Although it is well known that genes, proteins, and pathways are conserved across evolution, conservation of interactions between genetic pathways having the potential to differentially regulate expression of phenotypes is only just beginning to be characterized in model systems [61, 62]. Therefore, the clinical relevance of such networks remains to be elucidated [5, 63, 64]. In this regard, our data suggest the intriguing possibility that quantitative phenotypic analysis of Yor1-ΔF gene interaction reports on a complex trait in yeast of relevance to biogenesis of CFTR-ΔF508. Thus, evolutionary conservation is sufficient to usefully model human genetic disease in yeast - at least in the case of CF. This opens a door for efforts to dissect gene interaction underlying phenotypic complexity through integration of yeast phenomic data with human genetic data. A few clinically relevant genetic modifiers of cystic fibrosis disease were recently identified, however these variants are not suspected to function in CFTR protein biogenesis pathways . The genetic interaction model we have developed could be useful to mine CFTR-ΔF508 GWAS data for variant alleles that that modulate disease through effects on protein biogenesis. The Yor1-ΔF model suggests the potential existence of a large number of such modifiers. Thus, the yeast phenomic model may inform human genetic studies, where systematic, comprehensive, and quantitative analysis of gene interaction is of interest. Furthermore, given the large number of interactions, it will likely be important in the future to analyze higher order epistasis networks (for example, comprehensive three-way gene-gene-gene interaction experiments), which is unforeseen employing human genetic data alone.
The outbred genetic structure of human populations, due to its combinatorial complexity, severely limits the power to analyze phenotypes with respect to gene interaction . Thus, tractable yeast phenomic models could provide a powerful and complementary tool for dissecting disease complexity if the principle of evolutionary conservation of gene interaction applies . Our work provides evidence in support of this concept, as we demonstrate that gene interactions discovered from the yeast Yor1-ΔF model resemble by homology gene interactions similarly characterized for CFTR-ΔF biogenesis in human cell models. The findings support the notion that even when the phenotypic manifestations of homologous gene interaction appear unrelated (for example, oligomycin resistance in yeast vs. maintenance of peri-ciliary fluid depth in lungs), the principle network modulating the associated phenotypes can nevertheless be similar [5, 66].
We examined whether homologous modifiers of CFTR-ΔF were among the stronger Yor1-ΔF interactions (Figure 5B). Conserved interactions were not necessarily the strongest overall, raising points for consideration in future studies: (1) although strong hits from genetic screens receive the most attention, weak and intermediate strength interactions are also important for understanding the evolution of phenotypic variation; (2) the throughput and precision of Q-HTCP, which provides over 50,000 growth curves per experiment, is an enabling technology to map disease-relevant gene interaction networks, particularly regarding high quantitative accuracy to detect weak and intermediate strength interaction with high confidence; (3) high confidence measures of gene interaction across the entire genome will advance the opportunity to assess conservation of between homologs at a systems level to deduce functional modules that are most rapidly evolving within gene networks [42, 67]; and (4) the elucidation of conserved aspects of a 'ΔF biogenesis network' provides a starting point to predict novel human homologs of Yor1-ΔF regulators, and ultimately define higher-order interactions from a gene network perspective [65, 68]. Thus, the Yor1-ΔF phenomic model can serve in several ways as a tool to discover and prioritize targets for therapeutic development as well as potential modifiers of CF disease severity.
We chose the CFTR-ΔF508 allele causing cystic fibrosis as proof of principle for modeling a human disease-relevant gene interaction network in yeast, because CFTR-ΔF508 is arguably the best-characterized human genetic disease mutation. However, we anticipate that other CFTR mutations in addition to CFTR-ΔF508 as well as other diseases entirely can be analogously modeled in yeast to generate useful insight and new hypotheses as to how networks of interacting genes might modulate disease expression. For diseases not having a single locus that accounts for a high fraction of the phenotypic variation, the power of experimentally tractable yeast epistasis models may be even more beneficial . Furthermore, yeast gene interactions also have been useful for uncovering genetic modifiers of foreign proteins; in one example, yeast gene interactions modulating alpha-synuclein toxicity uncovered homologs that functioned similarly in animal models of Parkinson's disease, even though alpha-synuclein is not encoded by yeast genomes . In a second example, an informatics approach discovered 'phenologs', defined as overlapping sets of homologous genes associated with diverse phenotypic outcomes across various species, thus discovering novel genetic relationships between diverse phenotypes. Multiple predictions were validated experimentally, including homologs of genes functioning in yeast cellular resistance to HMG-CoA reductase inhibition influence angiogenesis in Xenopus embryos . In a third example, a genome-wide screen revealed unexpectedly that threonine metabolism is required to buffer a deficiency of dNTP biosynthesis, through augmenting provision of metabolic intermediates to overcome inhibition of a key enzyme, ribonucleotide reductase . Although threonine biosynthesis does not occur in multicellular eukaryotes, it was nevertheless shown that threonine catabolism is required in a developmentally-regulated way for DNA synthesis in mouse embryonic stem cells , and also for maintenance of stem cell chromatin state through S-adenosyl-methionine metabolism and histone methylation . Our study, together with these and other models indicate the power and utility of yeast genetic screens for generating useful new hypotheses about the role of gene interaction in phenotypic diversity, including human disease [5, 72].
A novel aspect of the phenomic approach described here is the acquisition and analysis of time series data from proliferating cell arrays. These data fit well to a logistic growth equation so that growth curve parameters of individual cultures can be employed to precisely and accurately quantify gene interaction (Figure 2). Coupling this method with a gradation in perturbation states (for example, multiple oligomycin concentrations) brings a new level of resolution to the powerful S. cerevisiae methods for analyzing gene interaction. Previous large-scale gene interaction studies have used endpoint measurements of phenotypes (for example, colony outgrowth at one time point) and binary perturbation states, which have less sensitivity for detecting gene interaction due to lower precision and accuracy of quantifying growth phenotypes . The enhancement in quantitative resolution provided by Q-HTCP was significant, because many conserved interactions were intermediate in strength, and thus were more likely to have been missed by less quantitative methods (Figures 3 and 4) . The validity of weak to intermediate strength interaction was further clarified biochemically in several cases (Figures 4 to 7).
The finding that gene interactions with Yor1-ΔF recapitulate homologous gene products interacting with human CFTR-ΔF in mammalian cell-based studies provides evidence that gene interaction networks can be conserved over great evolutionary distances (Figure 5). Thus, despite differential selective pressure that these distantly related ABC transporters have been subjected to, the cellular context in terms of interacting proteins that govern the biogenesis of Yor1 and CFTR is conserved and renders yeast a useful and powerful model for cystic fibrosis. Although it remains to be tested, we speculate that GWAS-based efforts to identify genetic modifiers of human disease could be aided by comprehensive and quantitative epistasis data from yeast models . An integrative/comparative approach could help prioritize findings diluted by multiple comparisons from human genetic analysis. The yeast phenomic model provides a biological framework for identifying, within quantitative trait loci, candidate genes with putative functions worthy of further study .
As another speculative example, it is plausible that deficiency of a cargo adapter protein, such as from Erv14 deletion, could give rise to a CF-like phenotype without mutations in CFTR itself (Figure 6). That Yor1 required an ER export adaptor was in fact somewhat surprising, because we had previously correlated ER export of Yor1 with interaction between a well-characterized basic binding pocket on the surface of the vesicle cargo adaptor, Sec24, and a di-acidic export motif on Yor1 . Thus a potential explanation for the present study findings is that Erv14 facilitates the Yor1/Sec24 interaction. CFTR also employs a di-acidic motif, albeit in a distinct domain from that of Yor1, and Erv14 is well conserved in metazoans , and therefore a similar mechanism of ERV14 facilitating interaction during capture into transport vesicles is plausible for selection of CFTR into ER-derived vesicles, and remains to be tested.
A potentially clinically relevant outcome of our study was the discovery of a novel function for the recently described ER membrane complex. The EMC was discovered in a screen to find ER folding factors in yeast . We now show that deletion of any one of the members of the evolutionarily conserved protein complex yields a quantitatively similar deletion enhancer phenotype with respect to Yor1-ΔF biogenesis (Figure 7). Interestingly, this interaction effect appears specific for the misfolded protein only, as deletion of members of the EMC did not affect oligomycin sensitivity in the context of wild-type Yor1 expression. Further studies are needed to clarify these findings, however we postulate a role for the EMC in the early secretory pathway, and suspect it acts in a pro-biogenesis manner as part of the co-translational mechanism - perhaps for proteins prone to misfolding. We did not see a role for the EMC proteins in protein turnover, since the half life or Yor1-ΔF was identical either in the presence or absence of their expression. Instead, we observed in the sop4-Δ0 mutant a reduced rate of production of Yor1-ΔF (Figure 7).
Consistent with the above hypothesis, it was previously noted that deletion of the EMC proteins yields a genetic interaction profile similar to over-expression of the sec61-2 mutation; thus, deletion of the EMC mimics genetic perturbation of the Sec61 translocon. Furthermore, deletion of UBC7 or CUE1 (genes functioning in ERAD) was aggravating in combination with deletion of either the EMC genes or sec61-2 overexpression [58, 75]. Our interpretation of these data is that EMC and Sec61 act in a functionally distinct pathway from ERAD, pathways that can buffer loss of one another [5, 76]. Other evidence suggesting a role for the EMC in the early secretory pathway comes from a high content microscopy screen, which discovered loss of the EMC causes increased ER retention of the Mrh1-GFP fusion protein . Importantly, we note that the role of the EMC and other secretory protein biogenesis network factors appears cargo-specific, since other factors that were found in the Mrh1-GFP screen exerted qualitatively different effects in our Yor1-ΔF screen . From a detailed comparison of our screen with the list of genes described by Bircham et al. to be required for forward transport of Mrh1-GFP, we noted that the EMC genes and SOP4 were ΔF-specific deletion enhancers; GYP1, RAV2, VAC14, and MON2 were ΔF-specific deletion suppressors; PKR1 was a non-specific deletion enhancer; and most other genes (GOS1, PEP4, SPF1, VPS51, VPS53, VPS60, VTA1, YPT6, and OPI3) showed no effect. Thus, while several genes were found in both studies, only loss of function alleles of the EMC complex appeared to have a consistent effect on prohibiting biogenesis of membrane proteins. Furthermore, for Yor1, prohibited biogenesis was specific to the misfolded Yor1-ΔF.
To test whether the EMC functions in a conserved manner as a pro-biogenesis factor for CFTR-ΔF, we knocked down TTC35/EMC2 in transfected HeLa cells expressing CFTR-ΔF under temperature rescue conditions. Since we did not observe an effect of disrupting the EMC on Yor1-ΔF turnover, but rather a defect in Yor1-ΔF production, we tested for a pro-biogenesis function of EMC2 on temperature-rescued CFTR-ΔF. We found that loss of EMC2 reduced the steady state level of CFTR-ΔF, consistent with our Yor1-ΔF findings. These results provide a strong rationale to utilize both yeast and human cells to clarify the pro-biogenesis mechanism for the EMC on ΔF-misfolded proteins (Figure 7).
In summary, the datasets provided here will serve as a resource for further identification and prioritization among candidate genes and pathways contributing to cellular processing of misfolded proteins. Novel cellular pathways in addition to the ones discussed, were suggested by this study to be of importance for biogenesis of misfolded ABC transporter proteins and include mRNA processing (for example, SKI complex) and ribosome-associated functions, both of which were strong Yor1-ΔF-specific deletion suppressors. The similarity in their impact on Yor1-ΔF biogenesis could occur by influencing rates of translation and/or altering protein-folding dynamics, although other mechanistic explanations are plausible. Future studies will be required to clarify the role of these and other genes in Yor1-ΔF biogenesis and their potential relevance to CFTR-ΔF. Additionally, given the abundance of pair-wise interactions revealed in our study, three-way interaction analysis will become increasingly important to understand functional hierarchies in higher-order epistasis networks that modulate Yor1-ΔF and, by extension, CFTR-ΔF protein biogenesis.