Epistasis and functional genomics
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages)
|
High-throughput methods of analyzing these types of interactions have been useful in expanding our knowledge of genetic interactions. Synthetic genetic arrays (SGA), diploid based synthetic lethality analysis on microarrays (dSLAM), and epistatic miniarray profiles (E-MAP) are three important methods which have been developed for the systematic analysis and mapping of genetic interactions. This systematic approach to studying epistasis on a genome wide scale has significant implications for functional genomics. By identifying the negative and positive interactions between an unknown gene and a set genes within a known pathway, these methods can elucidate the function of previously uncharacterized genes within the context of a metabolic or developmental pathway.
Inferring function: alleviating and aggravating mutations
In order to understand how information about epistatic interactions relates to gene pathways, consider a simple example of vulval cell differentiation in C. elegans. Cells differentiate from Pn cells to Pn.p cells to VP cells to vulval cells. Mutation of lin-26[4] blocks differentiation of Pn cells to Pn.p cells. Mutants of lin-36[5] behave similarly, blocking differentiation at the transition to VP cells. In both cases, the resulting phenotype is marked by an absence of vulval cells as there is an upstream block in the differentiation pathway. A double mutant in which both of these genes have been disrupted exhibits an equivalent phenotype that is no worse than either single mutant. The upstream disruption at lin-26 masks the phenotypic effect of a mutation at lin-36 [1] in a classic example of an alleviating epistatic interaction.
Aggravating mutations on the other hand give rise to a phenotype which is worse than the cumulative effect of each single mutation. This aggravated phenotype is indicative of two genes in compensatory pathways. In the case of the single mutant a parallel pathway is able to compensate for the loss of the disrupted pathway however, in the case of the double mutant the action of this compensatory pathway is lost as well, resulting in the more dramatic phenotype observed. This relationship has been significantly easier to detect than the more subtle alleviating phenotypes and has been extensively studied in S. cerevisiae through
It should be pointed out that these conclusions from double-mutant analysis, while they apply to many pathways and mutants, are not universal. For example, genes can act in opposite directions in pathways, so that knocking out both produces a near-normal phenotype, while each single mutant is severely affected (in opposite directions). A well-studied example occurs during early development in Drosophila, wherein gene products from the hunchback and nanos genes are present in the egg, and act in opposite directions to direct anterior-posterior pattern formation. Something similar often happens in signal transduction pathways, where knocking out a negative regulator of the pathway causes a hyper-activation phenotype, while knocking out a positively acting component produces an opposite phenotype. In linear pathways with a single "output", when knockout mutations in two oppositely-acting genes are combined in the same individual, the phenotype of the double mutant is typically the same as the phenotype of the single mutant whose normal gene product acts downstream in the pathway.
Methods of detecting SSL mutants
SGA and dSLAM
Epistatic miniarray profiles (E-MAPs)
In order to develop a richer understanding of genetic interactions, experimental approaches are shifting away from this binary classification of phenotypes as wild type or synthetic lethal. The E-MAP approach is particularly compelling because of its ability to highlight both alleviating and aggravating effects and this capacity is what distinguishes this method from others such as SGA and dSLAM. Furthermore, not only does the E-MAP identify both types of interactions but also recognizes gradations in these interactions and the severity of the masked phenotype, represented by the interaction score applied to each pair of genes.
E-MAPs exploit an SGA approach in order to analyze genetic interactions in a high-throughput manner. While the method has been particularly developed for examining epistasis in S. cerevisiae, it could be applied to other model organisms as well. An E-MAP collates data generated from the systematic generation of double mutant strains for a large clearly defined group of genes. Each phenotypic response is quantified by imaging colony size to determine growth rate. This fitness score is compared to the predicted fitness for each single mutant, resulting in a genetic interaction score. Hierarchical clustering of this data to group genes with similar interaction profiles allows for the identification of epistatic relationships between genes with and without known function. By sorting the data in this way, genes known to interact will cluster together alongside genes which exhibit a similar pattern of interactions but whose function has not yet been identified. The E-MAP data is therefore able to place genes into new functions within well characterized pathways. Consider for example E-MAP presented by Collins et al. which clusters the transcriptional elongation factor Dst1[6] alongside components of the mid region of the Mediator complex, which is involved in transcriptional regulation.[7] This suggests a new role for Dst1, functioning in concert with Mediator.
The choice of genes examined within a given E-MAP is critical to achieving fruitful results. It is particularly important that a significant subset of the genes examined have been well established in the literature. These genes are thus able to act as controls for the E-MAP allowing for greater certainty in analyzing the data from uncharacterized genes. Clusters organized by sub-cellular localization and general cellular processes (e.g.
High throughput methods of examining epistatic relationships face difficulties, however as the number of possible gene pairs is extremely large (~20 million in S. cerevisiae) and the estimated density of genetic interactions is quite low.[8] These difficulties can be countered by examining all possible interactions in a single cluster of genes rather than examining pairs across the whole genome. If well chosen, these functional clusters contain a significantly higher density of genetic interactions than other regions of the genome and thus allows for a higher rate of detection while dramatically decreasing the number of gene pairs to be examined.[8]
Generation of mutant strains: DAmP
Generating data for the E-MAP depends upon the creation of thousands of double mutant strains; a study of 483 alleles, for example, resulted in an E-MAP with ~100,000 distinct double mutant pairs. The generation of libraries of essential gene mutants presents significant difficulties however, as these mutations have a lethal phenotype. Thus, E-MAP studies rely upon strains with intermediate expression levels of these genes. The decreased abundance by messenger RNA perturbation (DAmP) strategy is particularly common for the high-throughput generation of mutants necessary for this kind of analysis and allows for the partial disruption of essential genes without loss of viability.
References
- ^ PMID 19486505.
- ^ S2CID 19392818.
- PMID 19269370.
- ^ "Lin-26 Transcription factor lin-26 [Caenorhabditis elegans] - Gene - NCBI".
- ^ "Lin-36 Protein lin-36 [Caenorhabditis elegans] - Gene - NCBI".
- ^ "DST1 Dst1p [Saccharomyces cerevisiae S288C] - Gene - NCBI".
- S2CID 4419709.
- ^ PMID 16269340.
- S2CID 34923823.