Evolution LettersEvolutionary BiologyPeer-reviewed journal article
Evolutionary trends in the emergence of skeletal cell types.
Amor M. DamataciD, Sara KoskaiD, Kristian K UllrichiD, Tomislav Domazet‐LošoiD, Alexander V. KlimovichiD, Markéta KauckáiD
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.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: statistical model
- approach: comparative / phylogenetic analysis
- Citations
- 6
- FWCI
- 1.24
- Field percentile
- top 26%
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