Genetic Novelty: How new genes are born
For half a century, most scientists believed that new protein-coding genes arise as a result of mutations in existing protein-coding genes. It was considered impossible for anything as complex as a functional new protein to arise from scratch. However, every species has certain genes, known as 'orphan genes', which code for proteins that are not homologous to proteins found in any other species. What do these orphan genes do, and how are they formed?
To date the roles of hundreds of orphan genes have been characterized. Although this is just a tiny fraction of the total, it is known that most of them code for proteins that bind to conserved proteins such as transcription factors or receptors. Some of these proteins are toxins, some are involved in reproduction, some integrate into existing metabolic and regulatory networks, and some confer resistance to stress (Carvunis et al., 2012; Li et al., 2009; Xiao et al., 2009; Arendsee et al., 2014; Belcaid et al., 2019). However, none of them are enzymes (Arendsee et al., 2014). Orphan genes arise quickly, so they may provide a disruptive mechanism that allows a given species to survive changes to its environment. Thus, the study of how orphan genes arise (and fall) is central to understanding the forces that drive evolution (Figure 1).
One possible mechanism is the 'de novo' appearance of a gene from an intergenic region or a completely new reading frame within an existing gene (Tautz and Domazet-Lošo, 2011). An alternative mechanism is that the coding sequence of the orphan gene arises by rapid divergence from the coding sequence of a preexisting gene: this would mean that an entire set of regulatory and structural elements would be available to the gene as it evolves. Now, in eLife, Nikolaos Vakirlis and Aoife McLysaght (both from Trinity College Dublin) and Anne-Ruxandra Carvunis (University of Pittsburgh) report how they have studied yeast, fly and human genes to compare the contributions of these two mechanisms to the emergence of orphan genes (Vakirlis et al., 2020).
Previous studies have used simulations to estimate the number of orphan genes that appear by divergence; until now, no one had relied on actual genomics data to study this phenomenon. Vakirlis et al. use a new approach to analyze orphan genes that have originated through divergence. They examine regions of the genome that correspond to each other (so-called syntenic regions) in related species to determine whether a gene exists in both regions and, if so, whether the proteins are non-homologous. If the genes have no homology, they may have originated by rapid divergence from the coding sequence of a preexisting gene.
Using this method, Vakirlis et al. infer that at most 45% of S. cerevisiae (yeast) orphan genes, 25% of D. melanogaster (fruit fly) orphan genes, and 18% of human orphan genes arose by rapid divergence, but this is an upper estimate. For example, it is possible that a new coding sequence might have arisen de novo within an existing gene, rather than the existing coding sequence having been modified beyond recognition.
But how can a protein sequence continue to be selected for as it rapidly diverges? Vakirlis et al. suggest that divergence might occur by a process of partial pseudogenation: the existing gene becomes non-functional, and then, with no selection pressure to retain the old protein, it diverges to form an orphan gene.
Many orphan genes may not have been identified yet, because they do not have homologs in other species, and have few recognizable sequence features. This means that up to 80% of orphan genes can be missed when a new genome is annotated (Seetharam et al., 2019). The approach detailed by Vakirlis, Carvunis and McLysaght evaluates specifically those annotated orphan genes for which a similar gene exists in a related species (which is ~50% of them; Arendsee et al., 2019). As high-quality genomes from more species become available, and as more orphan genes are annotated, the approach will provide yet deeper insights into the origin of these genes.
One of the many open questions in this field deals with genes of ‘mixed age’. Some such genes have incorporated ‘chunks’ of orphans into their coding sequences. A gene that has done this is (somewhat arbitrarily) considered to be the age of its most ancient segment, but we know little about the mechanism of this process or its significance. Another question involves the unique strategies and rates of evolution of each gene (Revell et al., 2018). How might the abundance and mechanisms of orphan gene origin vary among species? And how do different environments affect the emergence of orphan genes?
References
-
Coming of age: orphan genes in plantsTrends in Plant Science 19:698–708.https://doi.org/10.1016/j.tplants.2014.07.003
-
Phylostratr: a framework for phylostratigraphyBioinformatics 35:3617–3627.https://doi.org/10.1093/bioinformatics/btz171
-
Comparing evolutionary rates between trees, clades and traitsMethods in Ecology and Evolution 9:994–1005.https://doi.org/10.1111/2041-210X.12977
-
The evolutionary origin of orphan genesNature Reviews Genetics 12:692–702.https://doi.org/10.1038/nrg3053
Article and author information
Author details
Publication history
Copyright
© 2020, Singh and Syrkin Wurtele
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 26
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Hypothalamic kisspeptin (Kiss1) neurons are vital for pubertal development and reproduction. Arcuate nucleus Kiss1 (Kiss1ARH) neurons are responsible for the pulsatile release of gonadotropin-releasing hormone (GnRH). In females, the behavior of Kiss1ARH neurons, expressing Kiss1, neurokinin B (NKB), and dynorphin (Dyn), varies throughout the ovarian cycle. Studies indicate that 17β-estradiol (E2) reduces peptide expression but increases Slc17a6 (Vglut2) mRNA and glutamate neurotransmission in these neurons, suggesting a shift from peptidergic to glutamatergic signaling. To investigate this shift, we combined transcriptomics, electrophysiology, and mathematical modeling. Our results demonstrate that E2 treatment upregulates the mRNA expression of voltage-activated calcium channels, elevating the whole-cell calcium current that contributes to high-frequency burst firing. Additionally, E2 treatment decreased the mRNA levels of canonical transient receptor potential (TPRC) 5 and G protein-coupled K+ (GIRK) channels. When Trpc5 channels in Kiss1ARH neurons were deleted using CRISPR/SaCas9, the slow excitatory postsynaptic potential was eliminated. Our data enabled us to formulate a biophysically realistic mathematical model of Kiss1ARH neurons, suggesting that E2 modifies ionic conductances in these neurons, enabling the transition from high-frequency synchronous firing through NKB-driven activation of TRPC5 channels to a short bursting mode facilitating glutamate release. In a low E2 milieu, synchronous firing of Kiss1ARH neurons drives pulsatile release of GnRH, while the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons.
-
- Computational and Systems Biology
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study bias affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.