bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.05.11.089367

Chromosome-autonomous feedback downregulates meiotic DSB competence upon synaptonemal complex formation

Mu, X.·Murakami, H.·Mohibullah, N.·Keeney, S.
View original source ↗Export metadata ↓

Abstract

The number of DNA double-strand breaks (DSBs) initiating meiotic recombination is elevated in Saccharomyces cerevisiae mutants that are globally defective in forming crossovers and synaptonemal complex (SC), a protein scaffold juxtaposing homologous chromosomes. These mutants thus appear to lack a negative feedback loop that inhibits DSB formation when homologs engage one another. This feedback is predicted to be chromosome autonomous, but this has not been tested. Moreover, what chromosomal process is recognized as "homolog engagement" remains unclear. To address these questions, we evaluated effects of homolog engagement defects restricted to small portions of the genome using karyotypically abnormal yeast strains with a homeologous chromosome V pair, monosomic V, or trisomy XV. We found that homolog-engagement-defective chromosomes incurred more DSBs, concomitant with prolonged retention of the DSB-promoting protein Rec114, while the rest of the genome remained unaffected. SC-deficient, crossover-proficient mutants ecm11 and gmc2 experienced increased DSB numbers diagnostic of homolog engagement defects. These findings support the hypothesis that SC formation provokes DSB protein dissociation, leading in turn to loss of a DSB competent state. Our findings show that DSB number is regulated in a chromosome-autonomous fashion and provide insight into how homeostatic DSB controls respond to aneuploidy during meiosis. bioRxiv version 2 (September 2020) We added one bioRxiv citation in the discussion section which shows increased breaks in gmc2 and ecm11 mutants. This work was done independently by Keun Kim, Miki Shinohara and colleagues. The pulse gel electrophoresis result shown in Figure 1D has been re-quantified. The conclusion remains that homeologous and monosomic chromosome V generate more DSBs compared to internally controlled chromosome III. One zip3 map included in PCA (Figure 6C) and hierarchical clustering analysis (Figure S5B) has been deleted because this particular map has not yet been published. This change does not affect the conclusion. An additional supplemental table (S5) has been added to show Spo11-oligo datasets used in this study. Figures and preprint have been reformatted. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/089367v2_fig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@871eaforg.highwire.dtl.DTLVardef@ef7995org.highwire.dtl.DTLVardef@176441org.highwire.dtl.DTLVardef@1b6e772_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Higher DSB levels on homolog-engagement defective chromosomes. (A) Cartoons of wild-type, homeologous, monosomic and trisomic chromosome configurations. Grey lines are S. cerevisiae chromosomes and orange lines are S. pastorianus. The homeologous chrV pair rarely synapses or recombines. The trisomic chromosomes can adopt different synaptic configurations ("II+I", partner switch, and triple synapsis). (B) Representative PFGE Southern blots probed for chrIII and chrV. P, signal from parental-length DNA; W, signal in wells. Asterisk indicates an ectopic recombination product between leu2::hisG (on chrIII) and ho::hisG (on chrIV) in the monosomic strain. The other strains do not form this product because they do not have a hisG insert at ho. (C) Poisson-corrected DSB quantification of PFGE Southern blots. (D) Quantification of time-averaged DSBs on chrV relative to chrIII for wild-type, homeologous and monosomic strains. * p<0.05, unpaired t test. Error bars in C, D are mean {+/-} SD except for the monosomic strain (mean {+/-} range). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/089367v2_fig6.gif" ALT="Figure 6"> View larger version (36K): org.highwire.dtl.DTLVardef@1152edforg.highwire.dtl.DTLVardef@1b853daorg.highwire.dtl.DTLVardef@89392dorg.highwire.dtl.DTLVardef@108d65a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO gmc2{Delta} and ecm11{Delta} mutants share similar homolog-engagement signatures as zip3{Delta}. (A, B) Representative labeling of Spo11-oligo complexes in gmc2{Delta} (A) and ecm11{Delta} (B) mutants and quantification relative to wild-type cultures processed in parallel. Error bars indicate mean {+/-} SD for 3 cultures. Radiolabeled Spo11-oligo complexes are detected by autoradiography (left panel, top) and total Spo11 is detected by anti-Flag western blot (WB; left panel, bottom). The two main labeled species differ in the sizes of oligos (Neale et al. 2005). Most Spo11 protein does not end up making DSBs, so nearly all of the visible western blot signal is from free Spo11 that does not have an oligo attached (Neale et al. 2005). (C) Principal component analysis of 21 wild-type and mutant Spo11-oligo maps. (D) Loss of anticorrelation between chromosome length and DSB density in homolog-engagement-defective mutants. Each point is one chromosome. Correlation coefficients (Pearsons r) are shown. (E) Fold change of Spo11-oligo counts in different chromosomal domains. Tel, within 20 kb of telomeres; Cen, within 10 kb of centromeres; rDNA, from 60 kb leftward to 30 kb rightward of rDNA; Interstitial, all others. Horizontal dashed lines mark values assumed as no change (black) and average change (1.8-fold for zip3{Delta}, cyan; 1.7-fold for ecm11{Delta}, purple). Boxes indicate median and interquartile range; whiskers indicate the most extreme data points that are [&le;] 1.5 times the interquartile range from the box; individual points are outliers C_FIG O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/089367v2_figS5.gif" ALT="Figure 5"> View larger version (35K): org.highwire.dtl.DTLVardef@130cbf6org.highwire.dtl.DTLVardef@9ebe8forg.highwire.dtl.DTLVardef@1f0a2b4org.highwire.dtl.DTLVardef@14a0fcf_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplemental Figure S5.C_FLOATNO DSB patterns in gmc2{Delta} and ecm11{Delta} mutants. (A) Meiotic progression showing percentage of cells completing the first divisions (total bi- and tetra-nucleate cells, mean {+/-} SD, 3 cultures each). At least 100 cells were counted at each time point for each culture. (B) Hierarchical clustering analysis using the "Ward D2" method for 21 wild-type and mutant maps. The y-axis represents height of the cluster dendrogram. In the label for each data set, the letters A, B, C and D stand for maps generated by Xiaojing Mu, Neeman Mohibullah, Megan van Overbeek, and Xuan Zhu, respectively, and the numerals distinguish biological replicate maps. (C) Regional variation in response to zip3{Delta} and ecm11{Delta} mutations for chromosome XII. Lines, local regression (loess) based on Spo11-oligo density changes in 5 kb-bins; green circle, centromere; rDNA location is labeled. (D, E) Fold change of Spo11-oligo densities for zip3{Delta} and ecm11{Delta} compared to wild type in pericentric (D) and telomere-proximal (E) regions. Lines, local regression (loess) for data in 5 kb-bins averaged across 32 chromosome arms; dashed line, no change in black and genome average change in cyan (1.8-fold, zip3{Delta}) and purple (1.7-fold, ecm11{Delta}); yellow shading, zones suppressed for DSB formation as previously defined (Pan etal. 2011). C_FIG O_TBL View this table: org.highwire.dtl.DTLVardef@15408baorg.highwire.dtl.DTLVardef@9171aorg.highwire.dtl.DTLVardef@cbabfaorg.highwire.dtl.DTLVardef@187d594org.highwire.dtl.DTLVardef@d6eb68_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOSupplemental Table S5.C_FLOATNO O_TABLECAPTIONSpo11-oligo datasets used in this study C_TABLECAPTION C_TBL

Source connections

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mu, X., Murakami, H., Mohibullah, N., Keeney, S.. 2020-05-12. Chromosome-autonomous feedback downregulates meiotic DSB competence upon synaptonemal complex formation. https://doi.org/10.1101/2020.05.11.089367

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗
bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗
bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗