Genetic Novelty: How new genes are born

  1. Urminder Singh
  2. Eve Syrkin Wurtele  Is a corresponding author
  1. Department of Genetics, Developmental and Cell Biology, Iowa State University, United States

Abstract

Analysis of yeast, fly and human genomes suggests that sequence divergence is not the main source of orphan genes.

Main text

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).

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

Article and author information

Author details

  1. Urminder Singh

    Urminder Singh is in the Department of Genetics, Developmental and Cell Biology, Iowa State University, Ames, United States

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3703-0820
  2. Eve Syrkin Wurtele

    Eve Syrkin Wurtele is in the Department of Genetics, Developmental and Cell Biology, Iowa State University, Ames, United States

    For correspondence
    mash@iastate.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1552-9495

Publication history

  1. Version of Record published: February 19, 2020 (version 1)

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

  • 3,533
    Page views
  • 327
    Downloads
  • 7
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

  1. Urminder Singh
  2. Eve Syrkin Wurtele
(2020)
Genetic Novelty: How new genes are born
eLife 9:e55136.
https://doi.org/10.7554/eLife.55136

Further reading

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Bitya Raphael-Mizrahi et al.
    Research Article

    The endocannabinoid system consists mainly of 2-arachidonoylglycerol and anandamide, as well as cannabinoid receptor type 1 (CB1) and type 2 (CB2). Based on previous studies, we hypothesized that a circulating peptide previously identified as Osteogenic Growth Peptide (OGP) maintains a bone-protective CB2 tone. We tested OGP activity in mouse models and cells, and in human osteoblasts. We show that the OGP effects on osteoblast proliferation, osteoclastogenesis, and macrophage inflammation in vitro, as well as rescue of ovariectomy-induced bone loss and prevention of ear edema in vivo are all abrogated by genetic or pharmacological ablation of CB2. We also demonstrate that OGP binds at CB2 and may act as both an agonist and positive allosteric modulator in the presence of other lipophilic agonists. In premenopausal women, OGP circulating levels significantly decline with age. In adult mice, exogenous administration of OGP completely prevented age-related bone loss. Our findings suggest that OGP attenuates age-related bone loss by maintaining a skeletal CB2 tone. Importantly, they also indicate the occurrence of an endogenous peptide that signals via CB2 receptor in health and disease.

    1. Cancer Biology
    2. Computational and Systems Biology
    Iurii Petrov, Andrey Alexeyenko
    Research Article

    Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become 'hard currency' of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in each tumour genome - as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.