Figures
Abstract
Intronic gene regions are mostly considered in the scope of gene expression regulation, such as alternative splicing. However, relations between basic statistical properties of introns are much rarely studied in detail, despite vast available data. Particularly, little is known regarding the relationship between the intron length and the intron phase. Intron phase distribution is significantly different at different intron length thresholds. In this study, we performed GO enrichment analysis of gene sets with a particular intron phase at varying intron length thresholds using a list of 13823 orthologous human-mouse gene pairs. We found a specific group of 153 genes with phase 1 introns longer than 50 kilobases that were specifically expressed in brain, functionally related to synaptic signaling, and strongly associated with schizophrenia and other mental disorders. We propose that the prevalence of long phase 1 introns arises from the presence of the signal peptide sequence and is connected with 1–1 exon shuffling.
Citation: Baulin EF, Kulakovskiy IV, Roytberg MA, Astakhova TV (2020) Brain-related genes are specifically enriched with long phase 1 introns. PLoS ONE 15(5): e0233978. https://doi.org/10.1371/journal.pone.0233978
Editor: Igor B. Rogozin, National Center for Biotechnology Information, UNITED STATES
Received: February 12, 2020; Accepted: May 16, 2020; Published: May 29, 2020
Copyright: © 2020 Baulin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The exon-intron structure of genes is essential for cell type- and condition-specific gene expression regulation [1], maintaining genetic stability [2] and genome evolution [3–6] of higher eukaryotes.
Comprehensive genome annotation allows studying the basic statistical properties of introns. Particularly, introns are usually characterized by an ordinal number within the gene, the region length, and features of the nucleotide sequence. Another important numeric characteristic is the phase, i.e. the sum of the lengths of the preceding exons modulo 3, e.g. the phase 1 intron occurs after the first nucleotide of a codon.
It’s known that there is an excess of phase 0 introns in all eukaryotic genomes [7]. The usual ratio of intron phases 0, 1, and 2 is nearly 5:3:2 [7]. The phase distribution of spliceosomal introns strongly correlates with the sequence conservation of splicing signals in the neighboring exons [8]. The relatively underrepresented phase 2 introns exhibit the lowest conservation rate, the relatively overrepresented phase 0 introns show the highest conservation level, and phase 1 introns have no special properties in regard to sequence conservation. In [9] it was shown that the creation of new exon-intron structures through genetic recombination, the exon shuffling, in metazoans is predominantly mediated by phase 1 introns (1–1 exon shuffling), whereas 0–0 exon shuffling prevails in non-metazoan organisms. 2–2 exon shuffling rate was insignificant in all organisms. Based on these data, the authors hypothesized that phase 0 introns are more ancient than phase 1 and 2 introns, but claimed that the genetic and selection mechanisms that lie behind the preference for shuffling 1–1 exons or domains remain largely unknown.
In [10] authors found a significant excess of phase 1 introns in the vicinity of the signal peptide cleavage site in human genes. It was suggested that the amino acid sequence of the signal peptide favors an enrichment of phase 1 AG|G proto-splice sites in the vicinity of the cleavage site and the depletion of these sites in any phase within the peptide-encoding RNA sequence. However, in [11] authors claimed there were no disproportional excess of phase 1 AG|G sites in the vicinity of the cleavage site and suggested an alternative scenario, where the excess of phase 1 introns was produced by 1–1 exon shuffling
A study of the H. sapiens, D. melanogaster, C. elegans, and A. thaliana genomes [12] showed increasing frequency of phase 0 introns and decreasing frequency of phase 1 when scanning genes from 5’ to 3’. This tendency was specifically exhibited in the genomes of Homo sapiens and Arabidopsis thaliana, for which the emergence of new introns in the 3’ region of genes was suggested to be the dominant process [12]. The authors also consider the high frequency of phase 1 introns immediately after the signal peptide to be a minor contributing factor for the observed gradient.
In [13] it was demonstrated that introns in tissue-specific and development-specific genes are longer than those in housekeeping genes. It was claimed that the share of the conserved sequence is higher in tissue-specific genes, which may indicate that the intron evolution is not neutral in general. It was also reported that the length of the conserved intronic DNA in a gene was correlated with the number of functional domains in the protein encoded by that gene. According to [14], the intronic burden of a gene (considering either the total length or the number of introns) is positively correlated with its evolutionary conservation which suggests functional importance of the genes with large intronic regions.
In [15] the authors claimed that the first introns are usually longer in eukaryotic genes, for first introns both inside 5'-untranslated regions (5' UTRs) or coding sequences (CDSs). A proposed explanation was that the increased length of first introns allowed presence of additional functional elements compared to ‘normal’ intron structure. Consistently, the fraction of conserved sequence in the first introns was found higher compared to that of all the downstream introns (see Fig 3A in [13]).
Despite the vast amount of information that is already known about introns, there is still a lack of work regarding the relationship between the intron length and the intron phase.
In our previous work [16] we considered 17 animal genomes of various taxons (including insects, fish, amphibians, reptiles, birds, and mammals) and demonstrated that shares of intron phases significantly varied with increasing lengths of considered introns, particularly, the share of phase 1 increased and reached phase 0 share at a certain threshold (~100 kilobases (kb) in mammals).
In this work, we performed functional annotation of human and mouse orthologs satisfying particular intron phase and length constraints.
Materials and methods
We used GENCODE [17] gene annotations for Homo sapiens hg38 (GENCODE version 28) and Mus musculus mm10 (GENCODE version M18) genomes. The set of considered genes has been limited to protein-coding curated (HAVANA) transcripts with the transcript support level of 1. For each gene with several eligible transcripts, we selected the one with the maximum number of exons. Human-mouse orthologous pairs were extracted from the MGI database [18]. The resulting set consisted of 13823 orthologous gene pairs.
All introns were segregated into 4 groups by length: less than 5 kb (denoted <5 below), between 5 kb and 10 kb (5–10), between 10 kb and 50 kb (10–50), and longer than 50 kb (>50). Intron phases were marked ph0, ph1, and ph2 respectively for phase 0, phase 1, and phase 2. Every gene from orthologous pairs was annotated with the number of introns belonging to each of 12 possible 'phase-length' groups, see S1 Table.
For GO enrichment analysis, the list of gene ontology (GO) terms associated with each gene was extracted from MGI [18]. Each pair of a 'phase-length' group of introns and a GO-term was annotated with the number of orthologs associated with the GO-term and containing at least one intron of the given group (a) both in human and mouse, (b) in human only, (c) in mouse only, and (d) not containing such introns neither in human nor mouse, see S2 Table. With these data, we used Fisher's exact test [19] to estimate the statistical significance of the GO term enrichment for each 'phase-length' group. P-values were corrected for multiple tested pairs using Benjamini-Hochberg (FDR) procedure [20]. GO terms with significant enrichment (passing 1% FDR) in both human and mouse were selected for further analysis, see S3 Table.
Results
Phase 1 introns demonstrate a distinct pattern with multiple GO terms enriched in the group of length > 50 kb unlike phases 0 and 2 that demonstrate strong enrichments of multiple GO terms only for shorter introns (Fig 1).
Rows—'phase-length' intron groups, columns—GO-terms. The cells with significant enrichment (1% FDR) are colored. Color scale reflects the total number of genes associated with a particular GO term. Columns have been arranged manually by placing the GO-terms enriched with the shorter groups of introns on the left, GO-terms enriched with the groups of long introns on the right, and then sorting within those two halves of GO-terms by their sizes keeping similar rows close to each other. The GO-terms enriched with phase 1 introns longer than 50 kilobases only are emphasized with the black frame. Curly brackets mark out the clusters of similar GO-terms and are followed with the particular terms that demonstrate the most significant enrichments.
To perform an in-depth analysis, we created a subset of genes with ph1_>50 introns by gathering all such genes with at least one GO term enriched in ph1_>50 introns only (see Table 1 for the list of GO-terms). The resulting list consisted of 153 genes composed of 2235 exons, see S4 Table. Within CDSs, the genes included 742, 917, and 423 introns in phases 0, 1, and 2 respectively. Consistently, the selected genes showed a significant abundance of phase 1 introns longer than 50 kilobases (S1 Fig), while in the complete set of genes shares of phases 0 and 1 introns longer than 50 kilobases were comparable.
Assuming the Gene Ontology annotation is not comprehensive, we assembled the complete list of genes with ph1_>50-introns (507 genes) for further analysis, see S4 Table. Indeed, analysis of tissue-specific gene expression showed that both 507 and 153 groups are specifically expressed in brain tissues (S2 Fig).
We also performed an additional enrichment analysis of the 153 genes with respect to associated diseases having the entire set of 13823 genes as background. In agreement with brain-specific expression, we found associations with a number of mental disorders (S3 Fig), including schizophrenia and depression, which were detected as significant in three out of four annotation databases.
The excess of long phase 1 introns in the primary sublist of 153 genes can be caused by the presence of the signal peptide or with the 1–1 exon shuffling events.
Indeed, 83 out of 153 (54%) genes started with the signal peptide sequence (according to the UniprotKB [21], see S4 Table), which is significantly greater than the overall percentage of 17% in human genes (3596 out of 20365, according to the UniprotKB [21]). Furthermore, long phase 1 introns are preferably found at the 5'-end of the spliced transcripts (Fig 2). Next, from 9 possible phase types of exons, only 1–1 exons are significantly enriched in the 153 genes (odds ratio of 2, FDR-corrected P-value < 10−30, see S4 Table). Thus, the observed enrichment of long phase 1 introns is associated with both the presence of the signal peptide and with the 1–1 exon shuffling.
(A) Introns shorter than 50 kilobases of the entire set of considered human genes. (B) Introns longer than 50 kilobases of the entire set of considered human genes. (C) Introns shorter than 50 kilobases of the group of 153 genes. (D) Introns longer than 50 kilobases of the group of 153 genes. Y axis: distribution density, X axis: the relative position of an intron within the spliced transcript. Phase 1 introns are shown in orange, phase 2 and 0 introns are shown together in gray.
Discussion
As it's mentioned in Introduction, according to [11] the connection between phase 1 introns and the signal peptide may be explained by 1–1 exon shuffling events, and, according to [9], the preference for shuffling 1–1 exons remain largely unknown. We propose the following scenario to explain the observed associations.
First, the prevalence of phase 1 introns immediately after the signal peptide sequence [10] is caused by the conserved A, V, and G amino acids in the vicinity of the cleavage site [22]. This conservation is dictated by the (-3, -1) rule that states the residues at positions -3 and -1 (relative to the cleavage site) must be small and neutral for correct cleavage [22]. These three amino acids are coded by GNN codons which favor phase 1 introns since among the three positions of the AG|G common exon-exon junction motif only the G| position is strongly conserved and the other two are hardly specific [8]. The prevalence of phase 0 introns immediately after the ATG start codons (i.e. between codons 1 and 2 of the gene) observed in [23] can serve as the evidence that a single G| position is enough to favor a specific intron phase. In turn, sharp depletion of introns in the middle of the signal peptide coding region [10] may be explained by the fact that the region can serve as a signal for an alternative nuclear export of an mRNA [24].
The excess of long phase 1 introns (and not short introns) is possibly caused by the generally longer first introns in eukaryotes [15]. The signal peptide sequence is always on the 5'-end of the gene and consequently the intron immediately after it is often the first intron of the gene.
Next, we state the preference for shuffling 1–1 exons is caused exactly by the excess of phase 1 introns immediately after the signal peptide sequence (and not in the opposite direction of causality as earlier proposed in [11]).
First, there are several examples of the evolution governed by exon shuffling events in the genes composed of the signal peptide sequence followed by a phase 1 intron and another protein domain coding region, in particular, this was observed for serine proteases [25, 26], β-defensins [27], and scorpion venom genes [28].
Second, according to [29], the extracellular proteins in mammals evolve faster than the intracellular ones, and extracellular protein must start with the signal peptide. The observed faster evolution must have been governed by exon shuffling events since the modular proteins are mainly membrane and secreted proteins and the modular domains are mainly coded by 1–1 exons [3, 4, 30, 31]. The additional argument in favor of the above statement is that most of the recently duplicated genes encode membrane and secreted proteins [32].
We believe that such fast evolution caused by 1–1 exon shuffling close to the 5'-end of the genes should have been made a significant contribution to the tendency of phase 1 to decrease and phase 0 to increase in the direction from 5'- to 3'-end of the gene observed in [12] (see Fig 2).
The selected group of 153 genes with a great excess of long phase 1 introns is consistently enriched with brain-specific genes, which are considered evolutionarily young and enriched with alternative splicing events [33], requiring high intronic burden.
Supporting information
S1 Fig. Phase distribution of long (longer than 50 kilobases) introns.
https://doi.org/10.1371/journal.pone.0233978.s001
(PNG)
S2 Fig. Tissue-specific gene enrichment.
(a) All 507 genes containing ph1>50 introns. (b) 153 genes selected based on GO enrichment. Graphs have been generated with TissueEnrich [1]. (c) The top 50 genes (out of the 153 genes) with the largest total phase 1 introns size. The graph is generated with GTEx Multi Gene Query tool [2].
https://doi.org/10.1371/journal.pone.0233978.s002
(PNG)
S3 Fig. Disease and pathway enrichment analysis of the 153 genes.
Graphs have been generated with WebGestalt [3].
https://doi.org/10.1371/journal.pone.0233978.s003
(PNG)
S1 Table. Number of introns of different phase and length in human-mouse orthologous gene pairs.
https://doi.org/10.1371/journal.pone.0233978.s004
(XLSX)
S2 Table. Number of human-mouse orthologous gene pairs containing introns of different phase and length associated with particular GO-terms.
https://doi.org/10.1371/journal.pone.0233978.s005
(XLSX)
S3 Table. GO-enrichment of specific phase-length intron groups.
https://doi.org/10.1371/journal.pone.0233978.s006
(XLSX)
S4 Table. Lists of human gene names of 153 orthologous human-mouse gene pairs of interest and of 507 orthologous human-mouse gene pairs containing ph1>50 introns both in human and mouse.
Particular spreadsheets present exon-intron structure of the 153 genes, the first phase 1 introns, and the unique functional protein domains coded by the 1–1 exons of the 153 genes. 1. Jain A, Tuteja G. TissueEnrich: Tissue-specific gene enrichment analysis. Bioinformatics. 2019 Jun 1;35(11):1966–7. doi: 10.1093/bioinformatics/bty890 2. Carithers LJ, Moore HM. The Genotype-Tissue Expression (GTEx) Project. Biopreservation and Biobanking. 2015 Oct 1;13(5):307. doi: 10.1089/bio.2015.29031.hmm 3. Wang J, Vasaikar S, Shi Z, Greer M, Zhang B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic acids research. 2017 Jul 3;45(W1):W130-7. doi: 10.1093/nar/gkx356.
https://doi.org/10.1371/journal.pone.0233978.s007
(XLSX)
References
- 1. Rose AB. Intron-mediated regulation of gene expression. Nuclear pre-mRNA processing in plants 2008 (pp. 277–290). Springer, Berlin, Heidelberg. pmid:18630758
- 2. Zhao C, Hamilton T. Introns regulate the rate of unstable mRNA decay. Journal of Biological Chemistry. 2007 Jul 13;282(28):20230–7. pmid:17478421
- 3. Patthy L. Genome evolution and the evolution of exon-shuffling—a review. Gene. 1999 Sep 30;238(1):103–14.
- 4. Kolkman JA, Stemmer WP. Directed evolution of proteins by exon shuffling. Nature biotechnology. 2001 May;19(5):423. pmid:11329010
- 5. Keren H, Lev-Maor G, Ast G. Alternative splicing and evolution: diversification, exon definition and function. Nature Reviews Genetics. 2010 May;11(5):345–55. pmid:20376054
- 6. Harr B, Turner LM. Genome‐wide analysis of alternative splicing evolution among Mus subspecies. Molecular ecology. 2010 Mar;19:228–39. pmid:20331782
- 7. Long M, Rosenberg C, Gilbert W. Intron phase correlations and the evolution of the intron/exon structure of genes. Proceedings of the National Academy of Sciences. 1995 Dec 19;92(26):12495–9. pmid:8618928
- 8. Long M, Deutsch M. Association of intron phases with conservation at splice site sequences and evolution of spliceosomal introns. Molecular biology and evolution. 1999 Nov 1;16(11):1528–34. pmid:10555284
- 9. França GS, Cancherini DV, de Souza SJ. Evolutionary history of exon shuffling. Genetica. 2012 Jun 1;140(4–6):249–57. pmid:22948334
- 10. Tordai H, Patthy L. Insertion of spliceosomal introns in proto‐splice sites: the case of secretory signal peptides. FEBS letters. 2004 Sep 24;575(1–3):109–11. pmid:15388343
- 11. Vibranovski MD, Sakabe NJ, de Souza SJ. A possible role of exon-shuffling in the evolution of signal peptides of human proteins. FEBS letters. 2006 Mar 6;580(6):1621–4. pmid:16487520
- 12. Ruvinsky A, Ward W. A gradient in the distribution of introns in eukaryotic genes. Journal of molecular evolution. 2006 Jul 1;63(1):136–41. pmid:16736103
- 13. Vinogradov AE. “Genome design” model: Evidence from conserved intronic sequence in human–mouse comparison. Genome research. 2006 Mar 1;16(3):347–54. pmid:16461636
- 14. Gorlova O, Fedorov A, Logothetis C, Amos C, Gorlov I. Genes with a large intronic burden show greater evolutionary conservation on the protein level. BMC evolutionary biology. 2014 Dec;14(1):50. pmid:24629165
- 15. Bradnam KR, Korf I. Longer first introns are a general property of eukaryotic gene structure. PloS one. 2008;3(8). pmid:18769727
- 16. Astakhova TV, Roytberg MA, Tsitovich II, Yakovlev VV. Regularities Related to Introns Lengths Distribution. Mathematical Biology & Bioinformatics. 2014; 9 (2): 482–490. [in Russian]
- 17. Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic acids research. 2018 Oct 24;47(D1):D766–73. pmid:30357393
- 18. Eppig JT, Smith CL, Blake JA, Ringwald M, Kadin JA, Richardson JE, et al. Mouse Genome Informatics (MGI): resources for mining mouse genetic, genomic, and biological data in support of primary and translational research. Systems Genetics 2017 (pp. 47–73). Humana Press, New York, NY. pmid:27933520
- 19. Sprent P. Fisher exact test. International encyclopedia of statistical science. 2011:524–5.
- 20. Haynes W. Benjamini–Hochberg method. Encyclopedia of systems biology. 2013:78-.
- 21. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. Uniprotkb/swiss-prot. Plant bioinformatics 2007 (pp. 89–112). Humana Press. pmid:18287689
- 22. Nielsen H, Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein engineering. 1997 Jan 1;10(1):1–6. pmid:9051728
- 23. Nielsen H, Wernersson R. An overabundance of phase 0 introns immediately after the start codon in eukaryotic genes. BMC genomics. 2006 Dec 1;7(1):256. pmid:17034638
- 24. Palazzo AF, Springer M, Shibata Y, Lee CS, Dias AP, Rapoport TA. The signal sequence coding region promotes nuclear export of mRNA. PLoS biology. 2007 Dec;5(12). pmid:18052610
- 25. Rogers J. Split-gene evolution: Exon shuffling and intron insertion in serine protease genes. Nature. 1985 Jun;315(6019):458–9. pmid:3889660
- 26. Patthy L. Evolution of the proteases of blood coagulation and fibrinolysis by assembly from modules. Cell. 1985 Jul 1;41(3):657–63.
- 27. Zhu S, Gao B. Evolutionary origin of β-defensins. Developmental & Comparative Immunology. 2013 Jan 1;39(1–2):79–84. pmid:22369779
- 28. Wang X, Gao B, Zhu S. Exon shuffling and origin of scorpion venom biodiversity. Toxins. 2017 Jan;9(1):10. pmid:28035955
- 29. Julenius K, Pedersen AG. Protein evolution is faster outside the cell. Molecular biology and evolution. 2006 Nov 1;23(11):2039–48. pmid:16891379
- 30. Basu MK, Carmel L, Rogozin IB, Koonin EV. Evolution of protein domain promiscuity in eukaryotes. Genome research. 2008 Mar 1;18(3):449–61. pmid:18230802
- 31. Lee BW. Comparison of exon-boundary old and young domains during metazoan evolution. Genomics & Informatics. 2009;7(2):131–5.
- 32. Kondrashov FA, Rogozin IB, Wolf YI, Koonin EV. Selection in the evolution of gene duplications. Genome biology. 2002 Jan;3(2):research0008–1. pmid:11864370
- 33. Blencowe BJ. Alternative splicing: new insights from global analyses. Cell. 2006 Jul 14;126(1):37–47. pmid:16839875