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Functional protein divergence in the evolution of Homo sapiens
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Abstract
Quantification of the divergence of proteins by functional category shows that morphological changes in metazoa have been driven by variation in regulatory genes.
Abstract
Background
Protein-coding regions in a genome evolve by sequence divergence and gene gain and loss, altering the gene content of the organism. However, it is not well understood how this has given rise to the enormous diversity of metazoa present today.
Results
To obtain a global view of human genomic evolution, we quantify the divergence of proteins by functional category at different evolutionary distances from human.
Conclusion
This analysis highlights some general systems-level characteristics of human evolution: regulatory processes, such as signal transducers, transcription factors and receptors, have a high degree of plasticity, while core processes, such as metabolism, transport and protein synthesis, are largely conserved. Additionally, this study reveals a dynamic picture of selective forces at short, medium and long evolutionary timescales. Certain functional categories, such as 'development' and 'organogenesis', exhibit temporal patterns of sequence divergence in eukaryotes relative to human. This framework for a grammar of human evolution supports previously postulated theories of robustness and evolvability.
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Most cited references37
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The Gene Ontology (GO) project in 2006
(2005)
The Gene Ontology (GO) project () develops and uses a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see ). The GO Consortium continues to improve to the vocabulary content, reflecting the impact of several novel mechanisms of incorporating community input. A growing number of model organism databases and genome annotation groups contribute annotation sets using GO terms to GO's public repository. Updates to the AmiGO browser have improved access to contributed genome annotations. As the GO project continues to grow, the use of the GO vocabularies is becoming more varied as well as more widespread. The GO project provides an ontological annotation system that enables biologists to infer knowledge from large amounts of data.
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Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution.
Alexander Stark, Julius Brennecke, Natascha Bushati … (2005)
MicroRNAs are small noncoding RNAs that serve as posttranscriptional regulators of gene expression in higher eukaryotes. Their widespread and important role in animals is highlighted by recent estimates that 20%-30% of all genes are microRNA targets. Here, we report that a large set of genes involved in basic cellular processes avoid microRNA regulation due to short 3'UTRs that are specifically depleted of microRNA binding sites. For individual microRNAs, we find that coexpressed genes avoid microRNA sites, whereas target genes and microRNAs are preferentially expressed in neighboring tissues. This mutually exclusive expression argues that microRNAs confer accuracy to developmental gene-expression programs, thus ensuring tissue identity and supporting cell-lineage decisions.
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A Scan for Positively Selected Genes in the Genomes of Humans and Chimpanzees
Rasmus Nielsen, Carlos J Bustamante, Andrew G Clark … (2005)
Introduction Genes, or regions of the genome, that have been affected by natural selection may show an excess of functionally important molecular changes, beyond what would be expected in the absence of selection. Genomic regions with such an excess of changes are said to have experienced positive selection, i.e., selection in favor of new genetic variants. The most common statistical technique for detecting positive selection takes advantage of the fact that mutations in coding regions of genes come in two classes: nonsynonymous mutations that change the resulting amino acid sequence of the protein and synonymous mutations, which do not change the encoded protein. An excess of nonsynonymous mutations over synonymous mutations, beyond what would be expected if the two types of mutations occur at the same rate, provides strong evidence for the past action of positive selection at the protein level. Using this logic, there have recently been numerous studies documenting positive selection in a variety of genes and organisms, including immune-response-related genes [1–3], viral genes [4–6], fertilization genes [7,8], and genes involved in sensory perception and olfaction in humans [9]. Clark et al. [10] compared 7,645 genes from humans to their orthologs from the chimpanzee and the mouse. For each gene, they tested if there was an excess of nonsynonymous substitutions on the evolutionary lineage leading to humans. They showed that there was an excess of putatively positively selected genes in several functional classes, including genes involved in sensory perception, olfaction, and amino acid catabolism. They also showed that human genes that have been targeted by positive selection are significantly more likely to harbor variation associated with known genetic diseases. We here report the results of an analysis of 20,361 human and chimpanzee genes (of which 6,630 later were eliminated in a very conservative quality control), which includes the 7,645 genes analyzed by Clark et al. [10]. While the objective of the study by Clark et al. [10] was to find genes that have experienced accelerated evolution on the human lineage, using the mouse as an outgroup, the aim of the current study is to find genes that have been targeted by positive selection at any point in time during the evolution of humans and chimpanzees, based on a larger set of genes. We use a likelihood ratio test to identify positive selection and do extensive simulations to find the appropriate critical values of the test. Positive selection is inferred if the ratio of nonsynonymous substitutions per nonsynonymous site to synonymous substitutions per synonymous site (dN /dS) is statistically significantly greater than one in a test of the neutral null hypothesis dN /dS = 1 [11,12]. The method used for detecting positive selection takes transition/transversion rate biases and unequal codon and amino acid frequencies into account. The test for positive selection applied in this study is a traditional test of dN /dS greater than one. It has more power than the test used in the Clark et al. study [10] if selection affects both the human and the chimpanzee lineages because it uses information from both lineages. Results Chimpanzee sequence was obtained by PCR using primers designed to flank exon sequence annotated in the human genome [10]. Our analysis begins with data from 20,361 coding regions, including 103,606 nucleotide differences and 403 indels among 17,687,331 aligned nucleotides. These numbers are significantly lower than the genome-wide averages [13,14], presumably due to selective constraints in the coding regions. The distributions of nonsynonymous and synonymous nucleotide differences among genes are shown in Figure 1. The average numbers of nonsynonymous and synonymous mutations per nucleotide site are 0.002578 and 0.003281, respectively. Eliminating reads without a hit to known genes in public databases (see Materials and Methods), there are 71,896 nucleotide differences in 13,731 genes. The remaining analysis is restricted to this set of genes. Among them, 5,574 were eliminated from the positive selection analysis because they had fewer than three mutations, and 797 were eliminated because the sequence was less than 50 bp long. Additionally, 45 genes were eliminated because they contained internal stop codons, presumably due to erroneous annotations or sequencing errors. Among the remaining 8,079 genes, 3,913 were also analyzed by Clark et al. [10]. The average level of sequence divergence was 0.60%, corresponding to a divergence level of 1.57% in silent sites. This figure matches well the level of divergence observed by Ebersberger et al. [14] for Chromosome 22 of 1.44% overall and 2.26% in CpG islands. Seven hundred thirty-three of the 8,079 genes evolved with dN /dS greater than one, but only 35 had p-values less than 0.05, as determined by a likelihood ratio test of the null hypothesis of dN /dS = 1 against the alternative hypothesis of dN /dS greater than one. The number of significant genes at the 5% level, in this one-sided test, is lower than the nominal level because the vast majority of genes are conserved and evolve with dN /dS less than one. Nonetheless, after using Simes's improved Bonferroni procedure [15] we can, at the 5% significance level, reject the hypothesis that none of the genes are evolving with dN /dS greater than one. This also implies that a 5% false discovery rate set is nonempty. Even though the level of divergence between humans and chimpanzees is very low, there is statistically significant evidence for positive selection in the DNA sequences of these two species. Results for all genes are available in Dataset S1. Biological Processes Affected by Positive Selection To identify functional groups of genes with an overrepresentation of putatively positively selected genes, we used the PANTHER [16,17] classification of biological processes and a Mann-Whitney U test (MWU) based on the p-values from the likelihood ratio test (Table 1). The classification based on the MWU identifies categories of genes with small p-values from the likelihood ratio test. It is important to notice that genes that evolve approximately neutrally will tend to have smaller p-values than genes evolving under strong functional constraints. The classification based on the MWUs, therefore, does not provide unambiguous evidence for positive selection, but it provides a key to which groups harbors the most candidates for positive selection. Immune-defense-related genes appear at the top of the list. It is not surprising that several of the genes experiencing most positive selection are involved in immune responses to viruses. Considering the speed at which many pathogens, such as viruses, evolve (e.g., [5]), a coevolutionary molecular arms race between pathogens and host cells might explain the presence of strong selection favoring new mutations in these genes. Other forces, including overdominant selection to diversify the spectrum of immune responses, may also cause positive selection in immune- and defense-related genes. Such explanations have previously been used to explain the presence of positive selection in the human major histocompatibility complex [18]. As in [10] we also identify genes involved in various forms of sensory perception, including olfaction and genes classified as “unknown biological function.” Many of the genes with unknown biological function show sequence similarity with known transcription factors (data not shown). Much of the selection on sensory genes is driven by the selection on olfactory receptors previously found by Gilad et al. [9]. In contrast to Clark et al. [10], we also find that genes involved in spermatogenesis appear to have an excess of positively selected genes. The genes involved in spermatogenesis showing the strongest evidence for positive selection include several KRAB-containing zinc finger proteins that serve as repressors of transcription and are believed to be involved in determining the differentiation of pluripotent stem cells [19]. Expression Patterns and Positive Selection We also categorized 3,464 of the 8,079 genes according to the tissue of expression in the Novartis Gene Expression Atlas [20]. Because of the relatively small number of tissue-selective genes in our dataset (204) and the large number of tissues analyzed (28), many tissues had fewer than 20 tissue-selective genes, providing little statistical power for further subdivision. Therefore, we examined instead whether the tissue of maximal expression for a gene was correlated with positive selection, since high expression levels and importance in tissue function are often, but not always, correlated. The set of genes that have their maximal expression in the testes is the only one showing an excess of positive selection, after a Bonferroni correction for multiple tests (Table 2). Genes with their maximal expression in the brain do not have an excess tendency toward positive selection. In fact, genes expressed in the brain seem to be among the most conserved genes with the least evidence for positive selection. MWUs, comparing genes with their maximal expression in the brain (83 genes) to all other genes, show that these genes tend to have significantly higher p-values of the likelihood ratio test for positive selection (p = 0.035), indicating high levels of selective constraint. Genes that are expressed in the brain at a level of twice the expression level found in blood show an even stronger tendency toward avoidance of positive selection (p = 0.0002). Although studies of gene expression in the brain tissue are complicated by low-abundance transcripts and heterogeneous specialized brain regions [21], the overall evidence points toward a deficiency of positively, or fast evolving, genes among those expressed in the brain. The causes for the cognitive differences may instead be sought in adaptive changes in just a few genes, in changes in gene expression [22], or in changes in copy number and/or organization of genes relating to cognitive function [23]. Dorus et al. [24] found that genes expressed in the nervous system showed a relative increase in the rate in primates relative to rodents when compared to housekeeping genes, but provided no direct evidence for positive selection on these genes. Nervous-system-specific genes appear to be so conserved that it is unlikely that direct evidence for positive selection will be discovered in this group of genes. Positive Selection in the X Chromosome We also tested if any chromosomes show an excess of genes with evidence for positive selection. The only chromosome enriched in genes with small p-values from the likelihood ratio test for positive selection is the X chromosome (p = 0.0049; MWU). Several factors influence the contrast between the X and autosomes in tests of selection, including hemizygosity of the X in males, resulting in more effective selection against deleterious recessive and in favor of positive recessive mutations [25]. Male hemizygosity also results in mutations, with male-specific effects being more readily fixed by selection on the X [26]. This increased efficiency of selection for male-specific genes on the X may explain the excess of X-linked genes expressed in spermatogonia [27]. The observation that reproductive proteins generally evolve at a greater rate, coupled with the overrepresentation of male-specific genes on the X, could produce the excess positive selection seen on the X. However, after eliminating all genes with highest expression levels in the testis, or annotated as functioning in spermatogenesis, there is still an excess of putatively positively selected genes on the X chromosome (p = 0.0131; MWU). Thus, it appears that the elevated positive selection on the X is likely due to the general tendency of mutations to be recessive, regardless of their tendency to be male-limited in expression. Although other factors, such as an elevated male mutation rate [28], differences in the efficacy of genetic hitchhiking between autosomes and the X chromosome [29], and correlations between recombination rate and divergence [30], may cause differences in variability and substitution rate between autosomes and the X chromosome, none of these factors alone can explain the excess of positively selected genes on the X chromosome. Analysis of the 50 Genes Showing Strongest Evidence for Selection We studied the 50 genes with the highest likelihood ratios in greater detail to further characterize the causes of positive selection and examine error rates (Table 3). To investigate the degree to which our results might be influenced by sequencing errors, we compared the data for these genes with the public data available for the same genes. In the regions with overlap between the public data and our data there were a total of 327 mutations in the public data and 306 mutations in our data. This demonstrates that there is not an excess of (potentially artifactual) mutations in our data in the genes that show evidence for positive selection. While most of the 50 genes also show strong evidence for positive selection in the public data, six of the genes do not. HC19953, HC2758, HC6579, HC7761, HC8067, and HC9844 do not have dN /dS ratios larger than one in the public data. In most cases, the difference is caused by the fact that our database and the public database contain different regions of the genes. Not all regions of a gene are expected to be targeted by positive selection, but this does not challenge the evidence for positive selection in the regions of the genes included in this analysis. In any case, using the public data would not change the qualitative conclusions of the analysis of the genes presented here. Immunity and Defense Genes Targeted by Positive Selection The top 50 genes include many genes that we might a priori expect to be targets of positive selection, including four genes involved in olfaction (OR2W1, OR5I1, OR2B2, and C20orf185) and several genes involved in host–pathogen interactions, such as CMRF35H, CD72 antigen, pre-T-cell antigen receptor α (PTCRA), APOBEC3F, and granzyme H (GZMH). Only one of these genes was among the 50 most significant entries in the Clark et al. [10] model 2 analysis. APOBEC3F encodes an antiviral factor that has previously been demonstrated to be under positive selection by Sawyer et al. [3] who note that this gene has been associated with anti-HIV activity. Presumably, most of these genes have been targeted by positive selection throughout the primate and mammalian phylogeny. The widespread evidence for positive selection in immune-related genes confirms the hypothesis that much positive selection in the human and mammalian genomes may be driven by a coevolutionary arms race between host immune system and pathogens. Spermatogenesis- and Apoptosis-Related Genes The list also contains many testis- or sperm-specific genes including Protamine-1 (PRM1), which previously has been shown to be under positive selection [31], possibly due to sperm competition (but see [32] for an alternative explanation). Other sperm-specific genes on the list include USP26, C15orf2, PEPP-2, TCP11, HYAL3, and TSARG1. The inclusion of these genes in the list of the genes showing the strongest evidence for positive selection is consistent with the results, based on the PANTHER annotation and the Novartis expression data, of excess positive selection in sperm/testis-specific genes. The possible causes include sperm competition (e.g., [31]), sexual conflict (e.g., [7,8]), selection for reproductive isolation, pathogen-driven selection in the reproductive organs, and selection related to the occurrence of mutations causing segregation distortion. We notice that at least one of these genes (TSARG1) is involved in apoptosis during spermatogenesis. Apoptosis of germ cells is conspicuous during normal spermatogenesis, eliminating up to 75% of the potential spermazoa [33–35], affecting cells both before and after the meiotic division [36]. It has been hypothesized that the main cause for the high rate of apoptosis during spermatogenesis is to maintain a proper cell-number ratio between maturing germ cells and Sertoli cells [35]. The natural process of elimination of germ cells by apoptosis creates a genomic conflict in which each individual germ cell will benefit from avoiding apoptosis, but apoptosis of a certain fraction of germ cells may be beneficial to the mature organism. New mutations occurring in cells during spermatogenesis, which reduces the probability of apoptosis, will be positively selected. This effect will be particularly strong for mutations in genes expressed after the meiotic division, potentially resulting in segregation distortion. A mutant with an even very small increase in the probability of escaping postmeiotic apoptosis will have a strong selective advantage. Compensatory mutations, reducing or eliminating the effect of the apoptosis avoidance mutation, may then later occur. These dynamics may lead to recurrent events of positive selection in genes affecting spermatogenesis apoptosis. The 40 genes in this study involved in inhibition of apoptosis show an excess of evidence for positive selection compared to other categories (p = 0.0047; see Table 2). Many of the genes showing most evidence for positive selection are known to be involved in either spermatogenesis, apoptosis, or both. For example, the apoptosis-related gene showing the strongest evidence for positive selection (DFFA) is an inhibitor of Fas-mediated apoptosis, which has been shown to be involved in apoptosis during spermatogenesis [36]. This may suggest that genomic conflict due to spermatogenesis apoptosis may be driving positive selection in many of the included genes. Cancer-Related Genes While we expected to find genes involved in olfaction, spermatogenesis, and immune defense among the 50 annotated genes showing the strongest evidence for positive selection, we were surprised to find a very large proportion of cancer-related genes, especially genes involved in tumor suppression, apoptosis, and cell cycle control. These genes include four putative tumor suppressors: HYAL3, DFFA, PEPP-2 (note that both HYAL3 and PEPP-2 also appear to be involved in spermatogenesis), and C16orf3, another gene associated with tumor progression (MMP26), and a gene with unknown function but high similarity to melanoma-associated antigens (FLJ32965). In addition, there are several genes involved in apoptosis (PPP1R15A, HSJ001348, TSARG1, and GZMH). Given that many of the genes have very little functional information, it is surprising to find such a large proportion of genes that may be related to tumor development and control. The factors causing positive selection on these genes are unknown, but genes important in tumor development and suppression may be positively selected due to other functional effects of the genes, particularly in immunity and defense or in spermatogenesis. Several of the genes involved in tumor suppression or progression show testis-specific expression, and models of genomic conflict may explain the presence of positive selection in these genes. It should be noted that there is no pattern of human-specific selection in these genes. The high number of nonsynonymous mutations in these genes is approximately evenly distributed between the human and the chimpanzee lineage (results not shown). PAML Analysis For each of the 50 genes, we searched public databases to find orthologous genes in other mammals. For 25 of the genes we were able to identify orthologs from mouse and rat, and for these 25 genes we estimated the dN /dS ratio of each lineage of the underlying phylogeny using PAML [37]. The dN /dS ratio was elevated (p if i is less than j. The polarity of the mutation was determined using the chimpanzee sequence as outgroup. Analysis of ascertainment bias. To assess the impact of the ascertainment scheme in the tests that contrast human polymorphism data to the human–chimp divergence, new datasets were simulated, using standard neutral coalescence simulations (e.g., [38]). Each simulated dataset generated one chimp sequence and 78 human sequences for each of the 13,731 genes. For each simulated gene, one human sequence was randomly chosen and compared to the chimp sequence using a chi-square statistic for the goodness-of-fit test of dN /dS = 1. The 50 genes with largest chi-square statistic among genes with dN /dS greater than one were selected for population genetic analysis. This scheme was repeated 1,000 times to investigate the effect of the ascertainment protocol of the 50 genes. The parameters of the simulations were estimated from the data, using the observed distribution of sequence lengths, and synonymous-site mutation rate and humans–chimp divergence time estimated from the concatenated data. The distribution of dN /dS ratios among genes was estimated assuming the dN /dS ratios follow a γ distribution among genes, keeping the synonymous rate constant among them. Power analysis. To analyze the power of the test for positive selection, we simulated pairs of sequences and performed likelihood ratio tests of H0: dN /dS equals one versus dN /dS is greater than one for each sequence pair. The simulations were done using the average value of synonymous sequence divergence observed in the data, while nonsynonymous divergence was varied. For more details regarding such simulations, see, e.g. [50]. PRF analysis. Assume nonlethal mutations enter a population of constant size 2N according to a Poisson process and are assigned to one of three categories: neutral (S = 0), positively selected with selection coefficient S +, and negatively selected with selection coefficient S –, according to probabilities p 0, p +, and p – (where p 0 + p + + p – = 1). Furthermore, assume mutations evolve independently. It follows from standard population genetic theory, the total law of probability, and the rules of conditional probability that the probability of an SNP being found at frequency i out of n chromosomes under this scheme [44] is where F(i,n,S) --> is given by The likelihood of observing counts x 1, x 2, . . ., xS where S is the total number of segregating sites out of n 1, n 2, …, nS chromosomes is, thus, The maximum likelihood value and the maximum likelihood parameter estimates can then be obtained by numerically maximizing this function with respect to the parameters. Likelihood ratio tests can be constructed by constraining certain of the parameters to take on particular values. For example, setting p 0 = 1 defines a model with no selected mutations. Likewise, setting p 0 + p – = 1 defines a model that allows negative selection, but no positive selection. This analysis assumes that mutations are independent. Because of linkage and the possibility of epistasis, the independence assumption may not be met by the data. However, a full analysis taking the correlation among SNPs into account is not computationally feasible. Fortunately, the average correlation is low between SNPs because they have been sampled among 50 genes distributed throughout the genome. The effect of the correlation among SNPs on this analysis should, therefore, be minimal. The maximum log likelihood value for the full model is –234.19. However, the maximum log likelihood values for models assuming only neutral mutations, or a single class of selected mutations, are –243.82 and –240.88, respectively. Under the independence assumption, both of these simpler models can be rejected against the model with three classes of mutations, using a likelihood ratio test (p = 0.0006 and p = 0.004). Supporting Information Dataset S1 Results File (3.1 MB XLS). Click here for additional data file. Dataset S2 Alignment File (9.8 MB ZIP). Click here for additional data file. Accession Numbers The sequence analyzed in this study has been submitted to GenBank (http://www.ncbi.nlm.nih.gov/Genbank/).
Author and article information
Journal
Journal ID (nlm-ta): Genome Biol
Title:
Genome Biology
Publisher:
BioMed Central
ISSN
(Print):
1465-6906
ISSN
(Electronic):
1465-6914
Publication date
(Print):
2008
Publication date
(Electronic):
15
February
2008
Volume: 9
Issue: 2
Page: R33
Affiliations
Article
Publisher ID: gb-2008-9-2-r33DOI: 10.1186/gb-2008-9-2-r33
PMC ID: 2374701
PubMed ID: 18279504
SO-VID: a8282d0d-a542-459c-8e49-9068614080bd
Copyright © Copyright © 2008 Lopez-Bigas et al.; licensee BioMed Central Ltd.
License:
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.
History
Date
received
: 18
October
2007
Date
revision received
: 24
December
2007
Date
accepted
: 15
February
2008
Categories
Subject:
Research
ScienceOpen disciplines: Genetics
Data availability:
ScienceOpen disciplines: Genetics