Im trying to find a proper and general file with chromosomal coordinates for meiotic recombination hotspots.
I know that ucsc hgtables, has a table with recombination regions and their recombination rate. However these regions are all 1Mb, and several articles states that these hotspots regions are more local with a regional length of 2Kb.
Do any of you know of a file or database containing the chromosome coordinates for humans which have been identified as a recombination hotspots?
Thanks a lot for your time and help.
Interplay between modifications of chromatin and meiotic recombination hotspots
Meiotic recombination lies at the heart of sexual reproduction. It is essential for producing viable gametes with a normal haploid genomic content and its dysfunctions can be at the source of aneuploidies, such as the Down syndrome, or many genetic disorders. Meiotic recombination also generates genetic diversity that is transmitted to progeny by shuffling maternal and paternal alleles along chromosomes. Recombination takes place at non-random chromosomal sites called ‘hotspots’. Recent evidence has shown that their location is influenced by properties of chromatin. In addition, many studies in somatic cells have highlighted the need for changes in chromatin dynamics to allow the process of recombination. In this review, we discuss how changes in the chromatin landscape may influence the recombination map, and reciprocally, how recombination events may lead to epigenetic modifications at sites of recombination, which could be transmitted to progeny.
Meiotic recombination forms crossovers important for proper chromosome segregation and offspring viability. This complex process involves many proteins acting at each of the multiple steps of recombination. Recombination initiates by formation of DNA double-strand breaks (DSBs), which in the several species examined occur with high frequency at special sites (DSB hotspots). In Schizosaccharomyces pombe, DSB hotspots are bound with high specificity and strongly activated by linear element (LinE) proteins Rec25, Rec27 and Mug20, which form colocalized nuclear foci with Rec10, essential for all DSB formation and recombination. Here, we test the hypothesis that the nuclear localization signal (NLS) of Rec10 is crucial for coordinated nuclear entry after forming a complex with other LinE proteins. In NLS mutants, all LinE proteins were abundant in the cytoplasm, not the nucleus DSB formation and recombination were much reduced but not eliminated. Nuclear entry of limited amounts of Rec10, apparently small enough for passive nuclear entry, can account for residual recombination. LinE proteins are related to synaptonemal complex proteins of other species, suggesting that they also share an NLS, not yet identified, and undergo protein complex formation before nuclear entry.
Detecting and measuring hotspots
DSB hotspots – mapping breaks and ssDNA
Meiotic recombination initiates from DNA DSBS, which can be directly measured to reveal hotspots. A major advantage to the study of meiosis in budding yeast Saccharomyces cerevisiae is the ability to synchronize large numbers of cells to enter meiosis simultaneously, which facilitates biochemical analysis of meiotic recombination. Due to the effect of DSBs on DNA molecule size, gel electrophoresis of staged meiotic DNA can be used to analyse DSBs in yeast (Nicolas et al., 1989 Baudat and Nicolas, 1997 Garcia et al., 2015 ). This is a versatile method that allows DSB variation to be measured on scales from whole chromosomes to single nucleotides, according to electrophoresis conditions and probe choice (Nicolas et al., 1989 Baudat and Nicolas, 1997 Garcia et al., 2015 ). The detection of DSBs can be increased by using DNA processing mutants such as rad50S and sae2D, which are deficient in endonucleolytic release of SPO11 from DSBs and accumulate unrepaired breaks (Buhler et al., 2007 ). However, it is important to note that mapping DSBs in rad50s and sae2D mutants may lead to biased detection of early forming DSBs over late DSBs, which account for a substantial fraction of breaks in S. cerevisiae (Buhler et al., 2007 Joshi et al., 2015 ). Although the application of gel electrophoresis assays to larger genomes is challenging, a modified Southern blotting method using terminal transferase and nested PCR was able to detect DSBs at the H2-Ea hotspot in mouse testicular germ cells (Qin et al., 2004 ).
A sensitive method to detect meiotic DSBs relies on the catalytic mechanism of the SPO11 endonuclease (Neale et al., 2005 ). SPO11 is related to topoisomerases and becomes covalently bound to 5′-target sites at a catalytic tyrosine residue (Bergerat et al., 1997 Keeney et al., 1997 Neale et al., 2005 ). Subsequent upstream or downstream cleavage by the MRX nuclease complex liberates SPO11 bound to oligonucleotides approximately 20–100 nt in length (Figure 1b) (Neale et al., 2005 Garcia et al., 2011 Lange et al., 2011 Pan et al., 2011 ). Immunoprecipitation of SPO11 and purification of the bound oligonucleotides can be used to analyse DSB target sites, using end-labelling and gel electrophoresis, or generation of sequencing libraries (Figure 1c) (Neale et al., 2005 Lange et al., 2011 Pan et al., 2011 ). These approaches have been performed successfully in budding yeast, fission yeast and mice (Lange et al., 2011 Pan et al., 2011 Fowler et al., 2014 ). This technique has also been repeated in mouse atm kinase signaling mutants, which accumulate higher DSB levels (Lange et al., 2011 ). In budding yeast Spo11 functions with a group of accessory proteins, including Rec114, Mer2 and Mei4 (Panizza et al., 2011 ). These proteins have been analysed by chromatin immunoprecipitation (ChIP) and shown to associate with cohesin-rich regions (Panizza et al., 2011 ), which may reflect a mechanism to tether DSBs formed on chromatin loops to repair sites at the meiotic chromosome axis (Kleckner et al., 2004 ). Alternatively, the locus where breaks will form is first tethered to the axis, where SPO11 is present, and then a DSB forms (Kleckner et al., 2004 Sommermeyer et al., 2013 ).
Following DSB resection, meiotic ssDNA can be directly purified and analysed using an affinity resin, which can also be combined with recombination/processing mutants that accumulate unrepaired breaks (Figure 1e) (Buhler et al., 2007 , 2009 ). As ssDNA is bound by the DMC1 and RAD51 recombinases, ChIP of these factors has been used to generate high-resolution recombination maps in yeast, mouse and humans, using an approach termed ssDNA-sequencing (SSDS) (Figure 1e) (Smagulova et al., 2011 Brick et al., 2012 Khil et al., 2012 He et al., 2013 Pratto et al., 2014 ). Similar methods have also been developed in maize (He et al., 2013 ). It will be important to apply these or related methods in plants to fully understand genomic distributions of meiotic DSBs. A variety of Arabidopsis meiotic mutants are available that may facilitate the study of DSBs, including those altering break processing (sae2/com1) (Uanschou et al., 2007 ), DNA damage kinase signaling (atm, atr) (Garcia et al., 2003 Roitinger et al., 2015 ) and chromatin modification (arp6, met1, ddm1) (Colomé-Tatché et al., 2012 Melamed-Bessudo and Levy, 2012 Mirouze et al., 2012 Yelina et al., 2012 Choi et al., 2013 ).
Crossover hotspots – direct mapping
A classical method to detect crossovers is via analysis of co-inheritance of linked heterozygous markers through meiosis (Hunt Morgan, 1916 ), in which change of linkage phase between markers indicates the occurrence of a crossover. Meiotic tetrad analysis is a powerful genetic technique, where all four daughter cells from a single meiosis are analysed (Lichten, 2014 ). In addition to distinguishing between two, three and four strand crossover events, tetrad analysis also allows measurement of gene conversion events via detection of 3:1 inheritance between sister gametes (Nicolas et al., 1989 Berchowitz and Copenhaver, 2008 Martini et al., 2011 Sun et al., 2012 ). Recently tetrad analysis has been extended to both mice and plants (Berchowitz and Copenhaver, 2008 Cole et al., 2014 ). In Arabidopsis the quartet1 (qrt1) mutant is altered in pollen wall biogenesis, such that the four products of male meiosis remain physically attached (Francis et al., 2006 ). Combination of qrt1 with linked heterozygous transgenes expressing different colours of fluorescent protein provides an elegant visual method to score crossovers (Francis et al., 2007 Berchowitz and Copenhaver, 2008 ), and an equivalent approach has been developed in budding yeast (Thacker et al., 2011 ). Alternatively, qrt1 can be complemented and single pollen grains analysed using flow cytometry to increase measurement throughput (Yelina et al., 2012 , 2013 ). Single qrt1 tetrad pollinations can also be used to isolate siblings related through a common meiosis, which are then sequenced to provide insight into genome-wide patterns of crossover and gene conversion (Wijnker et al., 2013 Qi et al., 2014 ). Equally, large numbers of crossover events can be readily mapped using genotyping or sequencing of F2 or backcross populations (Salomé et al., 2012 Rowan et al., 2015 ). New methods that use multiple annealing- and looping-based amplification cycles (MALBEC) have allowed sequencing and identification of crossovers in a single human sperm or oocyte (Lu et al., 2012 Hou et al., 2013 ). This outcome has also been achieved in maize via sequencing of DNA from isolated single microspores (the male meiotic products) (Li et al., 2015 ). However, a major limitation to all of these approaches for the study of hotspots is the paucity of crossovers per meiosis. For example, known plant hotspots have genetic distances between approximately 0.1–0.5 cM (Table 2) (Yelina et al., 2012 Choi et al., 2013 Drouaud et al., 2013 ). Therefore, to analyse 100s of crossovers at a given hotspot it is necessary to screen 100 000s of meioses, which is generally not possible when generating populations of plants.
|Species||Hotspot||Interval (base-pairs)||cM||cM/Mb||Location||Chromosome cM/Mb||Reference|
|Arabidopsis thaliana||3b||5746||0.11||20.01||Intergenic||4.77||Choi et al. ( 2013 )|
|Arabidopsis thaliana||3a||5825||0.21||36.22||Gene 5′ and 3′ end||4.77||Choi et al. ( 2013 ) Yelina et al. ( 2012 )|
|Arabidopsis thaliana||14a||7283||0.55||75.52||Gene 5′ end & intergenic||4.8||Drouaud et al. ( 2013 )|
|Arabidopsis thaliana||130x||12 488||0.53||42.44||Intergenic||4.8||Drouaud et al. ( 2013 )|
|Zea mays||a1||1900||0.08||40.00||a1||2.1||Brown and Sundaresan ( 1991 )|
|Zea mays||a1||710||0.04||59.00||Gene promoter||0.5–1.5||Yao and Schnable ( 2005 )|
|Zea mays||a1||500||0.005||9.10||Gene 5′ end||0.5–1.5||Yao and Schnable ( 2005 )|
|Zea mays||Yz1||1900||0.06||32.00||Gene 5′ end||0.5–1.5||Yao and Schnable ( 2005 )|
|Zea mays||Yz1||540||0.03||48.00||Gene 3′ end||0.5–1.5||Yao and Schnable ( 2005 )|
|Zea mays||B||620||0.03||52.00||Gene 5′ end||nd||Patterson et al. ( 1995 )|
|Zea mays||bronze||1056||0.051||48.30||Gene 5′ end||nd||Fu et al. ( 2002 )|
|Zea mays||bronze||793||0.033||41.61||Gene 5′ end||nd||Fu et al. ( 2002 )|
|Triticum aestivum||HGA3||23 000||0.20||8.82||Gene 5′ end||0.85||Saintenac et al. ( 2009 )|
To isolate large numbers of crossovers at individual hotspots a general approach is to use allele-specific amplification from post-meiotic gamete DNA, termed sperm typing or pollen typing (Tiemann-Boege et al., 2006 Baudat and de Massy, 2007 Cole et al., 2010 Berg et al., 2011 Drouaud and Mézard, 2011 Yelina et al., 2012 Choi et al., 2013 Drouaud et al., 2013 ). These approaches rely on the generation of individuals that are heterozygous over a hotspot region of interest. Collection of gametes and isolation of DNA means that samples consist of a mixture of non-recombinant (parental) and crossover molecules that are distinguishable by patterns of polymorphisms. Allele-specific primers are designed to anneal to polymorphisms flanking a hotspot and used in different configurations to amplify either parental or crossover molecules (Figure 1i) (Baudat and de Massy, 2009 Kauppi et al., 2009 Cole and Jasin, 2011 Drouaud and Mézard, 2011 ). Typically, the products of male meiosis (pollen or sperm) are collected due to the ease of isolating the required large numbers of cells (Kauppi et al., 2009 Drouaud and Mézard, 2011 ), although analysis of female gametes is also possible (de Boer et al., 2013 ). This analysis is important as sex-specific differences in meiotic recombination are widespread, yet poorly understood (Lenormand and Dutheil, 2005 Giraut et al., 2011 Campbell et al., 2015 ). Estimating recombination rate within the amplified region is possible by diluting the gamete template DNA in order to titrate parental and crossover molecules. It is possible to then sequence or genotype amplification products from single crossover molecules to identify internal crossover positions to the resolution of individual polymorphisms (Tiemann-Boege et al., 2006 Baudat and de Massy, 2007 Cole et al., 2010 Berg et al., 2011 Drouaud and Mézard, 2011 Yelina et al., 2012 Choi et al., 2013 Drouaud et al., 2013 ). These methods allow the fine-scale characterization of crossover patterns within single hotspots. A further method to map crossover recombination is via ChIP of associated factors, for example Zip3 within the ZMM pathway has been analysed in budding yeast (Serrentino et al., 2013 ). In some cases early and late cytogenetic foci are observed with distinct properties (e.g. RNF212), and so it is important to consider whether ChIP approaches will profile different foci classes equally (Reynolds et al., 2013 Qiao et al., 2014 ).
Crossover hotspots – historical mapping
The signature of crossover can also be detected via analysis of natural genetic polymorphisms, due to the effect recombination has on non-random associations between mutations (Auton and McVean, 2012 ). Specifically, crossovers cause decay of linkage disequilibrium (LD) between linked polymorphisms, which can be analysed using coalescent theory (Charlesworth and Charlesworth, 2010 Auton and McVean, 2012 ). For example, consider two linked, independently arising mutations a and b. The only way an a–b haplotype can occur is through recurrent mutation, or via recombination joining a and b onto the same chromosome (Hudson and Kaplan, 1985 Charlesworth and Charlesworth, 2010 Auton and McVean, 2012 ). Packages such as LDhat and SequenceLDhot use this principle to analyse SNP patterns and estimate the population-scaled recombination rate 4Ner (where r is the per generation recombination rate and Ne is the effective population size) (Fearnhead, 2006 Auton and McVean, 2007 ). These approaches are powerful as they sample the very large numbers of meioses occurring in the history of the individuals compared. However, there are a number of limitations to these methods, including that SNPs are influenced by population genetic forces in addition to recombination, including selection, drift and migration (Charlesworth and Charlesworth, 2010 Auton and McVean, 2012 ). Further caveats associated with these approaches include the potential error introduced by structural variation between individuals, for example insertions, deletions, inversions and translocations. Mis-calling of SNP positions relative to a given reference sequence may under- or over-estimate actual physical distances between variants and thus cause erroneous recombination rate estimates. This may be a particular problem in repetitive regions, where accurately identifying SNPs from short read sequencing data can be problematic. Therefore, it is important to combine historical and experimental mapping of crossover recombination.
Current Research Projects
The mechanism of recombination in the absence of Prdm9
Male Wolf (Canis lupus lupus)
The mammalian family of Canidae, such as dogs and wolves, as well as several orders of birds and fish lack a functional copy of PRDM9. They are nevertheless able to complete meiosis successfully. The genomes of dogs as well as zebra finch and long-tailed finch even possess typical signatures of punctate historical recombination, present as hotspots of breakdown of linkage disequilibrium.
Historical linkage disequilibrium patterns revealing a canid recombination hotspot.
We are experts in investigating sites of historical recombination for de-novo recombination events using sperm-typing approaches. We are determining not only the frequency of recombination but also the placement of recombination breakpoints at the fine-scale, deciphering exactly where recombination events are distributed. In addition we use immune-fluorescent staining of meiotic proteins in cell spreads to assess meiotic timing as well as meiotic defects such as arrest and asynapsis (see banner image).
Comparing fine-scale recombination patterns of several animals with PRDM9 independent hotspot regulation, to organisms with PRDM9 regulated hotspots will give detailed insights into the difference between the dynamics and regulating factors of recombination in the absence of PRDM9, contributing to a better understanding of the function and role of PRDM9 in normal meiosis.
To achieve this aim, we are investigating numerous samples of Prdm9-deficient organisms, including several species of the Canidae family and members of the order of Passeriformes. Of course, we also have samples of organisms with Prdm9 regulated hotspots, mainly samples of several species and subspecies of mice across the genus Mus, such as Mus musculus musculus, Mus musculus domesticus, Mus spretus, Mus spicilegus as well as Apodemus uraliensis as an outgroup.
Recombination Landscape Evolution
Sequencing the Prdm9 Exon coding for the C2H2-zinc-finger domain can reveal the underlying population diversity in the positions that are relevant for DNA binding.
Recombination landscapes evolve rapidly. PRDM9-regulated hotspots, at least, are suppressed by mutations that disrupt the recombination initiation motif that PRDM9 binds to. In individuals heterozygous for the mutation, the suppressing allele always gets over transmitted through biased gene conversion, eventually silencing the recombination hotspot at the population level.
Additional biases, such as CG-biased gene conversion, do not affect crossovers or hotspot activity but nevertheless, generate meiotic drive and change genome composition over time. Recombination events are isolated from both orientations of recombination separately, using allele-specific PCR approaches. Comparing the frequency and distribution of reciprocal events uncovers incidences of bias and meiotic drive.
When observed, this information is used to predict the long-term evolutionary consequences via computer simulations.
Biased gene conversion of different strength changes allele-frequencies over generations.
Detection of COs at three meiotic hotspots by “pollen-typing”
A previous study of CO distribution over the entire A. thaliana chromosome 4, in large populations of hybrids between “Columbia-0” (Col) and “Landsberg erecta-4” (Ler), identified the regions 14a and 130× as good candidates for true CO hotspots . In these two regions of a few kilobases only, CO rates were found to be 20 to 30 times higher than the chromosomal average (4.8 cM/Mb). However, classical genetic techniques could not be used to further investigate these regions, as several tens of thousands of plants would have been needed to obtain enough COs to characterize these regions. Thus we set up a “pollen-typing” technique (see Materials and Methods Figure 1 ), which parallels the “sperm-typing” technique used for hotspot studies in mice and humans –. Briefly (see Material and Methods for more details), genomic DNA (gDNA) was extracted from millions of pollen grains collected from a series of F1 ColxLer hybrids and precisely quantified by PCR (see Material and Methods). Taking into account the CO frequency estimated by our genetic map, the gDNA was then diluted to obtain less than one putative recombinant molecule per PCR reaction. Recombinant products were detected by two rounds of allelic PCRs (Figure 1). When an appropriate dilution was reached, a large series of PCR reactions was performed to detect the presence or absence of at least one template molecule in each reaction. CO rates could then be estimated using the Bayesian inference approach described in the Material and Methods. The meiotic origin of these molecules was assessed with control reactions carried out in parallel with pollen and leaf (somatic) DNA. Using the same amounts of gDNA, CO molecules could be detected in pollen DNA, but never in leaf DNA (Figure 2). Indeed, when the few positive PCR products amplified from a large input of leaf genomes (at least 15 times more genomes than in the PCR reaction used to detect recombinant molecules on pollen DNA) were sequenced, they were found to result from non-specific amplification. They did not correspond to a single locus of the Arabidopsis genome but rather to a complex mixture of loci from different chromosomes (data not shown) in contrast to products obtained from pollen DNA (see below).
PCR was performed with allele-specific oligonucleotides (ASOs) designed for the amplification of CO molecules (see Material and Methods, Figure 1), using decreasing amounts of genomic DNA (the number of template molecules is indicated on the photograph) extracted from F1 Col x Ler hybrid plants, either from leaf (two top rows) or pollen (two bottom rows). Eight aliquot reactions were carried out for each dilution. No PCR products were amplified from leaf. However, using equivalent low concentrations of DNA extracted from pollen (4096 molecules and less), CO molecules were strongly and specifically amplified .
We were able to amplify and characterize hundreds of recombinant CO molecules at both regions in gDNA extracted from the pollen of ColxLer hybrid plants (167 and 104 COs at 130× and 14a respectively). The CO rate in pollen gDNA was 0.55% (Confidence Intervals (CI): 0.29–0.95) at 14a, and 0.53% (CI:0.34–0.78) at 130×. The recombinant molecules were confirmed by sequencing and their exchange point mapped precisely. All CO molecules characterized contained a single transition between the parental haplotypes.
Two distinct CO peaks were observed in the 14a region (subsequently referred to as 14a1 and 14a2), which both fit a Gaussian distribution (Figure 3A). The width of the hotspots within which 95% of COs occurred (determined by best fit normal distributions, see Material and Methods) was 1,475 bp and 3,775 bp for 14a1 and 14a2, respectively. Their respective medians are 3,047 bp apart, with a “valley” in between where just a few COs were detected. The CO frequency was null on either side of this region (Figure 3A). At 14a1 and 14a2, CO rates peak at 261 and 127 cM/Mb, respectively (54 and 26 times the chromosomal average). To investigate the relationship between CO frequency at 14a and chromatin we plotted published low nucleosome density (LND) data over the same region (Figure 3C), where a high signal represents an absence of nucleosomal DNA . Regions of LND are typically observed at the 5′ of genes coincident with transcriptional start sites (TSS). Consistent with this observation, the LND peaks were located upstream of the two genes within 14a. Strikingly we observed an overlap of the 14a CO frequency peaks and the LND peaks (Figure 3C) which suggest that DNA accessibility promotes COs at the 14a hotspot.
One hundred and four COs were analyzed at 14a (62 at 14a1 and 42 at 14a2) and 167 at 130×. Grey dotted vertical lines: median positions of the hotspots. On the x axis: triangles are positions of insertions-deletions larger than 7 bp (from the left to the right of 130×: 20, 13, 12, 70, 10, 7 and, 11 bp) green triangles are positions of indels used to detect NCOs green dots are SNPs used to detect NCOs the black dot on the x axis in (A) is the position of the microsatellite. Gene structures along the hotspot regions are displayed on top of graphical areas (light hashed grey: exons, dark grey: UTR, broken lines: introns). (C)(D) Low nucleosome density (LND) at 14a and 130×. LND is from published nucleosomal DNA microarray hybridization experiments, where a high signal represents an absence of nucleosomal DNA .
At 130×, the CO rate was also null on both sides of the region and reached a maximum (close to the center) at 167 cM/Mb, which is 35 times the chromosomal average (Figure 3B). The distribution of COs differed from that observed in 14a: it was broad (more than 7 kb) and irregular with alternating “peaks” and “valleys” (Figure 3B) which does not fit well with a unique Gaussian curve. Interestingly, there was little correlation between CO peaks and LND at 130× (Figure 3D), suggesting that hotspots exist, which have different relationships to nucleosome density. Altogether, both the CO rate and distribution at 14a and 130× clearly indicate the existence of hotspots in A. thaliana.
We then looked at the distribution of exchange points in the recombinant molecules in each orientation at both loci (Figure 4). At the 14a hotspots (14a1+14a2), discrepancies between CO distribution in the reciprocal orientations “Col to Ler” and “Ler to Col” (i.e. ‘CtoL’ and ‘LtoC’) were observed: ‘LtoC’ exchanges were shifted to the left of ‘CtoL’ exchanges (Figure 4A). A comparison of cumulative CO distribution patterns showed that this leads to an excess of the Col allele at the center of both hotspots (Figure 4A). At 14a1, the Col allele was over-transmitted by 68% and this difference was highly significant (p-value = 0.00111), while it was only barely significant (p-value = 0.0734) for the 14a2 hotspot, probably due to the lower CO number. These patterns are consistent with the hypothesis that the Ler allele has a stronger initiation activity than the Col allele at these hotspots . The mean position of the two reciprocal distributions ‘CtoL’ and ‘LtoC’ was separated on average by 213 bp and 483 bp for the 14a1 and 14a2 hotspots respectively. In contrast, at the 130× hotspots both alleles appeared equally proficient at initiating recombination (Figure 4B).
Twenty nine ‘CtoL’ and 33 ‘LtoC’ COs were analyzed at 14a1, 23 and 19 at 14a2, and 75 and 92 at 130×. The ‘LtoC’ distribution is presented in red and the ‘CtoL’ in blue. For all three hotspots and both reciprocal orientations, cumulated (from left to right) relative CO rates were scored at successive polymorphic sites along the region. Blue curves: cumulated relative ‘CtoL’ CO rates. Red curves: cumulated relative ‘LtoC’ CO rates. Blue histogram: distribution of ‘CtoL’ CO rates. Red histogram: distribution of ‘LtoC’ CO rates. Grey dotted vertical lines: median positions of the hotspots.
Detection of NCO events at meiotic recombination hotspots
At meiotic recombination hotspots, DSBs are repaired as either COs or NCOs. In plants, very few meiotic NCOs have been characterized because of the difficulty in detecting molecular events unless they are linked to a phenotypic change. We characterized NCO events at both the 14a1 and 130× hotspots, with different molecular approaches adapted to the polymorphisms available at each hotspot (Figure 1 see Material and Methods ).
For 14a1, the polymorphisms at the center of the hotspot were not suitable for a pollen typing strategy. Thus we used a cloning strategy based on a method described in  (Figure 1 see Material and Methods, ): after two rounds of allele-specific PCR performed on pollen DNA, the fragment corresponding to the 14a1 hotspot region was cloned. 3000 clones were individually genotyped at three SNPs: two (#35 and #37) located on opposite sides of the center of the hotspot and one (#33) on the left border (Figure 5). For the control reaction, a similar series of PCRs, cloning and genotyping was performed with DNA extracted from F1 ColxLer leaves. Positives clones obtained with pollen DNA were sequenced (see Material and Methods Figure 1). Among 3,000 molecules tested, 8 and 7 NCO events were detected at polymorphism #35 and #37 respectively, and none at the most external SNP #33 (Figure 5). No positive clones were obtained in DNA extracted from leaves (0/2850) at SNP#35 and #37 demonstrating that NCOs were specific to pollen DNA. The cumulative NCO frequency for both SNPs (#35 and #37) (1/203, 0.50% (CI: 0.30–0.82)) was similar to the overall CO frequency estimated with the pollen typing approach suggesting that this hotspot is equally prone to produce NCOs and COs (0.55%).
The SNPs #33, #35 and #37 are indicated by filled green circles. The polymorphisms are indicated by filled black circles along the chromosome coordinate axes. The position of the microsatellite located between #35 and #37 is indicated as an orange circle. Thick blue or red horizontal lines: converted SNPs, Thick dotted grey horizontal lines: interval in which NCO tract ends are located. Blue: Col Red: Ler.
NCO events at both sites were all restricted to a single polymorphism, either #35 or #37, i.e. without co-conversion of left and/or right flanking markers, which are located 111 bp and 482 bp away for #35 and 166 bp and 340 bp for #37 (Figure 5). Thus, the mean minimal tract was 1 bp if only the polymorphism converted was considered and, the mean maximal tract was 552 bp if the tract was extended to either side just before the next non-converted polymorphism (276 bp when the minimal and maximal mean are averaged).
We recovered unequal numbers of NCOs in both directions: two ‘LtoCtoL’ and six ‘CtoLtoC’ were detected at polymorphism #35 while one ‘LtoCtoL’ and six ‘CtoLtoC’ at polymorphism #37 (Figure 5). When all NCOs were pooled, the difference between NCO rates in reciprocal orientations (‘LtoCtoL’ versus ‘CtoLtoC’) was significant (p-val = 0.018). This result strengthens the hypothesis that initiation occurs preferentially on the Ler allele at the 14a1 hotspot (see above).
At 130×, NCO molecules were characterized using a PCR-based “pollen-typing” strategy (see Materials and Methods Figure 1 ). Allele-specific PCR was performed using either Col specific or Ler specific primers on 96 samples, each containing 4,145 F1 pollen genomes or 4,800 F1 leaf genomes. Then, to specifically detect NCO molecules, allele-specific PCR was carried out at three different SNPs (Figure 1 see Material and Methods ): the three polymorphic sites #21, #44 and #52 were 2339 and 2052 bp away, respectively (see green dots in Figure 3B). SNP#44 is next to the center of the hotspot where the CO frequency is maximal, #21 is located in the left section of 130× where the CO rate is low and #52 is to the right where CO rates were average. For the control, DNA extracted from leaves (almost 468,000 genomes), no PCR product was amplified at SNP #44. In DNA extracted from pollen (almost 398,000 genomes), 29 NCO events were found at SNP#44 (Figure 6A) demonstrating that NCOs were specific to pollen DNA. Thirty and four NCO events were found at SNP#21 and #52, respectively (Figure 6B, 6C). The observed NCO frequency was approximately 0.007% (CI: 0.005–0.010), 0.008% (CI: 0.005–0.011) and 0.001% (CI: 0.0004–0.0033) at #44, #21 and #52, respectively. When the results obtained at the three SNPs were pooled, the NCO rate is 0.017%, which is roughly thirty times less than the overall CO frequency (0.53%).
(A) SNP #44, (B) SNP #21, (C) SNP #52. The positions of the SNPs genotyped are indicated by green filled circles (SNPs) or triangles (indels). The polymorphisms are indicated by filled black circles along the chromosome coordinate axes. Triangles: insertion or deletion greater than 7 bp. Thick red or blue horizontal lines: converted SNPs, Thick grey dotted horizontal lines: interval in which NCO tract ends are located. Blue: Col Red: Ler.
At SNP #44, 23/29 NCO tracts extended to the right toward the neighboring polymorphism (89 bp away), while polymorphisms to the left were co-converted in only five tracts, but over a greater distance (up to 791 bp) (Figure 6B) similarly, at #21, 20/30 NCOs included the first two polymorphisms on the left (275 bp) whereas only five extended to the right but again over a greater distance (up to 1028 bp) (Figure 6A). This apparent non-symmetrical distribution of the NCO tracks reflects the asymmetrical scattering of the SNPs on either side of #44 and #21. At both sites, numerous SNPs are present only on one side, leading to an accurate analysis of the breakpoints whereas on the other side only distant SNPs were available. The minimal track length means were comparable for both SNPs (160 bp at #21 and 278 bp at #44) whereas the maximal mean track length was more than three times longer at #21 (1798 bp) when compared to #44 (492 bp). The longest NCO track was found at #52: six SNPs were co-converted along a tract of 1882 bp that could extend up to 3045 bp. Interestingly, none of the NCO tracks covered either #21 and #44 or #44 and #52. At #21 three NCO tracts were chimeric (with more than two exchange points): two ‘CtoLtoCtoLtoC’ and one ‘LtoCtoLtoCtoL’ (Figure 6A).
We also noticed that as observed for 14a1, an excess of ‘LtoCtoL’ (36) NCOs compared to ‘CtoLtoC’ (23) NCOs were detected at both #21 and #44. At #52, only ‘LtoCtoL’ NCOs were obtained. In the latter case, it was not possible to determine whether there was an absence of ‘CtoLtoC’ NCOs in our starting pool of almost 398000 genomes or if these were missed by pollen-typing. However, when the results were pooled for #21 and #44, the difference was not significant (‘LtoCtoL’: 0.009% (CI: 0.007–0.013) ‘CtoLtoC’: 0.006% (CI: 0.004–0.009)).
CO and NCO rates in the Atmsh4 mutant
In A. thaliana, on the basis of chiasma counts in mutant backgrounds, it is assumed that 85% of COs belong to the interference dependent pathway (class I), while the remaining 15% are interference-free (class II) . To test the contribution of both CO pathways at the 130× and 14a hotspots, we analyzed CO rate and distribution in an atmsh4 mutant background in which interfering COs are absent. Crosses were made between hemizygous Col and Ler lines containing a T-DNA insertion in the AtMSH4 gene (see Material and Methods). Meiosis appeared regular in both AtMSH4 +/− Col and Ler parents and the F1s AtMSH4 +/+ or AtMSH4 +/− . Meiosis, however, was disturbed in the F1 Atmsh4 −/− with a dramatic reduction in chiasma number as described in (Higgins et al.2004 data not shown). We set up pools of Atmsh4 −/− or AtMSH4 +/− or AtMSH4 +/+ F1 plants, extracted gDNA from their pollen and performed pollen-typing PCR to detect CO molecules. CO rates were not statistically different in AtMSH4 +/+ and AtMSH4 +/− at either 14a or 130× (data not shown). Thus pollen gDNA from AtMSH4 +/+ and AtMSH4 +/− was pooled and is referred to as “MSH4” in the following experiments. As expected, in the Atmsh4 −/− pollen, when we conducted the experiment at the 14a locus, we detected a dramatic decrease (12 fold) in CO frequency compared to the “MSH4” CO rate (Table 1). However, this frequency is likely to be slightly over-represented because the proportion of viable pollen grains depends on the number of bivalents (i.e. pairs of homologous chromosomes containing a CO). We then analyzed CO distribution (Figure 7A). Surprisingly, the two hotspots, 14a1 and 14a2, were affected differently by the mutation. In “MSH4”, the majority of COs (61%) occurred in 14a1 (ratio 14a1/14a2: 1.6). At contrario in Atmsh4 −/− , the proportion of COs between 14a1 and 14a2 was inversed (ratio 14a1/14a2: 0.5 chi2 p-value = 8.7 10 −5 ) (Figure 7A Table 2). At 130×, we performed two series of overlapping PCRs to cover the whole area (see Material and Methods Figure 7B). We also obtained a lower rate of CO frequency in Atmsh4 −/− , but at a different level in the left (13 times lower), and right (78 times lower) sections of the loci (Figure 7B Table 1).
(A) One hundred and eight and 48 COs were analyzed in “MSH4” and Atmsh4 −/− respectively. Grey distribution: “MSH4”. Green distribution: Atmsh4 −/− . Green dotted line: 5× magnification of the Atmsh4 −/− distribution. (B) Two hundred and forty five and 58 COs were analyzed in “MSH4” and Atmsh4 −/− respectively. Grey distribution: “MSH4”. Green distribution: Atmsh4 −/−. The localization of the two overlapping PCRs is shown with orange (left part) and pink lines (right part). Green dotted line: 5× magnification of the Atmsh4 −/− distribution.
Next, we tested the NCO frequency in the Atmsh4 mutant background and “MSH4” at both loci. At the SNP#35 in the center of 14a1, 41 NCOs were detected and confirmed among 11,896 colonies in pollen DNA extracted from Atmsh4 −/− and 20 among 3,600 colonies for “MSH4” pollen DNA. Thus, at this marker the NCO rate was comparable in both genetic backgrounds (Table 3). At 130×, in the Atmsh4 −/− pollen DNA, among 83,656 molecules, two NCOs were detected at #44, which gave a NCO frequency of 0.0024% (CI: 0.00074–0.0086). In “MSH4”, the NCO frequency (84 events/545844 genomes) was significantly higher at the same SNP 0.015% (0.012–0.019) (p-value = 0.0035) (Table 3 Figure S3). Therefore, the NCO frequency at #44 decreased considerably (six fold) in the absence of MSH4.
Hotspot strength and landscape vary depending on the genetic background
We selected two other Arabidopsis accessions for which we could use the same allele-specific primers to perform pollen typing but which have different levels of polymorphisms within the DNA sequence at the 14a hotspot: Pyl-1 (8AV) and Ws-4 (530AV), (Material and Methods). Between Col and the three accessions Ler, Pyl1 and Ws-4, there are 0.43%, 0.53% and 0.63% of polymorphisms distributed along the 5 kb of the 14a hotspot (Figure S1). We also included the “MSH4” data in this study because it is another ColxLer F1with exactly the same sequence at both the 14a and 130× loci. We observed considerable variation in CO rates at the 14a loci. Strikingly, the 14a hotspots almost disappeared in ColxPyl-1. There were 100 times less COs than in “MSH4”, 60 times less than in ColxLer and 23 times less than in ColxWs and even 12 times less than in the mutant Atmsh4 −/− background (Table 4 Figure 8A). In ColxWs, the CO rate (0.21%) was in the same range as in ColxLer (0.55%) but significantly less than in “MSH4” (1.27% Table 4).
(A). One hundred and four, 108 and 87 COs were analyzed in ColxLer (black), “MSH4” (orange) and ColxWs (purple), respectively. Thin dotted vertical lines: median position at 14a1 and 14a2 for each distribution. (B). One hundred and sixty seven and 245 COs were analyzed in ColxLer and “MSH4”, respectively. Thin dotted vertical lines: median position for each distribution. Using a Fisher exact test, the two distributions were found to be highly significantly different (p-value = 2.2×10 −16 ).
Surprisingly, we observed that the CO and NCO rates and CO distribution at 130× differed significantly between the two ColxLer F1s used in this study (ColxLer and “MSH4”), whereas no significant variation was obtained at 14a (Table 4 Figure 8). We also observed another difference, with ‘LtoC’ and ‘CtoL’ exchanges peaking in the same interval in ColxWs and in “MSH4” but distant in ColxLer (see above) (Figure S2). Moreover in “MSH4” we recovered a comparable number of ‘LtoCtoC’ and ‘CtoLtoC’ NCOs (Figure S3). Thus the bias in recombination appears to only exist in ColxLer. We believe that the differences in CO rates and distribution between these two F1s are robust, as they were observed in several different experiments (data not shown) but as mentioned above the hotspot sequences are identical in these two lines.
Results and Discussion
Identification and Distribution of Recombination Hotspots Along the Chromosome.
The alignment is 287 kb long and there are 464 nonsingleton SNPs in the European population and 232 in the Far East Asian, giving an average density of one SNP every 0.6 kb in Europe and 1.2 kb in the Far East (Table S1). For each population no more than two alternative nucleotides were found at each site. We estimated the population recombination parameter ρ between neighboring pairs of SNPs using the program rhomap (20) (Fig. 1A). In an idealized population, ρ is equal to 4Ner(1−F)M, where Ne is the effective population size, r the rate of recombination between the two SNPs, F the inbreeding coefficient, and M the frequency of sex (18, 21). Both populations show heterogeneity in the rate of recombination along the chromosome, particularly the European population, which also shows higher rates on average. This difference is not simply because of the smaller sample size in the Far East population, as it persists when the European population is reduced to eight strains (Fig. S1).
Distribution of recombination on S. paradoxus chromosome III. (A) The population-recombination parameter ρ, calculated between consecutive pairs of SNPs along the chromosome, is shown for the European and Far East populations (the first and last genes in the alignment are VBA3 and HMRA1 SNPs are shown as dots along the lines). The hotspot regions identified in each population are highlighted in red, and the corresponding regions in the other population in blue (for visual clarity, these extend 2 kb on either side). Hotspot numbers correspond to those used in Table 1. Positions of the centromere (CEN) and mating type locus (MAT) are indicated by arrows. The distribution of haplotype blocks, representing regions with no evidence of recombination, is also shown (black lines) (see Methods). Haplotype blocks are longer in the Far East population, consistent with the finding of lower recombination rate. (B) The average ρ for the two populations of S. paradoxus is plotted and hotspots from the combined analysis shown in red. Diamonds indicate the position of DSB hotspots in S. cerevisiae (11). (C) Comparison of hotpsot locations. Vertical red bars show the position of hotspots in S. paradoxus. Diamonds again show the position of DSB hotspots in S. cerevisiae (11) and horizontal lines show the hotspots identified by Mancera et al. (12) in their “crossover” and “total” analyses.
Physical locations and parameter estimates for recombination hotspots on chromosome III of S. paradoxus
To assess our population-genomic estimates of recombination rates, we tested whether two well-supported experimental results from S. cerevisiae are replicated in our dataset. In S. cerevisiae, rates of recombination are relatively low between the centromere and mating-type locus, and also higher in intergenic regions containing at least one promoter than in those with none (22, 23). We confirm both these results in S. paradoxus. Recombination in the ∼100-kb region between the centromere (CEN) and the mating type (MAT) locus is about half that for the rest of the chromosome (ρ = 1.9 vs. 4.5 Morgans/kb in Europe and 0.9 vs. 2.0 in the Far East). In addition, intergenic regions that are 5′ to at least one of the flanking genes have higher ρ than those that are 5′ to none (P = 0.004 and P = 0.07 for Europe and Far East) (Table S2). This correspondence of results gives us confidence that our dataset is sufficient to detect major features of recombination.
To identify recombination hotspots, we tested for statistically significant increases in ρ compared to flanking regions, using the program sequenceLDhot (24). For each 2-kb window (with a 1-kb offset), the program tests the null hypothesis that ρ in the window is equal to ρ in the flanking 50-kb region (centered on that window). Six recombination hotspots were found in each population (Fig. 1A and Table 1). They are 2 to 5 kb in length, similar to the 3 kb average found in S. cerevisiae chromosome III and the 1 to 2 kb length of human hotspots (3, 12, 25). Exactly the same hotspots are found if each 2-kb window along the chromosome is tested against the flanking 6-kb region. We further evaluated the significance of these hotspots by calculating the number of hotspots expected by chance in chromosomes with constant recombination along their length. Twenty simulated alignments were obtained by evolving chromosomes of the same length and level of polymorphism as the original dataset, using the program ms (26). Only an average of about one such “false” hotspot region was found per simulated alignment (1.2 and 1.4 for Europe and Far East, respectively). All hotspots in Europe have likelihood ratios greater than 10, and in the simulated alignments only 0.2 hotspots per alignment had likelihood ratios at least that high.
Detecting statistically significant recombination hotspots from population genomic data are a challenging problem, and even the best algorithms can have low power (20, 24). This low power probably accounts in part for the fact that some peaks in the rhomap estimates are not identified as statistically significant by sequenceLDhot (Fig. 1A). Nevertheless, one region is identified as a hotspot in both populations independently (the IMG1-BUD23-ARE1 hotspot ∼9 kb to the right of MAT). Moreover, it is apparent from Fig. 1A that hotspots in one population correspond to local peaks of recombination in the other population, even if these are not identified as statistically significant by sequenceLDhot. For the 10 hotspots that are statistically significant in only one population, the corresponding regions in the other population have higher ρ than their respective 6-kb flanking regions (paired randomization test, n = 10, P = 0.02). Therefore, to maximize our power to detect hotpots in S. paradoxus, we performed a combined analysis of the two populations. Ten of the 11 hotspots found in the separate analyses of the two populations are also significant in the combined analysis, and no new hotspots were identified (Fig. 1B) (the expected false-positive rate in the combined data is 0.9 hotspots per alignment).
Conservation of Recombination Hotspots.
To test whether recombination hotspots are conserved between S. paradoxus and S. cerevisiae, we compared our hotspots to those identified in two recent experimental studies of S. cerevisiae. The first study is an analysis of double-strand breaks (DSBs) formed during meiosis, in which 17 hotspots were identified on chromosome III that had DSB rates more than 8-fold greater than background (11). Eight of these are in regions homologous to the hotspots we identified in the combined S. paradoxus dataset (Fig. 1 B and C). As our hotspots occupy only 11.2% of the chromosome, the probability of such overlap under the null hypothesis of random placement is (from the binomial distribution) P = 0.00024. In addition, five more of the hotspots identified by Buhler et al. (11) correspond to local peaks in recombination in S. paradoxus that did not reach statistical significance in our analyses (Fig. 1B, hotspots a, j, k, l, and n).
The distribution of meiotic DSBs along a chromosome may not be identical to the distribution of crossovers, as breaks can be repaired without crossing over, for example, using the sister chromatid as a template for repair, or using the homologous chromosome without crossing over (27). Therefore we have also compared our data to the results of an analysis of 51 meioses in S. cerevisiae that identified five crossover hotspots on chromosome III (12). Three of these hotspots overlap hotspots in S. paradoxus (i.e., the middle of the smaller region is contained within the larger region) (Fig. 1C). To test if this much overlap would be expected by chance, we randomized hotspot locations separately in each of the two species, and for each randomization recorded the number of overlaps. In only 2% of randomizations were three or more overlaps observed. Mancera et al. (12) also identified five additional hotspots when combining both crossover and noncrossover data, and two of these overlap with another S. paradoxus hotspot (Fig. 1C). Overall, 4 of the 10 hotspots found in S. paradoxus overlap hotspots in S. cerevisiae, and 5 of the 10 hotspots in S. cerevisiae overlap those in S. paradoxus.
All three studies identify the same region as the hottest hotspot, corresponding to the well-known ARE1 (YCR048R) hotspot region (23, 28, 29) (hotspot 7 in Fig. 1B), suggesting there may be similarities between species in both location and intensity of recombination hotspots. These similarities are found despite the fact that completely different methodologies were used in the two species (population genomic vs. experimental). For those hotspots found in only one of the two species, it is not clear if this is because of real differences in the location of hotspots or to low power in the analyses.
Thus, the distribution of hotspots in S. cerevisiae and S. paradoxus appears to be much more similar than that between humans and chimpanzees, despite the fact that the yeasts are more than 10-times more divergent at the sequence level. These similarities between yeast species are most parsimoniously explained by conservation from the common ancestor, although we cannot formally exclude the possibility of recurrent hotspot evolution at the same sites because of limited availability of alternative sites for hotspots in a smaller genome. This relatively high conservation presumably reflects a slower rate of hotspot loss by gene conversion. We attribute this difference to the low frequency of sex and outcrossing in yeast. At the population level, the change in gene frequencies due to biased gene conversion, and hence the rate of deterioration of a particular hotspot, will be proportional to the frequency of sex and the level of heterozygosity (5). Previous work has indicated that S. paradoxus in nature goes through meiosis only once every 1,000 generations, and only 1% of matings are outcrossed (15, 18). The effect of gene conversion will therefore be reduced by a factor of 10 5 relative to that in an otherwise comparable obligately outcrossed species. Hotspots may also be maintained if the hotspot sequence is functionally relevant for a reason other than recombination, and it is likely that a smaller fraction of the yeast genome is selectively neutral than in the human genome [upper bounds of ∼35% and ∼88%, respectively (18, 30)].
The low frequency of sex and outcrossing in natural populations means that features of recombination that are observed in the laboratory (e.g., a conversion bias in favor of recombination-suppressing alleles) may have relatively small evolutionary effects. We now analyze two other laboratory-observed features of recombination, and estimate the magnitude of their effects on yeast genome evolution.
Recombination and Mutation Rate.
Recombinational repair of DSBs in mitotic cells is associated with a 100-fold increase in the mutation rate (31). Assuming that meiotic recombination shows an equivalent increase in mutation rate—for which there is some evidence (32)—and if sex is common, one might expect increased sequence divergence at recombination hotspots. However, S. paradoxus hotspots are neither more diverse nor more divergent than the chromosome average (Table 1). In addition, there is no correlation between rates of recombination and either levels of polymorphism or rates of divergence across 5-kb segments of the chromosome (Fig. S2). The lack of positive association between recombination rates and divergence (also reported by ref. 32) is fully consistent with recombination being rare, as the mutational effect of recombination will be swamped by the mutations occurring in the intervening asexual generations. For example, in S. cerevisiae the hottest hotspot recombines in about 25% of meioses (12). If sex occurs only once every 1,000 generations, then its average mutation rate over the whole life cycle is only 100 × 0.25 × 1/1,000 = 2.5% higher than a region that never recombines, too small a difference to be detected. Again, if hotspots tend to be in more functionally constrained regions of the genome, this could also contribute to the lack of association with increased divergence.
Recombination and Base Composition.
Experiments have shown that biased gene conversion in S. cerevisiae not only favors recombination-suppressing alleles, but also, independently, G and C nucleotides over A and T (12, 33). This bias occurs as a result of the mismatch-repair system acting on heteroduplex DNA formed during meiosis converting AT nucleotides into GC nucleotides. This repair bias may in turn have evolved to counteract the AT-bias in mutation (34). Recombination hotspots in S. cerevisiae have higher GC content than the rest of the genome (23), a result also found in S. paradoxus (43 vs. 39%, for both Europe and Far East, P = 0.004 and 0.02, respectively) (Table 1), and there is a highly significant positive correlation between ρ and GC content across nonoverlapping 5-kb segments of the genome (n = 56 windows Kendall's τ: 0.30, P = 0.001, in both populations) (Fig. 2). Correlations between recombination rate and GC content can be explained by biased gene conversion in favor of Gs and Cs, or by high GC content promoting recombination (35). Similar correlations have previously been found in humans (36). However, if we consider only the substitutions that have occurred since the common ancestor of the European and Far East lineages, and are therefore recent, hotspots show a pronounced bias in the opposite direction, toward increased AT content (Table 2). The absolute number of AT to GC changes in hotspots is about 40% lower than the number of GC to AT changes. By contrast, the numbers of changes in the nonhot regions are about equal, indicating that GC content is at equilibrium.
Rates of nucleotide substitution in ancestral GC or AT sites during the differentiation of European or Far East sequences from their common ancestor
The regulatory mechanism of meiotic recombination hotspots is a fundamental problem in biology, with broad impacts on areas ranging from disease study to evolution. Recently, many genomic and epigenomic features have been associated with recombination hotspots, but none of them can explain hotspots consistently. It is highly desirable to integrate the different features into a predictive model, and study the relation of the features with hotspots and themselves with a systems approach. Moreover, due to rapid and dynamic evolution of recombination hotspots, regulatory mechanisms of hotspots that are evolutionarily conserved among species remain unclear.
We propose a machine learning approach that encode genomic and epigenomic features into a support vector machine (SVM). Trained on known hotspots and coldspots in human and mouse genomes, the model is able to predict hotspots based on the features with good performance in both species. Moreover, the model reports a ranking of feature importance, uncovering the interactions of the features with hotspots and themselves. Applying the method to large-scale data, we identified evolutionarily conserved patterns of trans-regulators and feature importance between human and mouse hotspots. This is the first attempt to build a predictive model to identify evolutionarily conserved mechanisms for recombination hotspots by integrating both genomic and epigenomic features.
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Vol 339, Issue 6116
11 January 2013
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By Laurent Acquaviva , Lóránt Székvölgyi , Bernhard Dichtl , Beatriz Solange Dichtl , Christophe de La Roche Saint André , Alain Nicolas , Vincent Géli
Science 11 Jan 2013 : 215-218
A protein involved in histone methylation targets the meiotic recombination machinery to chromatin.
Wayne P. Wahls, Ph.D.
Ph.D., University of Illinois, Chicago
E-mail: [email protected]
Office: (501) 686-5787 – Biomedical Research Center B421C
Lab: (501) 686-7876 – Biomedical Research Center B428, B430, B432
FAX: (501) 526-7008
Chromosome Dynamics in Meiosis Stress Response Pathways
During meiosis, homologous chromosomes replicate once, pair, enjoy a high rate of recombination, and undergo two rounds of chromosome segregation to produce haploid meiotic products. We use a combination of genetic, molecular, biochemical, cytological and proteomic approaches to study meiotic chromosome dynamics in fission yeast. Our primary focus is on how recombination hotspots and chromatin remodeling regulate recombination throughout the genome. We are also investigating how complexes of meiotic recombination enzymes assemble and function. A third focus is on the relationship between recombination, sister chromatid cohesion, and proper segregation of chromosomes in each meiotic division.
Proteins of the ATF/CREB/AP-1 family are components of signal transduction pathways that monitor intracellular and extracellular conditions and transmit those signals to downstream targets. These proteins share a conserved bZIP domain that mediates both protein dimerization and sequence-specific DNA binding activity. We are interested in how these protein-DNA complexes regulate chromatin structure, transcription and RNA decay, and how cross-talk between pathways confers plasticity to environmental stress responses.