De novo gene birth is the process by which new genes evolve from DNA sequences that were ancestrally non-genic.De novo genes represent a subset of novel genes, and may be protein-coding or instead act as RNA genes. The processes that govern de novo gene birth are not well understood, although several models exist that describe possible mechanisms by which de novo gene birth may occur.
Although de novo gene birth may have occurred at any point in an organism's evolutionary history, ancient de novo gene birth events are difficult to detect. Most studies of de novo genes to date have thus focused on young genes, typically taxonomically restricted genes (TRGs) that are present in a single species or lineage, including so-called orphan genes, defined as genes that lack any identifiable homolog. It is important to note, however, that not all orphan genes arise de novo, and instead may emerge through fairly well-characterized mechanisms such as gene duplication (including retroposition) or horizontal gene transfer followed by sequence divergence, or by gene fission/fusion.
Although de novo gene birth was once viewed as a highly unlikely occurrence,several unequivocal examples have now been described, and some researchers speculate that de novo gene birth could play a major role in evolutionary innovation.
As early as the 1930s, J. B. S. Haldane and others suggested that copies of existing genes may lead to new genes with novel functions.In 1970, Susumu Ohno published the seminal text Evolution by Gene Duplication. For some time subsequently, the consensus view was that virtually all genes were derived from ancestral genes, with François Jacob famously remarking in a 1977 essay that "the probability that a functional protein would appear de novo by random association of amino acids is practically zero."
In the same year, however, Pierre-Paul Grassé coined the term "overprinting" to describe the emergence of genes through the expression of alternative open reading frames (ORFs) that overlap preexisting genes.These new ORFs may be out of frame with or antisense to the preexisting gene. They may also be in frame with the existing ORF, creating a truncated version of the original gene, or represent 3’ extensions of an existing ORF into a nearby ORF. The first two types of overprinting may be thought of as a particular subtype of de novo gene birth; although overlapping with a previously coding region of the genome, the primary amino-acid sequence of the new protein is entirely novel and derived from a frame that did not previously contain a gene. The first examples of this phenomenon in bacteriophages were reported in a series of studies from 1976 to 1978, and since then numerous other examples have been identified in viruses, bacteria, and several eukaryotic species.
The phenomenon of exonization also represents a special case of de novo gene birth, in which, for example, often-repetitive intronic sequences acquire splice sites through mutation, leading to de novo exons. This was first described in 1994 in the context of Alu sequences found in the coding regions of primate mRNAs.Interestingly, such de novo exons are frequently found in minor splice variants, which may allow the evolutionary “testing” of novel sequences while retaining the functionality of the major splice variant(s).
Still, it was thought by some that most or all eukaryotic proteins were constructed from a constrained pool of “starter type” exons.Using the sequence data available at the time, a 1991 review estimated the number of unique, ancestral eukaryotic exons to be < 60,000, while in 1992 a piece was published estimating that the vast majority of proteins belonged to no more than 1,000 families. Around the same time, however, the sequence of chromosome III of the budding yeast Saccharomyces cerevisiae was released, representing the first time an entire chromosome from any eukaryotic organism had been sequenced. Sequencing of the entire yeast nuclear genome was then completed by early 1996 through a massive, collaborative international effort. In his review of the yeast genome project, Bernard Dujon noted that the unexpected abundance of genes lacking any known homologs was perhaps the most striking finding of the entire project.
In 2006 and 2007, a series of studies provided arguably the first documented examples of de novo gene birth that did not involve overprinting.An analysis of the accessory gland transcriptomes of Drosophila yakuba and Drosophila erecta first identified 20 putative lineage-restricted genes that appeared unlikely to have resulted from gene duplication. Levine and colleagues then confirmed the de novo origination of five candidate genes specific to Drosophila melanogaster and/or the closely related Drosophila simulans through a rigorous pipeline that combined bioinformatic and experimental techniques. These genes were identified by combining BLAST search-based and synteny-based approaches (see below), which demonstrated the absence of the genes in closely-related species.
Despite their recent evolution, all five genes appear fixed in D. melanogaster, and the presence of paralogous non-coding sequences that are absent in close relatives suggests that four of the five genes may have arisen through a recent intrachromosomal duplication event.Interestingly, all five were preferentially expressed in the testes of male flies (see below). The three genes for which complete ORFs exist in both D. melanogaster and D. simulans showed evidence of rapid evolution and positive selection. This is consistent with a recent emergence of these genes, as it is typical for young, novel genes to undergo adaptive evolution, but it also makes it difficult to be completely sure that the candidates encode truly functional products. A subsequent study using methods similar to Levine et al. and an expressed sequence tag library derived from D. yakuba testes identified seven genes derived from six unique de novo gene birth events in D. yakuba and/or the closely related D. erecta.
Three of these genes are extremely short (<90 bp), suggesting that they may be RNA genes,although several examples of very short functional peptides have also been documented. Around the same time as these studies in Drosophila were published, a homology search of genomes from all domains of life, including 18 fungal genomes, identified 132 fungal-specific proteins, 99 of which were unique to S. cerevisiae.
Since these initial studies, many groups have identified specific cases of de novo gene birth events in diverse organisms.The BSC4 gene in S. cerevisiae, identified in 2008, shows evidence of purifying selection, is expressed at both the mRNA and protein levels, and when deleted is synthetically lethal with two other yeast genes, all of which indicate a functional role for the BSC4 gene product. Historically, one argument against the notion of widespread de novo gene birth is the evolved complexity of protein folding. Interestingly, Bsc4 was later shown to adopt a partially folded state that combines properties of native and non-native protein folding. Another well-characterized example in yeast is MDF1, which both represses mating efficiency and promotes vegetative growth, and is intricately regulated by a conserved antisense ORF. In plants, the first de novo gene to be functionally characterized was QQS, an Arabidopsis thaliana gene identified in 2009 that regulates carbon and nitrogen metabolism. The first functionally characterized de novo gene identified in mice, a noncoding RNA gene, was also described in 2009. In primates, a 2008 informatic analysis estimated that 15/270 primate orphan genes had been formed de novo. A 2009 report identified the first three de novo human genes, one of which is a therapeutic target in chronic lymphocytic leukemia. Since this time, a plethora of genome-level studies have identified large numbers of orphan genes in many organisms, although the extent to which they arose de novo, and the degree to which they can be deemed functional, remain debated.
There are two major approaches to the systematic identification of novel genes: genomic phylostratigraphyand synteny-based methods. Both approaches are widely used, individually or in a complementary fashion.
Genomic phylostratigraphy involves examining each gene in a focal species and inferring the presence or absence of ancestral homologs through the use of the BLAST sequence alignment algorithmsor related tools. Each gene in the focal species can be assigned an “age” (aka “conservation level” or “genomic phylostratum”) that is based on a predetermined phylogeny, with the age corresponding to the most distantly related species in which a homolog is detected. When a gene lacks any detectable homolog outside of its own genome, or close relatives, it is said to be a novel, taxonomically-restricted or orphan gene, although such a designation is of course dependent on the group of species being searched against.
Phylogenetic trees are limited by the set of closely related genomes that are available, and results are dependent on BLAST search criteria.Because it is based on sequence similarity, it is often difficult for phylostratigraphy to determine whether a novel gene has emerged de novo or has diverged from an ancestral gene beyond recognition, for instance following a duplication event. This was pointed out by a study that simulated the evolution of genes of equal age and found that distant orthologs can be undetectable for the most rapidly evolving genes. When accounting for changes in the rate of evolution to portions of young genes that acquire selected functions, a phylostratigraphic approach was much more accurate at assigning gene ages in simulated data. A subsequent pair of studies using simulated evolution found that phylostratigraphy failed to detect an ortholog in the most distantly related species for 13.9% of D. melanogaster genes and 11.4% of S. cerevisiae genes. Similarly, a spurious relationship between a gene’s age and its likelihood to be involved in a disease process was claimed to be detected in the simulated data. However, a reanalysis of studies that used phylostratigraphy in yeast, fruit flies and humans found that even when accounting for such error rates and excluding difficult-to-stratify genes from the analyses, the qualitative conclusions were unaffected for all three studies. The impact of phylostratigraphic bias on studies examining various features of de novo genes (see below) remains debated.
To increase the detectability of ancestral homologues, sensitive sequence-based similarity searches, such as CS-BLAST and Hidden Markov Model (HMM)-based searches, may also be used, alone or in combination with BLAST-based phylostratigraphy analysis, to identify de novo genes. The PSI-BLAST techniqueis particularly useful for detecting ancient homologs. A benchmarking study found that some of these “profile-based” analyses were more accurate than conventional pairwise tools. The impact of false positives, when genes are incorrectly inferred to have an ancestral homolog when they are new in reality, on our understanding of de novo gene birth has not yet been specifically assessed.
It is important to disentangle the technical difficulties associated with detection of the oldest ancestor of a gene, and estimates of how old a gene is (the ultimate goal of phylostratigraphy), from challenges linked to inferring the mechanisms by which a gene has evolved.Young and ancestral genes can all have evolved de novo, or through other mechanisms. The current approach of choice to determine whether a gene has emerged de novo is synteny, and can generally only be applied to young genes.
Approaches based on the analysis of syntenic sequences in outgroups – blocks of sequence in which the order and relative positioning of features has been maintained – allow for the identification of non-genic ancestors of candidate de novo genes.Syntenic alignments are anchored by short, conserved “markers.” Genes are the most common marker in defining syntenic blocks, although k-mers and exons are also used. Assuming that a high-quality syntenic alignment can be obtained, confirmation that the syntenic region lacks coding potential in outgroup species allows a de novo origin to be asserted with higher confidence. The strongest possible evidence for de novo emergence is the inference of the specific mutation(s) that created coding potential, typically through the analysis of microsyntenic regions of closely related species.
One challenge in applying synteny-based methods is the fact that synteny can be difficult to detect across longer timescales. To address this, various techniques have been tried, such as using exons clustered irrespective of their specific order to define syntenic blocksor algorithms that use well-conserved genomic regions to expand microsyntenic blocks. There are also difficulties associated with applying synteny-based approaches to genome assemblies that are fragmented or in lineages with high rates of chromosomal rearrangements, as is common in insects. Although synteny-based approaches have conventionally been lower-throughput in nature, they are now being applied to genome-wide surveys of de novo genes and represent a promising area of algorithmic development for gene birth dating. Some have used synteny-based approaches in combination with similarity searches in an attempt to develop standardized, stringent pipelines that can be applied to any group of genomes in an attempt to address discrepancies in the various lists of de novo genes that have been generated (see below).
Even when the evolutionary origin of a particular sequence has been rigorously established computationally, it is important to note that there is a lack of consensus about what constitutes a genuine de novo gene birth event. One reason for this is a lack of agreement on whether or not the entirety of the newly genic sequence must be non-genic in origin. With respect to protein-coding de novo genes, it has been proposed that de novo genes be divided into subtypes corresponding to the proportion of the ORF in question that was derived from previously noncoding sequence.Furthermore, for de novo gene birth to occur, the sequence in question must not just have emerged de novo but must in fact be a gene. Accordingly, the discovery of de novo gene birth has also led to a questioning of what constitutes a gene, with some models establishing a strict dichotomy between genic and non-genic sequences, and others proposing a more fluid continuum (see below). All definitions of genes are linked to the notion of function, as it is generally agreed that a genuine gene should encode a functional product, be it RNA or protein. There are, however, different views of what constitutes function, depending in part on whether a given sequence is assessed using genetic, biochemical, or evolutionary approaches.
It is generally accepted that a genuine de novo gene is expressed in at least some context,allowing selection to operate, and many studies use evidence of expression as an inclusion criterion in defining de novo genes. The expression of sequences at the mRNA level may be confirmed individually through conventional techniques such as quantitative PCR, or globally through more modern techniques such as RNA sequencing (RNA-seq). Similarly, expression at the protein level can be determined with high confidence for individual proteins using techniques such as mass spectrometry or western blotting, while ribosome profiling (Ribo-seq) provides a global survey of translation in a given sample. Ideally, to confirm that the gene in question arose de novo, a lack of expression of the syntenic region of outgroup species would also be demonstrated.
Confirmation of gene expression is only one approach to infer function. Genetic approaches, where one seeks to detect a specific phenotype or change in fitness upon disruption of a particular sequence, are considered by some to be the gold standard;however, for large-scale analyses of entire genomes, obtaining such evidence is often not feasible. Other experimental approaches, including screens for protein-protein and/or genetic interactions, may also be employed to confirm a biological effect for a particular de novo ORF. As more is learned about a particular locus, standard molecular biology techniques can be applied to dissect its specific cellular role.
Alternatively, evolutionary approaches may be employed to infer the existence of a molecular function from computationally-derived signatures of selection. In the case of TRGs, one common signature of selection is the ratio of nonsynonymous to synonymous substitutions (dN/dS ratio), calculated from different species from the same taxon. The neutral expectation for this ratio is 1; most protein-coding genes have a ratio below 1, indicating selective constraint, although a gene under strong directional selection may have a ratio above 1. A ratio below 1 is thus taken as evidence for selection against loss of function.Similarly, in the case of species-specific genes, polymorphism data may be used to calculate a pN/pS ratio from different strains or populations of the focal species. Given that young, species-specific de novo genes lack deep conservation by definition, detecting statistically significant deviations from 1 can be difficult without an unrealistically large number of sequenced strains/populations. An example of this can be seen in Mus musculus, where three very young de novo genes lack signatures of selection despite well-demonstrated physiological roles. For this reason, pN/pS approaches are often applied to groups of candidate genes, allowing researchers to infer that at least some of them are evolutionarily conserved, without being able to specify which. Other signatures of selection, such as the degree of nucleotide divergence within syntenic regions, conservation of ORF boundaries, or for protein-coding genes, a coding score based on nucleotide hexamer frequencies, have instead been employed.
Despite these and other challenges in the identification of de novo gene birth events, there is now abundant evidence indicating that the phenomenon is not only possible, but has occurred in every lineage systematically examined thus far.
Estimates regarding the frequency of de novo gene birth and the number of de novo genes in various lineages vary widely and are highly dependent on methodology. Studies may identify de novo genes by phylostratigraphy/BLAST-based methods alone, or may employ a combination of computational techniques (see above), and may or may not assess experimental evidence for expression and/or biological role.Furthermore, genome-scale analyses may consider all or most ORFs in the genome , or may instead limit their analysis to previously annotated genes.
The D. melanogaster lineage is illustrative of these differing approaches. An early survey using a combination of BLAST searches performed on cDNA sequences along with manual searches and synteny information identified 72 new genes specific to D. melanogaster and 59 new genes specific to three of the four species in the D. melanogaster species complex. This report found that only 2/72 (~2.8%) of D. melanogaster-specific new genes and 7/59 (~11.9%) of new genes specific to the species complex were derived de novo,with the remainder arising via duplication/retroposition. Similarly, an analysis of 195 young (<35 million years old) D. melanogaster genes identified from syntenic alignments found that only 16 had arisen de novo. In contrast, an analysis focused on transcriptomic data from the testes of six D. melanogaster strains identified 106 fixed and 142 segregating de novo genes. For many of these, ancestral ORFs were identified but were not expressed. Highlighting the differences between inter- and intra-species comparisons, a study in natural Saccharomyces paradoxus populations found that the number of de novo polypeptides identified more than doubled when considering intra-species diversity. In primates, one early study identified 270 orphan genes (unique to humans, chimpanzees, and macaques), of which 15 were thought to have originated de novo, while a later report identified 60 de novo genes in humans alone that are supported by transcriptional and proteomic evidence. Studies in other lineages/organisms have also reached different conclusions with respect to the number of de novo genes present in each organism, as well as the specific sets of genes identified. A sample of these large-scale studies is described in the table below.
A reanalysis of three such studies in murines that identified between 69 and 773 candidate de novo genes argued that the various estimates included many genes that were not in fact de novo genes.Many candidates were excluded on the basis of no longer being annotated in the major databases. A conservative approach was applied to the remaining genes, which excluded candidates with paralogs, distantly related homologs or conserved domains, or that lacked syntenic sequence information in non-rodents. This approach validated ~40% of candidate de novo genes, resulting in an upper estimate of only 11.6 de novo genes formed (and retained) per million years, a rate ~5-10 times slower than what was estimated for novel genes formed by duplication. It is notable that even after application of this stringent pipeline, the 152 validated de novo genes that remained still represents a significant fraction of the mouse genome likely to have originated de novo. Generally speaking, however, it remains debated whether duplication and divergence or de novo gene birth represent the dominant mechanism for the emergence of new genes, in part due to the fact that de novo genes are likely both to emerge and to be lost more frequently than other young genes (see below).
It is important to distinguish between the frequency of de novo gene birth and the number of de novo genes in a given lineage. If de novo gene birth is frequent, it might be expected that genomes would tend to grow in their gene content over time; however, the gene content of genomes is usually relatively stable.This implies that a frequent gene death process must balance de novo gene birth, and indeed, de novo genes are distinguished by their rapid turnover relative to established genes. In support of this notion, recently emerged Drosophila genes are much more likely to be lost, primarily through pseudogenization, with the youngest orphans being lost at the highest rate; this is despite the fact that some Drosophila orphan genes have been shown to rapidly become essential. A similar trend of frequent loss among young gene families was observed in nematode genus Pristionchus . In wild S. paradoxus populations, de novo ORFs emerge and are lost at similar rates. Similarly, an analysis of five mammalian transcriptomes found that most ORFs in mice were either very old or species specific, implying frequent birth and death of de novo transcripts. Nevertheless, there remains a positive correlation between the number of species-specific genes in a genome and the evolutionary distance from its most recent ancestor. In addition to the birth and death of de novo genes at the level of the ORF, mutational and other processes also subject genomes to constant “transcriptional turnover”. One study in murines found that while all regions of the ancestral genome were transcribed at some point in at least one descendant, the portion of the genome under active transcription in a given strain or subspecies is subject to rapid change. The transcriptional turnover of noncoding RNA genes is particularly fast as compared to that of coding genes.
Recently emerged de novo genes differ from established genes in a number of ways. Across a broad range of species, young and/or taxonomically restricted genes or ORFs have been reported to be shorter in length than established genes, to evolve more rapidly, and to be less expressed.Although these trends are also expected to occur as a result of homology detection bias (see Genomic phylostratigraphy section above), a reanalysis of several studies that reduced this bias by removing genes whose ages are more challenging to determine found that the qualitative conclusions reached in these studies were unaffected. In addition, the tendency for young genes to have fewer hydrophobic amino acids, and to have them more clustered near one another along the primary sequence, have been statistically controlled for evolutionary rate and for length, and so are not due to homology detection bias.
The expression of young genes has also been found to be more tissue- or condition-specific than that of established genes.In particular, relatively high expression of de novo genes was observed in male reproductive tissues in Drosophila, mice, and humans (see below), and, in humans, in the cerebral cortex or the brain more generally. In animals with adaptive immune systems, higher expression in the brain and testes may at least in part be a function of the immune-privileged nature of these tissues. An analysis in mice found specific expression of intergenic transcripts in the thymus and spleen (in addition to the brain and testes), and it has been proposed that in vertebrates de novo transcripts must first be expressed in these tissues before they can be expressed in tissues subject to surveillance by immune cells.
Older genes have more transcription factor regulation, indicative of their integration into larger molecular networks, displays a similar trend. Similarly, the likelihood of physical interactions, as well as the likelihood and strength of genetic interactions, is correlated with ORF age as determined by phylostratigraphy.
Features of de novo genes can depend on the species or lineage being examined. This appears to partly be a result of the fact that genomes vary in their GC content, and young genes bear more similarity to non-genic sequences from the genome in which they arose than do established genes.Features such as the percentage of transmembrane residues and the relative frequency of various predicted secondary structural features show a strong GC dependency in orphan genes, whereas in more ancient genes these features are only weakly influenced by GC content.
The relationship between gene age and the amount of predicted intrinsic structural disorder (ISD) in the encoded proteins has been subject to considerable debate. It has been claimed that ISD is also a lineage-dependent feature, exemplified by the fact that in organisms with relatively high GC content, ranging from D. melanogaster to the parasite Leishmania major , young genes have high ISD,while in a low GC genome such as budding yeast, several studies have shown that young genes have low ISD. However, a study that excluded young genes with dubious evidence for functionality, defined in binary terms as being under selection for gene retention, found that the remaining young yeast genes have high ISD, suggesting that the yeast result may be due to contamination of the set of young genes with ORFs that do not meet this definition, and hence are more likely to have properties that reflect GC content and other non-genic features of the genome. Beyond the very youngest orphans, this study found that ISD tends to decrease with increasing gene age, and that this is primarily due to amino acid composition rather than GC content per se. Within shorter time scales, a focus on de novo genes that have the most validation suggests that younger genes are more disordered in Lachancea, but less disordered in Saccharomyces.
An examination of de novo genes in A. thaliana found that they are both hypermethylated and generally depleted of histone modifications.In agreement with either the proto-gene model or contamination with non-genes (see below), methylation levels of de novo genes were intermediate between established genes and intergenic regions. The methylation patterns of these de novo genes are stably inherited, and methylation levels were highest, and most similar to established genes, in de novo genes with verified protein-coding ability. In the pathogenic fungus Magnaporthe oryzae, less conserved genes tend to have methylation patterns associated with low levels of transcription. A study in yeasts also found that de novo genes are enriched at recombination hotspots, which tend to be nucleosome-free regions.
In Pristionchus pacificus , orphan genes with confirmed expression display chromatin states that differ from those of similarly expressed established genes.Orphan gene start sites have epigenetic signatures that are characteristic of enhancers, in contrast to conserved genes that exhibit classical promoters. Many unexpressed orphan genes are decorated with repressive histone modifications, while a lack of such modifications facilitates transcription of an expressed subset of orphans, supporting the notion that open chromatin promotes the formation of novel genes.
Several theoretical models and possible mechanisms of de novo gene birth have been described. The models are generally not mutually exclusive, and it is possible that multiple mechanisms may give rise to de novo genes.
For birth of a de novo protein-coding gene to occur, a non-genic sequence must both be transcribed and acquire an ORF before becoming translated. These events may in theory occur in either order, and there is evidence supporting both an “ORF first” and a “transcription first” model.An analysis of de novo genes that are segregating in D. melanogaster with respect to their expression found that sequences that are transcribed had similar coding potential to the orthologous sequences from lines lacking evidence of transcription, supporting the notion that many ORFs, at least, exist prior to being expressed. The antifreeze glycoprotein gene AFGP, which emerged de novo in Arctic codfishes, provides a more definitive example in which the de novo emergence of the ORF was shown to precede that of the promoter region. Furthermore, putatively non-genic ORFs long enough to encode functional peptides are numerous in eukaryotic genomes, and expected to occur at high frequency by chance. At the same time, transcription of eukaryotic genomes is far more extensive than previously thought, and documented examples also exist of genomic regions that were transcribed prior to the appearance of an ORF that became a de novo gene. The proportion of de novo genes that are protein-coding is unknown, but the appearance of “transcription first” has led some to posit that protein-coding de novo genes may first exist as RNA gene intermediates. The case of bifunctional RNAs, which are both translated and function as RNA genes, shows that such a mechanism is plausible.
The two events may occur simultaneously when chromosomal rearrangement is the event that precipates gene birth.
An early case study of de novo gene birth, which identified five de novo genes in D. melanogaster, noted preferential expression of these genes in the testes,and several additional de novo genes were identified using transcriptomic data derived from the testes and male accessory glands of D. yakuba and D. erecta (see above). This was in keeping with the rapid evolution of genes related to reproduction that has been observed across a range of lineages, suggesting that sexual selection may play a key role in adaptive evolution and de novo gene birth. A subsequent large-scale analysis of six D. melanogaster strains identified 248 testis-expressed de novo genes, of which ~57% were not fixed. It has been suggested that the large number of de novo genes with male-specific expression identified in Drosophila is likely due to the fact that such genes are preferentially retained relative to other de novo genes, for reasons that are not entirely clear. Interestingly, two putative de novo genes in Drosophila (Goddard and Saturn) were shown to be required for normal male fertility.
In humans, a study that identified 60 human-specific de novo genes found that their average expression, as measured by RNA-seq, was highest in the testes.Another study looking at mammalian-specific genes more generally also found enriched expression in the testes. Transcription in mammalian testes is thought to be particularly promiscuous, due in part to elevated expression of the transcription machinery and an open chromatin environment. Along with the immune-privileged nature of the testes (see above), this promiscuous transcription is thought to create the ideal conditions for the expression of non-genic sequences required for de novo gene birth. Testes-specific expression seems to be a general feature of all novel genes, as an analysis of Drosophila and vertebrate species found that young genes showed testes-biased expression regardless of their mechanism of origination.
With the development and wide use of technologies such as RNA-seq and Ribo-seq, eukaryotic genomes are now known to be pervasively transcribedand translated. Many ORFs that are either unannotated, or annotated as long non-coding RNAs (lncRNAs), are translated at some level, under at least some condition, or in a particular tissue. Though infrequent, these translation events expose non-genic sequence to selection. This pervasive expression forms the basis for several models describing de novo gene birth.
Most non-genic ORFs that are translated appear to be evolving neutrally.The preadaptation and proto-gene models both predict, however, that expression of non-genic ORFs will occasionally provide an adaptive advantage to the cell. Differential translation of proto-genes in stress conditions, as well as an enrichment near proto-genes of binding sites for transcription factors involved in regulating stress response, support the adaptive potential of proto-genes. Furthermore, it is known that novel, functional proteins can be experimentally evolved from random amino acid sequences. Random sequences are generally well-tolerated in vivo; many readily form secondary structures, and even highly disordered proteins may take on important biological roles. The pervasive nature of translation suggests that new proto-genes emerge frequently, usually returning to the non-genic state. In wild S. paradoxus populations, some ORFs with exaggerated gene-like features are found among the pool of translated intergenic polypeptides. It is not clear whether such ORFs are preferentially retained.
It has been speculated that the epigenetic landscape of de novo genes in the early stages of formation may be particularly variable between and among populations, resulting in variable levels of gene expression and thereby allowing young genes to explore the “expression landscape.”The QQS gene in A. thaliana is one example of this phenomenon; its expression is negatively regulated by DNA methylation that, while heritable for several generations, varies widely in its levels both among natural accessions and within wild populations. Epigenetics are also largely responsible for the permissive transcriptional environment in the testes, particularly through the incorporation into nucleosomes of non-canonical histone variants that are replaced by histone-like protamines during spermatogenesis.
The preadaptation model of de novo gene birth uses mathematical modeling to show that when sequences that are normally hidden are exposed to weak or shielded selection, the resulting pool of “cryptic” sequences (i.e. proto-genes) can be purged of “self-evidently deleterious” variants, such as those prone to lead to protein aggregation, and thus enriched in potential adaptations relative to a completely non-expressed and unpurged set of sequences.This revealing and purging of cryptic deleterious non-genic sequences is a byproduct of pervasive transcription and translation of intergenic sequences, and is expected to facilitate the birth of functional de novo protein-coding genes. This is because by eliminating the most deleterious variants, what is left is, by a process of elimination, more likely to be adaptive than expected from random sequences.
The mathematics of the preadaptation model assume that the distribution of fitness effects is bimodal, with new sequences of mutations tending to break something or tinker, but rarely in between.From this it is derived that populations may either evolve local solutions, in which selection operates on each individual locus and a relatively high error rate is maintained, or the global solution of a low error rate which permits the accumulation of deleterious cryptic sequences. De novo gene birth is thought to be favored in populations that evolve local solutions, as the relatively high error rate will result in a pool of cryptic variation that is “preadapted” through the purging of deleterious sequences. Local solutions are more likely in populations with a high effective population size.
This proto-gene model agrees with the preadaptation model about the importance of pervasive expression, and refers to the set of pervasively expressed sequences that do not meet all definitions of a gene as “proto-genes”.Where it differs is that it that envisages a more gradual process under selection from non-genic to genic state, rejecting binary classification, with proto-genes expected to exhibit features intermediate between genes and non-genes.
Using the evolutionary definition of function (i.e. that a gene is by definition under purifying selection against loss), the preadaptation model assumes that “gene birth is a sudden transition to functionality”that occurs as soon as an ORF acquires a net beneficial effect. In order to avoid being deleterious, newborn genes are expected to display exaggerated versions of genic features associated with the avoidance of harm. This is in contrast to the proto-gene model, which expects newborn genes to have features intermediate between old genes and non-genes.
Several features of ORFs correlate with ORF age as determined by phylostratigraphic analysis (see above), with young ORFs having properties intermediate between old ORFs and non-genes; this has been taken as evidence in favor of the proto-gene model, in which proto-gene state is a continuum .This evidence has been criticized, because the same apparent trends are also expected under a model in which identity as a gene is a binary. Under this model, when each age group contains a different ratio of genes vs. non-genes, Simpson's paradox can generate correlations in the wrong direction.
More specifically, in support of the preadaptation model, an analysis of ISD in mice and yeast found that young genes have higher ISD than old genes, while random non-genic sequences tend to show the lowest levels of ISD.Although the observed trend may have partly resulted from a subset of young genes derived by overprinting, higher ISD in young genes is also seen among overlapping viral gene pairs. Reaching consensus over ISD values of the very youngest genes is made difficult by different annotation standards, as well as by disagreement over whether genes represent a binary or a continuous category. When proto-genes with less evidence for a selected function are excluded from the data in which a continuum was seen , the slope of the ISD trend is reversed. However, there remains uncertainty about whether the observed trends hold consistently over shorter timescales. With respect to other predicted structural features such as β-strand content and aggregation propensity, the peptides encoded by proto-genes are similar to non-genic sequences and categorically distinct from canonical genes.
The “grow slow and moult” model describes a potential mechanism of de novo gene birth, particular to protein-coding genes. In this scenario, existing protein-coding ORFs expand at their ends, especially their 3’ ends, leading to the creation of novel N- and C-terminal domains.Novel C-terminal domains may first evolve under weak selection via occasional expression through read-through translation, as in the preadaptation model, only later becoming constitutively expressed through a mutation that disrupts the stop codon. Genes experiencing high translational readthrough tend to have intrinsically disordered C-termini. Furthermore, existing genes are often close to repetitive sequences that encode disordered domains. These novel, disordered domains may initially confer some non-specific binding capability that becomes gradually refined by selection. Sequences encoding these novel domains may occasionally separate from their parent ORF, leading or contributing to the creation of a de novo gene. Interestingly, an analysis of 32 insect genomes found that novel domains (i.e. those unique to insects) tend to evolve fairly neutrally, with only a few sites under positive selection, while their host proteins remain under purifying selection, suggesting that new functional domains emerge gradually and somewhat stochastically.
In addition to its significance for the field of evolutionary biology, de novo gene birth has implications for human health. It has been speculated that novel genes, including de novo genes, may play an outsized role in species-specific traits;however, many species-specific genes lack functional annotation. Nevertheless, there is evidence to suggest that human-specific de novo genes are involved in disease processes such as cancer. NYCM, a de novo gene unique to humans and chimpanzees, regulates the pathogenesis of neuroblastomas in mouse models, and the primate-specific PART1, an lncRNA gene, has been identified as both a tumor suppressor and an oncogene in different contexts. Several other human- or primate-specific de novo genes, including PBOV1, GR6, MYEOV, ELFN1-AS1, and CLLU1, are also linked to cancer. Some have even suggested considering tumor-specifically expressed, evolutionary novel genes as their own class of genetic elements, noting that many such genes are under positive selection and may be neofunctionalized in the context of tumors.
The specific expression of many de novo genes in the human brainalso raises the intriguing possibility that de novo genes influence human cognitive traits. One such example is FLJ33706, a de novo gene that was identified in GWAS and linkage analyses for nicotine addiction and shows elevated expression in the brains of Alzheimer’s patients. Generally speaking, expression of young, primate-specific genes is enriched in the fetal human brain relative to the expression of similarly young genes in the mouse brain. Most of these young genes, several of which originated de novo, are expressed in the neocortex, which is thought to be responsible for many aspects of human-specific cognition. Many of these young genes show signatures of positive selection, and functional annotations indicate that they are involved in diverse molecular processes, but are enriched for transcription factors.
In addition to their roles in cancer processes, de novo originated human genes have been implicated in the maintenance of pluripotencyand in immune function. The preferential expression of de novo genes in the testes (see above) is also suggestive of a role in reproduction. Given that the function of many de novo human genes remains uncharacterized, it seems likely that an appreciation of their contribution to human health and development will continue to grow.
|Organism/Lineage||Homology Detection Method(s)||Evidence of Expression?||Evidence of Selection?||Evidence of Physiological Role?||# Orphan/De Novo Genes||Notes||Ref.|
|Arthropods||BLASTP for all 30 species against each other, TBLASTN for Formicidae only, searched by synteny for unannotated orthologs in Formicidae only||ESTs, RNA-seq; RT-PCR on select candidates||37 Formicidae-restricted orthologs appear under positive selection (M1a to M2a and M7 to M8 models using likelihood ratio tests); as a group, Formicidae-restricted orthologs have a significantly higher Ka/Ks rate than non-restricted orthologs||Prediction of signal peptides and subcellular localization for subset of orphans||~65,000 orphan genes across 30 species||Abundance of orphan genes dependent on time since emergence from common ancestor; >40% of orphans from intergenic matches indicating possible de novo origin|
|Arabidopsis thaliana||BLASTP against 62 species, PSI-BLAST against NCBI nonredundant protein database, TBLASTN against PlantGDB-assembled unique transcripts database; searched syntenic region of two closely related species||Transcriptomic and translatomic data from multiple sources||Allele frequencies of de novo genes correlated with their DNA methylation levels||None||782 de novo genes||Also assessed DNA methylation and histone modifications|
|Bombyx mori||BLASTP against four lepidopterans, TBLASTN against lepidopteran EST sequences, BLASTP against NCBI nonredundant protein database||Microarray, RT-PCR||None||RNAi on five de novo genes produced no visible phenotypes||738 orphan genes||Five orphans identified as de novo genes|
|Brassicaceae||BLASTP against NCBI nonredundant protein database, TBLASTN against NCBI nucleotide database, TBLASTN against NCBI EST database, PSI-BLAST against NCBI nonredundant protein database, InterProScan||Microarray||None||TRGs enriched for expression changes in response to abiotic stresses compared to other genes||1761 nuclear TRGs; 28 mitochondrial TRGs||~2% of TRGs thought to be de novo genes|
|Drosophila melanogaster||BLASTN of query cDNAs against D. melanogaster, D. simulans and D. yakuba genomes; also performed check of syntenic region in sister species||cDNA/ expressed sequence tags (ESTs)||Ka/Ks ratios calculated between retained new genes and their parental genes are significantly >1, indicating most new genes are functionally constrained||List includes several genes with characterized molecular roles||72 orphan genes; 2 de novo genes||Gene duplication dominant mechanism for new genes; 7/59 orphans specific to D. melanogaster species complex identified as de novo|
|Drosophila melanogaster||Presence or absence of orthologs in other Drosophila species inferred by synteny based on UCSC genome alignments and FlyBase protein-based synteny; TBLASTN against Drosophila subgroup||Indirect (RNAi)||Youngest essential genes show signatures of positive selection (α=0.25 as a group)||Knockdown with constitutive RNAi lethal for 59 TRGs||195 “young” (>35myo) TRGs; 16 de novo genes||Gene duplication dominant mechanism for new genes|
|Drosophila melanogaster||RNA-seq in D. melanogaster and close relatives; syntenic alignments with D. simulans and D. yakuba; BLASTP against NCBI nonredundant protein database||RNA-seq||Nucleotide diversity lower in non-expressing relatives; Hudson-Kreitman-Aguade-like statistic lower in fixed de novo genes than in intergenic regions||Structural features of de novo genes (e.g. enrichment of long ORFs) suggestive of function||106 fixed and 142 segregating de novo genes||Specifically expressed in testes|
|Homo sapiens||BLASTP against other primates; BLAT against chimpanzee and orangutan genomes, manual check of syntenic regions in chimpanzee and orangutan||RNA-seq||Substitution rate provides some evidence for weak selection; 59/60 de novo genes are fixed||None||60 de novo genes||Enabling mutations identified; highest expression seen in brain and testes|
|Homo sapiens||BLASTP against chimpanzee, BLAT and Search of syntenic region in chimpanzee, manual check of syntenic regions in chimpanzee and macaque||EST/cDNA||No evidence of selective constraint seen by nucleotide divergence||One of the genes identified has a known role in leukemia||3 de novo genes||Estimated that human genome contains ~ 18 human-specific de novo genes|
|Lachancea and Saccharomyces||BLASTP of all focal species against each other, BLASTP against NCBI nonredundant protein database, PSI-BLAST against NCBI nonredundant protein database, HMM Profile-Profile of TRG families against each other; families then merged and searched against four profile databases||Mass Spectrometry (MS)||Ka/Ks ratios across Saccharomyces indicate that candidates are under weak selection that increases with gene age; in Lachancea species with multiple strains, pN/pS ratios are lower for de novo candidates than for "spurious TRGs"||None||288 candidate de novo TRGs in Saccharomyces, 415 in Lachancea||MS evidence of translation for 25 candidates|
|Mus musculus and Rattus norvegicus||BLASTP of rat and mouse against each other, BLASTP against Ensembl compara database; searched syntenic regions in rat and mouse||UniGene Database||Subset of genes shows low nucleotide diversity and high ORF conservation across 17 strains||Two mouse genes cause morbidity when knocked out||69 de novo genes in mouse and 6 "de novo" genes in rat||Enabling mutations identified for 9 mouse genes|
|Mus musculus||BLASTP against NCBI nonredundant protein database||Microarray||None||None||781 orphan genes||Age-dependent features of genes compatible with de novo emergence of many orphans|
|Oryza||Protein-to-protein and nucleotide-to-nucleotide BLAT against eight Oryza species and two outgroup species; searched syntenic regions of these species for coding potential||RNA-seq (all de novo TRGs); Ribosome Profiling and targeted MS (some de novo TRGs)||22 de novo candidates appear under negative selection, and six under positive selection, as measured by Ka/Ks rate||Expression of de novo TRGs is tissue-specific||175 de novo TRGs||~57% of de novo genes have translational evidence; transcription predates coding potential in most cases|
|Primates||BLASTP against 15 eukaryotes, BLASTN against human genome, analysis of syntenic regions||ESTs||Ka/Ks ratios for TRGs below one but higher than established genes; coding scores consistent with translated proteins||Several genes have well-characterized cellular roles||270 TRGs||~5.5% of TRGs estimated to have originated de novo|
|Pristionchus pacificus||BLASTP and tBLASTN, syntenic analysis||RNA-Seq||2 cases complete de novo gene origination||27 other high-confidence orphans whose methods of origin included annotation artifacts, chimeric origin, alternative reading frame usage, and gene splitting with subsequent gain of de novo exons|
|Rodentia||BLASTP against NCBI nonredundant protein database||None||Mouse genes share 50% identity with rat ortholog||None||84 TRGs||Species-specific genes excluded from analysis; results robust to evolutionary rate|
|Saccharomyces cerevisiae||BLASTP and PSI-BLAST against 18 fungal species, HMMER and HHpred against several databases, TBLASTN against three close relatives||None||None||Majority of orphans have characterized fitness effects||188 orphan genes||Ages of genes determined at level of individual residues|
|Saccharomyces cerevisiae||BLASTP, TBLASTX, and TBLASTN against 14 other yeast species, BLASTP against NCBI nonredundant protein database||Ribosome Profiling||All 25 de novo genes, 115 proto-genes under purifying selection (pN/pS < 1)||None||25 de novo genes; 1,891 “proto-genes”||De novo gene birth more common than new genes from duplication; proto-genes are unique to Saccharomyces ( Sensu stricto ) yeasts|
|Saccharomyces cerevisiae||BLASTN, TBLASTX, against nt/nr, manual inspection of syntenic alignment||transcripts believed to be non-coding, manual inspection of ribosome profiling traces||None||None||1 de novo candidate gene, 217 ribosome-associated transcripts||Candidate de novo gene is polymorphic. Ribosomal profiling data is the same as in|
|Saccharomyces sensu strictu||BLASTP against NCBI nonredundant protein database, TBLASTN against ten outgroup species; BLASTP and phmmer against 20 yeast species reannotated using syntenic alignments||Transcript isoform sequencing (TIF-seq), Ribosome Profiling||Most genes weakly constrained but a subset under strong selection, according to Neutrality Index, Direction of Selection, Ka/Ks, and McDonald-Kreitman tests||Subcellular localization demonstrated for five genes||~13,000 de novo genes||>65% of de novo genes are isoforms of ancient genes; >97% from TIF-seq dataset|
Note: For purposes of this table, genes are defined as orphan genes (when species-specific) or TRGs (when limited to a closely related group of species) when the mechanism of origination has not been investigated, and as de novo genes when de novo origination has been inferred, irrespective of method of inference. The designation of de novo genes as “candidates” or “proto-genes” reflects the language used by the authors of the respective studies.
A transposable element is a DNA sequence that can change its position within a genome, sometimes creating or reversing mutations and altering the cell's genetic identity and genome size. Transposition often results in duplication of the same genetic material. Barbara McClintock's discovery of them earned her a Nobel Prize in 1983.
The human genome is a complete set of nucleic acid sequences for humans, encoded as DNA within the 23 chromosome pairs in cell nuclei and in a small DNA molecule found within individual mitochondria. These are usually treated separately as the nuclear genome, and the mitochondrial genome. Human genomes include both protein-coding DNA genes and noncoding DNA. Haploid human genomes, which are contained in germ cells consist of three billion DNA base pairs, while diploid genomes have twice the DNA content. While there are significant differences among the genomes of human individuals, these are considerably smaller than the differences between humans and their closest living relatives, the bonobos and chimpanzees.
Non-coding DNA sequences are components of an organism's DNA that do not encode protein sequences. Some non-coding DNA is transcribed into functional non-coding RNA molecules. Other functions of non-coding DNA include the transcriptional and translational regulation of protein-coding sequences, scaffold attachment regions, origins of DNA replication, centromeres and telomeres.
Molecular evolution is the process of change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes. Major topics in molecular evolution concern the rates and impacts of single nucleotide changes, neutral evolution vs. natural selection, origins of new genes, the genetic nature of complex traits, the genetic basis of speciation, evolution of development, and ways that evolutionary forces influence genomic and phenotypic changes.
In genetics, an operon is a functioning unit of DNA containing a cluster of genes under the control of a single promoter. The genes are transcribed together into an mRNA strand and either translated together in the cytoplasm, or undergo splicing to create monocistronic mRNAs that are translated separately, i.e. several strands of mRNA that each encode a single gene product. The result of this is that the genes contained in the operon are either expressed together or not at all. Several genes must be co-transcribed to define an operon.
Pseudogenes are segments of DNA that are related to real genes. Pseudogenes have lost at least some functionality, relative to the complete gene, in cellular gene expression or protein-coding ability. Pseudogenes often result from the accumulation of multiple mutations within a gene whose product is not required for the survival of the organism, but can also be caused by genomic copy number variation (CNV) where segments of 1+ kb are duplicated or deleted. Like other kinds of noncoding DNA, segments of pseudogenes can perform regulatory functions, and somehave important roles in normal physiology and abnormal pathology.
Regulation of gene expression, or gene regulation, includes a wide range of mechanisms that are used by cells to increase or decrease the production of specific gene products. Sophisticated programs of gene expression are widely observed in biology, for example to trigger developmental pathways, respond to environmental stimuli, or adapt to new food sources. Virtually any step of gene expression can be modulated, from transcriptional initiation, to RNA processing, and to the post-translational modification of a protein. Often, one gene regulator controls another, and so on, in a gene regulatory network.
Hox genes, a subset of homeobox genes, are a group of related genes that specify regions of the body plan of an embryo along the head-tail axis of animals. Hox proteins encode and specify the characteristics of 'position', ensuring that the correct structures form in the correct places of the body. For example, Hox genes in insects specify which appendages form on a segment, and Hox genes in vertebrates specify the types and shape of vertebrae that will form. In segmented animals, Hox proteins thus confer segmental or positional identity, but do not form the actual segments themselves.
Cis-regulatory elements (CREs) are regions of non-coding DNA which regulate the transcription of neighboring genes. CREs are vital components of genetic regulatory networks, which in turn control morphogenesis, the development of anatomy, and other aspects of embryonic development, studied in evolutionary developmental biology.
In biology, a gene is a sequence of nucleotides in DNA or RNA that encodes the synthesis of a gene product, either RNA or protein.
Gene structure is the organisation of specialised sequence elements within a gene. Genes contain the information necessary for living cells to survive and reproduce. In most organisms, genes are made of DNA, where the particular DNA sequence determines the function of the gene. A gene is transcribed (copied) from DNA into RNA, which can either be non-coding (ncRNA) with a direct function, or an intermediate messenger (mRNA) that is then translated into protein. Each of these steps is controlled by specific sequence elements, or regions, within the gene. Every gene, therefore, requires multiple sequence elements to be functional. This includes the sequence that actually encodes the functional protein or ncRNA, as well as multiple regulatory sequence regions. These regions may be as short as a few base pairs, up to many thousands of base pairs long.
Orphan genes are genes without detectable homologues in other lineages. Orphans are a subset of taxonomically-restricted genes (TRGs), which are unique to a specific taxonomic level. In contrast to non-orphan TRGs, orphans are usually considered unique to a very narrow taxon, generally a species.
Long non-coding RNAs are a type of RNA, defined as being transcripts with lengths exceeding 200 nucleotides that are not translated into protein. This somewhat arbitrary limit distinguishes long ncRNAs from small non-coding RNAs such as microRNAs (miRNAs), small interfering RNAs (siRNAs), Piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), and other short RNAs. Long intervening/intergenic noncoding RNAs (lincRNAs) are sequences of lncRNA which do not overlap protein-coding genes.
RNA-based evolution is a theory that posits that RNA is not merely an intermediate between Watson and Crick model of the DNA molecule and proteins, but rather a far more dynamic and independent role-player in determining phenotype. By regulating the transcription in DNA sequences, the stability of RNA, and the capability of messenger RNA to be translated, RNA processing events allow for a diverse array of proteins to be synthesized from a single gene. Since RNA processing is heritable, it is subject to natural selection suggested by Darwin and contributes to the evolution and diversity of most eukaryotic organisms.
Genome evolution is the process by which a genome changes in structure (sequence) or size over time. The study of genome evolution involves multiple fields such as structural analysis of the genome, the study of genomic parasites, gene and ancient genome duplications, polyploidy, and comparative genomics. Genome evolution is a constantly changing and evolving field due to the steadily growing number of sequenced genomes, both prokaryotic and eukaryotic, available to the scientific community and the public at large.
Robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
An overlapping gene is a gene whose expressible nucleotide sequence partially overlaps with the expressible nucleotide sequence of another gene. In this way, a nucleotide sequence may make a contribution to the function of one or more gene products. Overprinting refers to a type of overlap in which all or part of the sequence of one gene is read in an alternate reading frame from another gene at the same locus. Overprinting has been hypothesized as a mechanism for de novo emergence of new genes from existing sequences, either older genes or previously non-coding regions of the genome. Overprinted genes are particularly common features of the genomic organization of viruses, likely to greatly increase the number of potential expressible genes from a small set of viral genetic information.
RNA silencing suppressor p19 is a protein expressed from the ORF4 gene in the genome of tombusviruses. These viruses are positive-sense single-stranded RNA viruses that infect plant cells, in which RNA silencing forms a widespread and robust antiviral defense system. The p19 protein serves as a counter-defense strategy, specifically binding the 19- to 21-nucleotide double-stranded RNAs that function as small interfering RNA (siRNA) in the RNA silencing system. By sequestering siRNA, p19 suppresses RNA silencing and promotes viral proliferation. The p19 protein is considered a significant virulence factor and a component of an evolutionary arms race between plants and their pathogens.
Transcriptomics technologies are the techniques used to study an organism’s transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology lies in understanding how the same genome can give rise to different cell types and how gene expression is regulated.
The G-value paradox arises from the lack of correlation between the number of protein-coding genes among eukaryotes and their relative biological complexity. The microscopic nematode Caenorhabditis elegans, for example, is composed of only a thousand cells but has about the same number of genes as a human. Researchers suggest resolution of the paradox may lie in mechanisms such as alternative splicing and complex gene regulation that make the genes of humans and other complex eukaryotes relatively more productive.