Evolutionary trends in the emergence of skeletal cell types

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Abstract

The emergence of novel cell types fuels evolutionary innovations and contributes to the diversity of life forms and their morphological and functional traits. Cell types are fundamental functional units of multicellular organisms defined by their specific gene expression programs. The evolution of these transcriptional programs is driven by genetic changes, such as gene co-option and cis- regulatory evolution, known to facilitate the assembly or rewiring of molecular networks and give rise to new cell types with specialized functions. However, the role of novel genes in this complex evolutionary process is underexplored. Here, we examine the trends in skeletal cell type evolution with a focus on lineage-specific genes. We find that immature chondrocytes express the oldest transcriptome and resemble ancestral skeletogenic cell type, supporting the existence of a conserved genetic program for cartilage development in bilaterians. The subsequent acquisition of lineage-restricted genes led to the individuation of the ancient gene expression program and powered the emergence of osteoblasts and hypertrophic chondrocytes. We found a significant enrichment of Vertebrate-specific genes in osteoblasts and Gnathostome-specific genes in hypertrophic chondrocytes. By identifying the functional properties of the recruited genes, coupled with the recently discovered fossil evidence, our findings challenge the long-standing view on the evolution of vertebrate skeletal structures and suggest that endochondral ossification and chondrocyte hypertrophy evolved already in the last common ancestors of gnathostomes. Finally, our findings highlight the critical role of novel genes in shaping cellular diversity.

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  1. The phylostratigraphy map of M. musculus and D.rerio was constructed by comparing 22,769 M. musculus and 25,787 D.rerio protein sequences with the protein sequence database by blastp algorithm V2.9.0 with a 10-3 e-value threshold[101].

    Can you expand on why you chose blastp? There are a number of other (likely more sensitive) alignment methods. Given that many of the analyses in this manuscript rely on specific assumptions with respect to evolutionary age, it seems that identifying the most accurate approach possible would be useful.

    Also, why use this specific e-value threshold for all proteins? Proteins often vary in e-value distributions due to differences in sequence length/composition, evolutionary history, etc. Methods that account for this (e.g. OrthoFinder) might be worth exploring.