Transcribed processed pseudogenes in the human genome: an intermediate
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《核酸研究医学期刊》
Department of Biology, McGill University Stewart Biology Building, 1205 Dr. Penfield Avenue, Montreal, Quebec, Canada H3A 1B1 1Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT, USA 2Department of Computer Science, Yale University New Haven, CT, USA 3Banting and Best Department of Medical Research, University of Toronto Toronto, Ontario, Canada
*To whom correspondence should be addressed. Tel: +1 514 398 6420; Fax: +1 514 398 5069; Email: paul.harrison@mcgill.ca
ABSTRACT
Pseudogenes, in the case of protein-coding genes, are gene copies that have lost the ability to code for a protein; they are typically identified through annotation of disabled, decayed or incomplete protein-coding sequences. Processed pseudogenes (Pgs) are made through mRNA retrotransposition. There is overwhelming genomic evidence for thousands of human Pgs and also dozens of human processed genes that comprise complete retrotransposed copies of other genes. Here, we survey for an intermediate entity, the transcribed processed pseudogene (TPg), which is disabled but nonetheless transcribed. TPgs may affect expression of paralogous genes, as observed in the case of the mouse makorin1-p1 TPg. To elucidate their role, we identified human TPgs by mapping expressed sequences onto Pgs and, reciprocally, extracting TPgs from known mRNAs. We consider only those Pgs that are homologous to either non-mammalian eukaryotic proteins or protein domains of known structure, and require detection of identical coding-sequence disablements in both the expressed and genomic sequences. Oligonucleotide microarray data provide further expression verification. Overall, we find 166–233 TPgs (4–6% of Pgs). Proteins/transcripts with the highest numbers of homologous TPgs generally have many homologous Pgs and are abundantly expressed. TPgs are significantly over-represented near both the 5' and 3' ends of genes; this suggests that TPgs can be formed through gene–promoter co-option, or intrusion into untranslated regions. However, roughly half of the TPgs are located away from genes in the intergenic DNA and thus may be co-opting cryptic promoters of undesignated origin. Furthermore, TPgs are unlike other Pgs and processed genes in the following ways: (i) they do not show a significant tendency to either deposit on or originate from the X chromosome; (ii) only 5% of human TPgs have potential orthologs in mouse. This latter finding indicates that the vast majority of TPgs is lineage specific. This is likely linked to well-documented extensive lineage-specific SINE/LINE activity. The list of TPgs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/bppg.tov (or) http:pseudogene.org.
INTRODUCTION
The search for novel functional elements in the human genome is imperative and ongoing (1–3). Pseudogenes (gene copies that have lost their protein-coding ability) are a form of sequence of potential functional utility (4). Substantial progress has been made in the annotation of pseudogenes (5–11). There may be twice as many pseudogenes (derived from protein-coding genes) in the human genome as protein-coding genes (6–10).
Pseudogenes (derived from protein-coding genes) are typically ‘diagnosed’ through searching for the ‘symptoms’ of a lack of protein-coding ability. These symptoms include: frame disablement (from premature stop codons and frameshifts), coding sequence decay (typically detectable through examination of non-synonymous and synonymous substitution rates) or incompleteness (either from sequence truncation or from the loss of essential signals for transcription, splicing and translation) (6–10). Processed pseudogenes (Pgs) are made through retrotransposition of mRNAs. There is ubiquitous genomic evidence for thousands of Pgs in mammals (5–10). Similarly, dozens of processed genes (i.e. genes made by retrotransposition of the complete sequence of other genes) have arisen in both the mouse and human genomes (12,13). This mass gene retrotransposition may arise, at least in part, as a by-product of long interspersed element (LINE) retrotransposition (14). Retrotransposition is clearly an active process in mammalian gene evolution (15). Here, we search for an intermediate type of retrotransposed gene sequence: the transcribed processed pseudogene (shortened as TPg), which is a Pg that is disabled but nonetheless transcribed.
Historically, there have been several isolated reports of transcribed pseudogenes, of either the duplicated or the processed form (16–21). Two recent studies have demonstrated that such transcribed pseudogenes can regulate transcription of homologous protein-coding genes. Transcription of a pseudogene in Lymnea stagnalis, that is homologous to the nitric oxide synthase gene, decreases the expression levels for the gene through formation of a RNA duplex; this is thought to arise via a reverse-complement sequence found at the 5' end of the pseudogene transcript (20). In a second example, transcription of the makorin1-p1 TPg in mouse was required for the stability of the mRNA from a homologous gene makorin1 (21). This regulation was deduced to arise from an element in the 5' areas of both the gene and the pseudogene (21).
In addition to helping to elucidate such regulatory roles, annotation of TPgs will further add to our understanding of the dynamics of gene evolution through retrotransposition (15). Also, it is crucial to annotate TPgs correctly as a part of the ongoing process of correct cDNA/expressed sequence tag (EST) mapping during genome annotation, and for more accurate interpretation of microarray expression data (22,23). Here, we have performed a data-mining expedition for human TPgs using a rigorous method that applies stringent filters to avoid data pollution. TPgs have a markedly distinct distribution in the genome when compared with other Pgs and processed genes. A key result is that TPgs are significantly likely to insert near the 5' and 3' ends of genes, implying that TPgs can be generated by co-option of promoter elements or by intrusion into untranslated regions (UTRs) as ‘molecular passengers’. Also, we find that the vast majority of TPgs are human-lineage specific compared with mouse.
Definitions and terms
An mRNA can be reverse transcribed and re-integrated into the genomic DNA, possibly as a by-product of LINE-1 retrotransposition (14). The parent gene of the mRNA need not be on the same chromosome as the retrotransposed copy. Such a retrotransposed mRNA has three possible fates in the present-day genome: (i) formation of a non-transcribed Pg, (ii) formation of a TPg or (iii) formation of a processed gene (or part of a gene).
A Pg can be defined as any disrupted, decayed or incomplete copy of a gene that has arisen through such retrotransposition. In the process of evolution, Pgs accumulate disablements (frameshifts and premature stop codons) in their apparent coding sequences. Procedures to annotate Pgs using disablement detection have been described previously (4,5,7), and serve as the basis for the present analysis.
Operationally, a TPg is defined as a Pg for which an expressed sequence is mappable across any of its coding-sequence disablements, i.e. the disablement occurs in both the expressed sequence and the genomic sequence (see Methods for details).
A processed gene is any undisrupted retrotransposed copy of a gene that also has low Ka/Ks values indicative of selection pressure on coding ability (see Methods for details).
Each of our TPgs has 1 disablement verified by alignment of the expressed sequences to genomic DNA, in a region of the TPg that maps to a known structural protein domain, or to a protein sequence that is conserved in non-mammalian eukaryotes. This three-level verification procedure (genome:transcript:protein) is termed triple alignment. Each verified disablement has an estimated probability of being the result of a sequencing error of 10–6, since the error rate for the genomic sequence build is 10–4 (24) and the error rate for cDNAs/ESTs is 10–2 (25,26). We made a subset of TPgs, termed the C set, which has further evidence of lack of coding ability. These have: (i) no continuous segment of sequence that can code for a protein domain (as defined in Methods); (ii) high Ka/Ks values (0.5).
As it is possible that a fraction of the TPgs that map to introns arise from intron retention in cDNAs or ESTs in the source expressed sequence data, we analyzed all of the data both including and excluding the 67 TPgs that map to introns (see Table 3 and below). Our results are unaffected by such potential contamination, as explained below.
Table 3 Position of TPgs, other Pgs and processed genes relative to annotated genes
METHODS
Detection of TPgs
(i) Mapping expressed sequence data onto existing Pgs annotations
Pgs were annotated previously using a method based on the detection of disabled protein homology in genomic DNA (4,5,7). We mapped >6200 of these onto human genome build 34 (from http://www.ensembl.org), through detection of 100% nucleotide sequence matches, removing overlap with coding exons. For each Pg, the genomic sequence was extracted, both with and without a 6000 nt extension added on to either end to allow for homology matching to ‘pseudo-UTR’ regions. (These sets of genomic DNA are named genPg and genPg+/–6000.) Three sources of expressed sequences (Refseq mRNAs, Unigene consensuses, and ESTs from dbEST) were downloaded from http://www.ncbi.nih.gov. They were mapped onto genPg and genPg+/–6000, using BLASTN with low-complexity masking (E-value 10–10, minimum match length 100 nt) (27,28). From the resulting significant matches, those that align with 95% identity were used to generate a second BLASTN search against genPg and genPg+/–6000, but this time without low-complexity masking, to insure correct sequence identity. Matches to both genPg and genPg+/–6000 with 99% identity over >0.998 of the length of the expressed sequence were then extracted. These expressed sequence matches were filtered to insure that they match more significantly to the Pg than to any homologous gene. The matching expressed sequences were then re-aligned to the Pg sequence using FASTY (29), to check that 1 disablement (frameshift or premature stop codon) in the Pg occurs in both the genomic sequence and expressed sequence. Each disablement verified in this way has an estimated probability of being the result of a sequencing error of 1 x 10–6; this is because the genomic sequence error rate is 1 x 10–4, and the cDNA/EST sequencing error rate is 1 x 10–2.
(ii) Extraction of Pgs that are in Refseq mRNAs
All human Refseq entries corresponding to known mRNAs (total = 20 741) were compiled from data downloaded from the NCBI website (http://www.ncbi.nih.gov). These were compared with all known, non-fragmentary human proteins in the SWISSPROT database (30), using a modification of the disabled protein homology-based procedure developed previously for Pg annotation (4,5,7,31–33). To insure that all of the candidate TPgs in Refseq mRNAs map to a single continuous piece of genomic DNA, we extracted the appropriate mRNA subsequences and mapped them to the human genome using BLASTN. Those segments that matched over their complete length exactly were retained. The resulting TPg data were then filtered along with those generated in (i), as detailed in (iii) below.
(iii) Filtering the (transcribed) Pg data
We applied a set of filters to insure that we were compiling a bona fide list of TPgs. All TPg data sets were filtered as follows:
Removal of homologies to purely hypothetical proteins or fragmentary proteins: TPgs based only on homology to predicted reading frames or reading-frame fragments were removed through BLASTP comparisons (E-value 10–4) against a library of hypothetical or fragmentary proteins from SWISSPROT (30). These are removed because their disablements may be erroneous (which is inappropriate for the method employed here). Also, they may be inaccurately dated (values for Ks, Ka, etc., may be incorrect).
Verification that the disablements are in conserved parts of a known protein sequence or domain: We verified that the disablements examined are in known conserved parts of sequences, as detailed below. This list of filters has an ‘if-else-if-else-if’ structure:
First, we assigned protein structural domains to the TPgs, by comparing them with the ASTRALSCOP 95% identity set of protein domains (34), using BLASTP (27) (E-value 10–4). The total assigned TPg subsequence was determined (from the most N-terminal residue that was assigned to a domain, to the most C-terminal). This assigned subsequence was considered disabled, if a frameshift or stop codon occurred >10 residues in from either terminus. This accounts for 54% of TPgs.
Otherwise, secondly, TPgs not meeting criterion (1) were checked manually for occurrence of disablements in conserved domains using the InterPro (http://www.ebi.ac.uk/interpro) and CDD (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) domain annotation tools.
Otherwise, thirdly, TPgs not meeting criterion (2) (<10% of the sequences) were checked for disablement within a part of the sequence that is conserved in other mammals, and in 1 non-mammalian species, using BLASTP (E-value 10–4).
Removal of candidates with small introns: Putative Pgs, TPgs, and the expressed sequences that match them, were filtered for small intron sequences. A library of introns of <1000 nt was made from genes on human genome build 34. TBLASTN (27) was used to annotate any significant matches to these introns of >0.80 of their length (E-value 10–4). Any such matches could either be from introns in an aberrant cDNA or be a previously disregarded small intron in the genome. Some additional examples of TPgs that map to introns may arise from intron retention in cDNAs or ESTs; however, the main points of the analysis reported in this paper are unaffected by such potential contamination, as explained below.
Removal of possible duplications of single-exon genes and large exons: We wished to insure that there were no single-exon gene duplications in our Pg and TPg sets. To do this, all Pgs and TPgs were compared using BLASTP (E-value 10–4), to the set of proteins for build 34 (removing those whose genes overlap putative Pgs) (27). All Pgs and TPgs that had closest-matching homologies to single-exon proteins were removed. Furthermore, we insured that they aligned to their closest-matching homologous human proteins around at least one ‘exon seam’, i.e. a position in a protein sequence that corresponds to an intron–exon boundary. This exon seam filter insures that the pseudogenes considered are processed, and is particularly useful for removing homologies to genes with large exons (e.g. some zinc-finger-containing proteins).
Filtering for processed genes: All TPgs were filtered for overlap with annotated processed genes (resulting in the removal of only one putative TPg).
After applying these rigorous filters, we had 3418 Pgs (both transcribed and non-transcribed), and 233 TPgs, 218 from mapping expressed sequences to Pgs and 15 from Pg extraction from Refseq mRNAs. Almost half (97/233, 42%) of the TPg set represent 100% exact matches of expressed sequences to Pgs. Restricting analysis to just these matches does not affect any of the main trends and results reported here.
Making an obviously decayed C set of TPg sequences
We derived a ‘core set’ of TPgs that have further evidence of coding-sequence decay. These are dubbed the C set (totaling 177/233, 76%). This set is the union of the following two subsets: (i) TPgs without continuous segment of sequence that can code for a protein domain (106/233 TPgs, 45%) or (ii) TPgs with high Ka/Ks values (>0.50) indicative of lack of coding ability (127/233 TPgs, 54%).
(i) Lack of protein domain coding ability
We parsed each TPg into subsequences according to the positions of its disablements. If all subsequences could be labeled as ‘unlikely to code for a protein domain’, then the TPg was included in the C set. This resulted in inclusion of 106 TPgs in the C set. We labeled a subsequence as ‘unlikely to code for a protein domain’ if:
Its length was 32 residues. The vast majority (95%) of non-cysteine-rich protein domains in the ASTRALSCOP 40% identity set have sequence lengths >32 residues (34). Cysteine-rich domains (which are likely disulfide-bridged or metal-chelating) are defined as having cysteine concentration <0.077/residue, a value suggested by a bimodality in cysteine concentration, in surveys of cysteine and cystine occurrence in proteins (35,36). Condition (a) was not applied to any fragments that were adjudged cysteine-rich.
It contained a disrupted SCOP domain, as defined in part (iii)(b)(1) above. Such fragments are likely not to constitute a large enough fragment; the reasoning behind this criterion is that evolution has defined and refined the integrity of a body of recurrent folding units (protein domains) (34), and we can therefore use their disruption to evaluate whether a piece of sequence is no longer protein-coding.
(ii) Ka/Ks analysis
We calculated the Ka/Ks values for whole TPgs, using the Yang and Nielsen method in PAML (37), using the present-day gene sequence to compare against the pseudogene, as described previously (7). Also, similarly, we calculated Ka/Ks values for subsequences of TPgs (50 residues) derived by parsing at disablement positions. This parsing allows for the possibility that some of the pseudogene subsequences have coding ability, while others do not, i.e. we can test for a coding ability ‘imbalance’. From these Ka/Ks calculations, we found that only 4% of both Pgs and TPgs have two adjacent regions where one is <0.25 (potentially coding) and the other >0.5 (potentially non-coding), indicating that such imbalance is rare. From consulting independent analysis of populations of human genes and Pgs (6), we ascertained that for a threshold value of Ka/Ks 0.5, >95% of sequences are predicted to be Pgs and not genes. We use this as the expectation for the distribution of Pgs in general. Calculation of Ka/Ks values for gene/pseudogene pairs errs on the side of under-estimation of coding-sequence decay (7).
Conservation of TPg in mouse
For each human TPg, we searched against potentially orthologous mouse TPgs. These ‘moTPgs’ were derived by mapping expressed sequences (Refseq mRNAs, Unigene consensus sequences and ESTs) for mouse onto a previously derived set of mouse Pgs (8), in a similar manner to the human mappings (see above). These were pooled with any existing moTPg annotations, and a small number of mouse genes that might be potentially misannotated moTPgs. A potentially orthologous moTPg was required to match 0.5 of the length of the human TPg (for BLASTP matches, E-value 10–4), and to share the same closest-matching human protein with any potential human TPg homologs. We did not require that the retrotranspositions be in syntenic positions, since orthologous gene retrotranspositions are not necessarily syntenic (38).
Processed genes
We mapped an independently derived list of processed genes (13) to human genome build 34. In addition to the criteria in (13), we required Ka/Ks values <0.25, and coverage of 0.95 of the parent gene's length. Any examples that overlap the TPg data set of annotations were removed; vice versa, any TPgs that have Ka/Ks < 0.25 and cover 0.95 of their parent gene were deleted from the TPgs list. Our definitions give two distinct sets of processed genes and TPgs; naturally, we miss some sequences that cannot be classified as either a TPg or a processed gene.
RESULTS AND DISCUSSION
Number of TPgs
In total, we found 233 human TPgs (Table 1). These TPgs form a subset of 3418 previous Pg annotations that were mapped to build 34 of the human genome (7). These Pgs were filtered in the same way as the TPgs (from a starting total of 6200), to remove predicted reading frames, retained introns and potential duplications of single-exon genes or large exons. Using these data, we can estimate that 6% (218/3418) of Pgs are TPgs. An additional 15 TPgs were derived from a reciprocal process of searching for Pgs in known Refseq mRNAs, followed by subsequent mapping to the genome.
Table 1 Summary of numbers of TPgs
A small fraction of the TPgs (8%) corresponds to known Refseq mRNAs (Table 1). About a third are supported by Unigene consensus sequences, with a large fraction (71%) matching individual ESTs . We sought additional expression verification from a series of high-density oligonucleotide microarrays, composed of 52 million 36mers (23). These microarrays were applied to probe the transcriptionally active regions of the human genome, in a strand-sensitive way. Using the same data and statistical method (i.e. a sign test) for scoring the genes' transcriptional activity (22,23), we found that 75/233 (32%) TPgs were transcriptionally active in liver (P < 0.05) (Table 1). In comparison, 64% of genes from RefSeq mRNAs, 57% of Ensembl annotated genes (39), and 35% of genes predicted with the program GENSCAN (40), were found to be transcribed in liver.
The C set of more obviously decayed TPgs comprises 76% of the total population; 45% of TPgs having no continuous segment likely to code for a protein domain, and 54% of TPgs having Ka/Ks 0.5 (Table 1). Obvious degradation of the coding sequences is demonstrated for this set from analysis of protein-domain mapping and Ka/Ks (see Methods). Additionally, other factors (not examined in the present analysis) are expected to cause lack of coding ability in TPgs or arise as further consequences. It is likely that TPgs will not have appropriate start codon context (41), therefore leading to little or no efficient translation initiation. Also, those TPgs that are inserted into 3'-UTRs of mRNAs will be unlikely to become protein-coding through being downstream of a clearly defined coding sequence (although it is conceivable that they may be translatable in the 5'-UTR). Furthermore, a consequence of any frameshift in a sequence is the likelihood of an additional 20 residues or so of non-coding DNA, added onto the end of the sequence truncation (on average, in randomly picked, conceptually translated intergenic DNA, a stop codon will appear 20 residues downstream of any starting point); such additional sequence may lead to aggregation or misfolding in the cell.
The proportions of TPgs break down in a similar fashion to that just described above for the total data set, when the 67 examples that map to introns are removed (Table 1).
Closest matching human proteins for TPgs
TPgs were grouped according to their closest-matching human protein (Table 2). Each table entry represents a single ‘parent gene’. The total counts are also shown for the TPgs that do not map to introns (in square brackets, Table 2). There are 4 human proteins that have 4 homologous TPgs. The highest number of TPgs (5) occur for cyclophilin A, which is required for cis-peptide isomerization (42). All of these proteins arise from highly expressed mRNAs. They also occur in the top 20 proteins when apportioning all Pgs, in the same way (7).
Table 2 Human proteins with four or more homologous TPgs
TPg position relative to genes and the implications for their expression mechanisms
A number of mechanisms for TPg expression are plausible. First, TPgs may co-opt nearby promoter elements of protein-coding genes. Secondly, they may intrude into the UTRs of another mRNA, as a sort of ‘molecular passenger’. Thirdly, they may make use of cryptic promoter elements in the intergenic DNA; such promoter elements may have originated from transposable elements, or from genomic duplication of genic promoter regions, or sporadically (de novo).
Such mechanisms for TPg expression may have a bearing on their overall positional distribution in the genome relative to genes. To investigate this, we classified the TPgs into those that: (i) overlap existing coding-sequence exons; (ii) appear inserted in introns; (iii) are inserted in a 3000 or 10 000 nt region 5' to annotated genes; (iv) are inserted in a 3000 or 10 000 nt region 3' to annotated genes.
Table 3 summarizes these data. A minor proportion (8%) of TPgs entail gene coding-sequence annotations, i.e. they are erroneously annotated reading frames. There are 67 TPgs that map to introns (Table 3); it is unclear how many of these may arise from intron retention in cDNAs or ESTs. Expectations based on random insertion in the genome were calculated for classes (ii) to (iv). We focus on (iii) and (iv) in particular.
TPgs are significantly more likely than random (P < 0.01, chi-squared tests) to be inserted in the regions 5' and 3' of annotated genes; this effect is most obvious in the 3000 nt regions 5' and 3' to genes, but is still significant up to 10 000 nt in either direction (Table 3). Similar results are observed for the C set of more obviously decayed TPgs. The enrichment of TPgs observed in the 5' and 3' areas of genes can be seen as a simple logical consequence of randomly inserted Pgs having an increased probability of being transcribed, and is clear support for either co-option of genic promoter elements, or insertion into UTRs as molecular passengers, leading to TPg expression. This result is also unaffected by possible contamination from intron retention in cDNAs/ESTs, as, in general, Pgs are significantly under-represented in introns (Table 3); if one assumed that, in the extreme, all of the TPg mappings near the 5' and 3' ends of genes were actually mappings to introns, then this would make their over-representation even more significant. The general dearth of Pgs in introns may be a reflection of an overall genomic tendency for a lack of retroelement insertion in introns (43).
Roughly half of the TPgs are located away from genes (>10 000 nt 5' and 3' to genes, and overlapping neither an exon nor an intron; Table 3). These thus may be co-opting cryptic promoters of unknown origin in the intergenic DNA, such as those derivable from transposable elements.
In summary, the distribution of TPgs in the vicinity of genes is significantly different from that observable for other non-transcribed Pgs (that have no transcription evidence), and for processed genes in the following ways (Table 3):
TPgs are significantly over-represented in the 10 000 nt 5' and 3' to genes, whereas other Pgs and processed genes are not;
Other Pgs are significantly over-represented in intergenic DNA and significantly under-represented in introns, and processed genes are significantly under-represented in introns; TPgs now show such trends for introns or intergenic DNA. In addition, there is a dearth of Pgs 3' to genes (Table 3). The reasons for this are unclear; there may be a compositional effect, similar to the relationship between genomic G+C content and ribosomal-protein Pgs insertion, observed previously (44).
We examined the distribution of 20 TPgs that are directly mappable onto known Refseq mRNAs. Thirteen of these overlap an erroneously predicted open reading frame, and two are already annotated as transcribed pseudogenes. None of the five remaining TPgs are inserted in the 5'-UTR of a messenger RNA. One explanation for this absence in 5'-UTRs is that a TPg would introduce upstream ORFs that interfere with translation initiation (41). The five TPgs inserted in the 3'-UTRs of mRNAs are all in the forward direction (i.e. they are all on the same DNA strand as the annotated coding sequence). An example of this is discussed below. In addition, we checked the list of TPgs 3' to annotated genes and within 3000 nt of the end of the coding sequence (Table 3), for additional examples of this ‘passenger’ phenomenon, through manual examination of cDNAs or ESTs for the 5' genes, but could find no further examples of cDNAs with polyadenylation signals to define the end of the mRNA. Such analysis is complicated by the fact that, in some cases, it may not be possible to distinguish between the original polyadenylation signal of the gene, and an inserted polyadenylation signal arising from the TPg.
Distribution on chromosomes
Analysis of the distribution of processed genes in the human and mouse genome has indicated that the X chromosome is a marked outlier, both for processed gene deposition onto the X chromosome and origination from X (13). A similar outlier preference was observed for Pg deposition onto the X chromosome (but not origination from X) (13). These phenomena may be due to selection pressures to compensate for X-chromosome inactivation during spermatogenesis, in combination with some unaccounted-for mutational biases (13,38).
To compare with this previous analysis, we examined the distribution of TPg ‘parent genes’ on each chromosome, and also the distribution of the number of TPgs per chromosome (Figure 1A and B). Figure 1A indicates the data for origination of TPgs, and Figure 1B shows the trend for deposition of TPgs onto each chromosome. In each case (origination and deposition), the X chromosome is not an outlier. This may indicate that, in general, TPg formation is deleterious, unlike processed gene and non-transcribed Pg formation, which are arguably, by comparison, beneficial and selectively neutral, respectively. Interestingly, there is some outlier behavior for TPg origination from chromosome 12. The same result is obtained, if the 67 TPgs that map to introns are removed.
Figure 1 Origination and deposition of TPgs for different chromosomes. (A) Origination of TPgs: this plot shows the number of parent genes of TPgs in a chromosome versus the chromosome size (in Mb). (B) Deposition of TPgs: this shows the number of TPgs per chromosome versus chromosome size (in Mb). Only retrotranspositions from one chromosome to another are considered in each plot. The X chromosome is ringed. Note that for each plot we have corrected for the probability of X and Y chromosome inclusion in gametes .
Search for potential orthologs in mouse
We investigated mouse/human cross-species conservation of TPgs, as an indicator of human-lineage specificity. The 233 human TPgs were compared against a set of 215 putative mouse TPgs (moTPgs) (see Methods for details). We found that 5% (11/233) have potential orthologous TPgs. Four of these are for the metabolic enzyme, glyceraldehyde-3-phosphate dehydrogenase, which is ubiquitously and highly expressed, giving this sequence the status of a notable ‘parent gene’ for TPgs (see also Table 2). If the human and mouse TPgs are not restricted to having the same closest-matching human gene homolog, 28/237 (12%) have potential orthologs.
These results suggest that a minor fraction of TPgs could be used in conserved functional roles in mammals. However, given that 40% of human Pgs are conserved in the mouse genome (8), these results imply that TPgs are significantly under-conserved between human and mouse (P < 0.001 using binomial statistics) compared with Pgs in general, and also compared with processed genes (13), which are at most 20% lineage-specific. The vast majority of TPgs are thus human lineage-specific compared with mouse; indeed, both Alus (which are primate-specific) and Pgs can be made as by-products of LINE retrotransposition (14), and have similar overall age profiles in the genome (8). These results are also evidence for a general evolutionary selection pressure to delete TPgs. This may be because they form a source of transcriptional interference for adjacent genes or homologous genes. However, one must stress that, in the future, increased cDNA coverage for both the mouse and human genomes may modify these statistics somewhat. Such a lack of saturation in current databases of expressed sequences can be demonstrated using some simple sampling analysis. Sampling of TPg-matching expressed sequences from random fractional subsets of the total expressed sequence database used in the present analysis (i.e. ESTs + Unigene consensuses + Refseq mRNAs), indicates that we are not near finding all of the TPgs in the human genome (or, at least, those discoverable through mapping of expressed sequences). (This sampling analysis is presented in Supplementary Figure 1.)
Examples of TPgs
A TPg derived from the prohibitin gene is shown in Figure 2A. A prohibitin TPg is inserted into the 3'-UTR of a Zn-finger-containing protein. Prohibitin is highly and ubiquitously expressed, and is involved in inhibition of DNA synthesis; its mRNA contains a putative functional RNA element in its own 3' UTR (45). It is beyond the scope of this present study to ascertain whether this RNA element, in this TPg, is intact, as it has not yet been characterized extensively by mutational and biophysical analysis. This TPg is one of four that derived from the prohibitin gene (Table 2).
Figure 2 Examples of TPgs. (A) This is a TPg derived from the human prohibitin gene. The prohibitin gene contains both a protein-coding region and an RNA in its 3'-UTR (45), but only the segment of the TPg corresponding to the protein-coding sequence is shown. In the center is an alignment of the TPg (in red) with prohibitin protein (in green). The graphic above it shows the position of the TPg (red segment) in the 3'-UTR of an mRNA that codes for a Zn-finger-containing protein (blue segment). (B) An example of a TPg that maps to a known globular protein domain. The TPg derives from the mRNA for the precursor sequence of mitochondrial 2-amino-3-ketobutyrate coenzyme A. The domain is from the closest-matching protein structure (from E.coli, PDB code 1fc4a). In the Molscript (54) picture, the protein chain trace color changes at the position of each disablement. The alignment of the E.coli domain sequence and the human TPg sequence is shown. The part of the sequence that maps to an EST (gi|6138420) is boxed and italicized.
The second example is derived from the precursor sequence of mitochondrial 2-amino-3-ketobutyrate coenzyme A. The crystal structure of the Escherichia coli homolog of this enzyme is known (PDB code 1fc4a). We have indicated how the ‘triple alignment’ of genomic sequence, EST and known protein-domain sequence overlap (Figure 2B). The protein chain is divided into colored segments, with each disablement defining a segment boundary. One can clearly see that the triple alignment covers two disablements in the TPg.
CONCLUSIONS
Diverse efforts to map novel elements of potential functional utility in our genome are ongoing (1–3). In the spirit of such endeavors, we have derived a rigorous procedure for annotating a specific novel type of element of potential functional utility, the TPg. Applying this method to the human genome, we discovered 166–233 TPgs, which represent 4–6% of all Pgs (the lower total arises from setting aside any examples that map to introns). One should point out that we might have missed some TPgs; e.g. those without extensive homology to a coding sequence (i.e. those consisting largely of UTR homologies), or TPgs formed from single-exon and large-exon genes, or TPgs that are transcribed in a low-level beyond detectability through EST/cDNA sequencing.
TPgs are significantly more likely in regions close to the 5' and 3' ends of genes, compared with both a random insertion model for them throughout the genome, and compared with the distribution observed in general for Pgs. Furthermore, if one assumes that these 5' and 3' regions are actually introns, the significance of the increased 5' and 3' density of TPgs improves. (This indicates that the increased 5' and 3' density is not an artifact of intron retention in cDNA/EST libraries.) This increased density provides evidence that TPgs may be expressed through co-option of genic promoter elements or through insertion into UTRs as ‘molecular passengers’. Specific detailed evidence was found for molecular passengers in the 3'-UTRs of known mRNAs; an example of this derived from the prohibitin gene was illustrated (Figure 2A). TPgs could thus also have a role as intermediates in protein-coding sequence evolution. A reasonable hypothesis that can be further investigated is that, TPgs may represent a source of evolutionary protein novelty, either as ‘molecular passengers’, or as part of alternative splicings (46), through being temporarily released from coding-sequence selection pressures (31,47–50). Use of additional sequence segments may underlie the influence of the prion on phenotypic variability in budding yeast (31,51); analogs of this phenomenon are possible in mammals.
Two examples of regulation by transcribed pseudogenes of homologous genic transcripts have been observed (20,21). Transcriptional analysis showed that the stability of the makorin1 mRNA in mouse relies upon the expression of its homologous makorin1-p1 TPg, through the action of an element at the 5' end of the makorin1-p1 sequence. However, makorin1-p1 only seems to be conserved in one line of Mus, and has not been found in the rat genome (52). In a second example, transcription of a pseudogene in Lymnea stagnalis, that is homologous to the nitric oxide synthase gene, decreases expression levels for the gene; this is thought to arise via a reverse-complement sequence found at the 5' end of the pseudogene transcript (20). Alternatively, TPgs near genes or in UTRs may also exert a controlling/interfering influence on the genes' transcription and translation, through upstream ORF formation, or the action of other undiscovered elements. Such TPgs could exert such effects through co-option as alternative splicings, as has been observed for Alus (53). Also, it is possible that some TPgs produce a short peptide that does not misfold or aggregate in the cell, but is still targeted and serves an alternative function as a truncated peptide. Certainly, TPgs represent a source of transcriptional ‘noise’, which may have implications for selection pressures on transcription levels, and the degree of variation on which such pressures can act.
Our survey provides evidence for the existence in the human genome of a small population of TPgs, which are an intermediate class of retrosequence derived from genes, since they have expression evidence (like genes), but also have evidence of lack of coding ability (like other pseudogenes). The distribution of TPgs near the 5' and 3' ends of genes indicates that TPgs can co-opt genic promoters or intrude into UTRs; furthermore, this is a robust observation that verifies our expression-data mappings. One must also point out, however, that about half of the TPgs are located away from genes in intergenic DNA (Table 3), and thus may be co-opting cryptic promoters of undesignated origin. Also, TPgs differ from other Pgs (without transcription evidence) and from processed genes in terms of their distribution per chromosome, and their projected conservation in mouse. Our analysis indicates that, unlike processed genes and other Pgs, the vast majority (95%) of TPgs are human lineage-specific. In combination, the chromosomal distribution and mouse conservation for TPgs suggests that there is some general evolutionary pressure to delete TPgs from the genome. One should point out that the cDNA coverage of both genomes is far from complete (as illustrated here, with some simple sampling analysis), so that the analysis of conservation in mouse should be regarded as tentative.
This TPg analysis has important implications for genome annotation. It is still common practice to assume that an mRNA contains one undisrupted open reading frame; however, it is clear that one should routinely check for TPgs in the manner described here. Also, this TPg annotation is useful for improved interpretation of microarray expression data (22,23). The list of TPgs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/tppg.tar (or) http://pseudogene.org.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at NAR Online.
ACKNOWLEDGEMENTS
Thanks to T. Bureau, N. Juretic and D. Hoen (McGill U.) for discussions. This work was supported in part by a Discovery Grant from the National Science and Engineering Council of Canada to P.M.H., and by National Institutes of Health grant # P50 HG02357-01 to M.G. Funding to pay the Open Access publication charges for this article was provided by McGill University.
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*To whom correspondence should be addressed. Tel: +1 514 398 6420; Fax: +1 514 398 5069; Email: paul.harrison@mcgill.ca
ABSTRACT
Pseudogenes, in the case of protein-coding genes, are gene copies that have lost the ability to code for a protein; they are typically identified through annotation of disabled, decayed or incomplete protein-coding sequences. Processed pseudogenes (Pgs) are made through mRNA retrotransposition. There is overwhelming genomic evidence for thousands of human Pgs and also dozens of human processed genes that comprise complete retrotransposed copies of other genes. Here, we survey for an intermediate entity, the transcribed processed pseudogene (TPg), which is disabled but nonetheless transcribed. TPgs may affect expression of paralogous genes, as observed in the case of the mouse makorin1-p1 TPg. To elucidate their role, we identified human TPgs by mapping expressed sequences onto Pgs and, reciprocally, extracting TPgs from known mRNAs. We consider only those Pgs that are homologous to either non-mammalian eukaryotic proteins or protein domains of known structure, and require detection of identical coding-sequence disablements in both the expressed and genomic sequences. Oligonucleotide microarray data provide further expression verification. Overall, we find 166–233 TPgs (4–6% of Pgs). Proteins/transcripts with the highest numbers of homologous TPgs generally have many homologous Pgs and are abundantly expressed. TPgs are significantly over-represented near both the 5' and 3' ends of genes; this suggests that TPgs can be formed through gene–promoter co-option, or intrusion into untranslated regions. However, roughly half of the TPgs are located away from genes in the intergenic DNA and thus may be co-opting cryptic promoters of undesignated origin. Furthermore, TPgs are unlike other Pgs and processed genes in the following ways: (i) they do not show a significant tendency to either deposit on or originate from the X chromosome; (ii) only 5% of human TPgs have potential orthologs in mouse. This latter finding indicates that the vast majority of TPgs is lineage specific. This is likely linked to well-documented extensive lineage-specific SINE/LINE activity. The list of TPgs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/bppg.tov (or) http:pseudogene.org.
INTRODUCTION
The search for novel functional elements in the human genome is imperative and ongoing (1–3). Pseudogenes (gene copies that have lost their protein-coding ability) are a form of sequence of potential functional utility (4). Substantial progress has been made in the annotation of pseudogenes (5–11). There may be twice as many pseudogenes (derived from protein-coding genes) in the human genome as protein-coding genes (6–10).
Pseudogenes (derived from protein-coding genes) are typically ‘diagnosed’ through searching for the ‘symptoms’ of a lack of protein-coding ability. These symptoms include: frame disablement (from premature stop codons and frameshifts), coding sequence decay (typically detectable through examination of non-synonymous and synonymous substitution rates) or incompleteness (either from sequence truncation or from the loss of essential signals for transcription, splicing and translation) (6–10). Processed pseudogenes (Pgs) are made through retrotransposition of mRNAs. There is ubiquitous genomic evidence for thousands of Pgs in mammals (5–10). Similarly, dozens of processed genes (i.e. genes made by retrotransposition of the complete sequence of other genes) have arisen in both the mouse and human genomes (12,13). This mass gene retrotransposition may arise, at least in part, as a by-product of long interspersed element (LINE) retrotransposition (14). Retrotransposition is clearly an active process in mammalian gene evolution (15). Here, we search for an intermediate type of retrotransposed gene sequence: the transcribed processed pseudogene (shortened as TPg), which is a Pg that is disabled but nonetheless transcribed.
Historically, there have been several isolated reports of transcribed pseudogenes, of either the duplicated or the processed form (16–21). Two recent studies have demonstrated that such transcribed pseudogenes can regulate transcription of homologous protein-coding genes. Transcription of a pseudogene in Lymnea stagnalis, that is homologous to the nitric oxide synthase gene, decreases the expression levels for the gene through formation of a RNA duplex; this is thought to arise via a reverse-complement sequence found at the 5' end of the pseudogene transcript (20). In a second example, transcription of the makorin1-p1 TPg in mouse was required for the stability of the mRNA from a homologous gene makorin1 (21). This regulation was deduced to arise from an element in the 5' areas of both the gene and the pseudogene (21).
In addition to helping to elucidate such regulatory roles, annotation of TPgs will further add to our understanding of the dynamics of gene evolution through retrotransposition (15). Also, it is crucial to annotate TPgs correctly as a part of the ongoing process of correct cDNA/expressed sequence tag (EST) mapping during genome annotation, and for more accurate interpretation of microarray expression data (22,23). Here, we have performed a data-mining expedition for human TPgs using a rigorous method that applies stringent filters to avoid data pollution. TPgs have a markedly distinct distribution in the genome when compared with other Pgs and processed genes. A key result is that TPgs are significantly likely to insert near the 5' and 3' ends of genes, implying that TPgs can be generated by co-option of promoter elements or by intrusion into untranslated regions (UTRs) as ‘molecular passengers’. Also, we find that the vast majority of TPgs are human-lineage specific compared with mouse.
Definitions and terms
An mRNA can be reverse transcribed and re-integrated into the genomic DNA, possibly as a by-product of LINE-1 retrotransposition (14). The parent gene of the mRNA need not be on the same chromosome as the retrotransposed copy. Such a retrotransposed mRNA has three possible fates in the present-day genome: (i) formation of a non-transcribed Pg, (ii) formation of a TPg or (iii) formation of a processed gene (or part of a gene).
A Pg can be defined as any disrupted, decayed or incomplete copy of a gene that has arisen through such retrotransposition. In the process of evolution, Pgs accumulate disablements (frameshifts and premature stop codons) in their apparent coding sequences. Procedures to annotate Pgs using disablement detection have been described previously (4,5,7), and serve as the basis for the present analysis.
Operationally, a TPg is defined as a Pg for which an expressed sequence is mappable across any of its coding-sequence disablements, i.e. the disablement occurs in both the expressed sequence and the genomic sequence (see Methods for details).
A processed gene is any undisrupted retrotransposed copy of a gene that also has low Ka/Ks values indicative of selection pressure on coding ability (see Methods for details).
Each of our TPgs has 1 disablement verified by alignment of the expressed sequences to genomic DNA, in a region of the TPg that maps to a known structural protein domain, or to a protein sequence that is conserved in non-mammalian eukaryotes. This three-level verification procedure (genome:transcript:protein) is termed triple alignment. Each verified disablement has an estimated probability of being the result of a sequencing error of 10–6, since the error rate for the genomic sequence build is 10–4 (24) and the error rate for cDNAs/ESTs is 10–2 (25,26). We made a subset of TPgs, termed the C set, which has further evidence of lack of coding ability. These have: (i) no continuous segment of sequence that can code for a protein domain (as defined in Methods); (ii) high Ka/Ks values (0.5).
As it is possible that a fraction of the TPgs that map to introns arise from intron retention in cDNAs or ESTs in the source expressed sequence data, we analyzed all of the data both including and excluding the 67 TPgs that map to introns (see Table 3 and below). Our results are unaffected by such potential contamination, as explained below.
Table 3 Position of TPgs, other Pgs and processed genes relative to annotated genes
METHODS
Detection of TPgs
(i) Mapping expressed sequence data onto existing Pgs annotations
Pgs were annotated previously using a method based on the detection of disabled protein homology in genomic DNA (4,5,7). We mapped >6200 of these onto human genome build 34 (from http://www.ensembl.org), through detection of 100% nucleotide sequence matches, removing overlap with coding exons. For each Pg, the genomic sequence was extracted, both with and without a 6000 nt extension added on to either end to allow for homology matching to ‘pseudo-UTR’ regions. (These sets of genomic DNA are named genPg and genPg+/–6000.) Three sources of expressed sequences (Refseq mRNAs, Unigene consensuses, and ESTs from dbEST) were downloaded from http://www.ncbi.nih.gov. They were mapped onto genPg and genPg+/–6000, using BLASTN with low-complexity masking (E-value 10–10, minimum match length 100 nt) (27,28). From the resulting significant matches, those that align with 95% identity were used to generate a second BLASTN search against genPg and genPg+/–6000, but this time without low-complexity masking, to insure correct sequence identity. Matches to both genPg and genPg+/–6000 with 99% identity over >0.998 of the length of the expressed sequence were then extracted. These expressed sequence matches were filtered to insure that they match more significantly to the Pg than to any homologous gene. The matching expressed sequences were then re-aligned to the Pg sequence using FASTY (29), to check that 1 disablement (frameshift or premature stop codon) in the Pg occurs in both the genomic sequence and expressed sequence. Each disablement verified in this way has an estimated probability of being the result of a sequencing error of 1 x 10–6; this is because the genomic sequence error rate is 1 x 10–4, and the cDNA/EST sequencing error rate is 1 x 10–2.
(ii) Extraction of Pgs that are in Refseq mRNAs
All human Refseq entries corresponding to known mRNAs (total = 20 741) were compiled from data downloaded from the NCBI website (http://www.ncbi.nih.gov). These were compared with all known, non-fragmentary human proteins in the SWISSPROT database (30), using a modification of the disabled protein homology-based procedure developed previously for Pg annotation (4,5,7,31–33). To insure that all of the candidate TPgs in Refseq mRNAs map to a single continuous piece of genomic DNA, we extracted the appropriate mRNA subsequences and mapped them to the human genome using BLASTN. Those segments that matched over their complete length exactly were retained. The resulting TPg data were then filtered along with those generated in (i), as detailed in (iii) below.
(iii) Filtering the (transcribed) Pg data
We applied a set of filters to insure that we were compiling a bona fide list of TPgs. All TPg data sets were filtered as follows:
Removal of homologies to purely hypothetical proteins or fragmentary proteins: TPgs based only on homology to predicted reading frames or reading-frame fragments were removed through BLASTP comparisons (E-value 10–4) against a library of hypothetical or fragmentary proteins from SWISSPROT (30). These are removed because their disablements may be erroneous (which is inappropriate for the method employed here). Also, they may be inaccurately dated (values for Ks, Ka, etc., may be incorrect).
Verification that the disablements are in conserved parts of a known protein sequence or domain: We verified that the disablements examined are in known conserved parts of sequences, as detailed below. This list of filters has an ‘if-else-if-else-if’ structure:
First, we assigned protein structural domains to the TPgs, by comparing them with the ASTRALSCOP 95% identity set of protein domains (34), using BLASTP (27) (E-value 10–4). The total assigned TPg subsequence was determined (from the most N-terminal residue that was assigned to a domain, to the most C-terminal). This assigned subsequence was considered disabled, if a frameshift or stop codon occurred >10 residues in from either terminus. This accounts for 54% of TPgs.
Otherwise, secondly, TPgs not meeting criterion (1) were checked manually for occurrence of disablements in conserved domains using the InterPro (http://www.ebi.ac.uk/interpro) and CDD (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) domain annotation tools.
Otherwise, thirdly, TPgs not meeting criterion (2) (<10% of the sequences) were checked for disablement within a part of the sequence that is conserved in other mammals, and in 1 non-mammalian species, using BLASTP (E-value 10–4).
Removal of candidates with small introns: Putative Pgs, TPgs, and the expressed sequences that match them, were filtered for small intron sequences. A library of introns of <1000 nt was made from genes on human genome build 34. TBLASTN (27) was used to annotate any significant matches to these introns of >0.80 of their length (E-value 10–4). Any such matches could either be from introns in an aberrant cDNA or be a previously disregarded small intron in the genome. Some additional examples of TPgs that map to introns may arise from intron retention in cDNAs or ESTs; however, the main points of the analysis reported in this paper are unaffected by such potential contamination, as explained below.
Removal of possible duplications of single-exon genes and large exons: We wished to insure that there were no single-exon gene duplications in our Pg and TPg sets. To do this, all Pgs and TPgs were compared using BLASTP (E-value 10–4), to the set of proteins for build 34 (removing those whose genes overlap putative Pgs) (27). All Pgs and TPgs that had closest-matching homologies to single-exon proteins were removed. Furthermore, we insured that they aligned to their closest-matching homologous human proteins around at least one ‘exon seam’, i.e. a position in a protein sequence that corresponds to an intron–exon boundary. This exon seam filter insures that the pseudogenes considered are processed, and is particularly useful for removing homologies to genes with large exons (e.g. some zinc-finger-containing proteins).
Filtering for processed genes: All TPgs were filtered for overlap with annotated processed genes (resulting in the removal of only one putative TPg).
After applying these rigorous filters, we had 3418 Pgs (both transcribed and non-transcribed), and 233 TPgs, 218 from mapping expressed sequences to Pgs and 15 from Pg extraction from Refseq mRNAs. Almost half (97/233, 42%) of the TPg set represent 100% exact matches of expressed sequences to Pgs. Restricting analysis to just these matches does not affect any of the main trends and results reported here.
Making an obviously decayed C set of TPg sequences
We derived a ‘core set’ of TPgs that have further evidence of coding-sequence decay. These are dubbed the C set (totaling 177/233, 76%). This set is the union of the following two subsets: (i) TPgs without continuous segment of sequence that can code for a protein domain (106/233 TPgs, 45%) or (ii) TPgs with high Ka/Ks values (>0.50) indicative of lack of coding ability (127/233 TPgs, 54%).
(i) Lack of protein domain coding ability
We parsed each TPg into subsequences according to the positions of its disablements. If all subsequences could be labeled as ‘unlikely to code for a protein domain’, then the TPg was included in the C set. This resulted in inclusion of 106 TPgs in the C set. We labeled a subsequence as ‘unlikely to code for a protein domain’ if:
Its length was 32 residues. The vast majority (95%) of non-cysteine-rich protein domains in the ASTRALSCOP 40% identity set have sequence lengths >32 residues (34). Cysteine-rich domains (which are likely disulfide-bridged or metal-chelating) are defined as having cysteine concentration <0.077/residue, a value suggested by a bimodality in cysteine concentration, in surveys of cysteine and cystine occurrence in proteins (35,36). Condition (a) was not applied to any fragments that were adjudged cysteine-rich.
It contained a disrupted SCOP domain, as defined in part (iii)(b)(1) above. Such fragments are likely not to constitute a large enough fragment; the reasoning behind this criterion is that evolution has defined and refined the integrity of a body of recurrent folding units (protein domains) (34), and we can therefore use their disruption to evaluate whether a piece of sequence is no longer protein-coding.
(ii) Ka/Ks analysis
We calculated the Ka/Ks values for whole TPgs, using the Yang and Nielsen method in PAML (37), using the present-day gene sequence to compare against the pseudogene, as described previously (7). Also, similarly, we calculated Ka/Ks values for subsequences of TPgs (50 residues) derived by parsing at disablement positions. This parsing allows for the possibility that some of the pseudogene subsequences have coding ability, while others do not, i.e. we can test for a coding ability ‘imbalance’. From these Ka/Ks calculations, we found that only 4% of both Pgs and TPgs have two adjacent regions where one is <0.25 (potentially coding) and the other >0.5 (potentially non-coding), indicating that such imbalance is rare. From consulting independent analysis of populations of human genes and Pgs (6), we ascertained that for a threshold value of Ka/Ks 0.5, >95% of sequences are predicted to be Pgs and not genes. We use this as the expectation for the distribution of Pgs in general. Calculation of Ka/Ks values for gene/pseudogene pairs errs on the side of under-estimation of coding-sequence decay (7).
Conservation of TPg in mouse
For each human TPg, we searched against potentially orthologous mouse TPgs. These ‘moTPgs’ were derived by mapping expressed sequences (Refseq mRNAs, Unigene consensus sequences and ESTs) for mouse onto a previously derived set of mouse Pgs (8), in a similar manner to the human mappings (see above). These were pooled with any existing moTPg annotations, and a small number of mouse genes that might be potentially misannotated moTPgs. A potentially orthologous moTPg was required to match 0.5 of the length of the human TPg (for BLASTP matches, E-value 10–4), and to share the same closest-matching human protein with any potential human TPg homologs. We did not require that the retrotranspositions be in syntenic positions, since orthologous gene retrotranspositions are not necessarily syntenic (38).
Processed genes
We mapped an independently derived list of processed genes (13) to human genome build 34. In addition to the criteria in (13), we required Ka/Ks values <0.25, and coverage of 0.95 of the parent gene's length. Any examples that overlap the TPg data set of annotations were removed; vice versa, any TPgs that have Ka/Ks < 0.25 and cover 0.95 of their parent gene were deleted from the TPgs list. Our definitions give two distinct sets of processed genes and TPgs; naturally, we miss some sequences that cannot be classified as either a TPg or a processed gene.
RESULTS AND DISCUSSION
Number of TPgs
In total, we found 233 human TPgs (Table 1). These TPgs form a subset of 3418 previous Pg annotations that were mapped to build 34 of the human genome (7). These Pgs were filtered in the same way as the TPgs (from a starting total of 6200), to remove predicted reading frames, retained introns and potential duplications of single-exon genes or large exons. Using these data, we can estimate that 6% (218/3418) of Pgs are TPgs. An additional 15 TPgs were derived from a reciprocal process of searching for Pgs in known Refseq mRNAs, followed by subsequent mapping to the genome.
Table 1 Summary of numbers of TPgs
A small fraction of the TPgs (8%) corresponds to known Refseq mRNAs (Table 1). About a third are supported by Unigene consensus sequences, with a large fraction (71%) matching individual ESTs . We sought additional expression verification from a series of high-density oligonucleotide microarrays, composed of 52 million 36mers (23). These microarrays were applied to probe the transcriptionally active regions of the human genome, in a strand-sensitive way. Using the same data and statistical method (i.e. a sign test) for scoring the genes' transcriptional activity (22,23), we found that 75/233 (32%) TPgs were transcriptionally active in liver (P < 0.05) (Table 1). In comparison, 64% of genes from RefSeq mRNAs, 57% of Ensembl annotated genes (39), and 35% of genes predicted with the program GENSCAN (40), were found to be transcribed in liver.
The C set of more obviously decayed TPgs comprises 76% of the total population; 45% of TPgs having no continuous segment likely to code for a protein domain, and 54% of TPgs having Ka/Ks 0.5 (Table 1). Obvious degradation of the coding sequences is demonstrated for this set from analysis of protein-domain mapping and Ka/Ks (see Methods). Additionally, other factors (not examined in the present analysis) are expected to cause lack of coding ability in TPgs or arise as further consequences. It is likely that TPgs will not have appropriate start codon context (41), therefore leading to little or no efficient translation initiation. Also, those TPgs that are inserted into 3'-UTRs of mRNAs will be unlikely to become protein-coding through being downstream of a clearly defined coding sequence (although it is conceivable that they may be translatable in the 5'-UTR). Furthermore, a consequence of any frameshift in a sequence is the likelihood of an additional 20 residues or so of non-coding DNA, added onto the end of the sequence truncation (on average, in randomly picked, conceptually translated intergenic DNA, a stop codon will appear 20 residues downstream of any starting point); such additional sequence may lead to aggregation or misfolding in the cell.
The proportions of TPgs break down in a similar fashion to that just described above for the total data set, when the 67 examples that map to introns are removed (Table 1).
Closest matching human proteins for TPgs
TPgs were grouped according to their closest-matching human protein (Table 2). Each table entry represents a single ‘parent gene’. The total counts are also shown for the TPgs that do not map to introns (in square brackets, Table 2). There are 4 human proteins that have 4 homologous TPgs. The highest number of TPgs (5) occur for cyclophilin A, which is required for cis-peptide isomerization (42). All of these proteins arise from highly expressed mRNAs. They also occur in the top 20 proteins when apportioning all Pgs, in the same way (7).
Table 2 Human proteins with four or more homologous TPgs
TPg position relative to genes and the implications for their expression mechanisms
A number of mechanisms for TPg expression are plausible. First, TPgs may co-opt nearby promoter elements of protein-coding genes. Secondly, they may intrude into the UTRs of another mRNA, as a sort of ‘molecular passenger’. Thirdly, they may make use of cryptic promoter elements in the intergenic DNA; such promoter elements may have originated from transposable elements, or from genomic duplication of genic promoter regions, or sporadically (de novo).
Such mechanisms for TPg expression may have a bearing on their overall positional distribution in the genome relative to genes. To investigate this, we classified the TPgs into those that: (i) overlap existing coding-sequence exons; (ii) appear inserted in introns; (iii) are inserted in a 3000 or 10 000 nt region 5' to annotated genes; (iv) are inserted in a 3000 or 10 000 nt region 3' to annotated genes.
Table 3 summarizes these data. A minor proportion (8%) of TPgs entail gene coding-sequence annotations, i.e. they are erroneously annotated reading frames. There are 67 TPgs that map to introns (Table 3); it is unclear how many of these may arise from intron retention in cDNAs or ESTs. Expectations based on random insertion in the genome were calculated for classes (ii) to (iv). We focus on (iii) and (iv) in particular.
TPgs are significantly more likely than random (P < 0.01, chi-squared tests) to be inserted in the regions 5' and 3' of annotated genes; this effect is most obvious in the 3000 nt regions 5' and 3' to genes, but is still significant up to 10 000 nt in either direction (Table 3). Similar results are observed for the C set of more obviously decayed TPgs. The enrichment of TPgs observed in the 5' and 3' areas of genes can be seen as a simple logical consequence of randomly inserted Pgs having an increased probability of being transcribed, and is clear support for either co-option of genic promoter elements, or insertion into UTRs as molecular passengers, leading to TPg expression. This result is also unaffected by possible contamination from intron retention in cDNAs/ESTs, as, in general, Pgs are significantly under-represented in introns (Table 3); if one assumed that, in the extreme, all of the TPg mappings near the 5' and 3' ends of genes were actually mappings to introns, then this would make their over-representation even more significant. The general dearth of Pgs in introns may be a reflection of an overall genomic tendency for a lack of retroelement insertion in introns (43).
Roughly half of the TPgs are located away from genes (>10 000 nt 5' and 3' to genes, and overlapping neither an exon nor an intron; Table 3). These thus may be co-opting cryptic promoters of unknown origin in the intergenic DNA, such as those derivable from transposable elements.
In summary, the distribution of TPgs in the vicinity of genes is significantly different from that observable for other non-transcribed Pgs (that have no transcription evidence), and for processed genes in the following ways (Table 3):
TPgs are significantly over-represented in the 10 000 nt 5' and 3' to genes, whereas other Pgs and processed genes are not;
Other Pgs are significantly over-represented in intergenic DNA and significantly under-represented in introns, and processed genes are significantly under-represented in introns; TPgs now show such trends for introns or intergenic DNA. In addition, there is a dearth of Pgs 3' to genes (Table 3). The reasons for this are unclear; there may be a compositional effect, similar to the relationship between genomic G+C content and ribosomal-protein Pgs insertion, observed previously (44).
We examined the distribution of 20 TPgs that are directly mappable onto known Refseq mRNAs. Thirteen of these overlap an erroneously predicted open reading frame, and two are already annotated as transcribed pseudogenes. None of the five remaining TPgs are inserted in the 5'-UTR of a messenger RNA. One explanation for this absence in 5'-UTRs is that a TPg would introduce upstream ORFs that interfere with translation initiation (41). The five TPgs inserted in the 3'-UTRs of mRNAs are all in the forward direction (i.e. they are all on the same DNA strand as the annotated coding sequence). An example of this is discussed below. In addition, we checked the list of TPgs 3' to annotated genes and within 3000 nt of the end of the coding sequence (Table 3), for additional examples of this ‘passenger’ phenomenon, through manual examination of cDNAs or ESTs for the 5' genes, but could find no further examples of cDNAs with polyadenylation signals to define the end of the mRNA. Such analysis is complicated by the fact that, in some cases, it may not be possible to distinguish between the original polyadenylation signal of the gene, and an inserted polyadenylation signal arising from the TPg.
Distribution on chromosomes
Analysis of the distribution of processed genes in the human and mouse genome has indicated that the X chromosome is a marked outlier, both for processed gene deposition onto the X chromosome and origination from X (13). A similar outlier preference was observed for Pg deposition onto the X chromosome (but not origination from X) (13). These phenomena may be due to selection pressures to compensate for X-chromosome inactivation during spermatogenesis, in combination with some unaccounted-for mutational biases (13,38).
To compare with this previous analysis, we examined the distribution of TPg ‘parent genes’ on each chromosome, and also the distribution of the number of TPgs per chromosome (Figure 1A and B). Figure 1A indicates the data for origination of TPgs, and Figure 1B shows the trend for deposition of TPgs onto each chromosome. In each case (origination and deposition), the X chromosome is not an outlier. This may indicate that, in general, TPg formation is deleterious, unlike processed gene and non-transcribed Pg formation, which are arguably, by comparison, beneficial and selectively neutral, respectively. Interestingly, there is some outlier behavior for TPg origination from chromosome 12. The same result is obtained, if the 67 TPgs that map to introns are removed.
Figure 1 Origination and deposition of TPgs for different chromosomes. (A) Origination of TPgs: this plot shows the number of parent genes of TPgs in a chromosome versus the chromosome size (in Mb). (B) Deposition of TPgs: this shows the number of TPgs per chromosome versus chromosome size (in Mb). Only retrotranspositions from one chromosome to another are considered in each plot. The X chromosome is ringed. Note that for each plot we have corrected for the probability of X and Y chromosome inclusion in gametes .
Search for potential orthologs in mouse
We investigated mouse/human cross-species conservation of TPgs, as an indicator of human-lineage specificity. The 233 human TPgs were compared against a set of 215 putative mouse TPgs (moTPgs) (see Methods for details). We found that 5% (11/233) have potential orthologous TPgs. Four of these are for the metabolic enzyme, glyceraldehyde-3-phosphate dehydrogenase, which is ubiquitously and highly expressed, giving this sequence the status of a notable ‘parent gene’ for TPgs (see also Table 2). If the human and mouse TPgs are not restricted to having the same closest-matching human gene homolog, 28/237 (12%) have potential orthologs.
These results suggest that a minor fraction of TPgs could be used in conserved functional roles in mammals. However, given that 40% of human Pgs are conserved in the mouse genome (8), these results imply that TPgs are significantly under-conserved between human and mouse (P < 0.001 using binomial statistics) compared with Pgs in general, and also compared with processed genes (13), which are at most 20% lineage-specific. The vast majority of TPgs are thus human lineage-specific compared with mouse; indeed, both Alus (which are primate-specific) and Pgs can be made as by-products of LINE retrotransposition (14), and have similar overall age profiles in the genome (8). These results are also evidence for a general evolutionary selection pressure to delete TPgs. This may be because they form a source of transcriptional interference for adjacent genes or homologous genes. However, one must stress that, in the future, increased cDNA coverage for both the mouse and human genomes may modify these statistics somewhat. Such a lack of saturation in current databases of expressed sequences can be demonstrated using some simple sampling analysis. Sampling of TPg-matching expressed sequences from random fractional subsets of the total expressed sequence database used in the present analysis (i.e. ESTs + Unigene consensuses + Refseq mRNAs), indicates that we are not near finding all of the TPgs in the human genome (or, at least, those discoverable through mapping of expressed sequences). (This sampling analysis is presented in Supplementary Figure 1.)
Examples of TPgs
A TPg derived from the prohibitin gene is shown in Figure 2A. A prohibitin TPg is inserted into the 3'-UTR of a Zn-finger-containing protein. Prohibitin is highly and ubiquitously expressed, and is involved in inhibition of DNA synthesis; its mRNA contains a putative functional RNA element in its own 3' UTR (45). It is beyond the scope of this present study to ascertain whether this RNA element, in this TPg, is intact, as it has not yet been characterized extensively by mutational and biophysical analysis. This TPg is one of four that derived from the prohibitin gene (Table 2).
Figure 2 Examples of TPgs. (A) This is a TPg derived from the human prohibitin gene. The prohibitin gene contains both a protein-coding region and an RNA in its 3'-UTR (45), but only the segment of the TPg corresponding to the protein-coding sequence is shown. In the center is an alignment of the TPg (in red) with prohibitin protein (in green). The graphic above it shows the position of the TPg (red segment) in the 3'-UTR of an mRNA that codes for a Zn-finger-containing protein (blue segment). (B) An example of a TPg that maps to a known globular protein domain. The TPg derives from the mRNA for the precursor sequence of mitochondrial 2-amino-3-ketobutyrate coenzyme A. The domain is from the closest-matching protein structure (from E.coli, PDB code 1fc4a). In the Molscript (54) picture, the protein chain trace color changes at the position of each disablement. The alignment of the E.coli domain sequence and the human TPg sequence is shown. The part of the sequence that maps to an EST (gi|6138420) is boxed and italicized.
The second example is derived from the precursor sequence of mitochondrial 2-amino-3-ketobutyrate coenzyme A. The crystal structure of the Escherichia coli homolog of this enzyme is known (PDB code 1fc4a). We have indicated how the ‘triple alignment’ of genomic sequence, EST and known protein-domain sequence overlap (Figure 2B). The protein chain is divided into colored segments, with each disablement defining a segment boundary. One can clearly see that the triple alignment covers two disablements in the TPg.
CONCLUSIONS
Diverse efforts to map novel elements of potential functional utility in our genome are ongoing (1–3). In the spirit of such endeavors, we have derived a rigorous procedure for annotating a specific novel type of element of potential functional utility, the TPg. Applying this method to the human genome, we discovered 166–233 TPgs, which represent 4–6% of all Pgs (the lower total arises from setting aside any examples that map to introns). One should point out that we might have missed some TPgs; e.g. those without extensive homology to a coding sequence (i.e. those consisting largely of UTR homologies), or TPgs formed from single-exon and large-exon genes, or TPgs that are transcribed in a low-level beyond detectability through EST/cDNA sequencing.
TPgs are significantly more likely in regions close to the 5' and 3' ends of genes, compared with both a random insertion model for them throughout the genome, and compared with the distribution observed in general for Pgs. Furthermore, if one assumes that these 5' and 3' regions are actually introns, the significance of the increased 5' and 3' density of TPgs improves. (This indicates that the increased 5' and 3' density is not an artifact of intron retention in cDNA/EST libraries.) This increased density provides evidence that TPgs may be expressed through co-option of genic promoter elements or through insertion into UTRs as ‘molecular passengers’. Specific detailed evidence was found for molecular passengers in the 3'-UTRs of known mRNAs; an example of this derived from the prohibitin gene was illustrated (Figure 2A). TPgs could thus also have a role as intermediates in protein-coding sequence evolution. A reasonable hypothesis that can be further investigated is that, TPgs may represent a source of evolutionary protein novelty, either as ‘molecular passengers’, or as part of alternative splicings (46), through being temporarily released from coding-sequence selection pressures (31,47–50). Use of additional sequence segments may underlie the influence of the prion on phenotypic variability in budding yeast (31,51); analogs of this phenomenon are possible in mammals.
Two examples of regulation by transcribed pseudogenes of homologous genic transcripts have been observed (20,21). Transcriptional analysis showed that the stability of the makorin1 mRNA in mouse relies upon the expression of its homologous makorin1-p1 TPg, through the action of an element at the 5' end of the makorin1-p1 sequence. However, makorin1-p1 only seems to be conserved in one line of Mus, and has not been found in the rat genome (52). In a second example, transcription of a pseudogene in Lymnea stagnalis, that is homologous to the nitric oxide synthase gene, decreases expression levels for the gene; this is thought to arise via a reverse-complement sequence found at the 5' end of the pseudogene transcript (20). Alternatively, TPgs near genes or in UTRs may also exert a controlling/interfering influence on the genes' transcription and translation, through upstream ORF formation, or the action of other undiscovered elements. Such TPgs could exert such effects through co-option as alternative splicings, as has been observed for Alus (53). Also, it is possible that some TPgs produce a short peptide that does not misfold or aggregate in the cell, but is still targeted and serves an alternative function as a truncated peptide. Certainly, TPgs represent a source of transcriptional ‘noise’, which may have implications for selection pressures on transcription levels, and the degree of variation on which such pressures can act.
Our survey provides evidence for the existence in the human genome of a small population of TPgs, which are an intermediate class of retrosequence derived from genes, since they have expression evidence (like genes), but also have evidence of lack of coding ability (like other pseudogenes). The distribution of TPgs near the 5' and 3' ends of genes indicates that TPgs can co-opt genic promoters or intrude into UTRs; furthermore, this is a robust observation that verifies our expression-data mappings. One must also point out, however, that about half of the TPgs are located away from genes in intergenic DNA (Table 3), and thus may be co-opting cryptic promoters of undesignated origin. Also, TPgs differ from other Pgs (without transcription evidence) and from processed genes in terms of their distribution per chromosome, and their projected conservation in mouse. Our analysis indicates that, unlike processed genes and other Pgs, the vast majority (95%) of TPgs are human lineage-specific. In combination, the chromosomal distribution and mouse conservation for TPgs suggests that there is some general evolutionary pressure to delete TPgs from the genome. One should point out that the cDNA coverage of both genomes is far from complete (as illustrated here, with some simple sampling analysis), so that the analysis of conservation in mouse should be regarded as tentative.
This TPg analysis has important implications for genome annotation. It is still common practice to assume that an mRNA contains one undisrupted open reading frame; however, it is clear that one should routinely check for TPgs in the manner described here. Also, this TPg annotation is useful for improved interpretation of microarray expression data (22,23). The list of TPgs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/tppg.tar (or) http://pseudogene.org.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at NAR Online.
ACKNOWLEDGEMENTS
Thanks to T. Bureau, N. Juretic and D. Hoen (McGill U.) for discussions. This work was supported in part by a Discovery Grant from the National Science and Engineering Council of Canada to P.M.H., and by National Institutes of Health grant # P50 HG02357-01 to M.G. Funding to pay the Open Access publication charges for this article was provided by McGill University.
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