The SAGA core module is critical during Drosophila oogenesis and is broadly recruited to promoters.
Jelly H M SoffersSergio G-M AlcantaraXuanying LiWanqing ShaoChristopher W SeidelHua LiJulia ZeitlingerSusan M AbmayrJerry L WorkmanPublished in: PLoS genetics (2021)
The Spt/Ada-Gcn5 Acetyltransferase (SAGA) coactivator complex has multiple modules with different enzymatic and non-enzymatic functions. How each module contributes to gene expression is not well understood. During Drosophila oogenesis, the enzymatic functions are not equally required, which may indicate that different genes require different enzymatic functions. An analogy for this phenomenon is the handyman principle: while a handyman has many tools, which tool he uses depends on what requires maintenance. Here we analyzed the role of the non-enzymatic core module during Drosophila oogenesis, which interacts with TBP. We show that depletion of SAGA-specific core subunits blocked egg chamber development at earlier stages than depletion of enzymatic subunits. These results, as well as additional genetic analyses, point to an interaction with TBP and suggest a differential role of SAGA modules at different promoter types. However, SAGA subunits co-occupied all promoter types of active genes in ChIP-seq and ChIP-nexus experiments, and the complex was not specifically associated with distinct promoter types in the ovary. The high-resolution genomic binding profiles were congruent with SAGA recruitment by activators upstream of the start site, and retention on chromatin by interactions with modified histones downstream of the start site. Our data illustrate that a distinct genetic requirement for specific components may conceal the fact that the entire complex is physically present and suggests that the biological context defines which module functions are critical.
Keyphrases
- genome wide
- gene expression
- dna methylation
- hydrogen peroxide
- transcription factor
- high resolution
- copy number
- nitric oxide
- circulating tumor cells
- single cell
- electronic health record
- dna damage
- oxidative stress
- deep learning
- big data
- machine learning
- genome wide identification
- data analysis
- dna binding
- bioinformatics analysis