to the extremely high levels observed in eukaryotes.

This study presents the first evidence that the evolutionary regimen of the complexity of archaeal genomes could be significantly different from that of their bacterial counterparts. Instead, certain key eukaryote-like evolutionary features seem to be already embedded in archaeal genomes. On the one hand, those observations seem striking, as the architecture of archaeal genomes is very similar to that of bacterial genomes (2). On the other hand, they may simply reflect the close evolutionary connections between Archaea and eukaryotes, since eukaryotes likely evolved within the Archaea (19). Although the archaeal samplings here are still limited and the similarities drawn between Archaea and eukaryotes here are preliminary, we believe our study serves as a sufficient reminder that it is probably no longer a safe practice to group archaeal and bacterial genomes into one category when testing evolutionary hypotheses. Rather, comparisons should be made among Archaea, Bacteria, and eukaryotes.

Data collection and coding density calculation.Genomic data from closely related species that belong to the same genus were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/ ). Genera or species that were not well established, i.e., those whose taxonomic names were not formally proposed or alternative names were available, were removed before further analysis. The coding density of each genome was calculated as follows: % coding density = (DNA length of all coding sequences/total DNA length of complete genome) ×100%.

dN/dS ratios and ANI analyses.For each pair of genomes, pairs of orthologs were identified by a hybrid procedure combining Bidirectional Best Hit and BLASTclust (20). The orthologous pairs identified were aligned by ClustalW2, and dN/dS ratios were calculated by using YN00 in the PAML package (21, 22). Synonymous sites and ratios of synonymous to nonsynonymous sites considered to be saturated or unreliable (that is, a dS of <0.1, a dS of >1.6, or a dN/dS ratio of >99) were discarded before the mean dN/dS ratio and standard error of the mean of each genome pair were both calculated and reported in Tables S1 and S2. Although data filtration is a common practice to ensure better estimation of selective pressure, it also substantially reduces the number of pairwise orthologs available for downstream analyses (7, 11). This would make the data set less representative for inferring genome-wide patterns. Therefore, only genome pairs retaining pairwise orthologs accounting for no less than 5% of the pairs’ average coding capacity after all filtrations were kept for ultimate analyses. This cutoff is the starting point to represent most of the essential genes determined in prokaryotic genomes (23). ANI values were retrieved from IMG (24).

Correlation analysis.Spearman rank correlation (v1.0.3) was employed to obtain correlation coefficients (Rs) and two-sided P values (25).