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Multilayered mechanisms ensure that short chromosomes recombine in meiosis

Nature volume 582, pages 124–128 (2020)Cite this article

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Abstract

In most species, homologous chromosomes must recombine in order to segregate accurately during meiosis1. Because small chromosomes would be at risk of missegregation if recombination were randomly distributed, the double-strand breaks (DSBs) that initiate recombination are not located arbitrarily2. How the nonrandomness of DSB distributions is controlled is not understood, although several pathways are known to regulate the timing, location and number of DSBs. Meiotic DSBs are generated by Spo11 and accessory DSB proteins, including Rec114 and Mer2, which assemble on chromosomes3,4,5,6,7 and are nearly universal in eukaryotes8,9,10,11. Here we demonstrate how Saccharomyces cerevisiae integrates multiple temporally distinct pathways to regulate the binding of Rec114 and Mer2 to chromosomes, thereby controlling the duration of a DSB-competent state. The engagement of homologous chromosomes with each other regulates the dissociation of Rec114 and Mer2 later in prophase I, whereas the timing of replication and the proximity to centromeres or telomeres influence the accumulation of Rec114 and Mer2 early in prophase I. Another early mechanism enhances the binding of Rec114 and Mer2 specifically on the shortest chromosomes, and is subject to selection pressure to maintain the hyperrecombinogenic properties of these chromosomes. Thus, the karyotype of an organism and its risk of meiotic missegregation influence the shape and evolution of its recombination landscape. Our results provide a cohesive view of a multifaceted and evolutionarily constrained system that allocates DSBs to all pairs of homologous chromosomes.

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Fig. 1: Rec114 and Mer2 accumulate preferentially on smaller chromosomes.
Fig. 2: The timing of replication and the proximity to centromeres and telomeres influence the timing and the level of Rec114 association.
Fig. 3: The Rec114 boost is intrinsic to short chromosomes.
Fig. 4: The role of axis proteins in the short chromosome boost and an integrated view of DSB control.

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Data availability

All sequencing data have been deposited in the Gene Expression Omnibus (GEO) with the accession numbers GSE52970 (Rec114 ChIP–seq, including tof1), GSE84859 (Spo11 oligonucleotides in hop1 and red1), GSE119786 (Mer2 ChIP–seq), GSE119787 (all Rec114 ChIP–seq data generated in this study) and GSE119689 (Spo11-oligonucleotide maps in the wild type at 4 h and 6 h).

Code availability

Custom code for Spo11-oligonucleotide mapping has been previously published and is available online (see Methods for references).

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Acknowledgements

We thank A. Viale and N. Mohibullah of the Memorial Sloan Kettering Cancer Center (MSKCC) Integrated Genomics Operation for DNA sequencing; N. Socci at the MSKCC Bioinformatics Core Facility for mapping ChIP–seq and Spo11-oligo reads; and members of the Keeney laboratory, especially S. Yamada for advice on data analysis and L. Acquaviva for sharing unpublished information. We thank V. Subramanian, A. Hochwagen and F. Klein for discussions and for sharing unpublished information; and M. Lichten, E. Louis, K. Ohta, A. Amon, W. Zachariae, J. Matos and R. Rothstein for strains or plasmids. I.L. and M.v.O. 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 S.K. MSKCC core facilities are supported by NCI Cancer Center Support Grant P30 CA008748.

Author information

Author notes

  1. Isabel Lam

    Present address: Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

  2. Megan van Overbeek

    Present address: Caribou Biosciences, Inc., Berkeley, CA, USA

Authors and Affiliations

  1. Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Hajime Murakami, Isabel Lam, Pei-Ching Huang, Jacquelyn Song, Megan van Overbeek & Scott Keeney

  2. Louis V. Gerstner, Jr., Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Isabel Lam & Scott Keeney

  3. Weill Graduate School of Medical Sciences, Cornell University, New York, NY, USA

    Pei-Ching Huang & Scott Keeney

  4. Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Scott Keeney

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Contributions

H.M. performed ChIP–seq, generated translocation strains, performed the fluorescent spore assay and analysed the data. P.-C.H. constructed strains for the fluorescent spore assay with inducible NDT80 and performed the assay. I.L. and M.v.O. performed Spo11-oligo mapping. J.S. performed ChIP–seq under the supervision of H.M. H.M. and S.K. conceived the project and wrote the paper. S.K. analysed data, procured funding and oversaw the research. H.M., S.K. and I.L. edited the manuscript.

Corresponding authors

Correspondence to Hajime Murakami or Scott Keeney.

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Extended data figures and tables

Extended Data Fig. 1 Dependence of DSB-protein binding on chromosome size.

a, Dependence of crossovers (centiMorgans (cM) per kb) on chromosome size. Data are from ref. 12. n = 51 tetrads. b, Example Rec114 ChIP–seq profiles for chr1 and chr4 at 4 h (left) and 6 h (right). c, Similarity of the ChIP–seq patterns of Rec114 and Mer2. Both strains are in an ars∆ background, in which all active replication origins on the left arm of chr3 are deleted. The similar profiles for both proteins suggest similar regulation. n = 1 time course for each strain. d, Full time course of average per-chromosome Rec114 ChIP densities (ARS+ strain). n = 1 time course. e, The size dependence of average per-chromosome Mer2 ChIP density changes over time, similar to that of Rec114 (compare to Fig. 1e). n = 1 time course.

Extended Data Fig. 2 Association and dissociation times of DSB proteins.

a, Within-chromosome organization of Rec114 association and dissociation times (ARS+ strain). Each point is a called Rec114 peak. Green lines indicate binding duration (Δt). Vertical lines mark the per-chromosome means for association and dissociation. b, Per-chromosome Rec114 (left) and Mer2 (right) binding duration in ars∆ strains. n = 1 time course for each strain. c, Association times, dissociation times, and binding duration of Rec114 (ARS+ strain) at Rec114 peaks, broken down by chromosome. n = 998 Rec114 peaks. In all box plots, thick horizontal bars denote medians, box edges mark the upper and lower quartiles, whiskers indicate values within 1.5-fold of the interquartile range, and points are outliers. d, Rec114 and Mer2 show early association and late dissociation on short chromosomes, and thus a longer duration of binding. Values were grouped for the shortest three chromosomes and the other thirteen chromosomes (‘long’). Numbers above brackets indicate P values from one-sided Wilcoxon tests. e, Dependence of Spo11-oligonucleotide distributions on chromosome size as a function of sampling time. n = 2 time courses.

Extended Data Fig. 3 Rec114 chromatin binding in the zip3 mutant.

a, Genome-average Rec114 levels. The dashed line represents an independent wild-type profile from Fig. 1d. b, Effects of zip3 mutation on absolute Rec114 ChIP densities. ChIP–seq coverage was calibrated by qPCR. Each point is the mean for a single chromosome. At 2 h, which is very early in the initial accumulation phase of Rec114 on chromatin, the zip3 mutant has approximately twofold less Rec114 signal, affecting all chromosomes equivalently. It is possible that this decrease reflects a role for Zip3 at early timepoints, but because Zip3 is not known to have any function this early in prophase, a more likely explanation is that the zip3 mutant culture lagged slightly behind the wild-type culture. At 4 h, Rec114 ChIP densities were very similar between the cultures, again with little or no difference between chromosomes. However, at 6 h, the zip3 mutant retained substantially more Rec114 signal. At least some of this increase may be attributable to delayed meiotic progression because of inhibition of Ndt807,16, but—importantly for the purposes of this experiment—the Rec114 levels were disproportionately increased on the 13 larger chromosomes. n = 1 time course for each strain. c, Per-chromosome profiles of absolute Rec114 ChIP density. d, Per-chromosome Rec114 ChIP density. This is the intermediate time point for the samples shown in Fig. 1h.

Extended Data Fig. 4 Replication timing and the centromere and telomere effects.

a, b, Comparison of per-chromosome Rec114 and Mer2 association time (a) and ChIP density at early time points (b, 2 and 2.5 h, normalized to chr15) with replication timing. Replication index is defined as –log2-transformed ratio of sequence coverages for ChIP input samples in S phase versus G1 phase; lower values indicate earlier replication18. The means for the left arm (chr3L, open red circle) and right arm (chr3R, open blue circle) of chr3 are plotted separately, connected to the means for the entire chromosome (solid black) by dashed lines. In wild type, chr3L showed both early replication and early Rec114 association (a.i). Origin deletion delayed replication and Rec114 association, but association was still on the early side compared with longer chromosomes despite extreme replication delay (a.ii). Mer2 was similar (a.v). Moreover, in tof1, the short three chromosomes still showed early Rec114 association (a.iii) and origin deletion delayed Rec114 association18 less than it delayed replication (a.iv). ChIP densities exhibited a complementary trend: Rec114 and Mer2 overrepresentation on short chromosomes was partially dependent on early replication (b). Rec114 was naturally overrepresented on chr3L (b.i). Origin deletion caused a substantial decrease on chr3L but still left Rec114 at higher levels than on the larger chromosomes (b.ii) and Mer2 showed a similar pattern (b.v). In the tof1 mutant, the short three chromosomes still showed high Rec114 signal (b.iii), and origin deletion reduced this signal on chr3L but left it in the higher part of the longer-chromosome range (b.iv). The similarity with Rec114 patterns, including much later Mer2 accumulation on the late-replicating left arm of chr3, suggests that Mer2 binding to chromatin is also coordinated with replication timing. Mer2 is able to bind chromatin in the absence of Rec1143,6,40, but interaction with Rec114 (which is promoted by replication-associated Rec114 phosphorylation18) might stabilize or otherwise modify the localization of Mer2. c, d, Intra-chromosomal distributions of Rec114 and Mer2 association times (c) and ChIP density at 2 h (d) in the indicated strains. c, Average values for 50-kb bins are presented as described in the caption of Fig. 2a. d, Each 50-kb bin is colour-coded according to the mean ChIP density within the bin. e, f, Centromere (e) and telomere (f) effects on the ChIP density of DSB proteins at 2 h. The two Rec114 and one Mer2 ChIP time points were binned, standardized and pooled as in Fig. 2b, c. g, Centromere (top) and telomere (bottom) effects on Rec114 association time in tof1 mutants. Rec114 ChIP–seq data from ARS+ tof1 and ars∆ tof1 strains were binned, standardized and pooled as in Fig. 2b, c. The centromere and telomere effects are still apparent in the tof1 mutants, but appear substantially weaker (particularly the telomere effect). h, i, Detailed time courses of Mer2 binding to chromatin near centromeres (h) and telomeres (i). j, The zip3 mutation did not affect the centromere effect on Rec114 ChIP density at 2 h.

Extended Data Fig. 5 DSB protein binding is boosting on smallest three chromosomes.

a, c, Results from multiple regression analyses using other Rec114 (ARS+) and Mer2 (ars∆) ChIP–seq datasets performed as described in Fig. 2d, f to model Rec114 (n = 505 bins) and Mer2 (n = 583 bins) association times (a) as well as ChIP densities at 2 h (n = 597 bins) (c). Each point is a bin; bins on short chromosomes are blue. b, d, Multiple regression models underperform on short chromosomes. Residuals from the three-factor models (b, association time; d, ChIP density) applied to each dataset in turn were grouped for the three shortest chromosomes compared with the others, except chr12 because of its exceptionally late association (Extended Data Fig. 2c). Numbers in parentheses indicate the number of bins. P values are from one-sided Wilcoxon tests. Box plots are as described in Extended Data Fig. 2c. e–i, Effects of targeted translocations on per-chromosome Rec114 binding. e, Strategy to target reciprocal translocation. To generate the translocation between chr1 and chr4, part of the 3′ end of the URA3 gene from K. lactis (‘RA3’) was integrated on chr1 along with the TRP1 gene. Separately, part of the 5′ end of K. lactis URA3 (‘KlUR’) was integrated on chr4 along with the HIS3 gene. The two parts of K. lactis URA3 partially overlap, so their shared region of homology enables reciprocal recombination between them to result in the formation of a functional URA3 gene. f, Confirmation of targeted translocations. High-molecular-weight DNA was prepared from control and translocation strains and separated on pulsed-field gels and stained with ethidium bromide (EtBr). The translocations were then verified by Southern blotting using probes against the right and left ends of both chromosomes involved in the translocation (n = 4 different probes). A representative result using the chr1L probe is presented. For gel source data, see Supplementary Fig. 1. g, A three-factor multiple regression underperforms on chr1-derived sequences in both wild-type and translocation contexts. Box plots show residuals from multiple regression performed as in d using the 2 h data. ‘Syn1’ indicates sequence syntenic to chr1. Numbers in parentheses indicate number of bins. P values are from one-sided Wilcoxon tests. h, Chr1-derived sequences retain high-level Rec114 binding when in a large-chromosome context. Per-chromosome Rec114 ChIP densities normalized to chr15 are shown at 4 h (the intermediate time point for the samples shown in Fig. 3b). i, Rec114 profiles for wild-type and translocated chromosomes at 6 h. ChIP–seq data were normalized relative to chr15 and smoothed with a 10-kb sliding window.

Extended Data Fig. 6 An artificially short chromosome fails to acquire a boost in Rec114 binding.

a, Top, strategy to target reciprocal translocation. To generate the translocation between chr8 and chr9, we introduced a plasmid expressing Cas9 and two guide RNAs that target cleavage within chr8 and chr9, respectively. This plasmid was introduced by co-transforming it along with 100-bp-long recombination templates matching the desired reciprocal recombination products. Bottom, targeted translocation between chr8 and chr9 (to scale). b, Confirmation of targeted translocations as described in Extended Data Fig. 5f. The translocations were verified by Southern blotting using probes against the right and left ends of both chromosomes involved in the translocation (n = 4 different probes). A representative result using the chr8L probe is presented. For gel source data, see Supplementary Fig. 1. c–f, Per-chromosome Rec114 ChIP densities (c), Rec114 profiles at 2 h (d) and 6 h (f), and multiple regression residuals (e) are shown as in Fig. 3b, c and Extended Data Fig. 5g–i. In e, numbers in parentheses indicate the number of bins. P values are from one-sided Wilcoxon tests. Box plots are as described in Extended Data Fig. 2c. g–j, High-level Rec114 binding to chr6-derived sequences is not retained in S. mikatae. Rec114 profiles (g, j), multiple regression residuals (h) and Rec114 ChIP density at 4 h (i) are shown as in Fig. 3b, c and Extended Data Fig. 5g–i. In h, the numbers in parentheses indicate the number of bins. P values are from one-sided Wilcoxon tests. Note that the model also tended to underperform for chr3 in S. mikatae, but the distribution of residuals was not statistically significant (P = 0.071).

Extended Data Fig. 7 Effect of chromosome axis proteins on Rec114 chromatin binding patterns.

a, Long axes on chr3. To assess axis lengths, we used published measurements of synaptonemal complexes on pachytene chromosomes (see supplementary table 1 from ref. 31). In that study, spread, immunostained synaptonemal complexes were traced from the positions of lacO arrays (bound by LacI–GFP) integrated at the right end of chr3, chr4 or chr15 to the left end of each chromosome. We therefore used the SK1 genome assembly coordinates51 of the lacO integration sites to estimate the nucleotide length corresponding to the synaptonemal complex measurement (0.30 Mb for chr3, 1.48 Mb for chr4, and 0.99 Mb for chr15) and calculated the per-Mb axis lengths. Box plots summarize results from eight independent experiments in the wild type, including synaptonemal complex length measured by Zip1 (experiments 1 and 2) or Rec8-HA (experiment 3) staining. Note that the greater variance for chr3 is a consequence of the absolute measurement error (in μm) being a much larger fraction of the total chromosome length compared with the longer chromosomes. Data were pooled by chromosome for application of two-sided Wilcoxon tests. Box plots are as described in Extended Data Fig. 2c. b, Distributions of inter-peak distances. To ask whether the preferential binding of DSB proteins on short chromosomes is due to the presence of a higher density of DSB-protein-binding sites, we measured the distribution of the distances between DSB-protein ChIP–seq peaks. Vertical bars indicate medians. Coloured numerals above histograms indicate the number of distance measurements. Black numerals are P values from one-sided Wilcoxon tests. There was no significant difference (P > 0.05) between short and long chromosomes in any dataset, indicating that the density of preferred binding sites does not track with chromosome size. DSB-protein-binding sites correspond to sites where Hop1, Red1 and Rec8 are also enriched—that is, presumptive sites where DNA is most likely embedded in the axis3,52. We can then question how to reconcile the similar DSB protein binding site densities between short and long chromosomes with the longer per-Mb axis length on chr3 (a). Notably, the preferred binding sites are defined on a population-average basis. Therefore, one straightforward interpretation is that smaller chromosomes have more of their potential DSB-protein-binding sites axis-associated in each cell, whereas larger chromosomes are more likely to have loops that skip over preferred axis sites. This would yield smaller loop sizes and correspondingly longer axes on the short chromosomes despite a similar density of preferred axis sites per unit length of DNA. c, The dependence of Rec114 binding to chromosomes on the size of the chromosomes is lost in the absence of Hop1 or Red1, but not Rec8. Results are presented as in Fig. 4b. Note that, although correlations with chromosome size remain statistically significant in both the hop1 and the red1 mutants, their slopes are negligible compared to that of the wild type (Fig. 1e). n = 1 time course for each strain. d, Spo11-oligonucleotide labelling to compare DSB levels between the wild type and the hop1;red1 double mutant. Flag-tagged Spo11 was immunoprecipitated from denaturing meiotic extracts, then Spo11-oligonucleotide complexes were end-labelled with terminal deoxynucleotidyl transferase and [α-32P]dCTP. Samples were separated on SDS–PAGE and imaged using a phosphorimager. Spo11-oligonucleotide complexes in yeast run as two prominent bands reflecting the different sizes of attached oligonucleotides53. Points and error bars represent mean and s.d. of three independent meiotic cultures. For gel source data, see Supplementary Fig. 1. e, Progression of meiosis in axis mutants. Samples from meiotic cultures were fixed and stained with DAPI, then fractions of cells completing meiosis I (MI) or both MI and meiosis II (MII) were counted. Identical wild-type data are presented in both panels to aid comparison. Points and error bars represent mean and s.d. of three independent meiotic cultures. f, Correlation matrix of DSB-protein ChIP datasets from wild type and axis mutants. n = 1 time course for each strain. g, Distribution of DSB proteins relative to open reading frames (ORFs). Using an R package provided by the Hochwagen laboratory17, ORFs were standardized to a length of 1 kb, then ChIP–seq profiles were averaged over the standardized ORFs plus 0.5 kb of upstream and downstream sequence. The 4-h wild-type pattern (light grey shading) is repeated in each panel to facilitate comparison. h, The centromere effect is retained (albeit spreading less far) in hop1 and red1 single mutants but is lost in rec8 mutants. Rec114 ChIP data at 2 h were binned and standardized as in Fig. 2b. The rec8 mutation was epistatic to hop1;red1 for loss of the centromere effect. i, The per-chromosome duration of DSB-protein binding has an inverse proportional relationship with chromosome size. Duration data from Fig. 1g and Extended Data Fig. 2b were combined, censoring the cold region between CEN3 and MAT.

Extended Data Fig. 8 Proximity to telomeres influences the timing and the degree of DSB-protein dissociation from chromosomes.

a, d, Intra-chromosomal distributions of Rec114 and Mer2 dissociation times (a) and ChIP density at 6 h (d) in the ARS+ and ars∆ strain. Each block represents a 50-kb bin colour-coded according to the average of the Rec114 or Mer2 dissociation times for peak positions within the bin, or average ChIP density for the bin. Chromosomes are ranked by size, with the left chromosomal end at position zero. b, c, e, f, Effects of proximity to the centromere and the telomere on dissociation time (b, c) and ChIP density (e, f) of DSB proteins. The two Rec114 and one Mer2 ChIP time courses were combined as follows. Dissociation time and ChIP density (6 h) data from three datasets were binned (20-kb windows), standardized, and plotted as in Fig. 2b, c and Extended Data Fig. 4e, f (grey dots). Green lines are fitted exponential models. Vertical bars indicate the distance at which the effect decays to half of the original value. For the telomere effect modelling on ChIP density at 6 h (e), we instead fitted a composite curve (green line) consisting of two exponential models (red and blue lines) to describe repression and enrichment, respectively. g, h, EAR (end-adjacent region) effects on DSB-protein dissociation time (g) and ChIP density at 6 h (h). Histograms show the distribution of dissociation time data at each peak position and ChIP density data binned in 10-bp windows located within EARs (defined as the regions from 20 to 110 kb from each telomere33) and interstitial regions (that is, all segments between EARs). ChIP density data were log-transformed to decrease skewness. Numerals in red and blue indicate the numbers of peaks located within EARs and interstitial regions, respectively (g). P values are from one-sided Wilcoxon tests (g) and one-sided t-tests (h).

Extended Data Fig. 9 Chromosome size, separate from EAR effects, is a major determinant of DSB-protein behaviour in late prophase.

a, d, A model incorporating association time, chromosome size, and the centromere and telomere effects. Association time, chromosome size, centromere, and telomere effects were binned in 20-kb windows and used as explanatory variables to model DSB-protein dissociation time (a) or ChIP density at 6 h (d) by multiple linear regression. Association time was excluded in d. Each point compares the observed and model-predicted value within a bin. Bins on short chromosomes are blue. Adjusted R2 values are shown for all data points and (in parentheses) for only the longer chromosomes. Note that the fits for the ChIP-density models are substantially worsened by removing the small chromosomes (d). This suggests that these models are mainly capturing between-chromosome differences rather than within-chromosome variation on the long chromosomes. n = 505, 539, and 583 bins for Rec114 ChIP ARS+, Rec114 ChIP ars∆ and Mer2 ChIP ars∆ datasets, respectively. b, c, e, f, Examples of within-chromosome patterns predicted by the multiple regression model and each of its component factors. Grey dots are observed Rec114 dissociation times (b, c) or Rec114 ChIP density at 6 h (e, f) in 20-kb bins from the ars∆ strain. Blue, cyan, green and magenta lines are the components chromosome size, association time, centromere effects and telomere effects, respectively, and the black line is the prediction from the four-factor (b, c) and three-factor (e, f) regression models. The 200 kb surrounding the rDNA on chr12 is shaded yellow in c and f. g, h, The multiple regression models do not underperform on the small chromosomes for DSB-protein dissociation (g) or ChIP density at 6 h (h). Residuals from the regression models were grouped for the three shortest chromosomes compared with the remaining chromosomes, except for chr12. Numbers in parentheses indicate the number of bins. P values are from one-sided Wilcoxon tests. Compare with the substantial underperformance on the smallest chromosomes of regression models applied to early DSB-protein patterns (Extended Data Fig. 5b, d). Box plots are as described in Extended Data Fig. 2c. i, Size dependence and integration of each component shaping late DSB-protein behaviour (analogous to Fig. 4e). Net effects for each chromosome (relative to the genome-wide means) were estimated from the three-factor or four-factor models in a and d (points and vertical bars are means ± s.d. of the three datasets). Of particular importance for our purposes, note that a separate parameter capturing chromosome size per se dominates the models even when accounting for the telomere effect (which includes contribution of the EARs). This result suggests that, although EARs contribute to chromosome-size-related differences in DSB-protein dissociation33, another size-related process(es) is quantitatively more important. Homologous pairing kinetics might be such a process2,16,54. See Supplementary Discussion 6 for further details.

Extended Data Fig. 10 Small chromosomes are at risk of chromosome missegregation.

a–c, Premature exit from prophase I compromises crossing over and chromosome segregation on an artificial short chromosome. a, Configuration of spore-autonomous fluorescent markers39 used to detect crossovers and MI nondisjunction of the artificial short chromosome (der(9), see Extended Data Fig. 6a–f). The strain also carries NDT80 under the control of a galactose-inducible promoter (PGAL-NDT80) and expresses the Gal4 transcription factor fused to a portion of the mammalian oestrogen receptor (Gal4-ER)55,56. These constructs enable induction of Ndt80 expression at defined times by addition of β-oestradiol to the medium. Ndt80 expression is sufficient to drive exit from pachynema and prophase I (see Supplementary Discussion 8 for further detail). Note that this experimental set-up enables us to query a small chromosome that lacks the normal short-chromosome boost and to drive cells out of prophase before the homologue engagement pathway is allowed to operate fully to assure DSB formation. b, Schematics of chromosome segregation patterns with and without crossovers. NPD, nonparental ditype. Note that configuration of markers (crossover or nonexchange) cannot be determined in MI nondisjunction tetrads. Note also that double NPD tetrads are indistinguishable from MI nondisjunction; however, double NPDs are expected to be rare. c, Frequencies of MI nondisjunction and nonexchange (E0) chromosomes and average number of crossovers detected per meiosis. For E0, filled dark green circles are estimates of total E0 by combining observed E0 + observed MI nondisjunction; open circles are the predicted frequencies of E0 assuming a Poisson distribution with the observed mean number of crossovers per meiosis. Note the good agreement between these independent estimates of E0 frequency. β-oestradiol was added at the indicated times, then chromosome segregation and crossing over were scored after the completion of meiosis and sporulation (54 h). Pachytene exit would normally begin to occur after about 5 h in wild-type cells under these culture conditions46. Early induction of NDT80 resulted in a decreased yield of crossovers and an increased frequency of chromosome missegregation, matching our expectation. The crossover and segregation defects were progressively attenuated the later NDT80 was induced. Thus, delaying pachytene exit is sufficient to improve the likelihood of der(9) achieving a crossover. At least some of this improvement is probably attributed to providing more time in a DSB-permissive state, but other aspects of prophase I chromosome pairing and recombination may also be facilitated. d, e, Configuration of spore-autonomous fluorescent markers (d) and example segregation patterns (e) to detect MI nondisjunction for three different chromosomes. The strain also contained PGAL-NDT80 and Gal4-ER. Data are presented in Fig. 4f. f, Residuals for each member of the shortest chromosome trio (not including the CEN3–MAT interval) from the three-factor multiple regression model applied to Rec114 association time (ARS+ dataset, Fig. 1f). The model performs poorly for all three chromosomes, but performs especially poorly for the parts of chr3 that must have a high DSB frequency to balance the suppression of DSB formation in the CEN3–MAT interval. See Supplementary Discussion 9 for further detail. Numbers in parentheses indicate the number of bins. Box plots are as described in Extended Data Fig. 2c.

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Murakami, H., Lam, I., Huang, PC. et al. Multilayered mechanisms ensure that short chromosomes recombine in meiosis. Nature 582, 124–128 (2020). https://doi.org/10.1038/s41586-020-2248-2

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