In all eukaryotic species examined, meiotic recombination, and crossovers in particular, occur non‐randomly along chromosomes. The cause for this non-random distribution remains poorly understood but some specific DNA sequence motifs have been shown to be enriched near crossover hotspots in a number of species. We present analyses using machine learning algorithms to investigate whether DNA motif distribution across the genome can be used to predict crossover variation in Drosophila melanogaster, a species without hotspots. Our study exposes a combinatorial non-linear influence of motif presence able to account for a significant fraction of the genome-wide variation in crossover rates at all genomic scales investigated, from 20% at 5-kb to almost 70% at 2,500-kb scale. The models are particularly predictive for regions with the highest and lowest crossover rates and remain highly informative after removing sub-telomeric and -centromeric regions known to have strongly reduced crossover rates. Transcriptional activity during early meiosis and differences in motif use between autosomes and the X chromosome add to the predictive power of the models. Moreover, we show that population-specific differences in crossover rates can be partly explained by differences in motif presence. Our results suggest that crossover distribution in Drosophila is influenced by both meiosis-specific chromatin dynamics and very local constitutive open chromatin associated with DNA motifs that prevent nucleosome stabilization. These findings provide new information on the genetic factors influencing variation in recombination rates and a baseline to study epigenetic mechanisms responsible for plastic recombination as response to different biotic and abiotic conditions and stresses.

BackgroundEvidence in yeast indicates that gene expression is correlated with recombination activity and double-strand break (DSB) formation in some hotspots. Studies of nucleosome occupancy in yeast and mice also suggest that open chromatin influences the formation of DSBs. In Drosophila melanogaster, high-resolution recombination maps show an excess of DSBs within annotated transcripts relative to intergenic sequences. The impact of active transcription on recombination landscapes, however, remains unexplored in a multicellular organism. We then investigated the transcription profile during early meiosis in D. melanogaster females to obtain a glimpse at the relevant transcriptional dynamics during DSB formation, and test the specific hypothesis that DSBs preferentially target transcriptionally active genomic regions.ResultsOur study of transcript profiles of early- and late-meiosis using mRNA-seq revealed, 1) significant differences in gene expression, 2) new genes and exons, 3) parent-of-origin effects on transcription in early-meiosis stages, and 4) a nonrandom genomic distribution of transcribed genes. Importantly, genomic regions that are more actively transcribed during early meiosis show higher rates of recombination, and we ruled out DSB preference for genic regions that are not transcribed.ConclusionsOur results provide evidence in a multicellular organism that transcription during the initial phases of meiosis increases the likelihood of DSB and give insight into the molecular determinants of recombination rate variation across the D. melanogaster genome. We propose that a model where variation in gene expression plays a role altering the recombination landscape across the genome could provide a molecular, heritable and plastic mechanism to observed patterns of recombination variation, from the high level of intra-specific variation to the known influence of environmental factors and stress conditions.

No abstract

No abstract

No abstract