Abstract
To segregate accurately during meiosis in most species, homologous chromosomes must recombine1. Small chromosomes would risk missegregation if recombination were randomly distributed, so the double-strand breaks (DSBs) initiating recombination are not haphazard2. How this nonrandomness is controlled is not understood, although several pathways ensuring that DSBs occur at the appropriate time, number, and place are known. Meiotic DSBs are made by Spo11 and accessory “DSB proteins,” including Rec114 and Mer2, which assemble on chromosomes3–7 and are nearly universal in eukaryotes8–11. Here we demonstrate how Saccharomyces cerevisiae integrates multiple temporally distinct pathways to regulate chromosomal binding of Rec114 and Mer2, thereby controlling the duration of a DSB-competent state. Engagement of homologous chromosomes with one another regulates the dissociation of Rec114/Mer2 later in prophase I, whereas replication timing and proximity to centromeres or telomeres influence Rec114/Mer2 accumulation early. Another early mechanism boosts Rec114/Mer2 binding specifically on the shortest chromosomes, subject to selection pressure to maintain hyperrecombinogenic properties of these chromosomes. Thus, an organism’s karyotype and risk of meiotic missegregation influence the shape and evolution of its recombination landscape. Our results create a cohesive view of a multifaceted and evolutionarily constrained system that ensures DSB allocation to all pairs of homologous chromosomes.
Main Text:
Homologous chromosomes can only recombine if they have at least one DSB. Simulations show that the shortest chromosomes (Chr1, 3 and 6) would risk DSB failure if DSBs were random (Fig. 1a), but mechanisms attenuating this risk are apparent because chromosome size negatively correlates with crossover density12–14, DSB density15,16, and DSB protein binding3,17 (Fig. 1b and Extended Data Fig. 1a). Thus, short chromosomes recruit more DSB proteins, presumably yielding higher density of DSBs and crossovers. How preferential DSB protein recruitment is achieved is unknown.
Rec114 perdures on short chromosomes
We analyzed published18 and new meiotic timecourses of calibrated chromatin immunoprecipitation sequencing (ChIP-seq) for myc-tagged Rec114 or Mer2 (Fig. 1c, d and Extended Data Fig. 1b, c). Two key patterns emerged from relative per-chromosome abundance (Fig. 1e and Extended Data Fig. 1d, e). First, both proteins were overrepresented on short chromosomes throughout. Second, early and late ChIP densities were markedly different. Early (2–3 h), the short chromosomes had starkly higher relative ChIP densities, while the rest had low densities uncorrelated with size. Later (4–6 h), the 13 largest chromosomes showed a clear negative correlation with size and the smallest three deviated less from a linear relationship.
These patterns suggested that distinct mechanisms regulate early vs. late chromosomal binding. To test this, we measured association and dissociation times at each DSB protein peak. These times showed large early and late domains along chromosomes (Extended Data Fig. 2a). Nevertheless, Rec114 on average associated earlier and dissociated later from the shortest chromosome (chr1) than the longest (chr4) (Extended Data Fig. 2a) and this trend extended in a size-related manner across the entire complement (Fig. 1f). Importantly, per-chromosome patterns mirrored the ChIP density patterns above. The shortest chromosomes had precocious association times while the rest showed no clear size relationship, whereas dissociation times negatively correlated with size across all chromosomes, with the short trio fitting a global linear relationship (Fig. 1f).
Mean Rec114 duration correlated negatively with chromosome size, and the short trio again stood out with especially long durations (Fig. 1g and Extended Data Fig. 2b). These patterns were highly reproducible (Extended Data Fig. 2c, d) and Mer2 behaved similarly (Extended Data Fig. 2b, d).
If duration of Rec114 and other pro-DSB factors dictates how long chromosomes are DSB-competent2,3,7,19, then Rec114 and Mer2 binding patterns should presage DSB distributions. To test this, we measured DSBs at 4 and 6 h by sequencing Spo11 oligonucleotides (oligos), quantitative byproducts of DSBs15. Spo11-oligo densities correlated negatively with chromosome size, but the short trio had substantially higher density than predicted from a linear relationship (Extended Data Fig. 2e). Moreover, the 13 largest chromosomes showed a better anticorrelation at 6 h than at 4 h. Thus, DSBs mirror Rec114 and Mer2 distributions.
We infer that DSB protein association and dissociation govern size-dependent DSB control on all chromosomes and ensure overrepresentation of DSBs and crossovers on very short chromosomes. We therefore set out to elucidate this temporal regulation.
Homolog engagement dictates dissociation
DSB formation is inhibited when chromosomes engage their homologs and more effective inhibition on longer chromosomes establishes an anticorrelation between DSB density and chromosome size2,7,16,20. We hypothesized that: 1) distinct mechanisms control association vs. dissociation of DSB proteins; 2) shorter chromosomes take longer to engage their partners; and 3) homolog engagement displaces DSB proteins (Supplementary Discussion 1), thereby making Rec114/Mer2 duration anticorrelated with chromosome size. This hypothesis predicts that disrupting homolog engagement should not affect Rec114 binding early, but should cause inappropriate retention on larger chromosomes.
We tested this prediction in a homolog engagement-defective mutant (zip3)16. Modest differences compared to ZIP3 in genome-wide absolute Rec114 ChIP signal at 2 and 4 h possibly reflected small differences in culture timing, but a ~;two-fold increase at 6 h was consistent with Rec114 persisting longer (Extended Data Fig. 3a–c). At 2 h and 4 h, Rec114 was overrepresented on the shortest chromosomes in zip3, similar to wild type (Fig. 1h and Extended Data Fig. 3b, d). At 6 h in contrast, the mutant failed to establish an anticorrelation between ChIP density and chromosome size (Fig. 1h) because of preferential Rec114 retention on larger chromosomes (Fig. 1i and Extended Data Fig. 3b, c). We conclude that feedback from homolog engagement establishes much of late-prophase DSB control by governing dissociation of Rec114 and other proteins.
Three pathways regulating association
Replication regulates Rec114 chromatin binding18, so short chromosomes replicating early21 might explain their early Rec114 association. We tested this by deleting origins on the left arm of chr3 (chr3L) to delay replication and using tof1 mutation to compromise replication-DSB coordination18. We found that overrepresentation of Rec114/Mer2 and their early association times on short chromosomes were partially dependent on early replication (Extended Data Fig. 4a, b). Thus, replication coordination contributes but is not sufficient to explain all size differences. We inferred that additional controls exist.
To delineate these controls, we examined subchromosomal domains. Color-coded maps suggested early association and higher ChIP densities around centromeres22 and the converse toward telomeres (Fig. 2a and Extended Data Fig. 4c, d). Fitting trend lines to all datasets and all 32 chromosome arms quantified these effects and showed how they decayed with distance (Fig. 2b, c and Extended Data Fig. 4e, f). The telomere effect may reflect a known DSB formation delay23. Both effects were retained in tof1 mutants but appeared weaker (Extended Data Fig. 4g), possibly because of constitutively early (centromeres) or late (telomeres) replication21.
More early time points in the Mer2 data afforded a detailed look. Pericentromeric enrichment was detectable at 0.5 h, reached a maximum at 1.5 h, then diminished as Mer2 accumulation elsewhere balanced binding near centromeres (Extended Data Fig. 4h). In contrast, there was little telomere-proximal depletion of Mer2 through 1 h, then the depletion became progressively more apparent (Extended Data Fig. 4i). The centromere effect was normal in zip3 (Extended Data Fig. 4j), consistent with homolog engagement influencing DSB protein binding late but not early.
We tested by multiple linear regression if replication timing plus centromere and telomere effects might explain DSB protein association. Regression models accounted for 37% to 51% of the variance in association timing (Fig. 2d and Extended Data Fig. 5a). Thus, a simple three-factor model gave a reasonably effective fit genome-wide (Fig. 2e). However, the model fit the shortest chromosomes poorly, with observed association earlier than predicted (Fig. 2d and Extended Data Fig. 5a, b). ChIP density at 2 h was complementary: models fit genome-wide data well but underperformed on small chromosomes by predicting less enrichment than was observed (Fig. 2f, g and Extended Data Fig. 5c, d).
Thus, for most chromosomes, DSB protein association early in meiotic prophase is shaped by replication timing and proximity to centromere or telomere. The shortest trio, however, accumulates these proteins earlier and at higher levels than these influences predict. We hypothesized that little chromosomes have an additional feature(s) boosting their ability to compete early for binding to a limited pool of DSB proteins. This idea makes two predictions if this feature is intrinsic to the DNA sequence: segments from short chromosomes should retain the boost when fused to a longer chromosome, and making an artificially small chromosome by bisecting a larger one should not be sufficient to establish a similar boost.
Boosting short chromosomes
To test if chr1 (230 kb) intrinsically boosts Rec114 binding, we artificially lengthened it by reciprocal translocation with chr4 (1.5 Mbp) (Fig. 3a and Extended Data Fig. 5e, f). We asked whether chr1 portions of derivative chromosomes (der(1) at 532 kb and der(4) at 1.2 Mb) still behaved like a short chromosome as predicted or if they now behaved like a longer chromosome as previous studies of crossing over might predict14.
At 2 h, segments from chr1 retained Rec114 overrepresentation (Fig. 3b, left), yielding sharp ChIP transitions between chr4- and chr1-derived sequences (Fig. 3c). Moreover, a three-factor regression model again underperformed in predicting Rec114 levels on chr1-derived sequences (Extended Data Fig. 5g). These results support the hypothesis that chr1 has an intrinsic feature(s) promoting preferential early Rec114 association. This feature acts in cis and independently of chromosome size per se.
Later (4 and 6 h), translocated chr1 segments still showed Rec114 overrepresentation, but less than for native chr1 (Fig. 3b, right, and Extended Data Fig. 5h, i). Lesser Rec114 abundance matches our conclusion that feedback from homolog engagement dominates late patterns: since homolog engagement is tied to chromosome size per se, chr1-derived segments should conform to their long-chromosome context. The remaining overrepresentation relative to naturally long chromosomes may be a residuum of high early enrichment.
A converse experiment created an artificial short chromosome (177 kb) by translocation between two medium-size chromosomes. As predicted, this chromosome did not behave like a natural short chromosome in early prophase, when effects of the boost should be apparent, but did so later, when homolog engagement dominates (Extended Data Fig. 6a–f and Supplementary Discussion 2).
Selective pressure maintains the boost
Most Saccharomyces species have the same three short chromosomes24. This conservation suggests that mechanisms mitigating risk of meiotic nondisjunction are shared, implying in turn that evolutionary selection maintains hyperrecombinogenic properties of small chromosomes.
Saccharomyces mikatae provides a natural experiment for hallmarks of such selection. In other species, chr6 is the second shortest chromosome, but in S. mikatae the regions syntenic to ancestral chr6 (hereafter, syn6) are on longer chromosomes24,25 (Fig. 3d). We previously showed that syn6 DSB densities match the chromosomal context, but inferred that density is tied to chromosome size and not DNA sequence26. To revisit this conclusion, we reasoned that lack of selective pressure to maintain a DSB protein-binding boost on syn6 during post-translocation generations would lead to absence of the boost in extant S. mikatae strains. Chr1 should preserve the boost because it is still small.
As predicted, chr1 in S. mikatae showed strong overrepresentation of Rec114 at 2 h, but Rec114 binding was lower on syn6 in S. mikatae than for chr6 in S. cerevisiae. Indeed, syn6 segments were indistinguishable from larger chromosomes (Fig. 3e, left, and Extended Data Fig. 6g). A three-factor regression model fit syn6 data well but still underperformed for chr1 (Extended Data Fig. 6h). Later (4 and 6 h), syn6 segments had relative Rec114 densities in line with their chromosome sizes (Fig. 3e, right, and Extended Data Fig. 6i, j).
The boost requires axis proteins
Meiotic chromosomes form axial structures anchoring chromatin loops27. Because DSB proteins assemble on axes, arranging a DNA segment as short loops on a long axis is proposed to yield higher DSB density than if arrayed as long loops on a short axis28–30. Analysis of published data31 showed that chr3 has a ~;1.2 fold larger axis:DNA ratio (3.89 μm/Mbp on average) than chr4 or chr15 (3.22 μm/Mbp; Extended Data Fig. 7a), although densities of preferred DSB protein binding sites were not different between small and large chromosomes (Extended Data Fig. 7b). These findings are consistent with loop-axis structure contributing to intrinsic DSB potential.
Axis proteins Hop1, Red1, and Rec8 promote normal chromatin association of Rec114 and Mer2, and Hop1 and Red1 are overrepresented on short chromosomes, suggesting that axis protein enrichment contributes to high DSB density3,17. To test this hypothesis, we assessed Rec114 ChIP density and DSB formation in axis mutants.
Both hop1 and red1 single mutants eliminated Rec114 overrepresentation on small chromosomes early (2 h) and ablated the anticorrelation of Rec114 binding with chromosome size at all times (Extended Data Fig. 7c). Both mutations greatly decreased Rec114 ChIP levels genome-wide3, but, unexpectedly, Rec114 binding was substantially higher in a hop1 red1 double mutant without rescue of either the DSB defects or Rec114 spatial patterns (Fig. 4a, Extended Data Fig. 7d–g, and Supplementary Discussion 3). Even with this more robust ChIP signal, short chromosomes still lacked Rec114 overrepresentation at 2 h, and only a weak size dependence emerged later (Fig. 4b and Extended Data Fig. 7c).
Absence of Rec8 did not eliminate relative enrichment of Rec114 on short chromosomes (Extended Data Fig. 7c)17, although total Rec114 binding was greatly reduced (Fig. 4a). A hop1 red1 rec8 triple mutant behaved like hop1 red1: improved Rec114 recruitment but no short-chromosome boost (Fig. 4a, b). Rec8, which is enriched at centromeres22,32, was required for preferential Rec114 binding near centromeres but Hop1 and Red1 were not (Extended Data Fig. 7h).
Spo11-oligo maps demonstrated functional significance of loss of Hop1 and Red1: mutants lacked any higher density of DSBs on short chromosomes (Fig. 4c). Instead, an inverted relationship of DSBs with chromosome size suggested complete loss of size-dependent control. These findings implicate the chromosome axis as a platform supporting DSB regulation.
Safeguarding chromosome segregation
Early in prophase, three mechanisms (replication timing and distances to centromere and telomere) govern spatial and temporal patterns of Rec114 and Mer2 binding to all chromosomes and a fourth mechanism(s) boosts binding on the smallest chromosomes (Fig. 4d). Each mechanism differs in magnitude and contributes differently to chromosome size dependence (Fig. 4e and Supplementary Discussion 4). Another pathway (homolog engagement) primarily regulates DSB protein dissociation (Fig. 4d, Extended Data Fig. 7i and Supplementary Discussion 4). Within the context of homolog engagement, a tendency for chromosome end-adjacent regions to prolong DSB formation33 contributes modestly compared to other feature(s) such as size-dependent pairing kinetics (Extended Data Fig. 8, 9 and Supplementary Discussion 5, 6).
We propose that these pathways collaborate to govern the amount of DSB protein binding and the duration of a DSB-permissive state, thus shaping per-chromosome and within-chromosome “DSB potential” (Supplementary Discussion 7). The early pathways proactively establish when chromosomal segments become competent to make DSBs, so they modulate the population-average DSB probability. In contrast, feedback from homolog engagement reacts to a favorable outcome, so it not only affects the population-average DSB probability, it also ensures a low failure rate by allowing DSB formation to continue until success is achieved. Regulatory circuits involving Tel1ATM and Mec1ATR further ensure success2,7,34–37.
The purpose of this pathway integration is presumably to assure every chromosome the opportunity to pair and recombine. To test this idea, we disrupted reactive pathways by inducing premature prophase I exit with an exogenously controlled Ndt80 transcription factor (Supplementary Discussion 8). As predicted, early Ndt80 induction increased the frequency of meioses in which the artificial short chromosome (der(9)) was non-exchanged and suffered meiosis I nondisjunction (Extended Data Fig. 10a–c). Accurate segregation of a naturally short chromosome (chr6) was more sensitive to premature prophase exit than a mid-sized chromosome (chr5), and der(9)—which lacks the boost—was more sensitive still (Fig. 4f and Extended Data Fig. 10d, e).
Targeted boosting of DSB protein binding, perhaps by organizing short loops on long axes, may be a versatile strategy to mitigate meiotic missegregation risk caused by karyotypic constraints, for example restriction of sex chromosome recombination to the pseudoautosomal region in mammalian males28 (Supplementary Discussion 9 and Extended Data Fig. 10f). Budding yeast’s multilayered control provides a paradigm for how cells solve the challenge of ensuring recombination on every chromosome, no matter how small.
Methods
Yeast strain and plasmid construction
Myc-epitope tagging of Rec114 and Mer2 in S. cerevisiae and S. mikatae
To perform ChIP-seq experiments in S. cerevisiae, Rec114 or Mer2 were C-terminally tagged with 8 and 5 copies of the Myc-epitope marked with the hphMX4 cassette and the URA3 gene, respectively (REC114-Myc and MER2-Myc), described in refs18,40. Rec114 in S. mikatae was C-terminally tagged with the Myc-epitope with the same construct used to tag Rec114 in S. cerevisiae. Epitope tagged Rec114 in S. mikatae was checked by western blotting and was functional as the REC114-Myc strain showed good spore viability (98%, 16 tetrads).
Targeting reciprocal translocation between chr1 and chr4
Reciprocal translocation between chr1 and chr4 was targeted as described in Extended Data Fig. 5e. The TRP1 gene with a 3′ portion of the K. lactis URA3 gene (Kl.URA3) was amplified from plasmid pWJ71648 using primers TL#1AF and TL#1AR (Supplementary Table 1), which each contain 50 nt from the terminator region of SWC3 in the left arm of chr1. The HIS3 gene with a 5′ portion of Kl.URA3 was amplified from pWJ107748 using primers TL#1BF and TL#1BR (Supplementary Table 1), which each contain 50 nt from the terminator region of SLX5 in the left arm of chr4. Each amplified DNA fragment was transformed into MATa and MATα haploid yeast (ura3, trp1, his3), respectively. After verifying transformants by PCR and sequencing, MATa and MATα transformants were mated. Since the two Kl.URA3 segments share identical sequence (448 bp), homologous recombination between these regions would produce uracil prototrophy along with reciprocal translocation between chr1 and chr4. We sporulated the diploid and screened for Ura+ haploids by spreading spores on SC-ura plates. A Ura+ haploid was verified by pulsed-field gel electrophoresis (PFGE) followed by Southern blotting using probes hybridizing to both ends of chr1 and chr4 generated by primers listed in Supplementary Table 1 (see Extended Data Fig. 5f for an example). We confirmed that native chr1 and chr4 had disappeared and derivative chromosomes der(1) and der(4) of the expected size had appeared. The verified haploid was crossed with a REC114-myc haploid which retains native chr1 and chr4 to isolate both mating types with der(1), der(4) and REC114-myc. These haploids were verified by Southern blotting again and mated to obtain a diploid with the homozygous translocation in the REC114-myc background.
Targeting reciprocal translocation between chr8 and chr9
Reciprocal translocation between chr8 and chr9 was targeted by CRISPR/Cas9 as described in Extended Data Fig. 6a. Two guide RNA sequences were cloned into pCRCT (URA3, iCas9, 2 micron ori)49 to target cleavages in the downstream regions of YHL012w (80468: chr8, left arm) and URM1 (342918, chr9, left arm). The plasmid was cotransformed with 100-bp recombination donor fragments that have translocated sequences into a MATα REC114-myc haploid. Ura+ transformants were first screened on SC-ura plates and then checked for translocation by PCR with primer pairs flanking the two junctions. Positive transformants were mated with a wild type MATa haploid. The resulting diploid turned out to be homozygous for the translocated chromosomes probably because of recombination induced by Cas9 cleavages using der(8) and der(9) as template. The sizes of the translocated chromosomes in the above haploids and diploids were confirmed by PFGE followed by Southern blotting using probes hybridizing to both ends of chr8 and chr9 generated by primers listed in Supplementary Table 1 (Extended Data Fig. 6b). Diploids that had lost the plasmid were selected on 5-FOA plates and subjected to sporulation followed by tetrad dissection to isolate MATa and MATα haploids with der(8), der(9), and REC114-myc. These haploids were mated and the resulting diploid was used for further experiments.
Axis mutants
The red1, hop1, and mek1 deletions were made by replacing the respective coding sequences with the hygromycin B drug resistance cassette (hphMX4) amplified from plasmid pMJ696 (identical to pAG3250). Yeasts were transformed using standard lithium acetate methods. Gene disruption was verified by PCR and Southern blotting. The SPO11-Flag construct (SPO11-6His-3FLAG-loxP-kanMX-loxP) was provided by Kunihiro Ohta, Univ. Tokyo22. All axis (hop1, red1, rec8, hop1 red1, and hop1 red1 rec8) and zip3 mutants in the REC114-Myc background were created by multiple crossing followed by tetrad dissection.
Plasmids for spore-autonomous fluorescent markers
Plasmids pSK1269 (PYKL050c-GFP*-KanMX) and pSK1271 (PYKL050c-CFP-natMX) were constructed by subcloning EcoRI fragments from pSK726 and pSK69239 into EcoRI sites on pFA6a-KanMX51 and pMJ695 (identical to pAG2550), respectively. Fluorescent markers and drug resistant cassettes are tandem orientation. For the RFP integration into the right arm of chr9, a 247 bp sequence within the downstream of YPS6 ORF was amplified using primers (inf9RightRFPF and inf9RightRFPR) listed in Supplementary Table 1 and cloned at the Tth111I site in pSK691 (PYKL050c-RFP-LEU2) using In-Fusion HD (TaKaRa), yielding an integrative plasmid (pSK1320).
Construction of strains with inducible NDT80 and spore-autonomous fluorescent markers
To introduce the inducible NDT80 system, a GAL4-ER PGAL-NDT80 strain (from Angelika Amon, MIT)52 was crossed with our strain with der(9). Resulting haploids from tetrad dissection were transformed with spore-autonomous fluorescent markers with 50 bp homology sequence amplified from pSK1269, pSK1271 or pSK69139. Transformants were checked by PCR designed at two junctions as following combination (integration marker and locus: primers to amplify marker; plasmid; primers to check integration). RFP at MSH4 downstream (cen6): TF_RFP_cen6F/R; pSK691; cen6_RFP_check1F/cen1_RFP_check1R and cen1_RFP_check2F/cen6_RFP_check2R. GFP at NAS2 downstream (translocation junction, cen9): TF_GFP_cen9v3F/R; pSK1269; cen9_GFP_check3F/ cen9_GFP_check1v2R and cen9_GFP_check2F/cen9_GFP_check3R. CFP at GIM4 downstream (cen5): TF_CFP_cen5F/R; pSK1271; cen5_CFP_check1F/R and cen5_CFP_check2F/R. CFP at YHL048w downstream (chr8L): TF_CFP_chr8LF/R; pSK1271; chr8L_CFP_check1F/R and chr8L_CFP_check2F/R. RFP integration at YPS6 (chr9R) was done by integrating pSK1320 linearized with NdeI and transformants were checked by PCR using chr9R_RFP_check1F/R and chr9R_RFP_check2F/R. Strains with appropriate marker configuration (Extended Data Fig. 10 and Supplementary Table 2) were created by crossing the above transformants followed by tetrad dissection.
Yeast growth conditions
Studies were performed using S. cerevisiae SK1 and S. mikatae IFO1815 strain backgrounds; strains are listed in in Supplementary Table 2. Synchronous meiotic cultures were with the SPS pre-growth method47. Briefly, saturated overnight cultures in 4 ml YPD (1% yeast extract, 2% peptone, 2% glucose) were used to inoculate 25 ml of SPS (0.5% yeast extract, 1% peptone, 0.67% yeast nitrogen base without amino acids, 1% potassium acetate, 0.05 M potassium biphthalate, pH 5.5, 0.002% antifoam 204 (Sigma)) to a density of 5 × 106 cells/ml and cultured at 30°C at 250 rpm for 7 h. Cells were then inoculated into an appropriate volume (900 ml for ChIP-seq experiments with 12 time points or 300 ml for experiments with 3 time points) of fresh SPS at a density of 3 × 105 cells/ml and cultured at 30°C at 250 rpm for 12–16 h until the density reached 3–4 × 107 cells/ml. Cells were collected by filtration, washed with water, then resuspended at 4 × 107 cells/ml in appropriate volume (610 ml for 12 time points or 200 ml for 3 time points) of SPM (2% potassium acetate, 0.001% polypropylene glycol) supplemented with 0.32% amino acid complementation medium (1.5% lysine, 2% histidine, 2% arginine, 1% leucine, 0.2% uracyl, 1% tryptophan). Cultures were shaken at 250 rpm at 30°C and 50 ml samples for ChIP-seq were collected at desired times after transfer to SPM. For 12-time-point cultures, we collected samples as follows: 0, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 7, 8 h for Rec114 ChIP in TOF1 background; 0, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7 h for Rec114 ChIP in tof1 background; 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6 h for Mer2 ChIP. For all 3-time-point cultures, cells were collected at 2, 4 and 6 h.
For the Spo11-oligo mapping experiments, synchronous meiotic cultures of S. cerevisiae SK1 were prepared as described in ref.53. Briefly, cells from a saturated overnight YPD culture were used to inoculate a 14-h pre-sporulation culture in YPA (1% yeast extract, 2% peptone, 1% potassium acetate) supplemented with 0.001% antifoam 204 and grown at 30°C (starting cell density (OD600) of 0.2). Cells were harvested, resuspended in 2% potassium acetate, 0.2 × supplements (2 μg/ml adenine, 2 μg/ml histidine, 6 μg/ml leucine, 2 μg/ml tryptophan, 2 μg/ml uracil), 0.001% antifoam 204 at OD600 = 6.0, and incubated in a 30°C shaker to induce sporulation.
To assess culture synchrony, meiotic division profiles were obtained by collecting aliquots at various times from synchronous meiotic cultures, fixing in 50% (v/v) ethanol, and staining with 0.05 μg/ml 4′, 6-diamidino-2-phenylindole (DAPI). Mono-, bi- and tetranucleate cells were scored by fluorescence microscopy.
Chromatin immunoprecipitation for Mer2-Myc and Rec114-Myc
We performed ChIP experiments as described previously18, with modifications in cell disruption and chromatin fragmentation. Cells were disrupted by vigorous shaking at 6.5 m/s for 1 min × 10 times in a FastPrep24 (MP Biomedicals). Chromatin in the whole cell extracts (WCE) was sheared by sonication with “M” intensity, 30 sec ON/ 30 sec OFF for 15 min × 3 times in a Bioruptor Sonication System UCD200 (Diagenode) in 15 ml polystyrene conical tubes. Insoluble fraction (cell debris) was removed by centrifugation at 21,130 g, 5 min, 4°C. WCE was further sonicated with the same conditions 3–5 times to yield average DNA size less than 500 bp.
For qPCR, we used eight and ten primer pairs for S. cerevisiae 12-time-point and 3-time-points datasets, respectively. For S. mikatae we used ten primer sets. All primer sets are listed in Supplementary Table 1. qPCR was performed using the LightCycler® 480 SYBR Green I Master (Roche) according to manufacturer recommendations. All measurements of ChIP samples were expressed relative to the standard (dilution series of corresponding input samples).
Spo11-oligo mapping
For Spo11-oligo mapping, ≥ 600 ml sporulation culture volume was harvested 4 h after transfer to sporulation media. Because of the severe DSB defect in red1 and hop1, Spo11 oligos from multiple (4–5) cultures of independent colonies were pooled to generate each Spo11-oligo map. The wild-type Spo11-oligo map in Fig. 4c was from a previous study38.
Spo11-oligo mapping in red1 and hop1 mutants was performed essentially as described previously26, with modifications to purify enough Spo11 oligos from red1 and hop1 strains. For example, Spo11 oligos from independent cultures were pooled after eluting from the first immunoprecipitation and at the last step of oligo purification (after proteinase K treatment and ethanol precipitation of the oligos). When pooling Spo11-oligo complexes from five cultures after the first immunoprecipitation step, the total volume of the second immunoprecipitation was increased to 4 ml, and 500 μl of Dynabeads Protein G slurry were pre-bound to 100 μl of 1 mg/ml anti-Flag antibody (as opposed to 125 μl of Dynabeads Protein G slurry pre-bound to 25 μl 1 mg/ml anti-Flag antibody, and a 2nd IP volume of 800 μl). Purified Spo11 oligos were quantified and used for library preparation as described previously16.
Sequencing (Illumina HiSeq 2500, 2 × 75 bp paired-end reads) was performed in the MSKCC Integrated Genomics Operation. Clipping of library adapters and mapping of reads was performed by the Bioinformatics Core Facility (MSKCC) using a custom pipeline as described15,16,26,38,54. Reads were mapped to the sacCer2 genome assembly of type strain S288C from SGD (Saccharomyces Genome Database).
ChIP-seq data processing: scaling and masking
ChIP-seq experiments were performed as described18. DNA from ChIP and input samples (same samples as used for ChIP-qPCR) were further sheared by sonication to an average fragment size of ~;300 bp. These were sequenced (50 bp paired-end) on the HiSeq platform (Illumina). Reads were mapped to the SacCer2 genome assembly and S. mikatae genome assembly55 using BWA (0.7) MEM to generate coverage maps for each time point from each strain. Each ChIP coverage map was divided by the corresponding input map for normalization. Then, to scale the ChIP-seq coverage relative to absolute ChIP efficiency, we calculated the total coverage within ± 1 kb of the center of each qPCR amplicon, plotted these as a function of the corresponding qPCR ChIP efficiency, and calculated regression lines by least squares. The resulting regression line for each time point was then used to scale the ChIP-seq coverage maps.
To remove regions with spurious mapping, we previously defined “mask regions” where the coverage from the 0 h sample of the wild-type ARS+ strain was out of a fixed range (>1.5 SD from mean coverage) with further extension by 1 kb on either side18. These regions were censored in all input and ChIP coverage maps from S. cerevisiae. Mask regions for S. mikatae were defined similarly where the coverage from the 2 h input sample exceeded a fixed range (mean coverage ± 4 SD, calculated between 50–150 kb region of chr1). After the same extension, these regions were censored from S. mikatae coverage maps.
Replication index generated by ChIP input coverage maps
All masked coverage maps from input samples were binned using 5 kb windows and normalized to genomic mean coverage. For 12-time-point datasets, coverage from an “S-phase time point” (1.5 h and 2.5 h for Rec114 ChIP ARS+ tof1Δ and Rec114 ChIP arsΔ tof1Δ, respectively; 2 h for the rest) was divided by the corresponding “G1-phase time point” (0-h sample) to generate a “relative coverage” map. For 3-time-point datasets, the 2-h time point map was divided by the 0 h map from the Rec114 ChIP ARS+ dataset to generate relative coverage. For the S. mikatae dataset, the mean normalized 2 h map was used as relative coverage. We defined the “replication index” as –log2(relative coverage). Outliers were removed from each dataset, defined as the replication index value exceeding a fixed range (mean ± 4 SD).
Estimating association and dissociation times by sequential curve fitting
The method to measure association time is described in ref.18. The scaled and masked ChIP-seq coverage maps from two Rec114 ChIP-seq and Mer2 ChIP-seq data sets were smoothed using a 2010-bp Parzen (triangular) sliding window. Using the smoothed, scaled coverage map at 3.5 h time points, a total of 1477 (Rec114 ChIP ARS+), 1545 (Rec114 ChIP arsΔ) and 1550 (Mer2 ChIP) peaks were called using as a threshold of 0.5× each chromosome’s mean coverage. A ChIP temporal profile at each peak position was assembled by collecting the ChIP signals from the smoothed, scaled coverage map for each time point.
To define the empirical maximum time in the ChIP profile (tmax), Gaussian curves were fitted to ChIP signals plotted as a function of time. To create positive skew in the regression curves, times (t, in hours) were log-transformed [t′ = ln(t+1)]. We employed an equation that is a modification of the Gaussian probability density function:
where y is ChIP signal, a is the background, b is the peak height, c is the peak position, and d is the equivalent of standard deviation. We set the background parameter (a) to the ChIP signal at 0 h, then fitted the equation to the data points by least squares to estimate the other parameters (b, c and d) using the “nls” function in R. The estimated parameter (c) was transformed back to hours in meiosis ().
Next, to estimate the association time of DSB protein, we used this peak to fit a saturating exponential growth (logistic) curve to just the upward slope of the ChIP temporal profile (data points before tmax):
where y is ChIP signal, a is the background, b is the maximum value, c is a shaping factor and d is the inflection point of the logistic function, respectively. We set the background and the maximum value parameters (a and b) to the ChIP signal at 0 h and the previously estimated peak height value (parameter (b) from skewed Gaussian fitting, bGauss), respectively, and then fitted the equation to the data points to estimate the other parameters (c and d) using the “nls” function in R. We used d as tassociation where the logistic curve reaches 50% of maximum.
We also estimated the dissociation time of DSB protein by fitting a logistic curve to the downward slope of the ChIP temporal profile (data points after tmax):
where y is ChIP signal, a is the background, b is the maximum value, c is a shaping factor and d is the inflection point of the logistic function, respectively. We used the “nls” function to estimate parameters (c and d) and used d as tdissociation where the logistic curve reaches 50% of maximum.
To evaluate the fitting quality for the kinetic profile at each peak, absolute distances between the data points and the fitted Gaussian curve (residuals) were summed and divided by the peak height (parameter bGauss) from the fitted curve (normalized-total residuals, rGauss). Total residuals from two logistic fittings were divided by bGauss, and the sum of these was defined as rlogistic. We excluded poorly fitted peaks with normalized residuals exceeding 1.2 for Rec114 ChIP arsΔ and Mer2 ChIP datasets. We used less stringent criteria (filtering out peaks with rGauss > 1.6 or rlogistic > 1.5) for Rec114 ChIP ARS+ dataset because overall quality of fittings was less good compared to the other two datasets. After these filtering steps, totals of 998 (Rec114 ChIP ARS+), 1081 (Rec114 ChIP arsΔ), and 1490 (Mer2 ChIP) peaks were processed for further analyses.
For the Rec114 association time in the two tof1 datasets, we used previously estimated values for 957 (tof1 ARS+) and 2020 (tof1 arsΔ) peaks that passed filtering18.
Estimating centromere and telomere effects on DSB protein association and dissociation time, and ChIP density at 2 and 6 h
Association and dissociation timing, and ChIP density (2 and 6 h) data from the two Rec114 and one Mer2 ChIP time courses were combined as follows. To capture the intra-chromosomal features separate from inter-chromosomal differences, we averaged each dataset in 20-kb bins, standardized the values for each chromosome to a mean of 0 and variance of 1, and standardized again within a dataset before pooling all datasets together. The first standardization minimizes differences between chromosomes and the second standardization minimizes differences between datasets. The pooled association, dissociation time or ChIP density (2 or 6 h) data were plotted as a function of distance from centromere or telomere. We fitted an exponential decay model to these data points:
where y is association time, dissociation time, or ChIP density at 2 or 6 h, x is distance from centromere or telomere, a is the initial value of the centromere or telomere effect, b is a shaping factor and c is the intercept, respectively. We used the “nls” function in R to estimate parameters (a, b and c) and used the parameter (b) to present the half distance where the initial value decays to half as the following:
For the telomere effect modeling of ChIP density at 6 h, to represent DSB protein repression within 20 kb from telomere and enrichment in the adjacent regions (~;100 kb), we fitted a composite model consisting of two exponential decay models:
where y is ChIP density at 6 h, x is distance from telomere, a and a’ are the initial values of the repression and enrichment effects, b and b’ are shaping factors and c is the intercept.
Multiple linear regression analysis
To perform multiple regression analyses, replication index, association time, dissociation time, and ChIP density at 2 or 6 h were averaged in 20-kb bins. Distances from centromere and telomere at the midpoints of the 20-kb bins were plugged into the centromere and telomere exponential models whose parameters were estimated as described in the preceding section. The same centromere and telomere models were used for multiple regression in strains with translocation and in S. mikatae. Regression coefficients and the standardized regression coefficients (beta) are shown along with t and P values based on the standardized coefficients in Supplementary Tables 3–5.
Fluorescent spore assay to measure crossing over and MI nondisjunction
Diploids with der(9), spore-autonomous fluorescent markers and inducible NDT80 (SKY7023 and 7034) were sporulated using the SPS presporulation method described above. One ml SPM culture was removed at the indicated times in Fig. 4f and Extended Data Fig. 10c and returned to the shaker after adding β-estradiol (SIGMA, 1 μM final). Cells were harvested 54 h after transfer to SPM and 200 (SKY7023) and more than 500 (SKY7034) tetrads per time point were scored as previously described39. We used 40× objective lens and analyzed the captured tetrad images using Fiji56. We scored only tetrads with four obvious spores. For tetrads where fluorescent signals in spores was difficult to call positive or negative from visual inspection, we quantified signal strength of spores within a tetrad and called a given spore positive if its signal exceeded two (RFP and CFP) or four (GFP) fold higher than a sister spore with lower signal. Tetrads with aberrant numbers of “positive” spores other than two positive (SKY7023) and two or four positive (SKY7034) were excluded for further analysis.
For the strain designed to detect crossovers, nonexchange chromosomes (E0), and MI nondisjunction (MINDJ) on der(9) (SKY7023, Extended Data Fig. 10a, b), 200 tetrads per time point were scored. Tetrads with an aberrant number of positive spores other than two positive were excluded (marker gain or loss in Supplementary Table 6). The rest of tetrads were categorized as listed. The number of crossovers was estimated for each interval using the equation, TT + 6NPD, where TT is tetratypes and NPD is nonparental ditypes. Since MI nondisjunction and double NPD events would produce tetrads with identical fluorescent signal pattern (Extended Data Fig. 10b), the number of MINDJ was corrected as observed MINDJ – fraction(NPDCFP-GFP)*fraction(NPDGFP-RFP)*(total number of tetrads). Similarly, the number of E0 was corrected as observed E0 + fraction(NPDCFP-GFP)*fraction(NPDGFP-RFP)*(total number of tetrad). Based on the assumption that most MINDJ events are also E0, the total E0 was estimated as the sum of corrected E0 and corrected MINDJ. We also independently estimated E0 assuming a Poisson distribution of crossovers in the population of cells with the measured average crossover number (dashed line in Extended Data Fig. 10c). Because the two ways of estimating E0 values agreed well, we conclude that these measurements are robust.
For the strain designed to detect MINDJ on der(9), chr6, and chr5 (SKY7034, Extended Data Fig. 10d, e), more than 500 tetrads per time point were scored. Tetrads with two positive and two negative fluorescent spores were counted as MINDJ (Supplementary Table 7). We did not score tetrad showing other than four or two positive spores.
Extended Data
Supplementary Material
Acknowledgments:
We are grateful to Agnès Viale and Neeman Mohibullah of the Memorial Sloan Kettering Cancer Center (MSKCC) Integrated Genomics Operation for DNA sequencing; Nicholas Socci at the MSKCC Bioinformatics Core Facility for mapping ChIP-seq and Spo11-oligo reads; and members of the Keeney laboratory, especially Shintaro Yamada for advice on data analysis and Laurent Acquaviva for sharing unpublished information. We thank Vijayalakshmi Subramanian (NYU), Andreas Hochwagen (NYU), and Franz Klein (Univ. of Vienna) for discussions and sharing unpublished information. We thank Michael Lichten (NCI), Ed Louis (Univ. of Nottingham), Kunihiro Ohta (Tokyo Univ.), Angelika Amon (MIT), Wolfgang Zachariae (MPI of Biochemistry), Joao Matos (ETH Zurich), and Rodney Rothstein (Columbia Medical Center), for strains or plasmids.
Funding: IL and MvO were supported in part by National Institutes of Health (NIH) fellowships F31 GM097861 and F32 GM096692, respectively. This work was supported by NIH grants R01 GM058673 and R35 GM118092 to SK. MSKCC core facilities are supported by NCI Cancer Center Support Grant P30 CA008748.
Footnotes
Additional Information: Supplementary Information is available for this paper. Correspondence and requests for materials should be addressed to murakamh@mskcc.org or s-keeney@ski.mskcc.org.
Competing interests: Authors declare no competing interests.
Data and materials availability: All sequencing data were deposited at the Gene Expression Omnibus (GEO) with the accession numbers GSE52970 (Rec114 ChIP-seq including tof1), GSE84859 (Spo11 oligos in hop1 and red1), GSE119786 (Mer2 ChIP-seq), GSE119787 (all Rec114 ChIP-seq generated in this study) and GSE119689 (Spo11-oligo maps in wild type at 4 and 6 h).
Code availability: All data analyses are described in Methods. Custom code for Spo11-oligo mapping is previously published and available online (references in Methods).
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