Unraveling the effect of genomic structural changes in the rhesus macaque - implications for the adaptive role of inversions
© Ullastres et al.; licensee BioMed Central Ltd. 2014
Received: 24 February 2014
Accepted: 19 June 2014
Published: 26 June 2014
By reshuffling genomes, structural genomic reorganizations provide genetic variation on which natural selection can work. Understanding the mechanisms underlying this process has been a long-standing question in evolutionary biology. In this context, our purpose in this study is to characterize the genomic regions involved in structural rearrangements between human and macaque genomes and determine their influence on meiotic recombination as a way to explore the adaptive role of genome shuffling in mammalian evolution.
We first constructed a highly refined map of the structural rearrangements and evolutionary breakpoint regions in the human and rhesus macaque genomes based on orthologous genes and whole-genome sequence alignments. Using two different algorithms, we refined the genomic position of known rearrangements previously reported by cytogenetic approaches and described new putative micro-rearrangements (inversions and indels) in both genomes. A detailed analysis of the rhesus macaque genome showed that evolutionary breakpoints are in gene-rich regions, being enriched in GO terms related to immune system. We also identified defense-response genes within a chromosome inversion fixed in the macaque lineage, underlying the relevance of structural genomic changes in evolutionary and/or adaptation processes. Moreover, by combining in silico and experimental approaches, we studied the recombination pattern of specific chromosomes that have suffered rearrangements between human and macaque lineages.
Our data suggest that adaptive alleles – in this case, genes involved in the immune response – might have been favored by genome rearrangements in the macaque lineage.
KeywordsGenome shuffling Inversions Macaque Recombination Adaptation Meiosis Tandem repeats Evolutionary breakpoints
Large-scale genomic changes, such as inversions, translocations, fusions and fissions, contribute to the reshuffling of the genomic architecture of organisms, providing new sources of variation on which natural selection can work. In recent years, there has been an increasing number of studies focusing on the role of chromosomal reorganizations in adaptation and speciation processes [1–4], and more specifically on the influence of genome shuffling in recombination ( and references therein). In this framework, the “suppressed recombination” model has provided compelling evidence and a theoretical framework to explain how chromosome rearrangements are involved in speciation [6, 7]. Under this model, reorganizations such as inversions would have a minimal influence on fitness when present in the heterokaryotype, but rather would suppress recombination between genomic regions involved in reorganization, leading to the reduction of gene flow between diverging populations. In this context, chromosomal rearrangements would act as genetic barriers, interfering in the fixation of favorable alleles and allowing for the accumulation of genetic incompatibilities . As a way to test this hypothesis, subsequent studies have analyzed sequence divergence (patterns of nucleotide differentiation) between species as an indirect estimation of recombination [9, 10]. High rates of sequence divergence detected in genes located at, or near, chromosomal rearrangements have often been interpreted as indirect evidence of chromosomal speciation through suppressed recombination [9–16]. However, few empirical data have focused on the relationship between evolutionary breakpoint regions (EBRs) and recombination rates. Initial studies in Drosophila described a strong reduction of recombination around inversion breakpoints and within the reorganization itself . Analogous studies in mammals are scarce, and the role of evolutionary regions in recombination has just started to be elucidated [5, 14].
Whole-genome comparisons of distantly related mammalian species have provided the basis for establishing models that can explain genome dynamics [18–22]. In this sense, different approaches have been developed in recent years to define homologous synteny blocks (HSBs; i.e., regions where gene order has been conserved among species) and EBRs (regions where the synteny has been disrupted by chromosomal reorganizations) among mammalian genomes [19, 23–26]. Such reconstructions have revealed that genomic regions implicated in structural changes which occurred during the evolution of species are not distributed randomly through the genome, but instead they are clustered in regions that are more prone to break and reorganize [19, 23–26]. The fact that some chromosomal regions have been reused during mammalian chromosomal evolution questions (i) whether these regions are physically labile due to their DNA sequence and/or structural chromatin conformation, and (ii) whether they represent regions where selection against breakpoints is minimal . Regarding the first assumption, previous studies on mammalian genomes have provided compelling evidence that EBRs can be linked to the presence of repetitive elements, such as transposable elements, segmental duplications and/or tandem repeats [19, 25–31]. However, given the diversity of repetitive elements in EBRs, it is likely that sequence composition is not alone influencing genome instability, clamoring for the involvement of additional factors such as the state of the chromatin (i.e., open chromatin may drive chromosomal reorganizations ) or selective constraints. In this latter case, comparative genomic studies have shown that mammalian EBRs tend to localize in gene-dense regions [22, 28, 32]. But there is a long-standing debate on the mechanisms behind this well-known phenomenon. Several lines of evidences indicate that EBRs are precisely located between genes (i.e., intergenic regions, see ) not necessarily affecting gene structure/function, while others have reported possible gene expression changes due to genome reshuffling (see ).
Given this context, the general picture of the genomic features and DNA organization of genomic regions affected by structural reorganizations is still incomplete, as is that of how genomic changes are transmitted to the offspring during the formation of germ cells and contribute to speciation. If genomic shuffling does affect evolutionary processes through the mechanical shearing at evolutionary breakpoints, how does it impact on meiotic recombination? In this sense, the analysis of the most recent human and chimpanzee recombination maps has revealed that rearranged chromosomes presented lower recombination rates than chromosomes that did not suffer any reorganization since the human-chimpanzee common ancestor . Elucidating upon whether this pattern also holds for other mammalian species would have a relevant impact on our understanding of the role of genome shuffling in speciation. Here, we have analyzed the effect of genomic structural changes on genetic recombination in the rhesus macaque to understand the mechanisms underlying chromosomal evolution in mammals and determine, in the long-term, the influence of chromosomal reorganizations on meiotic recombination. To this end, we have firstly characterized the genomic regions involved in chromosomal rearrangements between human and macaque genomes. The rhesus monkey (Macaca mulatta, Tribe Papionini, Catarrhini) is a primate species widely used in both biomedicine and evolutionary studies [34–37]. All members of the Tribe Papionini (Macaca, Papio, Mandrillus and Cercocebus) are characterized by highly stable karyotypes that have been regarded to retain the ancestral Catarrhini karyotype [34, 35]. Due to such characteristics, the macaque has often been used as a reliable primate out-group candidate for evolutionary studies when studying great apes. But despite its importance, little effort has been made in characterizing the genomic landscape of HSBs and EBRs in this species since the initial release of the rhesus macaque genome [32, 36, 37]. Here we provide a detailed genomic map of the structural rearrangements between human and macaque. We have refined the genomic position of known rearrangements previously reported by cytogenetic approaches and described new putative micro-rearrangements (inversions and indels) between human and macaque genomes. Moreover, we have analyzed the repetitive DNA content and gene density in relation to chromosomal reorganizations, as well as the effect of inversions in meiotic recombination, detecting immune-related genes in evolutionary breakpoint regions in the macaque genome.
Results and discussion
Homologous synteny blocks (HSBs) and evolutionary breakpoint regions (EBRs) in human and rhesus macaque genomes
Summary of the evolutionary breakpoint regions (EBRs) detected in the human (HSA) and macaque (MMU) genomes
Number of EBRs
Minimum length (Kbp)
Maximum length (Kbp)
Median length (Kbp)
Our own study represents a departure from those conducted previously [27, 28, 30] in that it relies on a detailed comparison between human and rhesus macaque genomes based on orthologous genes and whole-genome sequence alignments. Previous cytogenetic studies delineated the primate ancestral karyotype, defining conserved syntenies among species and the direction of chromosomal rearrangements in a phylogenetic context [24, 34, 38, 39]. Species from the Tribe Papionini – including Macaca, Papio, Mandrillus and Cercocebus – are characterized by sharing the same karyotype and large-scale chromosomal reorganizations since their divergence from a common primate ancestor [34, 35]. When comparing these species with the human karyotype, previous cytogenetic studies described the presence of 20 intra- and inter-chromosomal reorganizations [34, 35, 37, 40]. These rearrangements include 12 pericentric inversions affecting eleven chromosomes, four paracentric inversions involving four chromosomes and four fusions/fissions [34, 35, 37, 40]. Overall, our in silico approach confirmed the presence of the above-mentioned macro-reorganizations and, thus, refined the breakpoints involved in both genomes (Additional file 1: Table S1). Moreover, we identified 39 and 41 previously undetected EBRs in the human and rhesus genome, respectively, affecting 13 different chromosomes (Additional file 1: Table S1). This resulted in 21 previously undetected reorganizations in the human genome and 23 in the macaque. Although previous cytogenetic studies have reported that six chromosomes (MMU6, MMU8, MMU11, MMU17 and MMU19) have been maintained collinear between both species, our results suggest that only chromosomes MMU6, MMU12 and MMU17 have maintained a complete conserved synteny. Six new indels (insertions or deletions) were also identified, ranging from 3.3 Kbp to 2,784.8 Kbp in five different chromosomes (Additional file 1: Table S1).
Tandem repeats do not accumulate in EBRs in the macaque genome; instead, they are correlated with the evolutionary history of chromosomes
Previous comparative genomic studies have revealed that mammalian breakpoint regions are especially rich in repetitive elements, such as segmental duplications [41–43], repetitive sequences [25, 42], transposable elements and long regulatory regions [26, 29, 44–46]. We tested whether this pattern applies to the rhesus macaque, a species that has retained the ancestral Catarrhini karyotype [34, 35]. To this end, the genome distribution of tandem repeats (TR) in this species was analyzed. A total of 701,128 loci, representing 60.9 Mbp of the whole genome was detected. In order to study the genome-wide distribution of TR, the number of base pairs involved in TR by screening non-overlapping windows of 100 Kbp along the genome was analyzed. When considering the number of base-pairs (per 100 Kbp) of each window covered by TR, we observed a significant increase of TR in telomeric and centromeric regions when compared to HSBs and EBRs (Kruskal-Wallis test, p-value < 0.0001), mirroring previous observations obtained in the human and great apes [25, 26]. Subsequently, we tested whether there was a correspondence between TR and EBRs in all macaque chromosomes. Our analysis indicated that EBRs are not significantly enriched by tandem repeats when compared to HSBs (Mann–Whitney U test, p-value > 0.05).
Recent studies in great apes have found differences in the genome-wide distribution of TR among species, suggesting that they might be correlated with the evolutionary history of each primate chromosome . More specifically, qualitative comparisons of TR distribution in great apes indicated that the TR landscape might have been conserved in collinear chromosomes, but altered in those reorganized chromosomes . Under this assumption, genomic regions that have suffered more rearrangements during their evolution are expected to concentrate more repetitive sequences than are conserved regions. In fact, our dataset supports this assumption since rearranged chromosomes in the macaque lineage (MMU3 and MMU5) have more TRs than do those that maintained the ancestral form (MMU2, MMU7, MMU10, MMU12, MMU13 and MMU18) (Mann–Whitney U test, p-value < 0.0001). Despite the limitations of the current rhesus macaque draft genome assembly and annotation [47, 48], this view is consistent with the lack of differences found in TR density between EBRs and HSBs in the macaque, which has maintained an ancestral karyotype within Catarrhini and, consequently, can be considered to have retained a more conserved chromosome complement (i.e., low degree of genome reshuffling) than have those of great apes [25, 26].
Defense-responsive genes are over-represented in EBRs
Once the evolutionary genomic landscape of the macaque was established, the genome-wide distribution of genes was further examined, paying special attention to gene ontology. A total of 28,595 genes was included in the analysis: 21,023 protein-coding genes; 5,913 non-coding RNA genes and 1,659 pseudogenes. We scrutinized each macaque chromosome’s complete sequence using non-overlapping windows of 100 Kbp in order to analyze the distribution of genes genome-wide. The mean distribution is 0.98 genes per 100 Kbp (including protein-coding genes, non-coding RNA genes and pseudogenes) in the whole macaque genome. When analyzing the distribution of protein-coding genes in more detail, a higher gene density in EBRs was detected (1.48 genes/100 Kbp), when compared with HSBs (0.73 genes/100 Kbp) (Kruskal-Wallis test, p-value < 0.0001). Therefore, our results indicate the presence of EBRs in gene-rich regions, in line with previous observations in mammals using multi-species comparative maps [22, 32]. In trying to understand the reasons behind this pattern, initial studies reported the intergenic location of mammalian EBRs , while recent studies have paid special attention to the adaptive role of EBRs . This has been the case of the pig, for example, where EBRs have been found to be especially rich in taste perception networks , suggesting that genome reshuffling significantly contributed to adaptation and the development of lineage-specific traits. Moreover, it has also been suggested that inversions can suppress recombination within the affected zones [5–8]. Consequently, rearranged sequences could accumulate alleles, which might be adaptive for the population, and this could generate reproductive isolation leading, eventually, to speciation.
Genes located in macaque EBRs
Distance from EBR edges (Kbp)
EBR position (chr: start-end)
C-C motif chemokine precursors
C-C motif chemokine precursor
Genes located in the MMU5 inversion
Distance from EBR
EBR position (start-end)
Term 1 (ER = 4.72)
Term 2 (ER = 3.52)
Our observation of an over-representation of defense-responsive genes in both EBRs and macaque-specific inversions might suggest an adaptive role of reorganizations in this species. Previous studies have reported that a small proportion of the mammalian genome, i.e., 4% in the case of the human genome, is under selective constraints, especially so for coding regions, introns and intergenic regions . This suggests that in certain regions the fitness cost is so pronounced (i.e., could be lethal or deleterious for the individual and the progeny) that rearrangements are not allowed (i.e., ). But it has also been shown that this constraint could be somewhat relaxed in the promoters of genes linked to the immune system, reproduction and perception , allowing for the generation of new variability to ensure adaptation to new environments. In light of our results, this might be the case for the genomic regions under study in the macaque. However, whether immune-related genes are directly involved in lineage-specific adaptation, as has been previously suggested for macaque , needs further validation.
Genome reshuffling and its effect on chromosome-specific recombination landscapes
Once the genomic structural changes were defined in the rhesus macaque, together with the genome distribution of coding-genes across evolutionary regions, we further experimentally analyzed the meiotic recombination landscape as a way to explore the adaptive role of chromosomal changes. Under the “suppressed recombination” model of chromosomal evolution, chromosome rearrangements would have a minimal influence on fitness, but would rather suppress recombination within the genomic regions affected, thus contributing to the accumulation of gene incompatibilities [2, 5–9]. The analysis of the most recent human and chimpanzee recombination maps inferred from genome-wide single-nucleotide polymorphism (SNP) data revealed that the standardized recombination rate was significantly lower in rearranged rather than in collinear chromosomes . In the case of rhesus macaque, chromosome-specific recombination maps are available for very few chromosomes , and whether or not chromosomal reorganizations that have been fixed in the macaque lineage have affected the recombination landscape was addressed in our study.
Chromosome-specific recombination analysis
Subsequently, CO density was analyzed inside and outside each inverted region using the selected BAC probes to label the location of the breakpoint in each chromosomal region affected by the inversion (Figures 2 and 3). As a general trend, we observed low CO densities within inverted regions (0.04 and 0.03 MLH1 foci/μm for CTO5 and CTO9, respectively), when compared to chromosomal regions outside the reorganized area (0.31 COs MLH1 foci per μm in both cases) in both rearranged chromosomes (Table 4). When analyzing the recombination rate within inverted regions among chromosomes, significant reduction of CO density was observed in CTO5 and CTO9 within the inverted regions, when compared with regions outside the inversion (Table 4, Mann–Whitney U test, p-value < 0.05). Such differences were not observed in the simulated inversion in CTO6, a chromosome that has been maintained collinear in the macaque lineage (Table 4).
Subsequently, we tested whether the suppression of recombination observed within reorganized areas was due to the low recombination rate characteristic of pericentromeric regions. To do so, we considered as a pericentromeric region an area extending 30% of each chromosome arm from the centromere towards the telomeric region and compared it to the CO density observed in the same region in the collinear chromosome (CTO6). When all chromosomes were compared, our results showed no statistical differences among the pericentromeric regions in the small arms (CTO5p, CTO6p and CTO9p) (Kruskal-Wallis test, p-value > 0.05); however, differences in CO density were significant when considering the long arms (CTO5q, CTO6q and CTO9q) (Kruskal-Wallis test, p-value = 0.018). Moreover, a significant reduction was observed in the CO density within the rearranged region in CTO9, when compared to the collinear chromosome CTO6 (Mann–Whitney U test, p-value = 0.016), (Figure 3 and Table 4). These differences were not significant, however, when the inverted region of CTO5 was compared to the collinear chromosome CTO6 (Mann–Whitney U test, p-value > 0.05).
What are the evolutionary implications of our observations in light of the “recombination suppression” model? Despite the fact that the “recombination suppression” was initially proposed to explain differences in recombination rates within reorganized genomics in heterokaryotypes (i.e., heterozygotes), we observed a reduction of recombination in fixed rearrangements, raising intriguing questions about the mechanisms involved. Previous studies in great apes have revealed that rearranged chromosomes presented significantly lower recombination rates than do chromosomes that have been maintained collinear since a common ancestor, and this was related to the lineage in which they become fixed . Importantly, inverted regions had lower recombination rates than did collinear and non-inverted regions, independently of the effect of centromeres . Although at this stage it would be premature to argue about the evolutionary forces behind this pattern, our results highlight the importance of the study of recombination framed by the evolutionary history of chromosomes and, in greater extent, genomes. Incorporating more chromosomes into the experimental study would be necessary to detect a clearer genomic effect of the inversions in the distribution of recombination patterns.
Genomic rearrangements might play an important role in local adaptation and species divergence by the modification of both the structure and regulation of genes located near the affected regions. Here, we provide a highly refined description of the chromosomal reorganizations and evolutionary breakpoint regions in the human and rhesus macaque genomes based on orthologous genes and genome sequence alignments. The high-resolution map of EBRs and HSBs defined in the macaque genome has revealed an interesting pattern: evolutionary breakpoints are gene-rich regions, with a significant functional clustering for genes related to the immune system. Furthermore, and in light of our observations, inversions can induce a reduction in the recombination rate among the different alleles contained in the inversion, which could be favoring adaptive alleles. Future comparative research on the effect of chromosomal reorganization on recombination as outlined above should be an effective means to enhance our knowledge of the role of genome reshuffling in evolution.
Whole-genome comparisons and evolutionary breakpoint definition
The experimental design of the study is represented in Figure 1. In order to detect the evolutionary breakpoint regions (EBRs) and homologous synteny blocks (HSBs) between human and macaque whole-genome sequences, two different algorithms were applied: SyntenyTracker and Cassis. Both approaches compare the order and orientation of orthologous markers (genes) between genomes, detecting changes both in the sequence order and the position of the HSBs and EBRs. Orthologous genes between human (GRch37.p7) and rhesus monkey (MMUL_1.0) genomes were obtained through the BioMart database (http://www.ensembl.org/index.html). SyntenyTracker determines the position in the chromosome sequences of both genomes, providing information of the relative orientation of each HSB. This enables the detection of chromosomal rearrangements such as inversions and/or translocations between two genomes. Once the HSBs were detected, genomic regions between consecutive HSBs were considered EBRs. Cassis, on the other hand, is especially designed to define breakpoint regions, providing information about different types of rearrangements, such as inversions, translocations or indels. Both algorithms were applied as previously described  using default parameters for SyntenyTracker and level 1 in the lastz alignment in Cassis. In order to obtain the genomic positions of EBRs in both genomes, the analysis was performed in two directions: (i) using both the human genome as reference genome, and (ii) the macaque genome as reference. Following previous studies [19, 22], we considered EBRs that were 4 Mbp in size or less. Regions larger than 4 Mbp in size were considered “gaps”. Furthermore, we labeled as telomeric/subtelomeric the 2 Mbp at the ends of each human chromosome and as centromeric/pericentromeric the 2 Mbp regions flanking the unknown nucleotides (Ns), as previously described .
Once the genomic positions of EBRs were obtained, we followed conservative criteria in order to avoid false positives. To do so, EBRs located at telomeres, centromeres and gaps were excluded from the analysis. The resulting EBRs were classified according to whether they are involved in macro-rearrangements (rearranged regions > 1.4 Mbp) or micro-rearrangements (rearranged regions < 1.4 Mbp). Simultaneously, we also classified each EBR depending on which type of chromosomal rearrangement was involved, that is, inversion, fusion or fission, following previous studies .
Analysis of repetitive elements and gene screening
The distribution of TR in the macaque genome using the eTandem algorithm was analyzed (part of EMBOSS 6.0.1 ). The eTandem algorithm was run with a minimum repeat unit of 2 bp and a maximum repeat unit of 100 bp, as previously described . The resulting output files were computed for the detection of overlapping TR, and the canonical motif was defined using in-home Perl scripts. In order to compare the distribution of TR along macaque chromosomes, we counted the base pairs of tandem repeats in 100 Kbp windows for each chromosome. Finally, each window was labeled according to its position: telomere, centromere, HSBs or EBRs. Using Perl scripts, we computed the density of TR and merged the positions of TR with the different types of genomic regions in the human genome.
Then, the number and the genomic position of annotated genes in the macaque genome were considered (RefSeq from the MMUL_1.0 assembly) to perform the gene distribution analysis using the BioMart browser of Ensembl (release 67). We grouped all genes with a known function in non-overlapping windows of 100 Kbp and labeled each window according to its position: telomere, centromere, HSBs or EBRs. In this case, the variable considered was gene count in each window, given that we analyzed presence/absence of genes, rather than the number of base-pairs covered by genes.
The Functional Annotation Clustering tool in DAVID (Database for Annotation, Visualization, and Integrated Discovery, v6.7) , was used in order to identify overrepresented biological terms contained in EBRs. Functional annotation clustering allows for the biological interpretation at a ‘biological module’ level of the most relevant biological terms (GO). Following algorithm’s recommendations, all clusters analyzed included a minimum of 10 genes and a maximum of 3,000 . In DAVID annotation system, Fisher Exact is adopted to measure the gene-enrichment in annotation terms by means of an EASE-score, a modified Fisher Exact P-Value. EASE-scores equal or smaller than 0.05 were considered statistically significant (i.e., strongly enriched in the annotation categories). Additionally, the system uses the group Enrichment Score (a geometric mean of member’s p-values in a corresponding annotation cluster) to rank the biological significance of the genes found in a cluster. Enrichment Scores ≥ 1.5 indicated significant over-represented of gene functions.
Metaphase chromosomes were prepared from peripheral blood samples obtained from one female rhesus macaque (Parc Zoològic de Barcelona, Spain). Cultures from peripheral blood samples were processed under standard conditions in order to obtain chromosome preparations as previously described . Additionally, testicular tissue from an adult individual of Cercocebus torquatus (CTO, 2n = 42) with proven fertility was used for the study of meiotic recombination. In order to obtain spermatocyte spreads, testicular tissue was processed as previously described [64, 65].
Immunostaining of meiocytes was performed as previously described [64, 65]. Different sets of antibodies were used: rabbit anti-SYCP3 (Abcam), human anti-CenP (human serum CREST, a kind gift from Dr. M. Fritzel) and mouse anti-MLH1 (Pharmigen) for the detection of meiotic crossovers (COs), all of them diluted in PTBG solution (0.05% Tween 20 in PBS) 1:200, 1:200 and 1:100, respectively. Fluorochrome-conjugated secondary antibodies (all from Jackson Immunoresearch) were used for detection: goat anti-rabbit conjugated with Cy3, goat anti-human conjugated with Cy5 and goat anti-mouse conjugated with FITC diluted 1:100 in PTBG.
Fluorescence in situhybridization
BAC clones spanning evolutionary breakpoints were obtained from the human library available at CHORI (Children’s Hospital Oakland Research Institute) (Additional file 6: Table S5). DNA from BACs was extracted according to standard protocols using a commercial kit (QIAGEN Plasmid). Fluorescence in situ hybridization (FISH) with specific BAC clones was performed on both metaphase chromosomes and spermatocyte spreads as previously described [40, 64]. Briefly, 1 μg of the DNA plasmid was labeled with dUTP-digoxygenine by Nick Translation (Abbot kit) and ethanol precipitated with competitor DNA (Cot-1 human DNA, Invitrogen, 1 mg/ml), salmon sperm DNA (Invitrogen, 10 mg/ml) and 1/10 volume of 3 mol/L sodium acetate overnight at -20°C. The precipitated probe mix was resuspended in 14 ml hybridization buffer (50% deionized formamide, 10% dextran sulfate, 2xSSC and 0.5 mol/L phosphate), denatured 80°C for 10 min and pre-annealed at 37°C for 1 h. Preparations were visualized using a Zeiss Axioskop epifluorescence microscope equipped with the appropriate filters and a charged coupled-device camera (ProgRes® CS10plus, Jenoptik).
The Micromeasure 3.3 software  was used for image analysis and for the construction of chromosome-specific recombination maps based on the relative distances between adjacent MLH1 foci, a marker for meiotic crossovers (COs). For each chromosome analyzed, the position of each MLH1 foci was recorded as a relative position (as the percentage of total length of the synaptonemal complex, SC) from the centromere, identified by the centromeric signal in each preparation as described previously [64, 65]. Using the centromere as a reference, the positions of each MLH1 focus were calculated along the SC, from the centromere to the telomere. Thus, for comparison among chromosomes, the position of MLH1 foci was expressed as the relative position of each CO to the length of the chromosome (the length of each SC was divided into 10% intervals). To convert the MLH1 foci to genetic distances, the number of MLH1 foci detected per SC was multiplied by a factor of 50 map units (1 crossover = 50 cM) [64, 65].
The effect of chromosome inversions on the CO distribution pattern was analyzed by calculating MLH1 foci density within inverted and non-inverted regions, considering the length (expressed in μm) for each region, so the differences due to SC lengths for each chromosome were normalized. In order to delimit the inversions directly in spermatocytes, the centromere position and the specific BAC probes labeling the breakpoint distal to centromere was used (Figure 1 and Additional file 6: Table S5). To allow for comparison among chromosomes, CO distribution was expressed as the relative position of each CO to the length of the chromosome (the length of each SC was divided into 10% intervals).
Furthermore, and in order to disentangle the centromeric effect on recombination, the CO distribution was compared between the rearranged chromosomes and the collinear one, using the last as a control. To this purpose, we simulated an inversion in the collinear chromosome, by analyzing the recombination pattern of a region of (proportionally) the same size as the observed inverted region. We constructed plots of cumulative frequency to study the pattern along the chromosome arms.
Statistical analysis was performed using JMP 10 software (SAS Institute Inc.) and IBM SPSS Statistics 20, using the Kolmogorov-Smirnov-Lilliefors test for normality, and the Kruskal-Wallis and Mann–Whitney U tests for comparisons.
Homologous syntenic blocks
Evolutionary breakpoint regions
Synaptonemal complex protein 3
MutL homolog 1
Bacterial artificial clones
Children’s Hospital Oakland Research Institute
The authors are especially thankful to Hugo Fernandez-Bellón, from the Barcelona Zoo, for his contribution in collecting biological samples. This work was supported by the Ministerio de Economia y Competitividad (CGL-2010-20170).
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