How evolution builds genes from scratch

Levy, Adam D.

doi:10.1038/d41586-019-03061-x

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…Understanding these issues is important, given that de novo genes have already changed our perceptions of how genomic novelties can arise [21]. In particular, it is often proposed that newly evolved genes in general, and de novo genes in particular, are involved in many important processes such as development, stress response and environmental adaptation [22,4].…”

Section: Introductionmentioning

confidence: 99%

Structural and functional characterization of a putativede novogene inDrosophila

Lange

Patel

Heames

et al. 2021

Preprint

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Comparative genomic studies have repeatedly shown that new protein-coding genes can emerge de novo from non-coding DNA. Still unknown is how and when the structures of encoded de novo proteins emerge and evolve. Combining biochemical, genetic and evolutionary analyses, we elucidate the function and structure of goddard, a gene which appears to have evolved de novo at least 50 million years ago within the Drosophila genus.Previous studies found that goddard is required for male fertility. Here, we show that Goddard protein localizes to elongating sperm axonemes and that in its absence, elongated spermatids fail to undergo individualization. Combining modelling, NMR and CD data, we show that Goddard protein contains a large central α-helix, but is otherwise partially disordered. We find similar results for Goddard’s orthologs from divergent fly species and their reconstructed ancestral sequences. Accordingly, Goddard’s structure appears to have been maintained with only minor changes over millions of years.

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Section: Introductionmentioning

confidence: 99%

Structural and functional characterization of a putativede novogene inDrosophila

Lange

Patel

Heames

et al. 2021

Preprint

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“…It is now widely accepted that many cytosolic lncRNAs possess short, ‘non-canonical’ open reading frames (sORFs) that are translated ( Bazzini et al, 2014 ; Makarewich and Olson, 2017 ; Ruiz-Orera et al, 2014 ). What fraction of these non-canonical ORFs is functional, and whether sORF translation serves a pure regulatory purpose or results in the production of stable microproteins, remains an active topic of debate ( Levy, 2019 ; Ruiz-Orera et al, 2018 ). Since high rates of conservation have historically been employed for the identification and annotation of canonical protein coding sequences ( Lin et al, 2011 ; Mudge et al, 2019 ), a primary reason for doubting the protein-coding capacity of sORFs in presumed lncRNAs is their generally poor sequence conservation across species.…”

Section: Introductionmentioning

confidence: 99%

A human ESC-based screen identifies a role for the translated lncRNA LINC00261 in pancreatic endocrine differentiation

Gaertner

Heesch

Schneider-Lunitz

et al. 2020

eLife

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Long noncoding RNAs (lncRNAs) are a heterogenous group of RNAs, which can encode small proteins. The extent to which developmentally regulated lncRNAs are translated and whether the produced microproteins are relevant for human development is unknown. Using a human embryonic stem cell (hESC)-based pancreatic differentiation system, we show that many lncRNAs in direct vicinity of lineage-determining transcription factors (TFs) are dynamically regulated, predominantly cytosolic, and highly translated. We genetically ablated ten such lncRNAs, most of them translated, and found that nine are dispensable for pancreatic endocrine cell development. However, deletion of LINC00261 diminishes insulin+ cells, in a manner independent of the nearby TF FOXA2. One-by-one deletion of each of LINC00261's open reading frames suggests that the RNA, rather than the produced microproteins, is required for endocrine development. Our work highlights extensive translation of lncRNAs during hESC pancreatic differentiation and provides a blueprint for dissection of their coding and noncoding roles.

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“…enzymes, begin as generalists with many interacting partners, and later evolve more specialized interactions [38, 39]. We speculate that novel proteins may be conserved prior to gaining a so called ‘important’ function, simply by being tolerated and adding to the network resilience, as suggested in research on de novo genes [6, 7]. De novo genes are found to have both high, stable and stochastic gene expression [40].…”

Section: Discussionmentioning

confidence: 99%

A computational exploration of resilience and evolvability of protein-protein interaction networks

Klein

Holmér

Smith

et al. 2020

Preprint

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AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

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How evolution builds genes from scratch

Cited by 22 publications

References 18 publications

Structural and functional characterization of a putativede novogene inDrosophila

Structural and functional characterization of a putativede novogene inDrosophila

A human ESC-based screen identifies a role for the translated lncRNA LINC00261 in pancreatic endocrine differentiation

A computational exploration of resilience and evolvability of protein-protein interaction networks

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