A toolbox for systematic discovery of stable and transient protein interactors in baker's yeast
Identification of both stable and transient interactions is essential for understanding protein function and regulation. While assessing stable interactions is more straightforward, capturing transient ones is challenging. In recent years, sophisticated tools have emerged to improve transient interactor discovery, with many harnessing the power of evolved biotin ligases for proximity labelling. However, biotinylation-based methods have lagged behind in the model eukaryote, Saccharomyces cerevisiae, possibly due to the presence of several abundant, endogenously biotinylated proteins. In this study, we optimised robust biotin-ligation methodologies in yeast and increased their sensitivity by creating a bespoke technique for downregulating endogenous biotinylation, which we term ABOLISH (Auxin-induced BiOtin LIgase diminiSHing). We used the endoplasmic reticulum insertase complex (EMC) to demonstrate our approaches and uncover new substrates. To make these tools available for systematic probing of both stable and transient interactions, we generated five full-genome collections of strains in which every yeast protein is tagged with each of the tested biotinylation machineries, some on the background of the ABOLISH system. This comprehensive toolkit enables functional interactomics of the entire yeast proteome.
This study presents a novel method for enhancing biotin-specific signal relative to noise and a complete protein interaction toolbox, which harnesses the power of exogenous biotin ligases in discovering stable and transient protein interactors in yeast.
Cellular architecture and function require the action of protein machines often formed by protein complexes. Characterising the subunit identity of such complexes can be done using classic protein–protein interaction (PPI) assays such as immunoprecipitation (IP) of an epitope-tagged protein followed by mass spectrometry (MS) (Dunham et al, 2012). However, to uncover their protein substrates and their regulators (such as posttranslational modification enzymes), it is essential to probe transient interactions. Transient PPIs cannot easily be captured by simple IP approaches since the nature of these experiments (such as the lysis conditions and long incubation times) selects only stable interactions between machinery subunits and cofactors. Therefore, when searching for transient PPIs between machineries and their clients or regulators, a more specialised approach is required.
One such approach is that of proximity labelling (PL) in which proteins proximal to an active enzyme are marked by a covalent tag that can be identified long after the interaction has ended. Biotin ligation represents a central PL method; with the first approach developed from the endogenous Escherichia coli biotin ligase, BirA (Cronan, 1990). BirA specifically biotinylates a lysine (K) residue within a short acceptor peptide sequence (Avi) (Beckett et al, 1999) in the presence of free biotin and ATP. Therefore, by tagging one protein with BirA and another with an Avi sequence (termed AviTag), stable and transient PPIs can be assessed in a pairwise manner using the high-affinity biotin binder, streptavidin, to detect biotinylated AviTag.
Having a pairwise assay enabled hypothesis-driven experiments but was less amenable to unbiased interactor discovery. Hence, a huge leap in the ability to utilise BirA-based methods for de novo discovery of interactions came with the creation of a promiscuous BirA mutant, BirAR118G (Choi-Rhee et al, 2004). This mutant is able to biotinylate available K residues on accessible proteins without the requirement for a specialised acceptor sequence, making it possible to capture and identify multiple biotinylated interactors in one experiment using streptavidin affinity-purification (AP)-MS. Indeed, this powerful tool, named BioID, was shown to enable the discovery of new PPIs in mammalian cells (Roux et al, 2012). Later, a smaller version of BioID (BioID2) was generated from the Aquifex aeolicus BirA (Kim et al, 2016). However, the most active biotin ligase to date, TurboID, was produced by the directed evolution of a BioID variant in Saccharomyces cerevisiae (from here on termed simply yeast) (Branon et al, 2018).
Surprisingly, despite the use of yeast to evolve TurboID, there has been limited use of these systems in yeast. BioID has so far principally been applied to elucidate PPIs for ribosome- and mitoribosome-associated proteins (Opitz et al, 2017; Singh et al, 2020). One reason for this may be that BioID functions optimally at 37°C (Kim et al, 2016) and is minimally active at 30°C—the temperature at which yeast is normally cultured. TurboID, on the contrary, displays high activity at 30°C (Branon et al, 2018) and was employed to discover interactors for soluble cytosolic and exosomal proteins in the fission yeast, Schizosaccharomyces pombe (Larochelle et al, 2019), but remains untested for PPI discovery in S. cerevisiae. A more general reason explaining why biotin-based approaches have lagged behind in this powerful model organism is the presence of several highly expressed native proteins that are endogenously biotinylated (Sumrada & Cooper, 1982; Stucka et al, 1991; Hasslacher et al, 1993; Brewster et al, 1994; Hoja et al, 2004; Kim et al, 2004; Nagaraj et al, 2012). These proteins hence make up a significant proportion of the signal following enrichment of biotinylated proteins and can therefore reduce the chance of identifying interactors—especially if they are low-abundance proteins or transient in nature.
Clearly, biotinylation-based tools have immense power to uncover PPIs as has been demonstrated in multiple model systems, including mammalian cells (Roux et al, 2012; Go et al, 2021), mice (Uezu et al, 2016; Kim et al, 2021; Liu et al, 2021), flies (Uçkun et al, 2021), worms (Artan et al, 2021; Sanchez et al, 2021) and plants (Mair et al, 2019; Zhang et al, 2019). This widespread utilisation incentivised our work to make this tool applicable for the systematic identification of PPIs, particularly transient ones, in yeast. To this end, we address the current gap in PL technology in yeast by optimising protocols for the discovery of stable and transient interactions using a variety of biotin-ligation-dependent techniques. Moreover, we developed a novel approach, which we term ABOLISH (Auxin-induced BiOtin LIgase diminiSHing), for the downregulation of endogenous biotinylation to increase the signal-to-noise ratio and make PPI discovery more robust. We showcase the power of these approaches by uncovering a set of new substrates for the endoplasmic reticulum (ER) localised insertase, the ER membrane complex (EMC). Most importantly, to enable these powerful tools to be used easily and rapidly by the entire yeast community and to promote systematic probing of interactions, we generated a collection of full-genome libraries in which each yeast gene is preceded by either TurboID-HA, BioID2-HA, BirA, or AviTag, with the ABOLISH system integrated into several of them. Altogether these freely available libraries provide a powerful platform for high-content PPI screening and ultimately substrate recognition and protein function discovery in yeast.
Developing ABOLISH—a strategy to enhance the signal-to-noise ratio for exogenous biotin ligases
To expand the arsenal of biotin-based tools available for protein interaction profiling in yeast it is essential to take into consideration endogenously biotinylated proteins (Sumrada & Cooper, 1982; Stucka et al, 1991; Hasslacher et al, 1993; Brewster et al, 1994; Hoja et al, 2004; Kim et al, 2004). This is because most biotinylated yeast proteins are highly abundant (Fig 1A) and therefore can mask many of the expected PPI assay signals on a streptavidin blot (Fig 1B) or take up a large percent of the reads from MS analyses. Since PL relies on biotinylated protein enrichment and detection, it would clearly be advantageous to reduce the background signal from the endogenously biotinylated proteins. To do this, we created a new method of endogenous biotinylation reduction that we call ABOLISH, for Auxin-induced BiOtin LIgase diminiSHing. In this method, Bpl1, the only endogenous yeast biotin ligase, is C-terminally tagged with an auxin-inducible degron (AID*, Nishimura et al, 2009; Morawska & Ulrich, 2013). Therefore, in the presence of auxin and the Oryza sativa transport inhibitor response 1 (OsTIR1, Nishimura et al, 2009) adaptor protein, the controlled and transient degradation of this essential enzyme ensues, leading to a reduction in biotinylation of its substrates.
Previously, many of the PL experiments using biotin ligases in yeast aimed to reduce the endogenous biotinylation by preculturing cells in reduced biotin (RB) media (Jan et al, 2014; Costa et al, 2018; Dahan et al, 2022). We therefore tested this condition followed by auxin addition to induce Bpl1 degradation and finally treatments with a biotin pulse (illustrated in Fig EV1A). This demonstrated that RB media alone was not an optimal condition since the initial dramatic reduction in endogenous biotinylation levels was rapidly reversed upon addition of free biotin (Fig EV1B), highlighting the limitations of this method for reducing background biotinylation. Importantly, this reversal was not observed if auxin was used to deplete Bpl1-AID*-9myc, proving that ABOLISH can achieve a more complete reduction in background biotinylation noise.
Next, we wanted to track whether ABOLISH could indeed increase the sensitivity of detection when using exogenous, promiscuous, biotin ligase fusions. To do that, we chose a complex for which we could follow both stable and transient PPIs: the most recently characterised ER-resident insertase; the ER membrane protein complex, EMC (Guna et al, 2018). This highly-conserved machinery is composed of eight subunits (Emc1-7 & Emc10) in yeast (Jonikas et al, 2009) and 10 (EMC1-10) in humans (Christianson et al, 2012). Since its discovery as an insertase for moderately hydrophobic tail-anchor (TA) proteins (Guna et al, 2018; Volkmar et al, 2019), it has also been found to insert multi-pass transmembrane domain (TMD)-containing proteins into the ER (Chitwood et al, 2018; Shurtleff et al, 2018; Tian et al, 2019; Bai et al, 2020; Miller-Vedam et al, 2020; O'Donnell et al, 2020; Pleiner et al, 2020). Furthermore, it is required for the biogenesis of single-pass TMD proteins, which do not contain a signal peptide (also known as type III membrane proteins, O'Keefe et al, 2021) and a subset of TMD proteins, which traffic from the ER to lipid droplets (LD) (Leznicki et al, 2021). To this end, it has a wide, and not yet fully characterised, substrate range and a clear set of stable interactions that we can track.
To compare the various biotin ligases in their capacity to label both stable and transient interactions, and to evaluate whether ABOLISH could enhance the detection of these labelled proteins, we tagged Emc6 at its N-terminus with either BioID2-HA or TurboID-HA (Fig 1C). A third strain expressing both TurboID-HA-Emc6 and the ABOLISH system was also generated, along with three control strains in which Sbh1, rather than Emc6, was tagged. Sbh1 was selected as a control to compare against Emc6 primarily because it is an ER membrane protein of similar abundance (Weill et al, 2018) whose N-terminus (used for tagging) also faces the cytosolic side of this organelle. Furthermore, it is part of the SEC machinery required for translocation across the membrane and is therefore functionally comparable, yet distinct from, the EMC. Hence, the comparison between these samples should lead to the discovery of specific EMC interactors. All promiscuous biotin ligase tags were preceded by the constitutive, moderate, CYC1 promoter to ensure that we do not dramatically overexpress our proteins leading to false positives, and all tagged proteins ran at their expected molecular weights as determined by SDS–PAGE (Fig EV1C).
Surprisingly, we found that overnight growth in RB media resulted in a decrease in the amount of TurboID-HA-tagged proteins (Fig EV1D); thus negating the signal-to-noise advantage conferred by ABOLISH. To uncover a condition where the TurboID-tagged protein levels are not reduced but endogenous biotinylation levels are, we tested several different parameters including: overnight growth in either regular YPD or SD media containing auxin; and growth in either RB or regular SD media with auxin and biotin addition at different time-points (Fig EV1E; see legend for time-point details). We found that overnight treatment with auxin in SD media (Fig EV1E, 2nd lane) resulted in the strongest background biotinylation reduction without a loss in TurboID-HA-Emc6. The requirement for this relatively lengthy auxin treatment is rationalised by the long half-lives (> 10 h, Christiano et al, 2014) of endogenously biotinylated proteins. Interestingly, the abundance of the ER translocon, Sec61, remained constant independent of the conditions tested. This suggests that the loss of TurboID-tagged proteins triggered by biotin depletion (in RB media) may be a regulatory adaptation to biotin starvation, rather than general protein degradation from the ER. We then calibrated the length of time for biotin treatment that would ensure sufficient labelling material and time for true PPI events to be captured by TurboID without increasing background biotinylation (Fig 1D). We found that even 4 h of exogenous biotin addition did not negate the effect of the auxin-induced depletion of endogenous biotinylated proteins and therefore this growth pipeline (using regular media and not RB media; illustrated in Fig 1E) was adopted for future ABOLISH experiments. These data collectively demonstrate that the ABOLISH method can be harnessed to reduce background biotinylation ‘noise’, paving the way for enhanced signal detection from exogenous proximity-labelling enzymes.
Comparing three biotin ligase systems identifies their ability to uncover both stable and transient protein–protein interactions by LC–MS/MS
While IPs of epitope-tagged proteins enrich for stable interactors (in this case EMC complex components), streptavidin APs should capture transient interactions labelled by exogenous biotin ligases (in this case clients inserted into the ER by the EMC or regulators of the EMC) and a subset of stable ones; depending on the protein topology and accessibility of K residues on the same side of the membrane. To directly compare the type of interactions that we can identify we analysed, either by HA-IP or streptavidin-AP, strains expressing either BioID2-HA-Emc6, TurboID-HA-Emc6, or TurboID-HA-Emc6 on the background of the ABOLISH system (Fig 2A).
Although we had previously observed the reduced background conferred by ABOLISH using Western blot (Fig 1), we first used our MS data to understand the impact of this system on proteomic experiments. We compared the intensities of the six known endogenously biotinylated proteins (Fig 1B) measured by LC–MS/MS from the strains subject to streptavidin-AP expressing TurboID-HA-Emc6 either with or without ABOLISH (Fig EV2). Consistent with our earlier observations (Fig EV1B), Arc1 was the most sensitive to ABOLISH; however, all other endogenously biotinylated proteins were also strongly and significantly reduced, with the exception of Hfa1, the levels of which did not change. Since Hfa1 abundance is very low compared with the other proteins, its insensitivity to ABOLISH has less impact on the background noise. Altogether, endogenously biotinylated proteins made up nearly half (~ 47%) of the total intensity from all proteins identified by LC–MS/MS (as measured by intensity-based absolute quantification (iBAQ)) from streptavidin-AP of the TurboID-HA-Emc6 strain without ABOLISH. Implementing ABOLISH decreased this number to ~ 27%, demonstrating how much proteomic ‘bandwidth’ is freed up by this approach thus increasing the probability of bona fide interactor identification.
Next, we wanted to identify the high-confidence protein interactors from the different LC–MS/MS runs (Fig 2A). To do this, Emc6 replicate samples were compared with their Sbh1 control counterparts to find high-confidence protein identifications (Dataset EV1 and via PRIDE, PXD033348). These were defined by the following criteria: a P-value of ≤ 0.05 (streptavidin samples) or ≤ 0.1 (HA samples); a fold-change of ≥ 2; and identification by two or more unique peptides (Fig 2B). From the HA-IP, as expected, several EMC complex components satisfied these requirements (Fig 2B and C; dotted outline). From the streptavidin-AP samples, only three high-confidence hits were found by BioID2, two of which were Emc1 and Emc4 (Fig 2B and C; blue fill). This confirms that although BioID2 is able to label bona fide interactors, its capacity is limited likely due to its relatively low catalytic activity at 30°C (Kim et al, 2016). On the contrary, 13 high-confidence identifications were made by TurboID (Fig 2B and C; black, solid outline). Looking at stable interactors, Emc2 was found in addition to Emc1 and Emc4, already hinting at increased labelling functionality relative to BioID2. Importantly, the luminal EMC component, Emc7, was not found, indicating that there was no postlysis biotinylation by the cytosolic-facing TurboID. Most encouragingly, of the remaining 10 putative interactors, nine had membrane protein features classically associated with EMC clients, suggesting an increased capacity to uncover transient interactions (Fig 2C; asterisks). Eight of these are multi-pass TMD secretory pathway proteins, and the remaining protein (Alg1) is a lipid-droplet (LD) protein with a single N-terminal TMD—a characteristic recently demonstrated to define EMC-dependence (Leznicki et al, 2021).
Furthermore, incorporating the ABOLISH system enabled the detection of even more putative interactors labelled by TurboID (Fig 2B and C; yellow fill). The overlap between both TurboID strategies was very large with the same stable interactions and all nine candidate substrates being found. Another 16 high-confidence identifications were made, five of which were secretory pathway multi-pass TMD proteins (Fig 2C; asterisks). Comparing our list of putative substrates to published yeast EMC client data found by ribosome profiling (Shurtleff et al, 2018) and proteomic analysis of WT vs EMC3 KO cells (Bai et al, 2020), revealed that eight out of the 14 identified had previously been found in either study (Fig 2D) supporting the validity of our transient PPI discovery. To our knowledge, this is the first time TurboID has been successfully used in baker's yeast, and our data demonstrate that it labels both stable and transient PPIs. In addition, the ABOLISH system enhances the capacity to detect TurboID-labelled interactors.
Validating new EMC substrates using genetic tools and a natively-expressed pairwise biotinylation method
The similarity between our list of candidate EMC clients and published datasets (Fig 2D) strongly suggested that TurboID-mediated proteomics, both with and without ABOLISH, identified bona fide EMC substrates. Such substrates should be affected by loss of the complex, and indeed, it was previously shown that the abundance and/or localisation of true EMC substrates changes upon Emc3 loss (Bai et al, 2020). We therefore deleted EMC3 on a selection of our candidates and observed a strong reduction in the abundance of GFP-Alg1 (Fig 3A, top panel) and, to a lesser, but still significant, extent, Gnp1-GFP (Fig 3A, 2nd panel). Deletion of EMC3 also changed the localisation of GFP-Pdr12 relative to the control strain (Fig 3A, 3rd panel). Pdr12 is a plasma membrane (PM) ATP-binding cassette (ABC) transporter, which first requires insertion into the ER before trafficking to its final destination. Therefore, the accumulation of Pdr12 on the ER (shown by colocalisation with ER-resident Sec63; Fig EV3A) in the Δemc3 strain likely signifies a preinserted population at the ER surface. Interestingly, Pho84-GFP was also affected by the loss of EMC3 (Fig 3A, bottom panel). Pho84 is an inorganic phosphate transporter, which traffics along the secretory pathway to the PM. In phosphate-rich media (used in these experiments) it is known to be internalised and degraded by the vacuole and hence very weakly detectable in the WT images (Petersson et al, 1999; Hürlimann et al, 2007). EMC3 deletion, however, led to a strong accumulation of Pho84-GFP signal at internal membranes. These changes were specific to the proposed clients since localisation and abundance of the ER membrane protein, Sec63, were not affected by perturbation of the EMC (Fig EV3A). Collectively, these functional assays support these proteins as newly-validated clients of the EMC complex.
More broadly, however, verifying transient interactions is, in itself, a challenging task as methods to validate PPIs (such as co-IP) are again optimised for very stable interactions. We therefore used a parallel biotinylation approach involving the BirA biotin ligase, which specifically biotinylates the AviTag sequence (Cronan, 1990; Beckett et al, 1999). In this setup, even transient protein–client interactions can be assayed in vivo and at physiological expression levels. To do this, a haploid strain expressing a BirA-tagged protein (e.g. Emc6) under its native promoter is mated with a haploid strain of the opposite mating type, which expresses a potential interactor N-terminally tagged with AviTag (also under native promoter control). The diploid strains can then be analysed for the appearance of a streptavidin-positive band that proves that BirA came sufficiently close to the AviTag (illustrated in Fig 3B). Initially, well-characterised and previously validated interactors were selected to test the utility and feasibility of this validation method: hence strains expressing either AviTag-Emc2, -Emc4, or -Spf1 (Jonikas et al, 2009; Shurtleff et al, 2018) were crossed with the BirA-Emc6 strain. To ensure the best signal-to-noise ratio in this setup, we integrated the ABOLISH system into these strains, and this clearly reduced the background signal and made the assay even cleaner (Fig EV3B and C). The ABOLISH system is therefore critically important for: lower abundance proteins; more transient interactions; proteins that are less efficiently biotinylated; or proteins whose molecular weight is similar to that of endogenously biotinylated proteins. Hence ABOLISH can also extend the dynamic range of BirA-AviTag for pairwise, gel-based assays.
This assay is highly specific since it did not falsely report an interaction for the highly abundant ER-resident protein, Pdi1 (Fig EV3D), in contrast to the clearly visible bands for Emc2, Emc4 and Spf1 (Fig EV3B and C). This made us confident that our assay reveals bona fide interactions in vivo. Indeed, Fks1, an interactor found by TurboID (Fig 2B and C) and a known EMC substrate (Shurtleff et al, 2018), was readily detected by streptavidin blot using the BirA-AviTag/ABOLISH system (Fig 3C). The AviTagged amino acid permease, Gnp1, was similarly easy to detect. Some clients required a higher contrast setting to be visualised. However, both AviTag-Pdr5 and AviTag-Pdr12 produced clear streptavidin-reactive bands compared with AviTag-Stv1, which did not produce a detectable Emc6 interaction (Fig 3C). Collectively, these data highlight the power of the BirA-AviTag/ABOLISH system for providing a rare, in vivo ‘snapshot’ of the transient interactions between the EMC insertase and both previously-confirmed (Shurtleff et al, 2018; Bai et al, 2020) and newly-validated (Fig 3A and C) substrates. More broadly it serves as a rapid, systematic pipeline for the validation of TurboID interactomes.
Generating a biotinylation toolkit: a collection of five full-genome libraries to facilitate high-throughput protein–protein interaction discovery
Through studying and comparing the promiscuous biotin ligases that can be used in yeast, we have demonstrated that TurboID, especially when combined with the ABOLISH system, serves as an unbiased tool to efficiently label stable and transient functional interactors in S. cerevisiae. This in turn can lead to the discovery of novel protein-machinery substrates, as highlighted for the EMC (Figs 2 and 3), and regulators. We have also demonstrated the suitability of the BirA-AviTag technology for assaying and validating native pairwise interactions and the capacity of this sensitive methodology to highlight even transient interactions.
To truly harness the power of these biotinylation tools and make them widely applicable, we created whole-proteome collections of yeast strains (also called libraries) using our recently developed approach for yeast library generation called SWAp Tag (SWAT) (Yofe et al, 2016; Meurer et al, 2018; Weill et al, 2018). This approach allows us to take an initial library and swap its tag to any one of our choice. Therefore, using the N′ GFP SWAT library and accompanying SWAT protocol (Yofe et al, 2016; Weill et al, 2018) we generated five whole-genome libraries (Fig 4). In the first two, each strain encodes one yeast protein fused at its N′ to a TurboID-HA tag expressed under the control of a medium-strength constitutive CYC1 promotor (Yofe et al, 2016; Weill et al, 2018) with, or without, the ABOLISH system. The third library is an N′ tag CYC1pr-BioID2-HA collection. All three libraries contain generic N′ localisation signals (signal peptides (SPs) and mitochondrial targeting signals (MTS) (Yofe et al, 2016; Weill et al, 2018)) where required. The last two full-genome libraries express N-terminally tagged proteins with either BirA or AviTag under their endogenous promoters and native N′ localisation signals, and the ABOLISH system is also integrated. In addition to PPI validation (as shown above) these libraries can, of course, be used for hypothesis-driven interrogation of interactions between any two proteins of interest.
All newly-generated libraries were subject to strict quality control checks (see Methods). Furthermore, a number of new library strains were selected and subject to SDS–PAGE analysis to confirm both protein expression and that the new tag had recombined in-frame during the SWAT process. This was demonstrated to be the case for the BioID2-HA (Fig EV4A), TurboID-HA (Fig EV4B) and TurboID-HA/ABOLISH (Fig EV4C) libraries. Hence these represent five high-coverage yeast libraries that will be freely distributed to enable high-throughput exploration, discovery and validation of stable and transient interactions throughout the yeast proteome.
Our work demonstrates the power of proximity biotin labelling tools for the exploration of both stable and transient protein interactions in yeast. We also show, for the first time, the utility of TurboID in this model organism. The combination of TurboID-based proximity-labelling and standard HA-IPs, which can be performed from the same sample thanks to our tag design, is the most powerful approach for finding an extensive repertoire of stable and transient PPIs. Surprisingly, we found that previously-utilised protocols of growth in low biotin media to reduce endogenous biotinylation levels (Jan et al, 2014) did not negate the rapid rebiotinylation of endogenous substrates, even upon a short biotin pulse, and also resulted in the downregulation of TurboID-tagged proteins. This highlights the advantage of using our ABOLISH system, which is designed to increase streptavidin-specific signal-to-noise through controlled Bpl1 degradation. The reduction in endogenous biotinylation levels leads to lower sample complexity and hence increased sensitivity—indeed, more interactors were found by TurboID when it was coupled to the ABOLISH system, despite the fact that advanced MS instrumentation was used. Furthermore, the ABOLISH approach should be applicable to several model organisms provided the endogenous biotinylation machinery has been identified and known to tolerate tagging. Naturally, model systems expressing high levels of background biotinylation would stand to gain the most from implementing ABOLISH.
The multi-subunit EMC (Jonikas et al, 2009; Christianson et al, 2012) has recently been characterised as an ER membrane insertase (Guna et al, 2018), and as such, several of its substrates have been elucidated, particularly in human cells (Chitwood et al, 2018; Guna et al, 2018; Shurtleff et al, 2018; Tian et al, 2019; Volkmar et al, 2019; Leznicki et al, 2021; O'Keefe et al, 2021). Using EMC as a test case for TurboID utility in baker's yeast, we discovered six new putative substrates of this complex, in addition to reproducing interactions previously identified. In the past, EMC substrates were uncovered using in vitro assays (Chitwood et al, 2018; Guna et al, 2018; Leznicki et al, 2021; O'Keefe et al, 2021), labour-intensive ribosome profiling (Shurtleff et al, 2018), or proteomic profiling comparing control vs ΔEMC cells (Shurtleff et al, 2018; Tian et al, 2019; Volkmar et al, 2019; Bai et al, 2020). While loss-of-function studies have clearly proven useful, they suffer from both false negatives (from the presence of backup systems, Ihmels et al, 2007) and false positives (resulting from off-target effects). Endogenous labelling of transiently interacting substrates in vivo can therefore offer a complementary approach to validate direct interactions and facilitate protein substrate discovery.
Of the six new candidate yeast EMC substrates that we identified, Alg1 stands out as unique. It is a highly-conserved and essential mannosyltransferase localised to LDs (Krahmer et al, 2013) and possesses a single N-terminal hydrophobic TMD. This type of substrate was only recently established to require the EMC for its biogenesis in humans (Leznicki et al, 2021). Notably, the free energy difference (ΔG, Hessa et al, 2007) for the TMD of Alg1 is −2.097, highly consistent with the ΔG values observed for the TMDs of human EMC-dependent LD proteins (Leznicki et al, 2021).
In addition to Alg1, we also found that Gnp1, Pdr12 and Pho84 behave as substrates. Both Gnp1 and Pdr12 were previously flagged as putative yeast EMC substrates (Shurtleff et al, 2018; Bai et al, 2020), however, remained unvalidated. Additionally, it seems that EMC-dependence for these proteins is conserved throughout evolution, with the levels of SLC7A1 and ABCA3 (human homologues of Gnp1 and Pdr12, respectively, Fenech et al, 2020) reduced in EMC KO cells (Tang et al, 2017; Tian et al, 2019). Interestingly, a physical association between EMC3 and ABCA3 was also reported (Tang et al, 2017), supporting our evidence for an EMC-Pdr12 interaction. Lastly, Pho84 was uniquely found by TurboID + ABOLISH (Fig 2B and C) and was not previously identified as a putative EMC substrate. Interestingly, however, the phosphate transporter paralogs Pho87 and Pho90 were identified as EMC client candidates (Shurtleff et al, 2018; Bai et al, 2020), suggesting a role for the EMC in phosphate uptake.
Naturally, not all putative substrates were identified or confirmed using proximity biotinylation methods. There are several aspects of each method that should therefore be thought of when choosing which of the libraries to utilise for PPI detection. For example, BirA specifically biotinylates AviTag and both modules must be proximal in space and on the same side of the membrane for biotinylation to occur. TurboID, on the contrary, has more labelling opportunities as it can biotinylate any topologically available lysine residue within an interactor sequence. Other differences to bear in mind include the fact that the BirA-AviTag assay is carried out in diploid cells; unlike the haploid TurboID-expressing strains. Also, the TurboID-tagged proteins are under the control of the constitutive CYC1 promoter and generic N′ localisation signals. This is in contrast to the proteins tagged with BirA/AviTag, which are under the control of their native promoter and localisation signals. These native features provide much more physiological conditions, even though some low-abundance proteins may be less easy to detect. Finally, as opposed to proteomic-based approaches, the BirA-AviTag system serves as a much cheaper and easy-to-use method requiring no specialist equipment.
In addition, despite the clear benefits of the ABOLISH system, we generated a TurboID ‘only’ library for instances where reduced biotinylation of endogenous substrates of Bpl1 or the addition of auxin itself may interfere with the proteins being studied. For example, it has been shown that auxin inhibits the TORC1 pathway (Nicastro et al, 2021). Similarly, a BioID2 library was also included as part of our toolkit since it has already been adopted by the yeast community (Opitz et al, 2017; Singh et al, 2020). It is a smaller ‘tag’ compared with TurboID (27 kDa vs. 35 kDa) and is known to have higher activity at higher temperatures; thus may prove useful for heat-shock experiments, for example.
We believe that the unique properties of the promiscuous and pairwise biotinylation machineries make the combination of both approaches the most powerful tool for stable, and even more so, transient PPI discovery and validation in baker's yeast. Our whole-genome libraries and accompanying sample preparation protocols provide a broad resource for functional proteome exploration. Altogether, we present a complete biotinylation toolkit to enable high-throughput interaction profiling in baker's yeast. This should now support the systematic characterisation of new protein functions, definition of substrate ranges for protein machineries, elucidation of signalling pathways and tracking of dynamic organellar processes.
Materials and Methods
Reagents and Tools table
|Reagent/Resource||Reference or Source||Identifier or Catalog Number|
|List cell lines, model organism strains, patient samples, isolated cell types etc. Indicate the species when appropriate|
|Saccharyomyces cerevisiae strains (S288C background)||Schuldiner lab||Dataset EV2|
|Indicate species for genes and proteins when appropriate|
|Tagging, knock-out and SWAT plasmids||Schuldiner lab||Dataset EV3|
|Include the name of the antibody, the company (or lab) who supplied the antibody, the catalogue or clone number, the host species in which the antibody was raised and mention whether the antibody is monoclonal or polyclonal. Please indicate the concentrations used for different experimental procedures|
|Mouse anti-HA||BioLegend||Cat # 901502|
|Rabbit anti-myc||Abcam||Cat # ab9106|
|Rabbit anti-Histone H3||Abcam||Cat # ab1791|
|Rabbit anti-Sec61||A gift from Matthias Seedorf and Marius Lemberg||N/A|
|Mouse anti-Actin||Abcam||Cat # ab8224|
|Goat anti-rabbit IgG 800||Abcam||Cat # ab216773|
|Goat anti-mouse IgG 800||Abcam||Cat # ab216772|
|Goat anti-rabbit IgG 680||Abcam||Cat # ab216777|
|Goat anti-mouse IgG 680||Abcam||Cat # ab216776|
|Oligonucleotides and sequence-based reagents|
|For long lists of oligos or other sequences please refer to the relevant Table(s) or EV Table(s)|
|PCR primers||This study||Dataset EV4|
|Chemicals, enzymes and other reagents|
|e.g. Drugs, peptides, recombinant proteins, dyes etc.|
|Auxin (3-indoleacetic acid)||Sigma||Cat # I3750|
|D-Biotin||Supelco||Cat # 47868|
|Nourseothricin (NAT)||Quimigen||Cat # AB-102-25G|
|Hygromycin (HYG)||Formedium||Cat # HYG5000|
|G418||Formedium||Cat # G4185|
|5-fluoro-orotic acid monohydrate (5-FOA)||Formedium||Cat # 5FOA05|
|Protease inhibitors||Merck||Cat # 539134|
|Benzonase||Sigma||Cat # E1014|
|Digitonin||Sigma||Cat # D141|
|Streptavidin magnetic beads||Sigma||Cat # 28-9857-99|
|Protein G magnetic beads||Sigma||Cat # 28-9513-79|
|SDS||BioRad||Cat # 1610418|
|Urea||Sigma||Cat # U5378|
|Iodoacetamide (IAA)||Sigma||Cat # I6125|
|Dithiothretol (DTT)||Sigma||Cat # D9779|
|Trypsin||Promega||Cat # V5111|
|Oasis desalting column||Waters||Cat # 186001828BA|
|C18 trapping column||Waters||Cat # 186008821|
|Fluorescent streptavidin||Thermo Scientific||Cat # S11378|
|Include version where applicable|
|ScanR Analysis Software v22.214.171.124||Olympus|
|ImageJ/FiJi||Schindelin et al (2012)|
|Prism v9.3.0||GraphPad Software|
|Kits, instrumentation, laboratory equipment, lab ware etc. that are critical for the experimental procedure and do not fit in any of the above categories can be listed here|
|FastPrep-24 cell homogeniser||MP Biomedicals|
|FastPrep-24 2ml tubes loaded with Lysing Matix C||MP Biomedicals||Cat # 116912100|
|Q Exactive HF quadrupole orbitrap mass spectrometer||Thermo Scientific|
|RoToR array pinning robot||Singer Instruments|
Methods and Protocols
Yeast strains and plasmids
All yeast strains used in this study are listed in Dataset EV2. Strains were constructed using the lithium acetate-based transformation protocol (Gietz & Woods, 2002). All plasmids used are listed in Dataset EV3 (see also Longtine et al, 1998; Janke et al, 2004) and primers were designed with the Primers-4-Yeast web tool (https://www.weizmann.ac.il/Primers-4-Yeast/, Yofe & Schuldiner, 2014, Dataset EV4). The original SWAT donor strain (yMS2085; Weill et al, 2018) was transformed with SWAT donor plasmids encoding BioID2-HA or TurboID-HA. SWAT donor strains encoding the ABOLISH system were constructed by C-terminally tagging Bpl1 with AID*-9myc and integrating the OsTIR1 adaptor into the HIS locus using a PmeI-cut, OsTIR1-encoding plasmid (Morawska & Ulrich, 2013; Orgil et al, 2015). These strains were transformed with SWAT donor plasmids encoding TurboID-HA, BirA, or AviTag.
Yeast cells were grown on solid media containing 2.2% agar or liquid media. YPD (2% peptone, 1% yeast extract, 2% glucose) was used for cell growth if only antibiotic selections were required, whereas synthetic minimal media (SD; 0.67% [w/v] yeast nitrogen base (YNB) without amino acids and with ammonium sulphate or 0.17% [w/v] YNB without amino acids and with monosodium glutamate, 2% [w/v] glucose, supplemented with required amino acid) was used for auxotrophic selection. Antibiotic concentrations were as follows: nourseothricin (NAT, Quimigen) at 0.2 g/l; G418 (Formedium) at 0.5 g/l; and hygromycin (HYG, Formedium) at 0.5 g/l. Yeast grown for transformation, protein extraction, or LC–MS/MS analysis was first grown in liquid media with full selections overnight at 30°C and subsequently back-diluted into YPD/SD media to an OD600 of ~ 0.2. Cells were collected after at least one division but before reaching an OD600 of 1 and either immediately used for transformation or snap-frozen for later processing. Cells grown for streptavidin-AP followed by LC–MS/MS were treated for ~ 18 h with 50 μM biotin as in Roux et al (2012), Branon et al (2018), Larochelle et al (2019), and Singh et al (2020). For all ABOLISH experiments, biotin was used at 100 nM with the exception of Fig EV1B, where it was used at 10 nM (Jan et al, 2014). For Fig EV1B, D and E, RB media was prepared as specified in (Jan et al, 2014). Auxin was used at 1 mM. Times of treatments are specified in the appropriate figure legends.
Protein extraction and SDS–PAGE analysis
Cell pellets were resuspended in 200 μl lysis buffer (8 M urea, 50 mM Tris–pH 7.5, protease inhibitors (Merck)). From there on, processing, SDS–PAGE separation, Western blotting and fluorescent-based imaging was done as described in Eisenberg-Bord et al (2021), with the exception of the HA blot in Fig EV1C. Here, the SDS–PAGE gel was blotted onto PVDF membrane (Millipore) by wet transfer, and imaging was done using X-ray film (FujiFilm) to detect signal from HRP-conjugated anti-mouse secondary antibody (1:7,500, Jackson ImmunoResearch, #111-035-003) incubated with ECL substrate (Thermo Scientific). The following antibodies were used for Western blot: anti-HA (1:1,000, BioLegend, #901502), anti-Myc (1:3,000, Abcam, #ab9106), anti-Histone H3 (1:5,000, Abcam, #ab1791), anti-Sec61 (1:5,000, a kind gift from Matthias Seedorf of Heidelberg University and Marius Lemberg of the University of Cologne), anti-Actin (1:2,000, Abcam, #ab8224), goat anti-rabbit IgG H&L 800CW (1:7,500, Abcam, #ab216773) and goat anti-mouse IgG H&L 680RD (1:7,500, Abcam, #ab216776). Membranes were incubated for 1 h at RT with fluorescent streptavidin (1:10,000, Invitrogen, #S11378) diluted in 2% (w/v) BSA/PBS containing 0.01% NaN3 to detect biotinylated proteins.
Immunoprecipitation and LC–MS/MS sample preparation
Cell pellets from a total of ~ 5ODs were resuspended in 400 μl lysis buffer (150 mM NaCl, 50 mM Tris–HCl pH 8.0, 5% Glycerol, 1% digitonin (Sigma, #D141), 1 mM MgCl2, protease inhibitors (Merck), benzonase (Sigma, #E1014)). The cell suspension was then transferred to a 2 ml FastPrep™ tube (lysing matrix C, MP Biomedicals) and lysis was carried out by 6 × 1 min maximum speed cycles on a FastPrep-24™ cell homogeniser (MP Biomedicals), with the samples being returned to ice for 5 min between each cycle. Lysates were cleared at 16,000 g for 10 min at 4°C, and the supernatant was transferred to a fresh microcentrifuge tube. For HA-IP, samples were first incubated for 1 h at 4°C with 2 μl of anti-HA antibody (BioLegend) and then for another hr after adding 30 μl of washed magnetic ProteinG beads (Cytiva). The beads were washed twice with 200 μl of digitonin wash buffer (150 mM NaCl, 50 mM Tris–HCl pH 8.0, 1% digitonin) and then four times in basic wash buffer (150 mM NaCl, 50 mM Tris–HCl pH 8.0) before being incubated with 50 μl elution buffer (2 M urea, 20 mM Tris–HCl pH 8.0, 2 mM DTT and 0.5 μl trypsin (0.5 μg/μl, Promega, #V5111)) per sample for 90 min. The eluate was removed from the beads and collected in a fresh microcentrifuge tube. 50 μl alkylation buffer (2 M urea, 20 mM Tris–HCl pH8.0, 50 mM iodoacetamide (IAA)) was then added to the beads and incubated for 10 min. This buffer was also removed from the beads and combined with the first eluate. Finally, the beads were washed with 50 μl urea buffer (2 M urea, 20 mM Tris–HCl pH 8.0) for another 10 min, and again, the buffer was removed and combined with the above mixture. All elution steps were carried out at room temperature (RT) in the dark with shaking (1,400 rpm). The eluted mixture (150 μl total volume) was incubated overnight at RT in the dark at 800 rpm. The following morning 1 μl 0.25 μg/μl trypsin was added to each sample and incubated for a further 4 h at RT in the dark at 800 rpm. For streptavidin-AP, the same protocol as for HA-IP was used with the following changes: (i) 100 μl streptavidin-conjugated beads (Cytiva) were used per sample and incubated overnight at 4°C; (ii) post-AP beads were washed twice in 500 μl 2% SDS wash buffer (2% v/v SDS (BioRad, #1610418), 150 mM NaCl, 50 mM Tris–HCl pH 8.0), twice in 500 μl 0.1% SDS wash buffer, then twice in 500 μl basic wash buffer. All washes were 5 min and were carried out at RT on overhead rotator. Following digestion, peptides were desalted using Oasis HLB, μElution format (Waters, Milford, MA, USA). The samples were vacuum dried and stored at −80°C until further analysis.
LC–MS/MS settings and analysis
ULC/MS-grade solvents were used for all chromatographic steps. Each sample was loaded using split-less nano-Ultra Performance Liquid Chromatography (10 kpsi nanoAcquity; Waters, Milford, MA, USA). The mobile phase was: (A) H2O + 0.1% formic acid, and (B) acetonitrile +0.1% formic acid. Desalting of the samples was performed online using a reversed-phase Symmetry C18 trapping column (180 μm internal diameter, 20 mm length, 5 μm particle size; Waters). The peptides were then separated using a T3 HSS nano-column (75 μm internal diameter, 250 mm length, 1.8 μm particle size; Waters) at 0.35 μl/min. Peptides were eluted from the column into the mass spectrometer using the following gradient: 4–30% B over 55 min, 30–90% B over 5 min, maintained at 90% for 5 min and then back to the initial conditions. The nanoUPLC was coupled online through a nanoESI emitter (10 μm tip; New Objective, Woburn, MA, USA) to a quadrupole orbitrap mass spectrometer (Q Exactive HF; Thermo Scientific) using a FlexIon nanospray apparatus (Proxeon). Data were acquired in data-dependent acquisition (DDA) mode, using a Top10 method. MS1 resolution was set to 120,000 (at 200 m/z), mass range of 375–1,650 m/z and AGC of 3e6, and maximum injection time was set to 60 ms. MS2 resolution was set to 15,000, quadrupole isolation 1.7 m/z, AGC of 1e5, dynamic exclusion of 20 s and maximum injection time of 60 ms.
LC–MS/MS raw data processing
Raw data were processed with MaxQuant v126.96.36.199. The data were searched with the Andromeda search engine against the SwissProt S. cerevisiae ATCC204508/S288c proteome database (November 2018 version, 6049 entries) in addition to the MaxQuant contaminants database. All parameters were kept as default except: Minimum peptide ratio was set to 1; maximum of 3 miscleavages were allowed; and match between runs was enabled. Carbamidomethylation of C was set as a fixed modification. Oxidation of M, deamidation of N and Q and protein N-term acetylation were set as variable modifications. The LFQ intensities were used for further calculations using Perseus v188.8.131.52. Decoy hits were filtered out, as well as proteins that were identified on the basis of a modified peptide only. The LFQ intensities were log2-transformed and only proteins that had at least 2 valid values in at least one experimental group were kept. The remaining missing values were imputed by a random low-range distribution. Student's t-tests were performed between the relevant groups to identify significant changes in protein levels. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al, 2021) partner repository with the dataset identifier PXD033348.
Cells were grown overnight in 100 μl SD media (with appropriate amino acid (AA) selections) in a round-bottomed 96-well plate (ThermoFisher) at 30°C with shaking. 5 μl of overnight culture was back-diluted into 95 μl YPD and incubated for ~ 4 h at 30°C with shaking. 50 μl of culture was subsequently transferred to a Concanavalin A (ConA, Sigma, 0.25 mg/ml)-coated 384-well glass-bottomed microscopy plate (Matrical Bioscience) and incubated for 20 min at RT. The media was removed and the cells were washed twice in 50 μl SD-riboflavin+completeAA prior to being imaged in the same media at RT. Images for the Alg1, Pdr12 and Pho84 panels of Figs 3A and EV3A were obtained using a VisiScope Confocal Cell Explorer system (Visitron Systems) coupled to an inverted IX83 microscope (Olympus), a CSU-W1-T1 50μm spinning disk scanning unit (Yokogawa) and an Edge sCMOS camera (PCO) controlled by VisiView software (V3.2.0, Visitron Systems). A 60x oil objective was used (NA = 1.42, Olympus) together with a GFP filter EX470/40 nm, EM525/50 nm (Chroma) and a 100 mW 488 nm laser (Visitron Systems). Images for the Gnp1 panel of Fig 3A were obtained using a SpinSR system (Olympus) coupled to a CSUW1-T2SSR spinning disk scanning unit (Yokogawa) and an ORCA-Flash 4.0 CMOS camera (Hamamatsu). A 60x air objective was used (NA = 0.9, Olympus) together with a GFP filter EX470/40 nm, EM525/50 nm (Chroma) and a 100 mW 488 nm laser system (Coherent OBIS LX). All images are single-focal planes. Fiji was used for image inspection and brightness adjustment (Schindelin et al, 2012). For quantitation (Gnp1-GFP in Fig 3A, Sec63-mScarlet in Fig EV3A), the images were first processed using the ScanR Analysis software (V184.108.40.206, Olympus). Processing used a neural network virtual channel module to segment the image (transmitted channel only) to cells using the intensity object segmentation module. Next, noise and objects that were poorly segmented were removed based on their area and circularity factor, and for each cell, the mean signal intensity in either the 488 or 561 channel was measured. The following Python script was used for data analysis: https://github.com/Maya-Schuldiner-lab/Analysis-of-GFP-quantified-and-segmented-cells. 100 randomly sampled cells from both the WT and Δemc3 strains were used for the quantitation of signal intensity. Quantified intensities were then compared and analysed using two-tailed unpaired t-tests and the P-values are written in the appropriate figure legends.
Diploid strain generation
The BirA-Emc6 strain was grown on YPD supplemented with NAT and AviTagged interactors were grown on SDMSG without histidine supplemented with HYG at 30°C overnight. Both strains were velveted onto a YPD plate and grown overnight at RT. The mated strains were then velveted onto SDMSG plates without histidine supplemented with NAT and HYG and grown overnight at 30°C. This step was repeated once more to select diploid strains containing the combination of desired traits.
BirA/AviTag interaction assay
Diploid strains were grown overnight at 30°C in SDMSG liquid media without histidine supplemented with NAT, HYG and auxin (1 mM, Sigma). Strains were then back-diluted to 0.2 OD600 and incubated for 4 h at 30°C in SDMSG liquid media without histidine supplemented with NAT, HYG, auxin and biotin (100 nM, Sigma). Cells were collected upon reaching 0.5 OD600 by centrifugation at 3,000 g for 3 min, washed once in double distilled water (DDW) and then processed for Western blotting (see above).
Yeast library generation
SWAT library generation was performed as described (Weill et al, 2018). Briefly, a RoToR array pinning robot (Singer Instruments) was used to mate the parental N′ tag GFP SWAT library with the required donor strain (Dataset EV2) and carry out the mating, sporulation and selection protocol to generate a haploid library selected for all the desired features (Tong & Boone, 2007). Growth of the library on YPGalactose (2% peptone, 1% yeast extract, 2% galactose) was used to induce SceI-mediated tag swapping, and subsequent growth on SD containing 5-fluoroorotic acid (5-FOA, Formedium) at 1 g/l, and required metabolic and antibiotic selections was used to select for strains, which had successfully undergone the SWAT process. Information on library genotypes, mating types, SWAp-Tag efficiency, percentage survival and other quality control checks can be found in Dataset EV5 along with the lists of tagged ORFs for each library.
Protein interaction IP/AP-MS data: PRIDE, PXD033348 (http://www.ebi.ac.uk/pride/archive/projects/PXD033348).
We thank Dr. Yury Bykov, Dr. Ofir Klein, Rosario Valenti and Sivan Arad from the Schuldiner lab for critical feedback on this manuscript. We are grateful to Dr. Yael Elbaz-Alon, Dr. Yoav Peleg and Prof. Itay Onn for plasmids, Prof. Matthias Seedorf and Prof. Marius Lemberg for reagents and Corine Katina (the de Botton Institute for Protein Profiling) for help with LC–MS/MS sample preparation. We are thankful to Dr. André C. Michaelis from Prof. Matthias Mann's lab for his time and assistance with the LC-MS/MS sample preparation protocol. The project was supported by the European Research Council Consolidator Grant OnTarget 864068, an Israel Science Foundation grant (ISF 760/17) and an SFB 1190 grant from the Deutsche Forschungsgemeinschaft (DFG). Emma Fenech was supported by the Weizmann Institute of Science Dean of Faculty Fellowship and a senior postdoctoral award. Maya Schuldiner is an incumbent of Dr. Gilbert Omenn and Martha Darling Professorial Chair in Molecular Genetics.
Emma J Fenech: Conceptualization; data curation; formal analysis; validation; investigation; visualization; methodology; writing – original draft; writing – review and editing. Nir Cohen: Conceptualization; data curation; formal analysis; validation; investigation; visualization; methodology; writing – review and editing. Meital Kupervaser: Data curation; formal analysis; writing – review and editing. Zohar Gazi: Formal analysis; visualization. Maya Schuldiner: Conceptualization; resources; supervision; funding acquisition; investigation; writing – original draft; project administration; writing – review and editing.
In addition to the CRediT author contributions listed above, the contributions in detail are:
EJF and NC designed, performed and analysed the experiments. MK ran LC–MS/MS samples and processed the LC–MS/MS data. ZG quantified microscopy data. EJF and MS wrote the manuscript, which all authors read and provided feedback on. MS supervised the work and secured funding.
Disclosure and competing interests statement
The authors declare that they have no conflict of interest. MS is an editorial advisory board member. This has no bearing on the editorial consideration of this article for publication.
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