- Open Access
Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes
© Liu et al.; licensee BioMed Central Ltd. 2004
- Received: 1 March 2004
- Accepted: 2 August 2004
- Published: 26 August 2004
Pseudogenes often manifest themselves as disabled copies of known genes. In prokaryotes, it was generally believed (with a few well-known exceptions) that they were rare.
We have carried out a comprehensive analysis of the occurrence of pseudogenes in a diverse selection of 64 prokaryote genomes. Overall, we find a total of around 7,000 candidate pseudogenes. Moreover, in all the genomes surveyed, pseudogenes occur in at least 1 to 5% of all gene-like sequences, with some genomes having considerably higher occurrence. Although many large populations of pseudogenes arise from large, diverse protein families (for example, the ABC transporters), notable numbers of pseudogenes are associated with specific families that do not occur that widely. These include the cytochrome P450 and PPE families (PF00067 and PF00823) and others that have a direct role in DNA transposition.
We find suggestive evidence that a large fraction of prokaryote pseudogenes arose from failed horizontal transfer events. In particular, we find that pseudogenes are more than twice as likely as genes to have anomalous codon usage associated with horizontal transfer. Moreover, we found a significant difference in the number of horizontally transferred pseudogenes in pathogenic and non-pathogenic strains of Escherichia coli.
- Horizontal Transfer
- Prokaryotic Genome
- Ureaplasma Urealyticum
- Pfam Family
- Cutoff Region
Genes that have recently fallen out of use for an organism are often detectable in the genome as pseudogenes - disabled copies of genes characterizable by disruptions of their reading frames due to frameshifts and premature stop codons [1–3]. Surveys of the pseudogene populations of eukaryotes (budding yeast, nematode worm, fruit fly and human) have recently been completed [4–10]. These pseudogene analyses have yielded insights into eukaryotic proteome evolution, showing that duplicated pseudogene formation tends to occur in younger, more lineage-specific, protein families, and is in many cases linked to the generation of functional diversity . However, pseudogene formation in most prokaryotes has not been analyzed as a matter of course, and has, historically, been assumed to be minimal . Some recent substantial populations of pseudogenes have been discovered in pathogenic bacteria, most notably in the leprosy bacillus Mycobacterium leprae, where around 1,100 pseudogenes (compared to around 1,600 genes) were found, with pseudogene formation providing a 'fossil record' of recent wholesale loss of pathways involved in lipid metabolism and anaerobic respiration .
Here we want to address the question of whether these large populations are exceptional, or whether there are substantial populations of pseudogenes in other prokaryotic genomes. If so, from a holistic 'polygenomic' perspective, what sorts of proteins tend to form prokaryotic pseudogenes? And are there any themes in common with the occurrence of pseudogenes in eukaryotes?
To address these broad questions, we have adapted a pipeline developed for eukaryotic pseudogene identification to 64 prokaryotic genomes . The species analyzed include archaea, pathogenic bacteria and non-pathogenic bacteria, and many of the pathogenic bacteria are also important organisms in current biodefense research. We have found nearly 7,000 pseudogenes, with notable numbers of pseudogenes for specific families linked to DNA transposition and also that have some role in environmental responses. Our results, which we have derived consistently across all the genomes, are available from our prokaryote pseudogene information website .
Pseudogenes are pervasive in prokaryotes
To check for a correlation with microbial 'lifestyle', we classified the 64 species into three categories: archaea, pathogenic bacteria and non-pathogenic bacteria. The pseudogene fractions for these groupings were assessed. M. leprae has a very large pseudogene fraction (36.5%) and is clearly a unique outlier. When this genome is set aside, the three groups have similar pseudogene fractions (3.6%, 3.9% and 3.3%). Note that three other pathogenic species/strains have relatively large pseudogene fractions, including Neisseria meningitidis MC58 (12.4%), N. meningitidis Z2491 (11.6%) and Rickettsia conorii (9.7%). The higher pseudogene fractions of some pathogenic species have previously been suggested to be a result of a rapidly changing environmental niche, with loss of metabolic and respiratory pathways .
We found that about 2,300 of our 6,895 candidate pseudogenes overlap with more than 2,600 annotated hypothetical open reading frames (ORFs), whose fractions were indicated in Figure 2. The overlap could arise from erroneous gene annotations or sequencing errors . In either case, the pseudogene annotation in prokaryotic genomes is evidently an important part of decontaminating gene annotation.
There are notable protein families that rank high in pseudogene number, but low in terms of gene number. They include the PPE family (PF00823) which is thought to be linked to antigenic variation in mycobacteria and is highly polymorphic ; the cytochromes P450 (PF00067), which are involved in processing diverse substrates; the GGDEF domain (PF00990), which is of unknown function and is associated with a wide diversity of other protein domains ; alpha/beta-hydrolase enzymes (PF00561), which have diverse catalytic functions; and pseudo-U-synthase-2 enzymes (PF00849), which help synthesize pseudouridine from uracil. Note that the first two families in this list have sequence diversity that has some link to environmental response.
Figure 3b shows the relationship between the number of pseudogenes and genes for Pfam families. One might expect this relationship to be linear, with bigger families having more pseudogenes, but Figure 3b shows this is not the case. Two large families that have a relatively high ratio of pseudogenes to genes are the transposase DDE domain (PF01609) and integrase core domain (PF00665). Transposase facilitates DNA transposition and horizontal gene transfer and its DDE domain may be responsible for DNA cleavage at a specific site followed by a strand-transfer reaction . Many transposons contain transposases for their transposition [29, 30]. We found that two strains of N. meningitidis (MC58 and Z2491) carry 26 and 22 copies of transposase pseudogenes, respectively, and have only 11 and 5 copies of transposase genes. In the MC58 strain, transposase pseudogenes have been found in most of the 29 remnant insertion sequences . This suggests that N. meningitidis strains probably undergo high selection pressure for transposases. The integrase core domain family (PF00665) is the catalytic domain of integrase, which mediates integration of a DNA copy of a viral/bacteriophage genome into the host genome . It catalyzes the DNA strand-transfer reaction by ligating the 3' ends of the viral DNA to the 5' ends of the integration site . The large number of transposase and integrase pseudogenes might result from harmful foreign genes being disabled in transposable elements. Several species contain many integrase pseudogenes, including Streptococcus pneumoniae, M. leprae, M. tuberculosis, and E. coli strain O157:H7. The large number of pseudogenes relative to genes for these two gene families may reflect an overall high selective pressure for them - that is, a gene family that is rapidly duplicating and evolving may generate many pseudogenes.
Origins of pseudogenes
Retrotransposition and genomic DNA duplication generate pseudogenes in mammals and other eukaryotes [2, 3]. In contrast, in prokaryotes, based on the experience annotating E. coli and M. leprae [12, 16], pseudogenes are suggested to arise from three process: the disablement of detectable native duplications; the decay of native single-copy host genes; and failed horizontal transfers.
Putative horizontally transferred genes and pseudogenes
Failed transfer index
Halobacterium sp. NRC-1
Synechocystis sp. PCC 6803
Nostoc sp. PCC 7120
L. lactis subsp. lactis
E. coli K12
Buchnera sp. APS
C. pneumoniae J138
C. pneumoniae AR39
C. pneumoniae CWL029
H. pylori J99
H. pylori 26695
S. pyogenes M1 GAS
N. meningitidis Z2491
N. meningitidis MC58
S. aureus subsp. aureus N315
S. aureus subsp. aureus Mu50
M. tuberculosis CDC1551
M. tuberculosis H37Rv
S. typhi CT18
S. typhimurium LT2
E. coli O157:H7
E. coli O157:H7 EDL933
To confirm our findings based on a method relying on GC content bias we applied the GeneTrace method (see Materials and methods). We analyzed a subset of pseudogenes and found that 18% result from failed horizontal transfer events, consistent with the previous method. Note that GeneTrace and the GC-content method are very different in the criteria they use to assess horizontal transfer and thus make for good independent verification of each other.
In summary, we report here for the first time an estimate of how often horizontal transfer in prokaryotes introduces genes that are redundant, useless or even detrimental. Firstly, ORFs from dangerous genetic elements are under strong selection pressure to be deleted from the host's genome . Secondly, horizontally transferred genes have a higher chance than non-transferred genes of becoming pseudogenes in most prokaryotes, which may be a result of deactivation/disablement of non-beneficial transferred genes.
By examining closely related strains of the same species, we found that most close strains have a similar value for the failed horizontal transfer index. In particular, M. tuberculosis (strains H37Rv and CDC1551), N. meningitidis (strains Z1491 and MC8), and Helicobacter pylori (strains 26695 and J99) share similar index values within species. However, E. coli has different index values in the three strains studied. The free-living E. coli K12 strain has an index value of 4.6, comparable to values calculated from previous results , while the two pathogenic E. coli strains O157:H7 and O157:H7 EDL933 have much lower values (1.8 and 0.8). This can be readily explained in two ways: the intracellular pathogenic E. coli strains could have moved into a different environment that results in lower exposure to incoming DNA and thus to a lower rate of horizontal gene transfer ; or these strains could have an increased rate of gene loss or pseudogene formation of their host genes.
A polygenomic power-law-like trend in pseudogene disablement
It is important to note here that the current sequence databases are derived from an uneven sampling of genomes. Therefore, genomes of organisms with more sequenced relatives may appear to have, on average, a seemingly younger population of pseudogenes, while others may appear to have older and fewer identifiable pseudogenes. Using data from 64 genomes, our results indicate an overall trend for pseudogenes observed in most of the genomes studied. However, these results have to be viewed as preliminary until more genome data is available.
We have shown that pseudogenes in prokaryotes are not uncommon, occupying 1-5% of all gene-like sequences. We find that specific gene families with clear links to DNA transposition and environmental responses have higher pseudogene/gene ratios.
The pseudogene data has many implications for the study of genome reduction and expansion [43, 44]. A significant proportion of the pseudogenes arose from putative failed horizontal transfer - at more than two times the rate for genes. Obligate pathogenic bacteria have high rates of disablement in younger pseudogene populations, consistent with recent accelerated genome reduction , while, in contrast, archaea and non-pathogenic bacteria have relatively older pseudogene populations, but similar rates of disablement.
In terms of methodological implications, it is evidently necessary to include prokaryote pseudogenes as part of systematic annotation pipelines in the future. In addition, it was also shown to be helpful to identify potential short ORFs . Furthermore, our survey shows that trends can be observed 'polygenomically' for prokaryotes, where they are not obvious or significant in individual genomes.
Database releases used
We used the following datasets in our prokaryotic pseudogene analysis: Swiss-Prot (release 40.19 and updated to 27 May, 2002)  containing 43,094 prokaryotic protein sequences; nucleotide sequences from 64 prokaryotic genomes from EMBL database release 70 on March-2002 , including 11 genomes from archaea and 53 from bacteria as listed in Figure 1; Pfam release 7.3 of May 2002, containing 3,849 families and 498,152 protein domains in the alignments .
Pseudogene identification pipeline
Figure 1a shows the basic procedure for identifying prokaryotic pseudogenes. The general schema was adapted from pipelines for pseudogene analysis in eukaryotes . We generated a prokaryotic proteome set by collecting all the prokaryotic protein sequences in the Swiss-Prot database and those annotated in the 64 prokaryotic genomes. To be conservative, we did not include hypothetical or putative proteins, a large proportion of which might be overannotated [47, 48]. All the protein sequences were masked by SEG using the default low-complexity filter parameters (122.22.5) . To maximize the efficiency of the pseudogene search, we only considered the intergenic DNA regions in the 64 prokaryote genomes (including the regions encoding hypothetical proteins) as query sequences, and searched their forward and reverse complement sequences against the proteome set using FastX . Significant homology matches (E-value less than 0.01) that contained more than one disablement (either a frameshift caused by insertion or deletion of nucleotides or a premature stop codon) were considered as potential pseudogenes. If an intergenic region had multiple matches, these matches were sorted by E-value (increasing) and then by the number of matching residues (decreasing), if they have the same E-value. The match with the most significant E-value and the maximum matching residues was selected and redundant matches were removed.
To ensure that spurious disablements were not introduced at ends of sequences as an alignment artifact, we excluded homology matches whose disablements occurred only within a 'cutoff region' at either end. We used 16 residues for the cutoff region for short sequences (160 amino acids or fewer) - a parameter that has been applied previously . For longer sequences (more than 160 amino acids), 10% of the sequence length was applied as the cutoff region as FastX tends to include more residues at the ends of alignments.
We also assessed the potential pseudogenes by examining the distribution of the disablements within pseudogene sequences. Given that mutations within pseudogenes are unconstrained, we would expect disablements on pseudogenes to be evenly distributed. Figure 1b shows the position of disablements within pseudogene fragments whose length is normalized to 100 residues. By removing those potential pseudogenes that only had disablements at their flanking regions at both ends, the distribution is almost evenly distributed. We used it as a 'control filter' to minimize false-positive pseudogenes. In the final pseudogene set, the length of pseudogenes ranges from 33 to 4,969 amino acids, with a median length of 130 amino acids, as compared with the proteome set, where the length ranges from 7 to 10,920 amino acids with a median length of 291 amino acids.
We considered non-standard codon usage in some bacteria, such as when TGA encodes tryptophan rather than a stop codon in mycoplasma species, including Mycoplasma pneumoniae, M. pulmonis and U. urealyticum. By manual examination of E. coli genes with translational frameshifts in the RECODE database , we found that those genes were included in coding sequences (CDS) and therefore were excluded from our pseudogene search.
Sequencing errors could also be a potential problem in the detection of pseudogenes. However, this effect is expected to be small, as comparison of independently sequenced isolates of the same E. coli strains indicated that only about 7% of candidate pseudogenes could be due to sequencing error . To further consider the possibility of sequencing error, we examined the stop codons in the pseudogenes detected in the S. pneumoniae genome (frameshift positions are not considered as they are difficult to locate.). This genome and eight others found in the trace archive of the National Center for Biotechnology Information (NCBI)  and Ensembl  were all sequenced by TIGR. We selected S. pneumoniae as a case study as it is a relatively big genome available in the archive. By adapting a previous method , we examined the overall quality values (Q) for each nucleic acid of stop codons in the pseudogenes. Pseudogene sequences were aligned to the archived sequences (≥ 95% identity), and the quality values for nucleotides in stop codons were summed up. We chose 10-2 as a cutoff of the error rate (err = 10SUM(-0.1Q)) for all nucleic acids. The stop codons with all three nucleic acids above the cutoff were validated. Out of 116 pseudogenes in this genome, 73 were found to contain 150 stop codons in total. Using the available data in the trace archive, we identified 54 pseudogenes with stop codons being aligned with the original sequences, and validated 47 of these (87%). In addition, a similar fraction of stop codons (101 out of 116) was confirmed.
Family classification of genes and pseudogenes
All genes in the 64 genomes were assigned to Pfam families by cross-referencing of their Swiss-Prot ID. Pseudogenes were assigned to Pfam families through ID of their closest homologs. Only the homologs that cover more than 70% of the Pfam domain were selected. A pseudogene could be assigned to multiple Pfam families if it contains multiple domains.
Estimation of horizontally transferred genes and pseudogenes
Here we used a method (GC-content) to estimate horizontal transferred genes on the basis of their base compositions [33, 39]. We analyzed each of the 64 genomes individually, and atypical genes and pseudogenes were identified if the GC content at first and third codon positions was two or more standard deviations higher or lower than the mean values at those positions in genes.
To ensure that we had the codon positions accurately assigned for the GC-content method, we only analyzed codons for pseudogenes that aligned well with annotated protein sequences, specifically excluding the regions of the alignment around frameshifts. While it is true that the local alignment in some regions of a pseudogene may be ambiguous, causing some difference in the GC-content calculation in that region, the impact on the overall GC-content estimation is minimal, given how many positions we average over to calculate the failed transfer index score.
The results for the 64 genomes are shown in Table 1. The failed transferred index in the last column represents the ratio of the fraction of putative horizontally transferred pseudogenes to the fraction of horizontally transferred genes
similar to the measure previously used in E. coli . This essentially gives a likelihood ratio for horizontal transfer for pseudogenes relative to that of genes.
Note that to minimize the effect of more divergent sequence alignments, for the horizontal-transfer calculations we only analyzed 1,748 'recent' pseudogenes, which have more than 50% sequence identity to their closest matches over an aligned subsequence of more than 100 residues.
We have investigated the statistical robustness of the failed transfer index using resampling approaches . For each of the 64 genomes, we randomly picked 90% of its genes and calculated their GC content. Using the new GC content, we then identified the putative horizontally transferred genes and pseudogenes and calculated the failed transfer index. We applied the process 1,000 times, generating a distribution of 1,000 indexes, which has a mean value of 2.32 with standard deviation of 0.01.
We also applied an alternative method (GeneTrace) to estimate horizontally transferred pseudogenes . In this method, potential horizontal transfer events are inferred within a protein family when it is present only in distantly related species and is absent from members of the same phylogenetic clade. We analyzed a subset of pseudogenes - 225 pseudogenes across 62 genomes - whose closest Swiss-Prot homologs share more than 70% sequence identity across at least 100 amino acids, and identified 41 of them (18%) as from failed horizontal transfer events.
M.G. thanks NIH/NIAID grant for Northeast Biodefense Center (1U54AI057158-01) for financial support. He also acknowledges support from the Ruth B. Williams Fund. Y.L. was partially supported by an NLM postdoctoral fellowship (NIH Grant T15 LM07056). We thank Zhaolei Zhang and Nick Carriero for helpful discussions and Duncan Milburn for technical help.
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