The role of exon shuffling in shaping protein-protein interaction networks

Cancherini, Douglas V.; França, Gustavo S.; Souza, Sandro J. de

doi:10.1186/1471-2164-11-s5-s11

Cited by 22 publications

(23 citation statements)

References 59 publications

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“…The first Nature paper on the draft human genome contained two figures showing "domain accretion" and "domain shuffling" in the eukaryotic evolution of transcription factors and chromatin binding proteins [21]. The sharing of domains has been an important factor in the evolution of protein interaction networks [209]. For proteins to evolve in this fashion, extended coding sequences for individual domains must be capable of both amplification (duplications) and rearrangement (cutting and splicing in new combinations).…”

Section: Protein Evolution By Amplification and Exon Shufflingmentioning

confidence: 99%

How life changes itself: The Read–Write (RW) genome

Shapiro

2013

Physics of Life Reviews

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The genome has traditionally been treated as a Read-Only Memory (ROM) subject to change by copying errors and accidents. In this review, I propose that we need to change that perspective and understand the genome as an intricately formatted Read-Write (RW) data storage system constantly subject to cellular modifications and inscriptions. Cells operate under changing conditions and are continually modifying themselves by genome inscriptions. These inscriptions occur over three distinct time-scales (cell reproduction, multicellular development and evolutionary change) and involve a variety of different processes at each time scale (forming nucleoprotein complexes, epigenetic formatting and changes in DNA sequence structure). Research dating back to the 1930s has shown that genetic change is the result of cell-mediated processes, not simply accidents or damage to the DNA. This cell-active view of genome change applies to all scales of DNA sequence variation, from point mutations to large-scale genome rearrangements and whole genome duplications (WGDs). This conceptual change to active cell inscriptions controlling RW genome functions has profound implications for all areas of the life sciences.

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Section: Protein Evolution By Amplification and Exon Shufflingmentioning

confidence: 99%

How life changes itself: The Read–Write (RW) genome

Shapiro

2013

Physics of Life Reviews

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“…This issue has been explored further by a few authors more recently. First, Cancherini et al [20] hypothesized that domain shuffling would impact the topology of PPI networks. That was confirmed when they observed that the average degree (number of interactions in a node) was significantly higher in proteins with evidence of at least one domain shuffling event when compared to proteins having no detected domain shuffling events.…”

Section: Domain Shuffling Goes Systems Biologymentioning

confidence: 99%

“…those with several interacting partners, in the domain shuffling events across metazoa. This evidence led Cancherini et al [20] to conclude that domain shuffling is one of the major mechanisms in the evolution of hubs (nodes with a high degree of interactivity) in PPI networks. All these aspects are illustrated in Fig.…”

Section: Domain Shuffling Goes Systems Biologymentioning

confidence: 99%

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Domain shuffling and the increasing complexity of biological networks

Souza

2012

BioEssays

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“…For example, multi-exon domain gain at the c-termini is due to gene fusion. Additionally, during metazoan evolution, new physical protein-protein interactions (PPIs) emerged consequent to exon shuffling of domains that mediated the interaction (3). An additional work, by Bornberg-Bauer and Mar Albà in 2013 (4), refined and expanded these mechanisms and introduced new concepts such as intrinsically disordered regions, implied links between the emergence of de-novo domains, and de-novo genes (4).…”

Section: Introductionmentioning

confidence: 99%

EvoProDom: Evolutionary model of protein families by means of translocations of protein domains

Carmi

Gorohovski

Frenkel‐Morgenstern

2020

Preprint

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† These authors contributed equally to this work. AbstractHere, we developed a novel evolution of protein domains (EvoProDom) model for evolution of proteins, which was based on mix and merge of protein domains. We collected and integrated genomic and proteome data for 109 organisms. These data include protein domain content and orthologous protein families. In EvoProDom, we defined evolutionary events, such as translocations, as reciprocal exchanges of protein domains between orthologous proteins of different organisms. We found that protein domains, which frequently appear in translocation events, were enriched in trans-splicing events, i.e., producing novel transcripts fused from two distinct genes. We presented in EvoProDom, a general method to obtain protein domain content and orthologous protein annotation, by predicting these data from protein sequences using the Pfam search tool and KoFamKOALA, respectively. This method can be implemented in other research such as proteomics, protein design and host-virus interactions.We hypothesized that proteins evolve by means of "mix and merge" or "shuffling" of 113 protein domains, which are distinct functional units (1, 20). The evolutionary model that 114 7 described protein evolution by means of protein domain dynamics was termed 115EvoProDom. The EvoProDom model defined and formulated standard evolutionary 116 mechanisms such as translocations, duplications and indel (insertion and deletion) events, 117 which acted on protein domains that were realized as Pfam domains (6, 7). Therefore, 118proteins, under the EvoProDom model, gained or lost their function based on the 119 presence or absence of respective function-conferring domains. Accordingly, proteins 120 were modeled as sets of protein domains; and evolutionary events, such as translocations, 121 were defined. These describe the gain and loss of particular domains between domain sets 122 or DAs. The KEGG database catalogs diverse taxa and forms groups of orthologous 123 proteins (KO) based on shared function. Thus, all members of the KO group were 124 orthologous proteins (8, 12, 13). Additionally, in the EvoProDom model, proteins were 125 assigned to KO groups (see Materials and Methods). Consequently, translocation events 126 were mapped to groups of organisms according to underlying changes in DA. Thus, 127 evolutionary events, which acted upon domains and manifested as changes in DA were 128 reflected at the organism level. A link between changes at these two levels was therefore 129 established. The EvoProDom model was implemented with and tested on EvoProDomDB 130 (see Materials and Methods). In total, 5,548 translocation events, involving 94 protein 131 super families, excluding an "unknown" super family, were found ( Table 2). This result 132 indicates the existence of multiple evolutionary translocation events as defined by the 133 model. 134Mapping of genes to proteins and alternative splicing 135 EvoProDom combined genomic information (genes) with proteins, and in turn, proteins 136 with Pfam doma...

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The role of exon shuffling in shaping protein-protein interaction networks

Cited by 22 publications

References 59 publications

How life changes itself: The Read–Write (RW) genome

How life changes itself: The Read–Write (RW) genome

Domain shuffling and the increasing complexity of biological networks

EvoProDom: Evolutionary model of protein families by means of translocations of protein domains

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