Evolutionary trends in the emergence of skeletal cell types

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Abstract

Cell types are fundamental functional units of multicellular organisms. The evolutionary emergence of new cell types is underpinned by genetic changes, such as gene co-option and cis-regulatory evolution, that propel the assembly or rewiring of molecular networks and give rise to new cell types with specialized functions. Here, we integrate genomic phylostratigraphy with single-cell transcriptomics to explore the evolutionary trends in the assembly of the skeletal cell type-specific gene expression programs. In particular, we investigate how the emergence of lineage-specific genes contributed to this process. We show that osteoblasts and hypertrophic chondrocytes (HC) express evolutionary younger transcriptomes compared to immature chondrocytes that resemble the ancestral skeletogenic program. We demonstrate that the recruitment of lineage-specific genes resulted in subsequent elaboration and individuation of the ancestral chondrogenic gene expression program, propelling the emergence of osteoblasts and HC. Notably, osteoblasts show significant enrichment of vertebrate-specific genes, while HC is enriched in gnathostome-specific genes. By identifying the functional properties of the recruited genes, coupled with the recently discovered fossil evidence, our study challenges the long-standing view on the evolution of vertebrate skeletal structures by suggesting that endochondral ossification and chondrocyte hypertrophy may have already evolved in the last common ancestors of gnathostomes.

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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.