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Comparative transcriptomics of primary cells in vertebrates

journal contribution
submitted on 2024-06-10, 09:03 and posted on 2024-06-10, 12:24 authored by Tanvir Alam, Saumya Agrawal, Jessica Severin, Robert S. Young, Robin Andersson, Erik Arner, Akira Hasegawa, Marina Lizio, Jordan A. Ramilowski, Imad Abugessaisa, Yuri Ishizu, Shohei Noma, Hiroshi Tarui, Martin S. Taylor, Timo Lassmann, Masayoshi Itoh, Takeya Kasukawa, Hideya Kawaji, Luigi Marchionni, Guojun Sheng, Alistair R.R. Forrest, Levon M. Khachigian, Yoshihide Hayashizaki, Piero Carninci, Michiel J.L. de Hoon

Gene expression profiles in homologous tissues have been observed to be different between species, which may be due to differences between species in the gene expression program in each cell type, but may also reflect differences in cell type composition of each tissue in different species. Here, we compare expression profiles in matching primary cells in human, mouse, rat, dog, and chicken using Cap Analysis Gene Expression (CAGE) and short RNA (sRNA) sequencing data from FANTOM5. While we find that expression profiles of orthologous genes in different species are highly correlated across cell types, in each cell type many genes were differentially expressed between species. Expression of genes with products involved in transcription, RNA processing, and transcriptional regulation was more likely to be conserved, while expression of genes encoding proteins involved in intercellular communication was more likely to have diverged during evolution. Conservation of expression correlated positively with the evolutionary age of genes, suggesting that divergence in expression levels of genes critical for cell function was restricted during evolution. Motif activity analysis showed that both promoters and enhancers are activated by the same transcription factors in different species. An analysis of expression levels of mature miRNAs and of primary miRNAs identified by CAGE revealed that evolutionary old miRNAs are more likely to have conserved expression patterns than young miRNAs. We conclude that key aspects of the regulatory network are conserved, while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species.

Other Information

Published in: Genome Research
See article on publisher's website:


National Cancer Institute (R01CA200859), Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational Learning.



  • English


Cold Spring Harbor Laboratory

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU