Cell-fate determination in the developing Drosophila eye: role of the rough gene

Development ◽  
1991 ◽  
Vol 112 (3) ◽  
pp. 703-712 ◽  
Author(s):  
U. Heberlein ◽  
M. Mlodzik ◽  
G.M. Rubin
Keyword(s):  
Cell Fate ◽  
Double Mutant ◽  
Homeobox Gene ◽  
Photoreceptor Cells ◽  
Cell Fate Determination ◽  
Cellular Interactions ◽  
Developmental Defects ◽  
Fate Determination ◽  
Drosophila Eye ◽  
Cell Type Specific

The homeobox-gene rough is required in photoreceptor cells R2 and R5 for normal ommatidial assembly in the developing Drosophila eye. We have used several cell-type-specific markers and double mutant combinations to analyze cell-fate determination in rough. We show that the cells that would normally become R2 and/or R5 express a marker, a lacZ insertion in the seven-up (svp) gene, which is indicative of the R1/3/4/6 cell fate. In addition, the analysis of mitotically induced svp, ro double mutant clones in the eye indicates that in rough all outer photoreceptors are under the genetic control of the svp gene. These results show that, in the absence of rough function, R2 and R5 fail to be correctly determined and appear to be transformed into cells of the R3/4/1/6 subtype. This transformation and the subsequent developmental defects do not preclude the recruitment of R7 cells. However, the presence of ommatidia containing more than one R7 and/or R8 cell in rough implies a complex network of cellular interactions underlying cell-fate determination in the Drosophila retina.

scholarly journals Evidence for additive and synergistic action of mammalian enhancers during cell fate determination

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jinmi Choi ◽  
Kseniia Lysakovskaia ◽  
Gregoire Stik ◽  
Carina Demel ◽  
Johannes Söding ◽  
...  
Keyword(s):  
Cell Fate ◽  
Target Genes ◽  
Human Leukemia ◽  
Cell Fate Determination ◽  
Cell Type ◽  
Enhancer Activity ◽  
Fate Determination ◽  
Enhancer Rna ◽  
Cell Type Specific ◽  
Factor C

Enhancer activity drives cell differentiation and cell fate determination, but it remains unclear how enhancers cooperate during these processes. Here we investigate enhancer cooperation during transdifferentiation of human leukemia B-cells to macrophages. Putative enhancers are established by binding of the pioneer factor C/EBPα followed by chromatin opening and enhancer RNA (eRNA) synthesis from H3K4-monomethylated regions. Using eRNA synthesis as a proxy for enhancer activity, we find that most putative enhancers cooperate in an additive way to regulate transcription of assigned target genes. However, transcription from 136 target genes depends exponentially on the summed activity of its putative paired enhancers, indicating that these enhancers cooperate synergistically. The target genes are cell type-specific, suggesting that enhancer synergy can contribute to cell fate determination. Enhancer synergy appears to depend on cell type-specific transcription factors, and such interacting enhancers are not predicted from occupancy or accessibility data that are used to detect superenhancers.

scholarly journals ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

2020 ◽  
Author(s):  
Quan Xu ◽  
Georgios Georgiou ◽  
Gert Jan C. Veenstra ◽  
Huiqing Zhou ◽  
Simon J. van Heeringen
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Cell Fate Determination ◽  
Cell Type ◽  
Fate Determination ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractProper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

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