We have identified a large number of mutants with varying degrees of silencing defects through systematic genetic screens in S. pombe using reporter genes that monitors silencing in vivo at different heterochromatic domains.
To understand the roles of these novel factors in heterochromatin we use functional genomics. This is a very powerful approach to assign individual genes to specific functions and pathways by evaluating their mutant phenotypes in the context of other mutations. We have done this for various mutants of interest at the genome-wide level by crossing them with the deletion library of S. pombe using the SGA (Synthetic Gene Arrays) method and a half-automatic high-density robotic approach. Analyzing these genetic interactions quantitatively allows us to dissect mutations within the same pathway (which display non-additive phenotypes) from mutants of parallel pathways (exhibiting synthetic phenotypes). See also: Verrier et al. Open Biol 2015; Barrales et al. Genes Dev 2016; Flury et al. Mol Cell 2017; Salas-Pino et al JCB 2017.
As functional readout, we monitor the heterochromatin state of each mutant. Silencing is monitored in vivo using reporter genes that are transcriptionally silent when inserted into heterochromatic domains, such as centromeres (CEN), telomeres (TEL) or the silent mating type loci (MAT). The expression of reporter genes involved in metabolism can be conveniently monitored by measuring growth, i.e. the size of a yeast colony on selective growth media, which allows us to quantitatively assess the silencing defect. This simple but effective approach allows measuring hundreds of different mutants on a single agar plate using high-density arrays (e.g. 384 or 768 arrays). Alternatively, we can monitor heterochromatin at the single-cell level by quantifying the expression of fluorescent proteins by flow cytometry, an approach that we developed in collaboration with Bassem Al-Sady (UCSF; Al-Sady et al. PloS-ONE 2016). This approach has the advantage that we can detect dynamic changes even in individual cells in a heterogeneous population.
An advanced approach of SGA is E-MAP (Epistasis Mini-Array Profile), which systematically assesses the pair-wise genetic interactions of a collections of mutants (mini array) and yields comprehensive data sets in which each mutant has a specific genetic interaction profile. Hierarchical clustering of these genetic interaction profiles gives rise to an epistasis map, in which clusters of genetic interactions indicate cellular components of shared physical complexes and/or linear pathways. Alternatively, mutants can also combined with different conditions (growth, stress) or the readout at different heterochromatin domains.