R E S E A R C H A R T I C L E
Open Access
Exploring the selective constraint on the sizes of
insertions and deletions in 5
’ untranslated
regions in mammals
Chun-Hsi Chen
1, Ben-Yang Liao
1*and Feng-Chi Chen
1,2,3*Abstract
Background: Small insertions and deletions ("indels” with size ≦ 100 bp) whose lengths are not multiples of three (non-3n) are strongly constrained and depleted in protein-coding sequences. Such a constraint has never been reported in noncoding genomic regions. In 5’untranslated regions (5’UTRs) in mammalian genomes, upstream start codons (uAUGs) and upstream open reading frames (uORFs) can regulate protein translation. The presence of non-3n indels in uORFs can potentially disrupt the functions of these regulatory elements. We thus hypothesize that natural selection disfavors non-3n indels in 5’UTRs when these regulatory elements are present.
Results: We design the Indel Selection Index to measure the selective constraint on non-3n indels in 5’UTRs. The index controls for the genomic compositions of the analyzed 5’UTRs and measures the probability of non-3n indel depletion downstream of uAUGs. By comparing the experimentally supported transcripts of human-mouse orthologous genes, we demonstrate that non-3n indels downstream of two types of uAUGs (alternative translation initiation sites and the uAUGs of coding sequence-overlapping uORFs) are underrepresented. The results hold well regardless of differences in alignment tool, gene structures between human and mouse, or the criteria in selecting alternatively spliced isoforms used for the analysis.
Conclusions: To our knowledge, this is the first study to demonstrate selective constraints on non-3n indels in 5’UTRs. Such constraints may be associated with the regulatory functions of uAUGs/uORFs in translational regulation or the generation of protein isoforms. Our study thus brings a new perspective to the evolution of 5’UTRs in mammals.
Background
Insertion and deletion mutations (indels) frequently occur during the evolution of mammalian genomes [1-4]. Most of these indels are selectively neutral when they occur in noncoding genomic regions [5,6]. Mean-while, indels indivisible by three (designated as“non-3n” indels, in contrast to “3n indels”, indels divisible by three) were found to be underrepresented in coding sequences (CDS) [4,7]. This is because non-3n indels lead to frameshift in CDS (which frequently results in pseudogenization), while 3n indels do not. Therefore, non-3n indels in CDS have larger chances of being
removed by natural selection. If reading frame preserva-tion is the only evolupreserva-tionary constraint on indel size, 3n indels and non-3n indels should have the same probabil-ity of being retained in noncoding regions. Indeed, a for-mer study only found an overrepresentation of 3n indels in CDS, but not in noncoding sequences in the human genome [4].
Among noncoding sequences, 5’-untranslated regions (5’UTRs) are of particular interest because of their tran-sient presence between transcriptome and proteome, and their regulatory effects on both transcription and translation [8-10]. The primary translational regulatory elements in 5’UTRs include translation initiation motifs [11], upstream start codons (uAUGs), and upstream open reading frames (uORFs) [12,13]. uORFs can be classified into three major categories [14]: (i) strictly upstream ORFs ("strict uORFs”) -uORFs entirely located
* Correspondence: liaoby@nhri.org.tw; fcchen@nhri.org.tw
1Division of Biostatistics and Bioinformatics, Institute of Population Health
Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan
Full list of author information is available at the end of the article
© 2011 Chen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
within 5’UTR; (ii) CDS-overlapping uORFs ("overlapping uORFs”) - uORFs with their start codons located in 5’UTR and their stop codons located within the CDS; and (iii) alternatively translated uORFs ("alternative uORFs”) -uORFs that start with an alternative initiation site of translation ("AIS”, a potential translation initia-tion site located in 5’UTR), and share the same reading frame and the same stop codon with the main CDS (Figure 1A). Note that the protein products that include alternative uORFs are currently unidentified. Therefore, for extensively studied species such as human and mouse, AISs are less likely to be misannotated canonical translation start sites. The scanning model for transla-tion [15] posits that a translatransla-tion process begins with the binding of 40S ribosomal complexes onto the 5’-cap structure of an mRNA. The ribosomal complexes then slide from 5’-cap to 3’ end base by base until they encounter the first AUG triplet of the mRNA and turn on the translation process. According to this model, dif-ferent types of uORF have distinct effects on the transla-tion of the main CDS: (1) strict uORFs compete for ribosomes, leading to reduced protein production of the main CDS [16]; (2) overlapping uORFs cause translation re-initiation downstream of the main start codon, result-ing in N-truncation of the peptide coded by the main CDS [10,17]; they may also cause strong translational inhibition by facilitating ribosome skipping of the main start codon [10,18,19]; and (3) alternative uORFs result in the generation of N-extended peptides [17,20,21]. Notably, non-3n indels downstream of uAUGs can lead to interchanges between uORF types (Figure 1B), while 3n indels usually can not. Such interchanges may result in either changes of protein abundance (quantitative changes) or gain/loss of protein isoforms (qualitative changes) (Figure 1B). Considering that the level of pro-tein translation is tightly constrained during evolution [22], most changes between uORF types are supposedly deleterious. Therefore, we hypothesize that in 5’UTRs, non-3n indels that occur downstream of uAUGs are subject to purifying selection.
To test our hypothesis, we develop the Indel Selection Index (ISI, see Methods) to examine whether the occur-rence of non-3n indels between the first uAUGs and the translation initiation site ("TIS”, also known as the main start codon) of the main CDS are selectively disfavored during the evolution of mammalian genes after primate-rodent divergence. Since non-3n indels may affect pro-tein expression more seriously when they occur down-stream of the uAUGs of alternative uORF and overlapping uORFs (Figure 1B), purifying selection on non-3n indels is expected to be particularly stringent in these regions, while relatively relaxed in the case of strict uORFs. Our results clearly support this hypothesis. This study thus offers a new perspective to the evolution
of 5’UTRs, in that the sizes of indels can be subject to selective constraint in these genomic regions conditional on the presence of certain regulatory elements.
Results
We compared human and mouse orthologous genes to examine whether non-3n indels are selectively con-strained downstream of uAUGs. While the range of 5’UTRs varies in alternatively spliced mRNA isoforms, we used three different criteria to select one transcript for each gene for this analysis (see Methods): (1) a ran-domly selected transcript, (2) the transcript with the longest 5’UTR, and (3) the transcript with a “pure” 5’UTR (i.e. a 5’UTR that does not overlap with the CDS of any other splicing isoforms). These three different selection criteria have distinct biological implications. The first dataset assumes that for each gene, all of the isoforms are equally important to the organism. The assumption may not be true, but random sampling may fairly rule out potential sampling biases. The second dataset contains the largest number of uAUGs. How-ever, some uAUGs in these 5’UTRs may overlap with the CDS of other isoforms of the same gene. In contrast, although the third dataset enables us to explore the selection pressure that works exclusively on 5’UTRs, only a small proportion of transcripts with biased prop-erties can be included in the analysis. These transcripts tend to be the products of the genes with short 5’UTR, genes that are lowly expressed, or genes not alternatively spliced, although > 90% of human genes have multiple isoforms [23]. Given the distinct properties of selected transcripts in the three datasets, we reason that if our hypothesis is correct, all three datasets will yield consis-tent results.
According to our criteria, more than 45% human genes and 41% mouse genes have at least one uORF (Table 1). These percentages are similar to those reported in previous studies [16,24]. Next, we divided the transcripts of each dataset into four subgroups for comparisons: (1) transcripts without uAUGs (G0); (2) transcripts with only alternative uORF(s) (Ga); (3) tran-scripts with only strict uORF(s) (Gs; their uAUGs are designated as“SuAUGs”); (4) transcripts with only over-lapping uORF(s) (Gv; their uAUGs are designated as “VuAUGs”). Transcripts with multiple types of uORFs were excluded from this study for simplicity.
To evaluate the evolutionary constraints on indel lengths in different parts of a 5’UTR, we developed the Indel Selection Index ("ISI"; see Methods). In brief, ISI measures the probability of observing a higher frequency of non-3n indels downstream of a uAUG (or a reference point), as compared to the 5’UTR region upstream of the uAUG (or reference point). In other words, a small ISI indicates a depletion of non-3n indels downstream
1 3 5’-cap AIS TIS 2 overlapping uORF alternative uORF strict uORF without uAUGs (A) (B)
Figure 1 Classification of upstream open reading frames (uORFs) and potential effects of non-3n indels when they occur between uAUGs and TISs. (A) The open circle represents the 5’-cap structure of the transcript. The solid- and dashed-line open boxes represent the main coding sequences and the uORFs, respectively. The open and solid inverted triangles, respectively, indicate the locations of the uAUGs and the translation initiation sites (TIS). The open and solid triangles indicate locations of the stop codons of the uORFs and the main coding sequences. (B) Symbols“○”, “×”, and “?” represent that the protein isoforms or protein expression “is affected”, “is not affected”, and “uncertain”, respectively.
of a certain 5’UTR position. The use of ISI thus controls for the properties potentially specific to 5’UTRs. We first analyzed the ISI distribution of the G0 transcripts, where the ratios of non-3n to 3n indels are expected to be approximately equal between the “upstream” and “downstream” regions to a given position of the 5’UTRs. We assigned a reference point to each of the transcript and shifted the point from 10% to 90% of the 5’UTR lengths (with intervals of 10%) from the cap to obtain the ISI values. ISI values of the G0transcripts vary with the reference point position (Additional file 1). For both human and mouse, the median ISI values remain approximately equal when the reference point is located at 30~70% of the 5’UTR lengths from the 5’ cap, but drop toward both ends of 5’UTR. Therefore, controlling the ratio of upstream/downstream length is necessary when analyzing the ISI values. We then compared the ISI values of uAUG-containing transcripts (Ga, Gs, and Gv) with those of G0 transcripts with corresponding upstream/downstream ratios. Note that the uAUG-con-taining transcripts may have multiple uAUGs of the same type. In such cases, we used the first uAUG from the cap as the reference point.
We found that Gvtranscripts consistently show signif-icantly lower ISIs than the corresponding G0transcripts in all of the six comparisons (P < 10E-8, all of the P values for the ISI value comparisons were estimated by using the Mann-Whitney U test, Figure 2). Meanwhile, the Gatranscripts have significantly lower-than-expected ISIs (P < 0.05) in five of the six comparisons. The only exception lies in mouse transcripts with pure 5’UTRs (P = 0.216), although the median ISI of the Gatranscripts is lower than that of G0 for this dataset. The lack of sta-tistical significance may have resulted from the relatively small sample sizes (only 33 indel-containing Ga tran-scripts are available for both human and mouse). In comparison, in only two of the six comparisons (ran-domly selected 5’UTRs in both human and mouse) do Gs transcripts have significantly lower ISIs than expected (P < 0.05). We noticed that the sizes of indels
might change with alignment tools [25], which could potentially affect our results. Therefore, we used Pecan [26], another alignment tool known for its accuracy, to re-align the human-mouse orthologous sequences and obtained similar results (Additional file 2).
Notably, the locations of reference points of the corre-sponding G0 transcripts actually differ significantly among Ga, Gs, and Gv. In view of the variations of ISI values with different reference point locations (Addi-tional file 1), the comparisons among the three tran-script groups appear unfair. Particularly, all of the Gs transcripts are compared against the G0 transcripts with their reference points located at ~25% from the cap, whereas the percentages for Gaand Gvfall between 54% ~69% (Figure 2). To address this issue, we divided the Gv transcripts into three equal-sized groups according to the relative positions of their uAUGs (we did not per-form the analysis for Ga because of its small sample size). As shown in Additional file 3 the first Gv sub-groups (Gv_1) were compared against G0 transcripts with reference points located at 14%~16% from the cap for human, and 24%~26% for mouse. These Gv tran-scripts have uAUGs located closer to the cap than their Gs counterparts, and they still have ISI values signifi-cantly lower than the corresponding G0 transcripts. Similar results are also observed for Gv_2 (Additional file 3). Gv_3 transcripts show a similar trend, although the differences in ISI values are statistically insignificant, possibly due to reduced sample sizes.
Apparently, non-3n indels are significantly more depleted when occurring downstream of AISs and VuAUGs than SuAUGs. However, since Ga, Gv, and Gs have different sample sizes, we are interested in comparing how the ISIs of Ga, Gv, and Gsdeviate from the expected values when controlling for the difference in sample size. We performed a bootstrap simulation (with 1,000 re-sam-plings with replacement) for each of the six comparisons by reducing the sample sizes of Gvand Gstranscripts to be the same as that of the Gatranscripts. As shown in Fig-ure 3, Ga-G0 and Gv-G0 comparisons have smaller P-Table 1 Transcripts of human-mouse orthologous genes analyzed in this study
No. of genes (%)b
Type Classa Randomly-selected 5’UTR Longest 5’UTR Pure 5’UTR
Human Mouse Human Mouse Human Mouse
Without uORF G0 3,265 (54.0%) 3,560 (58.9%) 2,701 (46.6%) 3,153 (54.4%) 3,144 (55.2%) 3,368 (59.1%)
Ga 73 (1.2%) 61 (1.0%) 99 (1.7%) 76 (1.3%) 38 (0.7%) 40 (0.7%)
Single uORF type Gs 1,558 (25.8%) 1,456 (24.1%) 1,564 (27.0%) 1,487 (25.6%) 1,638 (28.7%) 1,523 (26.7%)
Gv 401 (6.6%) 385 (6.4%) 356 (6.1%) 333 (5.7%) 380 (6.7%) 345 (6.1%)
Multiple types of uORF 749 (12.4%) 584 (9.7%) 1080 (18.6%) 751 (12.9%) 500 (8.8%) 424 (7.4%)
Total 6,046 6,046 5,800 5,800 5,700 5,700
a
G0, Ga, Gs, and Gvindicate transcripts without uAUGs, with AISs, with SuAUGs, and with VuAUGs, respectively. b
values (more significant differences) than the Gs-G0 com-parisons (P < 4.0E-12 by the Kolmogorov-Smirnov Test). Therefore, when the factor of sample size is controlled, the overall result that indels are more depleted down-stream of AISs and VuAUGs holds well. Notably, even in the Gs-G0comparison in the case of pure 5’UTRs (which gives the most conservative estimation), more than 6% of the P values are smaller than 0.05 for both human and mouse. This observation suggests that non-3n indels may
be subject to weak purifying selection pressure down-stream of SuAUGs.
Discussion
Possible reasons for non-3n indel depletion in 5’UTRs We have demonstrated that non-3n indels are subject to purifying selection in mammalian 5’UTRs conditional on the presence of uORFs. We show that both alterna-tive and overlapping uORFs contribute to decreased
Randomly selected 5’UTR
Longest 5’UTR
Pure 5’UTR
(A)
Randomly selected 5’UTR
Longest 5’UTR
Pure 5’UTR
(B)
Figure 2 Distributions of ISI values of (from left to right) (GaVS. G0), (GsVS. G0), and (GvVS. G0) for (A) human and (B) mouse. The
numbers in the parentheses following G0indicate the median distances of the uAUGs from 5’ cap in terms of percentage of 5’UTR length in the
non-G0transcripts. These proportions of length are referenced to determine which G0distributions to use in the comparisons. The P values of
pair-wise differences (calculated by using the Mann-Whitney U test) are shown at the top. The symbols“*”, “**”, and “***” represent 0.01 ≦ P < 0.05, 0.001≦ P < 0.01, and P < 0.001, respectively.
non-3n indels downstream of their uAUGs, and that strict uORFs have only minor effects in this regard. These results hold well when technical issues in tran-script isoform selection, difference in alignment tool, and differences in transcript structures between human and mouse are controlled.
The suppression of non-3n indels downstream of AISs and VuAUGs implies the functional importance of these two uAUG types. Two possible reasons may explain this observation. The first is the functional importance of uORF-associated protein products. Translation of over-lapping uORFs always generates radically different
Randomly selected 5’UTR
Longest 5’UTR
Pure 5’UTR
(A)
Randomly selected 5’UTR
Longest 5’UTR
Pure 5’UTR
(B)
Figure 3 Distributions of theP values in different ISI comparisons by controlling the sample sizes (indicated by “n”) of Gsand Gv
transcripts to be the same as those of the Gatranscripts. One thousand times of re-sampling with replacement was performed for each
transcript subgroup to derive the P value distributions. The P values were estimated by using the Mann-Whitney U test. The dashed line indicates P = 0.05 (or -log (P) = 1.301).“Fsig“ indicates the fraction of P values in the distribution that is smaller than 0.05. The statistical
significance between P value distributions of different transcript subgroups was estimated by using the Kolmogorov-Smirnov test and shown in the upper right corner of each panel.
peptides from those translated from the main CDS because of the difference in reading frame [27]. Strict uORFs can also produce functional proteins when trans-lated [28,29]. Therefore, additional constraints on non-3n indels unrelated to peptide coding may have sepa-rated strict uORFs from the other two types of uORFs. Another explanation is that overlapping uORFs can give rise to in-frame N-truncated peptides, which may have different molecular functions from the original, full-length peptides [17]. Such N-truncated peptides may change in length or simply disappear if non-3n indels occur downstream of VuAUGs. On the other hand, alternative uORFs can lead to the generation of N-extended peptides, which could have different functions from original peptides (e.g. the human regulators of G-protein signalling (RGS2)) [30]. The functional disrup-tion of such N-extended or N-truncated peptides by non-3n indels may be detrimental to the organism in general, and thus could have been removed by natural selection. A recent study provides evidence of the importance of alternative translation start sites by show-ing that start codons downstream of TISs are evolutio-narily conserved [31]. It is suggested that alternative start sites may work as“backup” translational initiation sites and thus may increase the efficiency of translation [31]. The same comment likely also applies to the AISs analyzed in this study.
The second possible explanation for the uAUG-related selection pressure on non-3n indels is the evolutionary conservation of protein abundance. As shown in Figure 1B, non-3n indels may cause interchanges between the three types of uORFs (Figure 1B). In the case of alterna-tive uORFs, downstream non-3n indels can lead to the generation of either strict or overlapping uORFs, causing strong inhibition of protein production of the main CDS in both cases. Such drastic changes in protein abun-dance are likely deleterious. By contrast, when a non-3n indel occurs downstream of an SuAUG, the affected strict uORF may either become an alternative or over-lapping uORF, or remain a strict uORF (but with a dif-ferent length) (Figure 1B). We suggest that the latter case is more likely, for changing a strict uORF to an alternative or overlapping uORF requires that the read-ing frame startread-ing from the uAUG remain open until it reaches the TIS. Furthermore, in the case of alternative uORF, the reading frame must be the same as that used by the main CDS. These requirements are difficult to fulfil considering that SuAUGs are relatively distant from the TIS (about 70~75% of the 5’UTR length). As such, non-3n indels that occur downstream of SuAUGs may not lead to changes in uORF type in most cases. Such indels may have no significant effects on changing the protein abundance of the downstream CDS, and thus may have small fitness effects. Lastly, in the case of
overlapping uORFs, the occurrence of non-3n indels has a higher possibility of changing them into alternative uORFs than in the case of strict uORFs, because VuAUGs are typically closer to the TIS (about 40% of the 5’UTR length). Overlapping uORFs can result in nearly complete inhibition of protein translation or gen-eration of N-truncated protein [10,18,19]. Furthermore, overlapping uORFs can serve important regulatory roles [10]. Therefore, non-3n indels in overlapping uORFs may be selectively disfavoured. A non-3n indel may also change an overlapping uORF to a strict uORF, or simply change the length of the original uORF (without chan-ging its type). In these cases, non-3n indels may not have significant effects in changing protein abundance, and thus may be tolerated by selection.
One unexpected observation from our results is that four of the six datasets demonstrate lower P values in the Gvtranscripts than in the Gatranscripts (Figure 3), indicating that overlapping uORFs may have contributed stronger constraints on non-3n indels in 5’UTRs than alternative uORFs. That said, the real cause of this Gv -Gadifference remains unclear.
Notably, it has been recently reported that 3’UTRs actually have a larger effect on protein abundance than 5’UTRs, which appear to account for ~1% of the varia-tions in protein abundance [32]. How can we observe any selection pressure on non-3n indels in 5’UTRs if these non-coding regions have such a“small” effect on protein abundance? There are three possible explana-tions. Firstly, as we mentioned above, the non-3n indels in 5’UTRs may affect both the abundance and the pep-tide sequence of the affected gene. The “qualitative” change may also be subject to selection pressure. Sec-ondly, even though on average 5’UTRs account for only a small proportion of the variations in protein abun-dance genome-widely, in individual genes the variations can be very large, as was demonstrated by a recent study [16]. The cases where 5’TURs have very small effects on protein abundance may actually add to the noise in our analysis. However, we have found clear sig-nals of selection pressure on non-3n indels despite these potential noises, which in fact reflects the strength of the “real” signals. Finally, the uORFs per se may be bio-logically functional, in terms of either their peptide pro-ducts or their regulatory roles. The disruption of functional uORFs is thus likely subject to selective constraint.
Limitations of the study
The current analysis contains a few limitations. Firstly, determining which transcript of a gene to analyze is controversial. This study used three different criteria for transcript selection. In the case of the transcripts with the longest 5’UTRs, one uORF may partly overlap with
CDS. In this case, the ISI value may be smaller than expected because of the strong constraint on reading frame preservation in coding sequences. In addition, the classification of the uORFs (alternative, strict, or over-lapping) in these 5’UTRs is sometimes ambiguous (see Additional file 3 for an example). Analyzing the tran-scripts with pure 5’UTRs avoids this problem. However, this practice will lead to significantly decreased numbers of uORFs and severely reduced sample sizes, which in turn may result in decreased statistic power and poten-tial sampling biases. These two criteria for transcript selection represent two extremes. The results derived using randomly selected transcripts fall in-between. Nevertheless, this study obtained consistent results across all datasets, indicating that depletion of non-3n indels is unlikely to result solely from constraints in the main coding sequences.
Secondly, since our study is based on pairwise sequence alignments, we cannot distinguish between insertions and deletions, nor can we infer the lineage specificity of the identified indels. We do not know exactly in which lineage the indels have affected the structures of the 5’UTRs (i.e. the types of uORFs). This is important because the 5’UTRs of human and mouse transcripts may have different lengths and uORFs. When a non-3n indel occurs to a lineage whose tran-script does not contain any uORFs or 5’UTR exons, this indel adds to the noise in our analysis. We cannot elimi-nate such noises without using multiple-species sequence alignments (which, however, will dramatically decrease the sample size and render the analysis infeasi-ble). To overcome this problem, this study performed analyses using the transcript structures of human and mouse separately. The results from both species turn out to be consistent with each other. Therefore, in spite of the above limitation, our results seem to have revealed a biological fact.
Conclusions
To the best of our knowledge, this is the first study to demonstrate the selective constraint on non-3n indels in 5’UTRs. This constraint may result from the require-ment to preserve either the translational regulatory ele-ments (uORFs) in 5’UTRs or the functions of the peptides whose translation is associated with uORFs. Our results thus demonstrate the impacts of indels in the evolution of 5’UTRs in mammalian genomes and re-assure the functional importance of uORFs from a dif-ferent angle.
Methods
Data collection
In this study, we analyze the human and mouse tran-scripts because their genomes have been fully sequenced
and extensively curated [33,34]. In addition, the tran-scriptomes of these two species have been well charac-terized. The annotations for 5’UTRs are thus fairly accurate for the two species. The sequences of experi-mentally verified transcripts with known protein pro-ducts of one-to-one human-mouse orthologous genes, based on the Ensembl release 54 http://www.ensembl. org, were retrieved through BioMart [35]. Non-protein-coding genes and protein-Non-protein-coding genes whose tran-scripts did not contain 5’UTRs were excluded. Poten-tially misannotated transcripts (whose locations of TISs were inconsistent with that observed in the DNA sequences) were also excluded. To avoid repetitive counts of the same indels, only one transcript was selected for each gene by three different criteria: (1) a randomly selected transcript, (2) the transcript with the longest 5’UTR, and (3) the transcript with a “pure” 5’UTR. A pure 5’UTR is one that does not overlap with any coding sequences in other splicing isoforms (See Additional file 4 for an example).
Sequence alignments and identification of indels and uORFs
Indels were identified based on the human-mouse pair-wise genomic sequence alignments downloaded from the University of California, Santa Cruz (UCSC) Gen-ome Browser http://genGen-ome.ucsc.edu/ [36]. The versions of the human and mouse genomes (hg18 and mm9, respectively) correspond to Ensembl release 54.
To ensure that human-mouse orthologous sequences were compared in our study, this work only retained the alignments that include one-to-one human-mouse orthologous genes annotated by Ensembl. In addition, to avoid mis-assignment of gaps (indels), the alignable exo-nic sequences in one species are required to overlap with > 80% of the annotated exonic sequences of the other species. The alignments must cover the entirety of the annotated 5’UTRs. Consequently, this study obtained ~6,000 human and mouse genes for subse-quent analyses (Table 1). To examine whether different alignment tools affect the overall results, the Pecan alignment program [26] was used with default para-meters to re-align the retrieved human-mouse ortholo-gous sequences.
The 5’UTRs of the retrieved transcripts were then scanned for the existence of uAUGs. Around half of the analyzed human and mouse transcripts were found to have at least one uAUG (Additional file 5). These pro-portions are similar to those observed in previous stu-dies [16,24]. Here, a uORF is defined as a putative open reading frame in 5’UTR starting with a uAUG and com-posed of at least nine nucleotides, including the stop codon. Human and mouse orthologous genes may have different 5’UTR structures and different numbers and
types of uORFs. Therefore, we performed our analyses according to the human and mouse gene annotations separately.
Measurement of selection pressure on indel lengths– the indel selection index
To evaluate the evolutionary constraints on indel lengths in different parts of a 5’UTR, we defined
R = log2 (Nn3n,d+ 0.5)/(N3n,d+ 0.5) (Nn3n,u+ 0.5)/(N3n,u+ 0.5) , (1)
where N represents the number of indels, the sub-scripts“n3n“ and “3n“ represent non-3n and 3n indels. The subscripts“d“ and “u“ indicate the 5’UTR regions downstream and upstream of the uAUGs (or reference points) of interest. Adding the pseudocount 0.5 ensures that the denominator is not zero. Using different pseu-docounts changes the magnitude, but not the sign (posi-tive or nega(posi-tive) of the R value (Additional file 6). The ratio of non-3n to 3n indels upstream of uAUGs serves as the “background” to measure the depletion (or enrichment) of non-3n indels downstream of uAUGs.
Theoretically, if selection has no preference on indel sizes across 5’UTRs, R should be equal to zero. How-ever, when non-3n indels downstream of uAUGs are suppressed by purifying selection, R should be smaller than 0. The statistical power of R is expected to increase with the number of indels. Yet in our dataset, the lengths of more than 90% of the 5’UTRs are shorter than 1 kb (and thus the numbers of indels are small). The relatively small sample sizes may lead to unex-pected biases. To address this issue, we developed the “Indel Selection Index” (ISI) to measure the probability that an R value is smaller than the random expectation. The ISI is defined as
ISI = PrRshuffled < R
, (2)
which represents the proportion of the randomized R-values (Rshuffled) that is smaller than the observed R-value. The distribution of Rshuffledwas generated by ran-domly shuffling the locations of the indels 1,000 times for each transcript, while retaining the lengths and num-bers of indels of the 5’UTR. An ISI value smaller than the neutral expectation indicates depletion of non-3n indels downstream of a uAUG. The neutral distribution of ISIs were derived from the transcripts without uAUGs (see Additional file 1), with the upstream/down-stream differentiation point moving from the cap to TIS by an increment of 1% of 5’UTR length. Note that we use ISI rather than comparing the non-3n to 3n indel ratios between 5’UTR and intergenic/intronic regions to control for the potential biological differences between 5’UTR and the other noncoding regions.
Additional material
Additional file 1: The indel selection index in 5’UTRs without uAUGs. The X-axis indicates the relative distance from the 5’-cap to the reference point in terms of percentage of the 5’UTR length. The results were derived from the genes with randomly selected transcripts. Additional file 2: Distributions of ISI values according to Pecan-aligned sequences, with reference to the gene annotations of (A) human; and (B) mouse. The numbers in the parentheses following G0
indicate the median distances of the uAUGs from 5’ cap in terms of percentage of 5’UTR length in the non-G0transcripts. These proportions
of length are referenced to determine which G0distributions to use in
the comparisons. The P values of pair-wise differences (calculated by using the Mann-Whitney U test) are shown at the top. The symbols“*”, “**”, and “***” represent 0.01 ≦ P < 0.05, 0.001 ≦ P < 0.01, and P < 0.001, respectively.
Additional file 3: The ISI distributions of Gvtranscripts with
different uAUG locations. Each GVdataset is divided into three
equal-sized subgroups according to the relative locations of uAUGs. The numbers in the parentheses following G0indicate the median distances
of the uAUGs from 5’ cap in terms of percentage of 5’UTR length in the GVtranscripts. The P values (by the Mann-Whitney U test) for the ISI
differences between GVand the corresponding G0transcripts are shown
at the top.
Additional file 4: The five alternatively spliced transcript isoforms of human gene ENSG00000119125. Only the 5’ proximal regions of the transcripts are shown here. Among the isoforms, ENST00000358399 is the only one that contains a pure 5’UTR, which includes no uAUGs at all. Note that this pure 5’UTR is also the shortest one. In comparison, ENST00000238018 contains the longest 5’UTR, which encompasses as many as 15 uAUGs. The exons are represented as blue bars (not to the scale). The black, red, green, and blue inverted triangles represent translation initiation sites, AISs ("A”), VuAUGs ("V”), and SuAUGs ("S”), respectively.
Additional file 5: The numbers and percentages of transcripts analyzed in this study.“Gam“, “Gsm“, and “Gvm“ indicate transcripts with
multiple AISs, SuAUGs, and VuAUGs, respectively. In the Gmgroup, more
than one type of uORF is found in the 5’UTRs. The subscripts “a”, “s”, and “v”, indicate the presence of AIS, SuAUG, and VuAUG, respectively. Note that only Ga, Gs, and Gvare analyzed in this study.
Additional file 6: The distributions of R values using different pseudocounts.“c” stands for the pseudocount. The black bar represents a 5’UTR, where solid and open circles indicate the locations of non-3n and 3n indels, respectively. The reference point is used to differentiate the upstream and downstream region of a 5’UTR.
Acknowledgements
The authors wish to thank Hsuan-Yu Lin for his assistance in data processing and analysis. We also thank Dr. Wen-Chang Wang and Tsung-Kai Chang for statistical advice. Intramural funding from the National Health Research Institutes, Taiwan, to BYL and FCC, supported this work.
Author details
1Division of Biostatistics and Bioinformatics, Institute of Population Health
Sciences, National Health Research Institutes, Zhunan, Miaoli County, 350 Taiwan.2Department of Life Science, National Chiao-Tung University, Hsinchu, 300 Taiwan.3Department of Dentistry, Chinese Medical University,
Taichung, 404 Taiwan. Authors’ contributions
CHC carried out all of the analyses. BYL and FCC conceived the study. CHC, BYL and FCC designed the study and drafted the manuscript. All authors have read and approved the final manuscript.
Received: 10 February 2011 Accepted: 5 July 2011 Published: 5 July 2011
References
1. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, et al: Initial sequencing and comparative analysis of the mouse genome. Nature 2002,
420(6915):520-562.
2. Consortium TCSaA: Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 2005, 437(7055):69-87. 3. Mills RE, Luttig CT, Larkins CE, Beauchamp A, Tsui C, Pittard WS, Devine SE:
An initial map of insertion and deletion (INDEL) variation in the human genome. Genome Res 2006, 16(9):1182-1190.
4. Chen FC, Chen CJ, Li WH, Chuang TJ: Human-specific insertions and deletions inferred from mammalian genome sequences. Genome Res 2007, 17(1):16-22.
5. Chen CH, Chuang TJ, Liao BY, Chen FC: Scanning for the signatures of positive selection for human-specific insertions and deletions. Genome Biol Evol 2009, 1(1).
6. Lunter G, Ponting CP, Hein J: Genome-wide identification of human functional DNA using a neutral indel model. PLoS Comput Biol 2006, 2(1): e5.
7. de la Chaux N, Messer PW, Arndt PF: DNA indels in coding regions reveal selective constraints on protein evolution in the human lineage. BMC Evol Biol 2007, 7(1):191.
8. Wilkie GS, Dickson KS, Gray NK: Regulation of mRNA translation by 5’- and 3’-UTR-binding factors. Trends Biochem Sci 2003, 28(4):182-188.
9. Sonenberg N, Hinnebusch AG: Regulation of translation initiation in eukaryotes: mechanisms and biological targets. Cell 2009, 136(4):731-745. 10. Jackson RJ, Hellen CU, Pestova TV: The mechanism of eukaryotic
translation initiation and principles of its regulation. Nat Rev Mol Cell Biol 2010, 11(2):113-127.
11. Kozak M: Effects of intercistronic length on the efficiency of reinitiation by eucaryotic ribosomes. Mol Cell Biol 1987, 7(10):3438-3445.
12. van der Velden AW, Thomas AA: The role of the 5’ untranslated region of an mRNA in translation regulation during development. Int J Biochem Cell Biol 1999, 31(1):87-106.
13. Mignone F, Gissi C, Liuni S, Pesole G: Untranslated regions of mRNAs. Genome Biol 2002, 3(3):REVIEWS0004.
14. Kochetov AV: Alternative translation start sites and hidden coding potential of eukaryotic mRNAs. Bioessays 2008, 30(7):683-691.
15. Kozak M: The scanning model for translation: an update. J Cell Biol 1989, 108(2):229-241.
16. Calvo SE, Pagliarini DJ, Mootha VK: Upstream open reading frames cause widespread reduction of protein expression and are polymorphic among humans. Proc Natl Acad Sci USA 2009, 106(18):7507-7512. 17. Kochetov AV, Sarai A, Rogozin IB, Shumny VK, Kolchanov NA: The role of
alternative translation start sites in the generation of human protein diversity. Mol Genet Genomics 2005, 273(6):491-496.
18. Kozak M: Regulation of translation via mRNA structure in prokaryotes and eukaryotes. Gene 2005, 361:13-37.
19. Kozak M: Pushing the limits of the scanning mechanism for initiation of translation. Gene 2002, 299(1-2)-1-34.
20. Sarrazin S, Starck J, Gonnet C, Doubeikovski A, Melet F, Morle F: Negative and translation termination-dependent positive control of FLI-1 protein synthesis by conserved overlapping 5’ upstream open reading frames in Fli-1 mRNA. Mol Cell Biol 2000, 20(9):2959-2969.
21. Song KY, Choi HS, Hwang CK, Kim CS, Law PY, Wei LN, Loh HH: Differential use of an in-frame translation initiation codon regulates human mu opioid receptor (OPRM1). Cell Mol Life Sci 2009, 66(17):2933-2942. 22. Schrimpf SP, Weiss M, Reiter L, Ahrens CH, Jovanovic M, Malmstrom J,
Brunner E, Mohanty S, Lercher MJ, Hunziker PE, Aebersold R, von Mering C, Hengartner MO: Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol 2009, 7(3): e48.
23. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O’Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321(5891):956-960.
24. Resch AM, Ogurtsov AY, Rogozin IB, Shabalina SA, Koonin EV: Evolution of alternative and constitutive regions of mammalian 5’UTRs. BMC Genomics 2009, 10:162.
25. Chen X, Tompa M: Comparative assessment of methods for aligning multiple genome sequences. Nat Biotechnol 2010, 28(6):567-572. 26. Paten B, Herrero J, Beal K, Fitzgerald S, Birney E: Enredo and Pecan:
genome-wide mammalian consistency-based multiple alignment with paralogs. Genome Res 2008, 18(11):1814-1828.
27. Klemke M, Kehlenbach RH, Huttner WB: Two overlapping reading frames in a single exon encode interacting proteins–a novel way of gene usage. EMBO J 2001, 20(14):3849-3860.
28. Cvijovic M, Dalevi D, Bilsland E, Kemp GJ, Sunnerhagen P: Identification of putative regulatory upstream ORFs in the yeast genome using heuristics and evolutionary conservation. BMC Bioinformatics 2007, 8:295.
29. Tautz D: Polycistronic peptide coding genes in eukaryotes–how widespread are they? Brief Funct Genomic Proteomic 2009, 8(1):68-74. 30. Gu S, Anton A, Salim S, Blumer KJ, Dessauer CW, Heximer SP: Alternative
translation initiation of human regulators of G-protein signaling-2 yields a set of functionally distinct proteins. Mol Pharmacol 2008, 73(1):1-11. 31. Bazykin GA, Kochetov AV: Alternative translation start sites are conserved
in eukaryotic genomes. Nucleic Acids Res 2010, 39(2):567-577. 32. Vogel C, Abreu Rde S, Ko D, Le SY, Shapiro BA, Burns SC, Sandhu D,
Boutz DR, Marcotte EM, Penalva LO: Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol 2010, 6:400.
33. Church DM, Goodstadt L, Hillier LW, Zody MC, Goldstein S, She X, Bult CJ, Agarwala R, Cherry JL, DiCuccio M, Hlavina W, Kapustin Y, Meric P, Maglott D, Birtle Z, Marques AC, Graves T, Zhou S, Teague B, Potamousis K, Churas C, Place M, Herschleb J, Runnheim R, Forrest D, Amos-Landgraf J, Schwartz DC, Cheng Z, Lindblad-Toh K, Eichler EE, et al: Lineage-specific biology revealed by a finished genome assembly of the mouse. PLoS Biol 2009, 7(5):e1000112.
34. Consortium IHGS: Finishing the euchromatic sequence of the human genome. Nature 2004, 431(7011):931-945.
35. Kasprzyk A, Keefe D, Smedley D, London D, Spooner W, Melsopp C, Hammond M, Rocca-Serra P, Cox T, Birney E: EnsMart: a generic system for fast and flexible access to biological data. Genome Res 2004,
14(1):160-169.
36. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D: The human genome browser at UCSC. Genome Res 2002, 12(6):996-1006.
doi:10.1186/1471-2148-11-192
Cite this article as: Chen et al.: Exploring the selective constraint on the sizes of insertions and deletions in 5’ untranslated regions in mammals. BMC Evolutionary Biology 2011 11:192.
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