Abstract

Most eukaryotic genomes encode multiple RecQ family helicases, including five such enzymes in humans. For many years, the yeast Saccharomyces cerevisiae was considered unusual in that it only contained a single RecQ helicase, named Sgs1. However, it has recently been discovered that a second RecQ helicase, called Hrq1, resides in yeast. Both Hrq1 and Sgs1 are involved in genome integrity, functioning in processes such as DNA inter-strand crosslink repair, double-strand break repair, and telomere maintenance. However, it is unknown if these enzymes interact at a genetic, physical, or functional level as demonstrated for their human homologs. Thus, we performed synthetic genetic array (SGA) analyses of hrq1Δ and sgs1Δ mutants. As inactive alleles of helicases can demonstrate dominant phenotypes, we also performed SGA analyses on the hrq1-K318A and sgs1-K706A ATPase/helicase-null mutants, as well as all combinations of deletion and inactive double mutants. We crossed these eight query strains (hrq1Δ, sgs1Δ, hrq1-K318A, sgs1-K706A, hrq1Δ sgs1Δ, hrq1Δ sgs1-K706A, hrq1-K318A sgs1Δ, and hrq1-K318A sgs1-K706A) to the S. cerevisiae single gene deletion and temperature-sensitive allele collections to generate double and triple mutants and scored them for synthetic positive and negative genetic effects based on colony growth. These screens identified hundreds of synthetic interactions, supporting the known roles of Hrq1 and Sgs1 in DNA repair, as well as suggesting novel connections to rRNA processing, mitochondrial DNA maintenance, transcription, and lagging strand synthesis during DNA replication.

The known and hypothesized roles of Sgs1 in homologous recombination, DNA replication, meiosis, excision repair, and telomere maintenance were recently reviewed (Gupta and Schmidt 2020). Much less is known about Hrq1, though it is linked to DNA inter-strand crosslink (ICL) repair, telomere maintenance, and the unwinding of noncanonical DNA secondary structures (Bochman et al. 2014; Rogers and Bochman 2017; Rogers et al. 2017; Nickens et al. 2018; Rogers et al. 2020) like human RECQL4 (Jin et al. 2008; Ghosh et al. 2011; Ferrarelli et al. 2013; Keller et al. 2014). Contemporaneous work using a multi-omics approach also suggests that Hrq1 has roles in transcription, chromosome/chromatin dynamics, rRNA processing/ribosomal maturation, and in the mitochondria (Rogers et al.; companion manuscript G3/2020/401864).

Despite these advances in yeast RecQ research, little is known about the genetic interactions that occur between HRQ1 and SGS1 or the physical interactions between Hrq1 and Sgs1. In humans, some of the RecQ helicases are partially functionally redundant (e.g., BLM and WRN), some display complementarity (e.g., WRN and RECQL5), and others exhibit functional synergism (reviewed in (Croteau et al. 2014)). The latter is exemplified by BLM and RECQL4, where BLM promotes the retention of RECQL4 at DNA double-strand breaks (DSBs), and RECQL4 stimulates BLM activity (Singh et al. 2012). Do such connections exist between their yeast homologs Hrq1 and Sgs1? Two reports demonstrate that various combinations of hrq1 and sgs1 alleles display differential responses to DNA damage compared to single mutants (Bochman et al. 2014; Rogers et al. 2020), suggesting that functional interactions among the RecQ helicases also exist in S. cerevisiae.

Materials & MethodsScreen design

The strains used in this study are listed in Table 1. The HRQ1 gene was deleted in Y8205 (Table 1) by transforming in a NatMX cassette that was PCR-amplified from the plasmid pAC372 (a gift from Amy Caudy) using oligonucleotides MB525 and MB526 (Table S1). The deletion was verified by PCR analysis using genomic DNA and oligonucleotides that anneal to regions up- and downstream of the HRQ1 locus (MB527 and MB528). The confirmed hrq1Δ strain was named MBY639. The hrq1-K318A allele was introduced into the Y8205 background in a similar manner. First, an hrq1-K318A(NatMX) cassette was PCR-amplified from the genomic DNA of strain MBY346 (Bochman et al. 2014) using oligonucleotides MB527 and MB528 and transformed into Y8205. Then, genomic DNA was prepared from transformants and used for PCR analyses of the HRQ1 locus with the same oligonucleotide set to confirm insertion of the NatMX marker. Finally, PCR products of the expected size for hrq1-K318A(NatMX) were sequenced using oligonucleotide MB932 to confirm the presence of the K318A mutation. The verified hrq1-K318A strain was named MBY644.

Table 1 Strains used in this study

The SGS1 gene was deleted from Y8205 (Table 1) in the same manner as the HRQ1::natMX deletion above by transforming in a NatMX cassette that was PCR-amplified using oligonucleotides MB1395 and MB768 (Table S1). The deletion was verified by PCR analysis of genomic DNA and oligonucleotides MB373 and MB374. The confirmed sgs1Δ strain was named MBY640. The sgs1-K706A allele was PCR amplified from plasmid pFB-MBP-Sgs1K706A-his (Cejka and Kowalczykowski 2010) (Table 2) using oligonucleotides MB765 and MB1396. The NatMX cassette was PCR-amplified from pAC372 using oligonucleotides MB1397 and MB768 and fused to the sgs1-K706A PCR product by Gibson assembly (Gibson et al. 2009). The resultant sgs1-K706A(natMX) cassette was reamplified with MB765 and MB768 and transformed into Y8205. Genomic DNA was then prepared from transformants and used for PCR analyses of the SGS1 locus with oligonucleotides MB373 and MB374 to confirm insertion of the cassette. Finally, PCR products of the expected size were sequenced using oligonucleotide MB769 to confirm the presence of the K706A mutation. The verified sgs1-K706A strain was named MBY642.

Table 2 Results of the SGA analyses for all query strains crossed to the single-gene deletion collection

The double mutant strains were constructed using similar techniques. Briefly, the hrq1Δ sgs1Δ and hrq1-K318A sgs1Δ strains were generated by deleting SGS1 in strains MBY639 and MBY644, respectively, using a URA3 cassette amplified from pUG72 (Gueldener et al. 2002) with oligonucleotides MB1395 and MB355 (Table S1). The strains verified by PCR of genomic DNA and sequencing were named MBY643 and MBY645, respectively. The hrq1Δ sgs1-K706A and hrq1-K318A sgs1-K706A strains were constructed by amplifying sgs1-K706A as above, amplifying the URA3 cassette with oligonucleotides MB1397 and MB355, and fusing the PCR products via Gibson assembly. The sgs1-K706A(URA3) cassette was then transformed into strains MBY639 and MBY644, and transformants were confirmed for proper integration by PCR and Sanger sequencing. The verified hrq1Δ sgs1-K706A and hrq1-K318A sgs1-K706A strains were named MBY674 and MBY676, respectively. Further details concerning strain construction are available upon request.

SGA analysis of the hrq1Δ, sgs1Δ, hrq1-K318A, sgs1-K706A, hrq1Δ sgs1Δ, hrq1Δ sgs1-K706A, hrq1-K318A sgs1Δ, and hrq1-K318A sgs1-K706A mutants was performed at the University of Toronto using previously described methods (Tong et al. 2001; Tong et al. 2004). All query and control strains were crossed in quadruplicate to both the S. cerevisiae single-gene deletion collection (Giaever and Nislow 2014) and the TS alleles collection (Kofoed et al. 2015) to generate double or triple mutants for analysis. For the double mutant screens, the control strain (Y8835) contained the NatMX marker inserted into the benign ura3 locus (MATα ura3Δ::natMX4 can1Δ::STE2pr-Sp_his5 lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 met15Δ0 LYS2+). For the triple mutant screens, the control strain was Y13096 (MATα ura3Δ::natMX4 hoΔ::KlURA3 can1Δ::STE2pr-Sp_his5 lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 met15Δ0 LYS2+), as described previously (Kuzmin et al. 2018).

Phenotypes

Quantitative scoring of the genetic interactions was based on colony size. The SGA score measures the extent to which the size of a double or triple mutant colony differs from the colony size expected from combining the query and tester mutations together (Baryshnikova et al. 2010). The data includes both negative (putative synthetic sick/lethal) and positive interactions (potential epistatic or suppression interactions) (Tables S2-17). The magnitude of the SGA score is indicative of the strength of the interaction. Based on statistical analysis, it was determined that a default cutoff for a significant genetic interaction is P < 0.05 and SGA score > |0.08| (Costanzo et al. 2010).

Verification of mutants

The top five negative and positive interactions for each query strain were confirmed by remaking and reanalyzing the double and triple mutants by hand, followed by spot dilution (Andis et al. 2018) and/or growth curve (Ononye et al. 2020) assays to compare the growth of the double or triple mutants to their parental strains and wild-type. Examples are shown in Figure S1.

Statistical analysis

Data were analyzed and graphed using GraphPad Prism 6 software. The reported values are averages of ≥ 3 independent experiments, and the error bars are the standard deviation. P-values were calculated as described in the figure legends, and we defined statistical significance as P < 0.01.

Data availability

Strains, plasmids, and other experimental reagents are available upon request. File S1 contains Table S1, a description of the other supplementary tables included in Files S2-S4, and Figures S1-S4. File S2 contains Tables S2-S9, File S3 contains Tables S10-S17, and File S4 contains the complete SGA data for all screens in the form of Tables S18-33. Supplemental material available at figshare: https://doi.org/10.25387/g3.13157519.

Results and DiscussionOverall results of the screen

Hundreds of synthetic interactions were detected for all query strains screened through both the single-gene deletion (Table 2) and TS mutant (Table 3) collections (Tables S2-17). For the single-gene deletion collection screen, the numbers of negative and positive genetic interactions were generally the same for all query strains, except hrq1Δ and hrq1-K318A, which yielded approximately twice as many negative as positive interactions (Table 2). These mutants also had the fewest number of synthetic interactions by a factor of > 2.3 compared to sgs1Δ and sgs1-K706A. This is consistent with the generally more modest phenotypes of hrq1Δ and hrq1-K318A strains compared to sgs1Δ and sgs1-K706A for DNA damage sensitivity (Bochman et al. 2014). The double mutant query strains yielded a greater than additive number of synthetic genetic interactions than the single mutant parental query strains, indicating that mutating both RecQ helicases had a synergistic effect. This synergism was strongest for the hrq1-K318A sgs1Δ mutant, which generated 880 synthetic interactions, a nearly twofold increase over the additive effect of the 132 hrq1-K318A and 312 sgs1Δ interactions individually (compared to ∼1.5- to 1.sixfold increases for the other combinations).

Table 3 Results of the SGA analyses for all query strains crossed to the temperature-sensitive allele collection

For the TS allele collection screen, the numbers of negative and positive genetic interactions were again generally similar for all query strains (Table 3). As above, the hrq1Δ and hrq1-K318A mutants had the fewest number of synthetic interactions by a factor of > 2.1 compared to sgs1Δ and sgs1-K706A. In this case, however, the double mutant query strains yielded approximately an additive number of synthetic genetic interactions compared to the single mutant parental query strains and thus did not display the synergism described for the single-gene deletion SGA analysis. It should also be noted that the numbers of synthetic genetic interactions listed in Table 3 are inflated because several different TS alleles of the same ORF are included in the collection for many individual genes (Kofoed et al. 2015).

Figure 1 shows the frequency distribution of all of the SGA scores as violin plots and separate box plots of the negative and positive synthetic genetic interactions, with outliers denoted as single points, for the single-gene deletion collection (Figure 1A-C) and the TS collection (Figure 1D-F). The outliers represent the mutants with the strongest synthetic phenotypes. As shown in Figures 1A and 1D, most synthetic phenotypes were mild decreases or increases in the growth of the double and triple mutant colonies. There were no significant differences in the distribution of the SGA scores among any of the mutant sets generated by crossing the query strains to the single-gene deletion collection. However, several significant differences were found in the distributions of positive SGA scores for the mutant sets yielded from the crosses to the TS collection. These includes mild differences between hrq1-K318A vs. hrq1Δ sgs1Δ (P = 0.0123) and sgs1Δ vs. hrq1-K318A sgs1Δ (P = 0.0303), intermediate differences for sgs1Δ vs. hrq1Δ sgs1Δ (P = 0.0016) and sgs1-K706A vs. hrq1-K318A sgs1-K706A (P = 0.0070), and strong differences between sgs1-K706A and hrq1Δ sgs1Δ, hrq1Δ sgs1-K706A, and hrq1-K318A sgs1Δ (all P < 0.0001). It is currently unclear why the strength of the positive synthetic genetic interactions significantly varied among these mutants, especially compared to the sgs1-K706A query strain, but we are actively following up on phenotypic difference among all of the hrq1 and sgs1 alleles. Regardless, as mutants giving the strongest growth effects, the outliers in Figures 1B, C, E, and F are summarized in Tables 4 and 5. For simplicity, only the negative genetic interactions are discussed in further detail below. Comparisons between the full SGA datasets for the hrq1Δ/hrq1-K318A, sgs1Δ/sgs1-K706A, and all pairwise combinations of triple mutants screens are available in Figures S2 and S3.

Figure 1

Analysis of the distribution of the magnitudes of the synthetic genetic interactions. Violin plots of the synthetic genetic interactions with the single-gene deletion collection (A) and TS collection (D). The median values are denoted with dashed lines, and the quartiles are shown as solid lines. The SGA data are also shown in separate box and whisker plots drawn using the Tukey method for the negative (B) and positive (C) interactions with the deletion collection, as well as for the negative (E) and positive (F) interactions with the TS collection. The individually plotted points outside of the inner fences represent outliers (i.e., interactions with mutants yielding the strongest SGA scores) and correspond to alleles whose SGA score is less than the value of the 25th quartile minus 1.5 times the inter-quartile distance (IQR) for negative interactions and alleles whose SGA score is greater than the value of the 75th quartile plus 1.5IQR for positive interactions. The significant differences between SGA data sets discussed in the main text were calculated using the Kruskal-Wallis test and Dunn’s multiple comparisons test.

Table 4 Genes whose deletion cause the strongest growth phenotypes when combined with the hrq1 and sgs1 mutantsTable 5 Temperature-sensitive alleles that cause the strongest growth phenotypes when combined with the hrq1 and sgs1 mutantshrq1Δ interactions

The deletion of HRQ1 displayed strong negative interactions with mutations in 10 genes (Tables 4 and 5), many of which correspond to the recently described Hrq1 interactome (Rogers et al.)1. For instance, RECQL4 is the only human RecQ found in both the nucleus and mitochondria (Croteau et al. 2014), and Hrq1 likewise localizes to both organelles (Koh et al. 2015) and physically interacts with mitochondrial proteins (Rogers et al.)1. Here, we found strong negative synthetic genetic interactions between hrq1Δ and mutation of MRM2, a mitochondrial 2’ O-ribose methyltransferase whose deletion results in mitochondrial DNA (mtDNA) loss (Pintard et al. 2002), and YSC83, a mitochondrial protein of unknown function (Sickmann et al. 2003). It is still unclear what the role of Hrq1 is in the mitochondria, but it is tempting to speculate that it is involved in mtDNA maintenance in a similar fashion to its maintenance of the nuclear genome.

This role in genome integrity is highlighted by the negative interactions of hrq1Δ with mutation of SPO16, which is involved in the meiotic cell cycle (Shinohara et al. 2008), and RAD14, a nucleotide excision repair protein (Guzder et al. 2006) and regulator of transcription (Chaurasia et al. 2013). Deletion of HRQ1 also negatively interacted with mutation of SLX9, an rRNA processing factor (Bax et al. 2006) that additionally binds G-quadruplex (G4) DNA structures (Gotz et al. 2019). This is provocative in light of the connection of Hrq1 to rRNA processing and ribosome biogenesis (Rogers et al.)1, as well as the fact that G4 structures are preferred substrates for Hrq1 in vitro (Rogers et al. 2017). Finally, mutations in YEF3, YUR1, MUP3, and PHO5 (encoding a translation elongation factor, protein glycosylase, methionine permease, and acid phosphatase, respectively), as well as the dubious open reading frame (ORF) YDR455C (Fisk et al. 2006), also negatively interacted with hrq1Δ.

hrq1-K318A interactions

Mutations in only two genes, RAD14 and YEF3, are shared between the lists of strong negative interactors with hrq1Δ and hrq1-K318A. This is not unexpected based on the ability of Hrq1-K318A to phenocopy wild-type in some pathways (Bochman et al. 2014). However, mutations in genes encoding proteins involved in processes shared between both sets are evident. This includes HAP2 and HAP3, which are activators of transcription (Xing et al. 1993), TCO89, a member of the TOR complex and global regulator of histone H3 K56 acetylation (Chen et al. 2012), and RAD14 as described above. Similarly, TOM70 encodes a subunit of the mitochondrial protein importer (Brix et al. 2000), which is likely important for localizing Hrq1 to the mitochondria where it may be involved in mtDNA maintenance. Genome integrity is also highlighted by CBC2, which encodes an RNA binding and processing factor involved in telomere maintenance (Lee-Soety et al. 2012). Hrq1 is known to regulate telomerase activity at both DSBs and telomeres (Bochman et al. 2014; Nickens et al. 2018; Nickens et al. 2019). Mutation of the gene encoding the Vps41 vacuolar membrane protein (Nakamura et al. 1997) also negatively interacted with hrq1-K318A.

The overall genetic interactome of HRQ1

In a companion manuscript, we present proteomic and transcriptomic data related to Hrq1 and Hrq1-K318A, highlighting similarities and differences between the wild-type and mutant to generate a wholistic picture of Hrq1 biology (Rogers et al.; manuscript #401710). We also touch on the SGA data included herein. Thus, it is useful to compare some of the hrq1Δ and hrq1-K318A data. In total, 117 significant (P < 0.05) genetic interactions (76 negative and 41 positive) were identified between hrq1Δ and the single-gene deletion collection, and 119 (65 negative and 54 positive) were identified between hrq1Δ and the TS alleles collection (Table S2). Similarly, 132 significant (P < 0.05) genetic interactions (84 negative and 48 positive) were identified between hrq1-K318A and the single-gene deletion collection, and 102 (41 negative and 61 positive) were identified between hrq1K318A and the TS alleles collection (Table S3). When comparing the hrq1Δ and hrq1-K318A data sets in aggregate, there was ∼39% overlap between the negative genetic interactions (Figure 2A) and > 30% overlap between the positive genetic interactions (Figure 2B). However, there was very little overlap when comparing negative to positive genetic interactions and vice versa (Figure 2C,D), consistent with these hrq1 alleles having similar effects in vivo and the wild-type and mutant proteins displaying similar activities in vitro, though often of different magnitudes (Bochman et al. 2014; Rogers and Bochman 2017; Rogers et al. 2017; Nickens et al. 2018; Nickens et al. 2019; Rogers et al. 2020b).

Figure 2

Venn diagrams of the shared synthetic genetic interactions displayed by hrq1Δ and hrq1-K318A. A) Sixty-one alleles negative interact with both the hrq1Δ and hrq1-K318A mutations. B) Similarly, 35 alleles positively interact with both the hrq1Δ and hrq1-K318A mutations. C) Very few of the negative genetic interactors with hrq1Δ are common to the set of positive hrq1-K318A interactors. D) Likewise, only 10 of the positive genetic interactors with hrq1Δ are shared by the set of negative hrq1-K318A interactors.

sgs1Δ interactions

Over 500 genetic interactions with sgs1 alleles have been reported (see: https://www.yeastgenome.org/locus/S000004802/interaction), including most of the hits from our screen, such as the genome integrity genes MMS4, RRM3, SLX1, SLX4, SRS2, and WSS1 (Fisk et al. 2006), as well as SLX9 (see above) and EFB1, which encodes a translation elongation factor (Hiraga et al. 1993). These hits serve as internal positive controls. It should also be noted that: 1) YBR099C is a dubious ORF that completely overlaps MMS4 (Fisk et al. 2006), 2) YBR100W was an originally misannotated ORF and more recently merged with an adjacent ORF such that the coding region is now named MMS4 (Xiao et al. 1998), and 3) the pby1Δ strain in the single-gene deletion collection is actually a deletion of MMS4 (ölmezer et al. 2015). Thus, multiple different mms4 alleles were hits in the screen, again acting as positive controls for our approach.

In addition to known effects, we also discovered three new negative interactions with sgs1Δ. These include the deletions of SWC4 and SWC5, which encode subunits of the SWR1 complex that replaces histone H2A with H2A.Z (Mizuguchi et al. 2004), preventing the spread of silent heterochromatin (Meneghini et al. 2003). This interaction could be connected to the role of Sgs1 in telomere maintenance (Huang et al. 2001; Johnson et al. 2001; Azam et al. 2006) because telomeric DNA is also silenced via the telomere position effect (Mondoux and Zakian 2005). As with hrq1Δ and hrq1-K318A, the yef3-f650s TS allele was also a negative genetic interactor with sgs1Δ (Table 5).

sgs1-K706A interactions

Unlike sgs1Δ, much less is known about the genetic interactome of the catalytically inactive sgs1-K706A allele. We found that the strong negative interactors were mutations in genes that completely overlap with the sgs1Δ set (SRS2, SLX4, SLX9, SLX1, SWC5, WSS1, MMS4, ELG1, YEF3, and SWC4). However, the sgs1-K706A interactors also included mutations in genes that were not ranked as causing the strongest negative effects with sgs1Δ. Nevertheless, alleles of some of these genes (RNH203, SLX8, RNH202, and MUS81) are previously reported negative interactors with sgs1Δ (see: https://www.yeastgenome.org/locus/S000004802/interaction).

Mutations in the remaining genes have not previously been reported to negatively interact with sgs1Δ, but three of them (SPO16, YSC83, and HAP3) overlap with the hrq1 interactors described above, perhaps suggesting some overlap in function between Hrq1 and Sgs1 in the pathways related to these genes. That leaves only two genes, SUA7 and ASK10, as unique interactors here. The SUA7 gene product is the yeast transcription factor TFIIB that is needed for RNA polymerase II transcriptional start site selection (Pinto et al. 1992). This may indicate that like the human RECQL5 helicase (Aygun et al. 2008; Izumikawa et al. 2008; Saponaro et al. 2014), Sgs1 is involved in transcription, a hypothesis also put forth for Hrq1 (Rogers et al.)1. In support of this, the remaining interactor