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R-loops and regulatory changes in chronologically ageing fission yeast cells drive non-random patterns of genome rearrangements

['David A. Ellis', 'Institute Of Healthy Ageing', 'Department Of Genetics', 'Evolution', 'Environment', 'University College London', 'London', 'United Kingdom', 'Félix Reyes-Martín', 'Centro Andaluz De Biología Del Desarrollo']

Date: 2021-11

Aberrant repair of DNA double-strand breaks can recombine distant chromosomal breakpoints. Chromosomal rearrangements compromise genome function and are a hallmark of ageing. Rearrangements are challenging to detect in non-dividing cell populations, because they reflect individually rare, heterogeneous events. The genomic distribution of de novo rearrangements in non-dividing cells, and their dynamics during ageing, remain therefore poorly characterized. Studies of genomic instability during ageing have focussed on mitochondrial DNA, small genetic variants, or proliferating cells. To characterize genome rearrangements during cellular ageing in non-dividing cells, we interrogated a single diagnostic measure, DNA breakpoint junctions, using Schizosaccharomyces pombe as a model system. Aberrant DNA junctions that accumulated with age were associated with microhomology sequences and R-loops. Global hotspots for age-associated breakpoint formation were evident near telomeric genes and linked to remote breakpoints elsewhere in the genome, including the mitochondrial chromosome. Formation of breakpoint junctions at global hotspots was inhibited by the Sir2 histone deacetylase and might be triggered by an age-dependent de-repression of chromatin silencing. An unexpected mechanism of genomic instability may cause more local hotspots: age-associated reduction in an RNA-binding protein triggering R-loops at target loci. This result suggests that biological processes other than transcription or replication can drive genome rearrangements. Notably, we detected similar signatures of genome rearrangements that accumulated in old brain cells of humans. These findings provide insights into the unique patterns and possible mechanisms of genome rearrangements in non-dividing cells, which can be promoted by ageing-related changes in gene-regulatory proteins.

DNA breaks followed by chromosomal rearrangements that join non-neighboring DNA sequences may critically affect gene function, evolution, and ageing. Such chromosomal rearrangements are difficult to spot in sequence data even if they are widespread, because they are individually rare and reflect diverse events. Here we establish sensitive analyses of DNA sequences and identify prevalent rearrangements that specifically accumulate during ageing in yeast cells. These rearrangements feature short repeated DNA sequences near the breaks, preferentially occur in certain locations of the chromosomes (e.g., near their ends), and can link sequences originating from different chromosomes. We show results indicating that DNA-RNA interactions, triggered by the ageing-associated suppression of an RNA-binding protein, can cause the non-random patterns of some chromosomal rearrangements. Our analyses suggest that similar patterns of chromosomal rearrangements accumulate in brain cells in older humans, raising the possibility that such DNA changes occurring in ageing cells are conserved from yeast to human.

Processes affecting ageing are remarkably conserved from yeast to human, including both genetic and environmental factors [ 21 , 33 ]. The fission yeast, Schizosaccharomyces pombe, is a potent model for cellular ageing; we and others have explored effects of nutrient limitation, signalling pathways and genetic variations on chronological lifespan in S. pombe [ 34 – 37 ]. Chronological lifespan is defined as the time a cell survives in a quiescent, non-dividing state, which models post-mitotic ageing of somatic metazoan cells [ 21 , 33 ]. Quiescent S. pombe cells feature distinct DNA-damage responses [ 28 , 38 , 39 ] and distinct mutational forces that can promote genetic diversity [ 26 , 27 ]. Here we interrogate aberrant genomic DNA-junction sequences in non-dividing S. pombe cells, revealing unique signatures of ageing-associated chromosomal rearrangements and suggesting their mechanistic underpinning. Similar patterns of rearrangements are also evident in ageing human brain cells.

Ageing has been associated with both an increase in DSBs [ 14 , 15 ] and a decline in the efficiency and accuracy of DNA repair [ 15 , 16 ]. Accordingly, increased genomic instability and chromosomal rearrangements are well-known hallmarks of ageing [ 17 – 22 ]. Impaired NHEJ leads to accelerated ageing in human patients and mouse models, and MMEJ increases with age [ 23 ]. Genome re-sequencing studies during ageing have been limited to mitochondrial DNA [ 24 , 25 ], small genetic variants [ 26 , 27 ] and duplications [ 28 ], or proliferating cells [ 29 ]. No systematic approaches have been applied to identify heterogeneous, rare chromosomal rearrangements in non-dividing, somatic cells [ 15 , 30 – 32 ].

Cellular processes like transcription and replication can trigger DNA lesions such as double-strand breaks (DSBs) [ 1 – 4 ]. A sensitive sequencing approach has revealed DSBs at hotspots in mouse brain cells, linked to transcribed genes with neuronal functions [ 5 ], suggesting that the physiological context can affect the landscape of DSBs. DSBs are normally repaired by homologous recombination or by non-homologous end-joining (NHEJ), two pathways which protect chromosomes from aberrant structural variations [ 6 – 8 ]. Under certain physiological conditions, e.g. when the regular DNA-repair pathways are compromised, alternate DNA end-joining processes take over, often involving short homologous sequences (microhomologies) that are typically unmasked through DNA-end resection from the DSBs [ 9 – 11 ]. Microhomology-mediated end-joining (MMEJ) can link chromosomal breakpoints that are normally far apart or even on different chromosomes [ 12 , 13 ]. Such events lead to genome rearrangements such as inversions, duplications, translocations or deletions, which can considerably affect the function of genomes. The patterns of genome rearrangements are shaped by the particular mechanisms of their formation and by the fitness effects they exert on the cell.

Results and discussion

Similar patterns of genome rearrangements accumulate in old human brain cells A recent study, looking at single-nucleotide polymorphisms in single cells, reports that somatic mutations accumulate with age in humans [55]. Non-dividing yeast cells are a model for the post-mitotic ageing of somatic human cells such as the long-lived cells of the brain [33,56]. To check whether similar rearrangements also occur in human cells, and to validate our method in an independent system, we applied our junction calling pipeline to published sequencing data of young and old adult brain tissue [24]. We found a subtle increase in junctions in older brain cells (Fig 2A), although differences were marginally significant at best, reflecting that the coverage and sample number in this data set were low (Materials and Methods). Note that the younger brains in this analysis will have already experienced some level of age-associated physiological decline. The most striking result from this analysis was the similar pattern of rearrangements: as in fission yeast (Fig 1D), the junctions were associated with microhomology in somatic human cells (Fig 2A). The microhomology-associated junctions in human brain cells showed a bimodal distribution: a large population featuring similar microhomology lengths to fission yeast (peaking around 4–6 bases), and a less abundant population featuring longer microhomology (median ~16 bases). Interestingly, in cancer genome sequences a transition in the probability of junction formation occurs at around 11 bases of microhomology [54]; the authors suggest that this transition reflects a shift in repair mechanisms from MMEJ to single-strand annealing. Notably, simulated data showed that there were fewer junctions in coding regions of human brain cells than would be expected by chance (Fig 2B). As for fission yeast (Fig 1B), this finding likely reflects selection against rearrangements that interfere with gene function, either through cell death or active culling of unfit cells [57]. These results raise the possibility that similar patterns of ageing-related DNA rearrangements occur in both yeast and human brain cells. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 2. Similar patterns of genome rearrangement in ageing human brain cells as in ageing yeast cells. Analysis was performed on sequence data from putamen samples of neurologically normal Caucasian males [24], six of whom were young (blue; 19, 24, 28, 28, 29 and 30 year old donors) and six of whom were old (red; 67, 71, 78, 83, 85 and 89 year old donors). The 100bp reads from these samples, which were crudely enriched for mtDNA, were re-mapped to the whole human genome and filtered (Materials and Methods). (A) Histograms showing number of junctions with various lengths of microhomology (right of dotted lines), blunt joints (between dotted lines), or non-homologous insertions (left of dotted lines) at breakpoint. Histograms at different ages are overlaid on top of each other, with samples from young donors in blue and older donors in red (as in A). In these sparse data, the difference between young and old brain tissue was marginally significant at best (comparison of number of junctions per mapped read: p Mann-Whitney = 0.23; correlation between sample age and JPMR: p Pearson = 0.17; comparison of average microhomology length: p T-test = 0.05, suggesting slightly shorter mean microhomologies in old tissues, 8.15bp vs 8.28bp). (B) Proportion of junctions expected to fall in coding regions vs proportion that do (based on simulated data; Materials and Methods). One-sided two sample Mann-Whitney U to test whether observed is less than expected (U = 3, p <0.001). Simulated proportions approached the 1.5% proportion of the genome reported as coding [127] when more junctions were simulated. https://doi.org/10.1371/journal.pgen.1009784.g002

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[1] Url: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009784

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