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Abstract
In nature, numerous mechanisms have evolved by which organisms fabricate biological structures with an impressive array of physical characteristics. Some examples of metazoan biological materials include the highly elastic byssal threads by which bivalves attach themselves to rocks, biomineralized structures that form the skeletons of various animals, and spider silks that are renowned for their exceptional strength and elasticity. The remarkable properties of silks, which are perhaps the best studied biological materials, are the result of the highly repetitive, modular, and biased amino acid composition of the proteins that compose them. Interestingly, similar levels of modularity/repetitiveness and similar bias in amino acid compositions have been reported in proteins that are components of structural materials in other organisms, however the exact nature and extent of this similarity, and its functional and evolutionary relevance, is unknown. Here, we investigate this similarity and use sequence features common to silks and other known structural proteins to develop a bioinformatics-based method to identify similar proteins from large-scale transcriptome and whole-genome datasets. We show that a large number of proteins identified using this method have roles in biological material formation throughout the animal kingdom. Despite the similarity in sequence characteristics, most of the silk-like structural proteins (SLSPs) identified in this study appear to have evolved independently and are restricted to a particular animal lineage. Although the exact function of many of these SLSPs is unknown, the apparent independent evolution of proteins with similar sequence characteristics in divergent lineages suggests that these features are important for the assembly of biological materials. The identification of these characteristics enable the generation of testable hypotheses regarding the mechanisms by which these proteins assemble and direct the construction of biological materials with diverse morphologies. The SilkSlider predictor software developed here is available at https://github.com/wwood/SilkSlider.
Fig 1. A. ROC curves displaying the performance of different predictors. B. Categorisation of silk-like proteins identified in the B. mori dataset. The pie chart shows division of the 178 predicted silk-like proteins into the categories indicated in the legend. ‘Known’ and ‘likely biological material’ categories refer to proteins that are known or likely to have a role in biological material formation. All 12 known B. mori silk-like sequences found in the literature (box) were identified by the predictor.
Fig 2. Results of survey of silk-like proteins in the Metazoa.Dendrogram on the left indicates currently accepted phylogenetic relationships of investigated taxa. Columns on the right indicate the total number of sequences evaluated by the predictor, the total number of predicted silk-like proteins, and the categorisation of identified silk-like proteins, for each taxon.
Fig 3. Spatial localization of genes encoding predicted silk-like proteins.Blue staining corresponds to cells expressing the gene. A. Dorsal view of juvenile H. asinina, removed from shell. Has-CL10Con2 expression is restricted to the mantle (arrows). B. Expanded view of boxed area in ‘A’. Has-CL10Con2 expression is in cells in the nacreous zone of the mantle. C. Dorsal view of juvenile H. asinina, removed from shell. Has-GRBP transcripts are localized to the mantle (arrows). D. Expanded view of boxed area in ‘C’. Has-GRBP expression is restricted to cells in the nacreous zone of the mantle, and is higher at the boundary between prismatic and nacreous zones. E. Late gastrula-stage (40 hours post fertilisation) S. purpuratus embryo. The sea urchin SM30E gene is expressed in PMCs (arrows). F. Late gastrula-stage (40 hours post fertilisation) S. purpuratus embryo. The sea urchin Cara7LA gene is expressed in PMCs (arrows). p, prismatic zone; n, nacreous zone.
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