bioRxivDevelopmental BiologyPreprint
The conserved genetic program of male germ cells uncovers ancient regulators of human spermatogenesis
Rion Brattig CorreiaiD, Joana M. AlmeidaiD, Margot Julia WyrwolliD, Irene JulcaiD, Daniel SobraliD, Chandra Shekhar MisraiD, Sara Di PersioiD, Leonardo Gastón GuilguriD, Hans‐Christian Schuppe, Neide SilvaiD, Pedro PrudêncioiD, Ana NóvoaiD, Ana S Leocádio, Joana BomiD, Sandra LaurentinoiD, Moisés MalloiD, Sabine KlieschiD, Marek MutwiliD, Luis Mateus RochaiD, Frank TüttelmanniD, Jörg Dieter BeckeriD, Paulo Navarro‐CostaiD
Abstract
Male germ cells share a common origin across animal species, therefore they likely retain a conserved genetic program that defines their cellular identity. However, the unique evolutionary dynamics of male germ cells coupled with their widespread leaky transcription pose significant obstacles to the identification of the core spermatogenic program. Through network analysis of the spermatocyte transcriptome of vertebrate and invertebrate species, we describe the conserved evolutionary origin of metazoan male germ cells at the molecular level. We estimate the average functional requirement of a metazoan male germ cell to correspond to the expression of approximately 10,000 protein-coding genes, a third of which defines a genetic scaffold of deeply conserved genes that has been retained throughout evolution. Such scaffold contains a set of 79 functional associations between 104 gene expression regulators that represent a core component of the conserved genetic program of metazoan spermatogenesis. By genetically interfering with the acquisition and maintenance of male germ cell identity, we uncover 161 previously unknown spermatogenesis genes and three new potential genetic causes of human infertility. These findings emphasize the importance of evolutionary history on human reproductive disease and establish a cross-species analytical pipeline that can be repurposed to other cell types and pathologies.
- organism: multiple species
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 20
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