scholarly journals Author response: Testis single-cell RNA-seq reveals the dynamics of de novo gene transcription and germline mutational bias in Drosophila

Author(s):  
Evan Witt ◽  
Sigi Benjamin ◽  
Nicolas Svetec ◽  
Li Zhao
2019 ◽  
Author(s):  
Evan Witt ◽  
Sigi Benjamin ◽  
Nicolas Svetec ◽  
Li Zhao

SummaryThe testis is a peculiar tissue in many respects. It shows patterns of rapid gene evolution and provides a hotspot for the origination of genetic novelties such as de novo genes, duplications and mutations. To investigate the expression patterns of genetic novelties across cell types, we performed single-cell RNA-sequencing of adult Drosophila testis. We found that new genes were expressed in various cell types, the patterns of which may be influenced by their mode of origination. In particular, lineage-specific de novo genes are commonly expressed in early spermatocytes, while young duplicated genes are often bimodally expressed. Analysis of germline substitutions suggests that spermatogenesis is a highly reparative process, with the mutational load of germ cells decreasing as spermatogenesis progresses. By elucidating the distribution of genetic novelties across spermatogenesis, this study provides a deeper understanding of how the testis maintains its core reproductive function while being a hotbed of evolutionary innovation.


Author(s):  
Dylan Kotliar ◽  
Adrian Veres ◽  
M Aurel Nagy ◽  
Shervin Tabrizi ◽  
Eran Hodis ◽  
...  

2018 ◽  
Author(s):  
Pierre-Cyril Aubin-Frankowski ◽  
Jean-Philippe Vert

AbstractSingle-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulation networks (GRN) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. In this work we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual data, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.


2017 ◽  
Author(s):  
William Blevins ◽  
Mar Albà ◽  
Lucas Carey

In de novo gene emergence, a segment of non-coding DNA undergoes a series of changes which enables transcription, potentially leading to a new protein that could eventually acquire a novel function. Due to their recent origins, young de novo genes have no homology with other genes. Furthermore, de novo genes may not initially be under the same selective constraints as other genes. Dozens of de novo genes have recently been identified in many diverse species; however, the mechanisms leading to their appearance are not yet well understood. To study this phenomenon, we have performed deep RNA sequencing (RNA-seq) on 11 species of yeast from the phylum of Ascomycota in both rich media and oxidative stress conditions. Furthermore, we performed ribosome profiling (Ribo-seq) experiments in both conditions with S. cerevisiae. These data have been used to classify the conservation of genes at different depths in the yeast phylogeny. Hundreds of genes in each species were novel (unannotated), and many were identified as putative de novo genes; these candidates were then tested for signals of translation using our Ribo-seq data. We show that putative de novo genes have different properties relative to phylogenetically conserved genes. This comparative phylotranscriptomic analysis advances our understanding of de novo gene origins.


2020 ◽  
Author(s):  
Anneke Dixie Kakebeen ◽  
Alexander Daniel Chitsazan ◽  
Madison Corinne Williams ◽  
Lauren M Saunders ◽  
Andrea Elizabeth Wills

2019 ◽  
Author(s):  
Mirko Francesconi ◽  
Bruno Di Stefano ◽  
Clara Berenguer ◽  
Luisa de Andrés-Aguayo ◽  
Marcos Plana-Carmona ◽  
...  

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