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

Abstract

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.

Aging-associated long non-coding RNA boosts longevity and reduces the ribosome content of non-dividing fission yeast cells

“…To further test the possibility that aal1 functions in translation, we analyzed the subcellular localization of the aal1 RNA using antisense probes for single-molecule RNA fluorescence in situ hybridization (smRNA-FISH) 52 . Given that the aal1 RNA expression is very low in proliferating cells (Figure 1A,B) and smRNA-FISH is challenging in stationary-phase cells 53 , we generated a strain expressing aal1 from the P41nmt1 promoter at its native genomic locus ( aal1-gOE ). This analysis revealed that aal1 RNAs predominantly localize in the cytoplasm in multiple foci (Figure 2B).…”

Approaches for Mapping and Analysis of R‐loops

“…Following read quality control, reads are aligned to the genome using short‐read aligners like bowtie (or bowtie2) and BWA or (BWA‐MEM). Then, duplicate alignments are often removed using SAMtools (L. Chen et al., 2017; Ellis et al., 2021), Picard tools (Abakir et al., 2019; Alecki et al., 2020; Zeng et al., 2021), or Sambamba (Bayona‐Feliu et al., 2017), and the alignment files are compressed in BAM format.…”

“…The result is a genomic track that shows a genome‐wide histogram of R‐loop mapping coverage (usually in bigWig or bedGraph formats). Previous R‐loop mapping studies have typically generated coverage tracks using BEDtools (Crossley et al., 2020; Kotsantis et al., 2020), SAMtools (Ellis et al., 2021; Sanz & Chédin, 2019), or deepTools (Alecki et al., 2020; Itoh & Tomizawa, 1980; K. Wang et al., 2021; Wulfridge & Sarma, 2021). Finally, BAM files are used to calculate “peaks,” discrete genomic ranges that show robust coverage enrichment compared to background or input control.…”

“…Following upstream data processing, a downstream analysis is performed using custom scripts in R (L. Chen et al., 2017; Sanz et al., 2016; Wahba et al., 2016), Python (Crossley et al., 2020; Dumelie & Jaffrey, 2017; Ellis et al., 2021), MATLAB (Xu et al., 2020), or Perl (L. Chen et al., 2017; Sanz et al., 2016). The simplest approach for exploring R‐loop mapping data is to visualize it using a genome browser; the Integrative Genomics Viewer (IGV) (Robinson et al., 2011) is a genome browser widely used in previous R‐loop mapping studies (Crossley et al., 2020; Dumelie & Jaffrey, 2017; Xu et al., 2020).…”

Low‐Molecular Weight Compounds that Extend the Chronological Lifespan of Yeasts, Saccharomyces cerevisiae, and Schizosaccharomyces pombe

“…[5][6][7] In addition to the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe is an insightful eukaryotic model yeast for understanding cellular aging and lifespan. [8][9][10][11][12][13] Yeast lifespans can be defined in two ways as the replicative lifespan (RLS), as measured by the number of divisions a cell can divide during its lifetime, and as the chronological lifespan (CLS), indicating the survival period of a nondividing cell population, which is measured by viability after entry into the stationary phase. [14][15][16][17] The RLS is a model for the aging process of dividing cells (such as stem cells) in higher eukaryotes, whereas CLS is a model for the aging of nondividing cells (such as neurons) in higher eukaryotes.…”