Genetic Novelty: How new genes are born
Analysis of yeast, fly and human genomes suggests that sequence divergence is not the main source of orphan genes.
- Department of Genetics, Developmental and Cell Biology, Iowa State University, United States
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).
Figure 1
Life cycle of orphan genes.
Every species has orphan genes that have no homologs in other species. This schematic shows the genome of the fruit fly (bottom) and the genome of an ancestor of the fruit fly (top). Each panel also shows (from left to right): genes that are highly conserved and can be traced back to prokaryotic organisms (yellow background); genes that are found in just a few related species (taxonomically restricted genes), orphan genes and potential orphan genes that are not currently expressed and are thus free from selection pressure (proto-orphan genes); and regions of the genome that do not code for proteins (blue background) (Van Oss and Carvunis, 2019; Palmieri et al., 2014). An orphan gene can form through the rapid divergence of the coding sequence (CDS) of an existing gene (1), or arise de novo from regions of the genome that do not code for proteins (including the non-coding parts of genes that evolve to code for proteins; 2). Some orphan genes will be important for survival, and will thus be selected for and gradually optimized (3). This means that the genes in a single organism will have a gradient of ages (Tautz and Domazet-Lošo, 2011). Many proto-orphan genes will undergo pseudogenation (that is, they will not be retained; 4). Coding sequences (shown as thick colored bars) with detectable homology are shown in similar colors. Vakirlis et al. estimate that a minority of orphan genes have arisen by divergence of the coding sequence of existing genes.
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
Eve Syrkin Wurtele
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
-
- 4,281
- views
-
- 385
- downloads
-
- 29
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
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)
Genetic Novelty: How new genes are born
eLife 9:e55136.
https://doi.org/10.7554/eLife.55136
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.
-
- Computational and Systems Biology
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
